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The World Health Organization (WHO) calls for the development of a rapid, biomarker-based, non-sputum test capable of detecting all forms of tuberculosis (TB) at the point-of-care to enable immediate treatment initiation. Lipoarabinomannan (LAM) is the only WHO-endorsed TB biomarker that can be detected in urine, an easily collected sample matrix. For obtaining optimal sensitivity, we and others have shown that some form of sample pretreatment is necessary to remove background from patient urine samples. A number of systems are paper-based often destined for resource limited settings. Our current work presents incorporation of one such sample pretreatment, proteinase K (ProK) immobilized on paper (IPK) and test its performance in comparison to standard proteinase K (SPK) treatment that involves addition and deactivation at high temperature prior to performing a capture ELISA. Herein, a simple and economical method was developed for using ProK immobilized strips to pretreat urine samples. Simplification and cost reduction of the proposed pretreatment strip were achieved by using Whatman no.1 paper and by minimizing the concentration of ProK (an expensive but necessary reagent) used to pretreat the clinical samples prior to ELISA. To test the applicability of IPK, capture ELISA was carried out on either LAM-spiked urine or the clinical samples after pretreatment with ProK at 400 μg/mL for 30 minutes at room temperature. The optimal conditions and stability of the IPK were tested and validation was performed on a set of 25 previously analyzed archived clinical urine samples with known TB and HIV status. The results of IPK and SPK treated samples were in agreement showing that the urine LAM test currently under development has the potential to reach adult and pediatric patients regardless of HIV status or site of infection, and to facilitate global TB control to improve assay performance and ultimately treatment outcomes., Competing Interests: The authors have declared that no competing interests exist.

Background: Adiponectin is an adipocyte-derived hormone that acts as a marker of insulin sensitivity. Bloodspot sampling by fingerstick onto filter paper may increase the feasibility of large-scale studies of the determinants of insulin sensitivity. We first describe the validation of an enzyme-linked immunoassay (ELISA) for quantifying adiponectin from dried blood spots and then demonstrate its application in a large trial (PROBIT). Methods: We quantified adiponectin from 3-mm diameter discs (≈3 µL of blood) punched from dried blood spots obtained from: i) whole blood standards (validation); and ii) PROBIT trial samples (application) in which paediatricians collected blood spots from 13,879 children aged 11.5 years from 31 sites across Belarus. We examined the distribution of bloodspot adiponectin by demographic and anthropometric factors, fasting insulin and glucose. Results: In the validation study, mean intra-assay coefficients of variation (n = 162) were 15%, 13% and 10% for ‘low’ (6.78 µg/ml), ‘medium’ (18.18 µg/ml) and 'high’ (33.13 µg/ml) internal quality control (IQC) samples, respectively; the respective inter-assay values (n = 40) were 23%, 21% and 14%. The correlation coefficient between 50 paired whole bloodspot versus plasma samples, collected simultaneously, was 0.87 (95% CI: 0.78 to 0.93). Recovery of known quantities of adiponectin (between 4.5 to 36 µg/ml) was 100.3–133%. Bloodspot adiponectin was stable for at least 30 months at −80°C. In PROBIT, we successfully quantified fasting adiponectin from dried blood spots in 13,329 of 13,879 (96%) children. Mean adiponectin (standard deviation) concentrations were 17.34 µg/ml (7.54) in boys and 18.41 µg/ml (7.92) in girls and were inversely associated with body mass index, fat mass, triceps and subscapular skin-fold thickness, waist circumference, height and fasting glucose. Conclusions: Bloodspot ELISA is suitable for measuring adiponectin in very small volumes of blood collected on filter paper and can be applied to large-scale studies. [ABSTRACT FROM AUTHOR]

The safety of chemicals, drugs, novel foods and genetically modified crops is often tested using repeat-dose sub-acute toxicity tests in rats or mice. It is important to avoid misinterpretations of the results as these tests are used to help determine safe exposure levels in humans. Treated and control groups are compared for a range of haematological, biochemical and other biomarkers which may indicate tissue damage or other adverse effects. However, the statistical analysis and presentation of such data poses problems due to the large number of statistical tests which are involved. Often, it is not clear whether a “statistically significant” effect is real or a false positive (type I error) due to sampling variation. The author's conclusions appear to be reached somewhat subjectively by the pattern of statistical significances, discounting those which they judge to be type I errors and ignoring any biomarker where the p-value is greater than p = 0.05. However, by using standardised effect sizes (SESs) a range of graphical methods and an over-all assessment of the mean absolute response can be made. The approach is an extension, not a replacement of existing methods. It is intended to assist toxicologists and regulators in the interpretation of the results. Here, the SES analysis has been applied to data from nine published sub-acute toxicity tests in order to compare the findings with those of the author's. Line plots, box plots and bar plots show the pattern of response. Dose-response relationships are easily seen. A “bootstrap” test compares the mean absolute differences across dose groups. In four out of seven papers where the no observed adverse effect level (NOAEL) was estimated by the authors, it was set too high according to the bootstrap test, suggesting that possible toxicity is under-estimated. [ABSTRACT FROM AUTHOR]

The single-nucleotide polymorphisms of genes related to DNA damage repair and inflammasomes and mutated gene expression in coal workers' pneumoconiosis (CWP) were analysed to identify the risk factors of CWP and potential biomarkers for early warning and diagnosis. Further, mutated gene pathways were analysed based on proteome and metabolome. Han Chinese male subjects were randomly selected and divided into 4 or 5 groups according to the process of CWP. MassARRAY was used to sequence single-nucleotide polymorphism genotypes. Mutated gene expression in plasma was tested using enzyme-linked immunosorbent assay (ELISA). Odds ratios (ORs) and receiver operating characteristic curves (ROC) were calculated. The serum different proteins and metabolites were identified by Ultra Performance Liquid Chromatography Quadrupole time of flight/Mass Spectrum (UPLC-Q-TOF/MS) and analysed using bioinformation software. As CWP progressed, the CC and CA genotypes of ERCC1 rs3212986 decreased and increased significantly, respectively. AA (OR = 3.016) and CA (OR = 2.130) genotypes were identified as risk factors for stage II. ERCC1 significantly decreased in processing of CWP. The cutoff value of ERCC1 was 5.265 pg/ml, with a sensitivity of 90.0% and specificity of 86.7%. ERCC1 had an indirect interaction with activator protein-1 and insulin and its pathways were mainly made with molecules related to lipid metabolism and actin dynamics. ERCC1 is a candidate biomarker for detection and precise intervention in CWP. If it reaches the threshold, workers will change other jobs in time and will not develop and diagnose as pneumoconiosis and will help the employers spend less money. Meanwhile, the signal molecules of ERCC1 pathway could be as a candidate target for drug discovery., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright: © 2024 Deng et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Infections are a major complication of open fractures and fracture fixation. In this study, an innovative bioactive medical device was used to experimentally treat MRSE-induced osteomyelitis in rabbit tibia. This paper investigates the clinical significance of inflammatory biomarkers (NLR, PLR, MLR and PMR), SII and IL-6 and assesses their role in the development of osteomyelitis. The main objective is to identify the utility of hematological reports derived from neutrophils, leukocytes, monocytes and platelets in the evolution of implant-related osteomyelitis and the estimation of treatment efficiency. In particular, this study compares the response of these inflammatory markers to different treatments in the presence or absence of bioactive materials and/or topical antibiotics over time. The analysis of the threads showed that NLR, PLR and SII had high values in the acute phase of the disease, so that after chronicization, they decrease. The animals treated with vancomycin nano-functionalized peptide-enriched silk fibroin-coated implants showed lower levels of inflammatory biomarkers compared to the other groups (empty implants and peptide-enriched silk fibroin-coated implants). NLR, PLR and SII, complemented by IL-6 can be used as fairly accurate biomarkers for the diagnosis of osteomyelitis., Competing Interests: The authors declare no conflict of interest., (Copyright: © 2024 Ancuța et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Myocardial ischemia-reperfusion injury (MIRI) refers to the secondary damage to myocardial tissue that occurs when blood perfusion is rapidly restored following myocardial ischemia. This process often exacerbates the injury to myocardial fiber structure and function. The activation mechanism of angiogenesis is closely related to MIRI and plays a significant role in the occurrence and progression of ischemic injury. In this study, we utilized sequencing data from the GEO database and employed WGCNA, Mfuzz cluster analysis, and protein interaction network to identify Stat3, Rela, and Ubb as hub genes involved in MIRI-angiogenesis. Additionally, the GO and KEGG analysis of differentially expressed genes highlighted their broad participation in inflammatory responses and associated signaling pathways. Moreover, the analysis of sequencing data and hub genes revealed a notable increase in the infiltration ratio of monocytes and activated mast cells. By establishing key cell ROC curves, using independent datasets, and validating the expression of hub genes, we demonstrated their high diagnostic value. Moreover, by scrutinizing single-cell sequencing data alongside trajectory analysis, it has come to light that Stat3 and Rela exhibit predominant expression within Dendritic cells. In contrast, Ubb demonstrates expression across multiple cell types, with all three genes being expressed at distinct stages of cellular development. Lastly, leveraging the CMap database, we predicted potential small molecule compounds for the identified hub genes and validated their binding activity through molecular docking. Ultimately, our research provides valuable evidence and references for the early diagnosis and treatment of MIRI from the perspective of angiogenesis., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright: © 2024 Zhao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Immune-based therapy is a promising type of treatment for hepatocellular carcinoma (HCC) but has only been partially successful due to the high heterogeneity in HCC tumor. The differences in the degree of tumor cell progression and in the activity of tumor immune microenvironment could lead to varied clinical outcome. Accurate subgrouping for recurrence risk is an approach to address the issue of such heterogeneity. It remains under investigation as whether integrating quantitative whole slide image (WSI) features with the expression profile of immune marker genes can improve the risk stratification, and whether clinical outcome prediction can assist in understanding molecular biology that drives the outcome. Methods: We included a total of 231 patients from the Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) project. For each patient, we extracted 18 statistical metrics corresponding to a global region of interest and 135 features regarding nucleus shape from WSI. A risk score was developed using these image features with high-dimensional survival modeling. We also introduced into the model the expression profile of 66 representative marker genes relevant to currently available immunotherapies. We stratified all patients into higher and lower-risk subgroup based on the final risk score selected from multiple models generated, and further investigated underlying molecular mechanisms associated with the risk stratification. Results: One WSI feature and three immune marker genes were selected into the final recurrence-free survival (RFS) prediction model following the best integrated modeling framework. The resultant score showed a significantly improved prediction performance on the test dataset (mean time-dependent AUCs = 0.707) as compared to those of other types (e.g: mean time-dependent AUCs of AJCC tumor stage = 0.525) of input data integration. To assess that the risk score could provide a higher-resolution risk stratification, a lower-risk subgroup (or a higher-risk subgroup) was arbitrarily assigned according to score falling below (or above) the median score. The lower risk subgroup had significantly longer median RFS time than that of the higher-risk patients (median RFS = 903 vs. 265 days, log-rank test p-value< 0.0001). Additionally, the higher-risk subgroup, in contrast to the lower-risk patients were characterized with a significant downregulation of immune checkpoint genes, suppressive signal in tumor immune response pathways, and depletion of CD8 T cells. These observations for the higher-risk subgroup suggest that new targets for adoptive or checkpoint-based combined systemic therapies may be useful. Conclusion: We developed a novel prognostic model to predict RFS for HCC patients, using one feature that can be automatically extracted from routine histopathological images, as well as the expression profiles of three immune marker genes. The methodology used in this paper demonstrates the feasibility of developing prognostic models that provide both useful risk stratification along with valuable biological insights into the underlying characteristics of the subgroups identified. Author summary: Prognosis of recurrence events is important for HCC patients due to a high likelihood of post-surgery recurrence. Recurrence risk is related to both intra-tumor cell differentiation, and functional characteristics of the immune microenvironment. It remains under investigation as whether integrating quantitative whole slide image (WSI) features with the expression profile of immune marker genes can improve the risk stratification, and whether clinical outcome prediction can assist in understanding molecular biology that drives the outcome. By combining information from WSI features and the expression of immune marker genes, we derived a risk score, supported with underlying molecular mechanisms, that was shown to effectively stratify patients according to recurrence risk whereby the higher-risk subgroup experienced tumor recurrence events almost two years earlier than the lower-risk patients. The methods provide an effective way to build prognostic models with good performance and internal validity that are able to combine information from sets of features of different types. [ABSTRACT FROM AUTHOR]

Borderline Personality Disorder (BPD) is often perceived to be a female-predominant disorder in both research and clinical contexts. Although there is growing recognition of possible sex differences, the current literature remains fragmented and inconclusive. This scoping review aimed to synthesize available research evidence on potential sex differences in BPD. PsycINFO, PubMed, Scopus and Web-of-Science were searched from January 1982 to July 2022 surrounding the key concepts of sex and BPD. Data searching and screening processes followed the Joanna Briggs Institute methodology involving two independent reviewers, and a third reviewer if necessary, and identified 118 papers. Data regarding BPD symptoms, comorbid disorders, developmental factors, biological markers, and treatment were extracted. Data was summarized using the vote counting method or narrative synthesis depending on the availability of literature. Males with BPD were more likely to present externalizing symptoms (e.g., aggressiveness) and comorbid disorders (e.g., substance use), while females with BPD were more likely to present internalizing symptoms (e.g., affective instability) and comorbid disorders (e.g., mood and eating disorders). This review also revealed that substantially more research attention has been given to overall sex differences in baseline BPD symptoms and comorbid disorders. In contrast, there is a dearth of sex-related research pertaining to treatment outcomes, developmental factors, and possible biological markers of BPD. The present scoping review synthesized current studies on sex differences in BPD, with males more likely to present with externalizing symptoms in contrast to females. However, how this might change the prognosis of the disorder or lead to modifications of treatment has not been investigated. Most studies were conducted on western populations, mainly North American (55%) or European (33%), and there is a need for future research to also take into consideration genetic, cultural, and environmental concomitants. As the biological construct of 'sex' was employed in the present review, future research could also investigate the social construct 'gender'. Longitudinal research designs are needed to understand any longer-term sex influence on the course of the disorder. [ABSTRACT FROM AUTHOR]

Background: The 8-aminoquinolines, primaquine and tafenoquine, are the only available drugs for the radical cure of Plasmodium vivax hypnozoites. Previous evidence suggests that there is dose-dependent 8-aminoquinoline induced methaemoglobinaemia and that higher methaemoglobin concentrations are associated with a lower risk of P. vivax recurrence. We undertook a systematic review and individual patient data meta-analysis to examine the utility of methaemoglobin as a population-level surrogate endpoint for 8-aminoquinoline antihypnozoite activity to prevent P. vivax recurrence. Methods and findings: We conducted a systematic search of Medline, Embase, Web of Science, and the Cochrane Library, from 1 January 2000 to 29 September 2022, inclusive, of prospective clinical efficacy studies of acute, uncomplicated P. vivax malaria mono-infections treated with radical curative doses of primaquine. The day 7 methaemoglobin concentration was the primary surrogate outcome of interest. The primary clinical outcome was the time to first P. vivax recurrence between day 7 and day 120 after enrolment. We used multivariable Cox proportional-hazards regression with site random-effects to characterise the time to first recurrence as a function of the day 7 methaemoglobin percentage (log base 2 transformed), adjusted for the partner schizonticidal drug, the primaquine regimen duration as a proxy for the total primaquine dose (mg base/kg), the daily primaquine dose (mg/kg), and other factors. The systematic review protocol was registered with PROSPERO (CRD42023345956). We identified 219 P. vivax efficacy studies, of which 8 provided relevant individual-level data from patients treated with primaquine; all were randomised, parallel arm clinical trials assessed as having low or moderate risk of bias. In the primary analysis data set, there were 1,747 patients with normal glucose-6-phosphate dehydrogenase (G6PD) activity enrolled from 24 study sites across 8 different countries (Indonesia, Brazil, Vietnam, Thailand, Peru, Colombia, Ethiopia, and India). We observed an increasing dose-response relationship between the daily weight-adjusted primaquine dose and day 7 methaemoglobin level. For a given primaquine dose regimen, an observed doubling in day 7 methaemoglobin percentage was associated with an estimated 30% reduction in the risk of P. vivax recurrence (adjusted hazard ratio = 0.70; 95% confidence interval [CI] [0.57, 0.86]; p = 0.0005). These pooled estimates were largely consistent across the study sites. Using day 7 methaemoglobin as a surrogate endpoint for recurrence would reduce required sample sizes by approximately 40%. Study limitations include the inability to distinguish between recrudescence, reinfection, and relapse in P. vivax recurrences. Conclusions: For a given primaquine regimen, higher methaemoglobin on day 7 was associated with a reduced risk of P. vivax recurrence. Under our proposed causal model, this justifies the use of methaemoglobin as a population-level surrogate endpoint for primaquine antihypnozoite activity in patients with P. vivax malaria who have normal G6PD activity. Ihsan Fadilah and colleagues conduct an individual patient data meta-analysis examining the utility and validity of blood methaemoglobin as a surrogate endpoint for recurrent vivax malaria. Author summary: Why was this study done?: Plasmodium vivax causes recurrent malaria due to dormant liver stage parasites. The pro-drugs primaquine and tafenoquine are the only available treatments to prevent relapsing malaria, but their optimal dosing remains unclear. The active metabolites of primaquine and tafenoquine cause predictable increases in blood methaemoglobin; these same metabolites also kill liver parasites. This suggests that increases in blood methaemoglobin could be used as a population-level surrogate endpoint for liver stage parasite killing. What did the researchers do and find?: We conducted an individual patient data meta-analysis examining the utility and validity of blood methaemoglobin as a surrogate endpoint for recurrent vivax malaria. We systematically reviewed and pooled the available methaemoglobin and clinical data from patients with P. vivax malaria treated with primaquine over the last 20 years. We fit statistical models to these data to quantify the relationship between day 7 methaemoglobin levels and the risk of recurrent P. vivax malaria. We observed an increasing dose-response relationship between weight-adjusted primaquine dose and methaemoglobin levels measured on day 7. We found that for a fixed primaquine dose regimen, a doubling in day 7 methaemoglobin was associated with an estimated 30% reduction in the risk of vivax recurrence. Using day 7 methaemoglobin levels as a surrogate endpoint for recurrent malaria could reduce sample sizes needed in future studies by about 40% and reduce follow-up duration by 94%. What do these findings mean?: Patients with higher methaemoglobin levels a week after starting primaquine treatment appear to have a lower risk of recurrence. Day 7 methaemoglobin levels can potentially be used as a proxy for later vivax recurrence in exploratory trials, making it more efficient (e.g., fewer volunteers and resources required) to determine whether new drugs or regimens are effective. Study limitations include the inability to determine whether P. vivax recurrences are due to treatment failure, reinfection, or relapse. [ABSTRACT FROM AUTHOR]

Nutritional status is a critical element of many aspects of animal ecology, but has proven difficult to measure non-invasively in studies of free-ranging animals. Urinary C-peptide of insulin (UCP), a small polypeptide cleaved in an equimolar ratio from proinsulin when the body converts it to insulin, offers great promise in this regard, and recent studies of several non-human primate species have utilized it with encouraging results. Despite this, there are a number of unresolved issues related to the collection, processing, storage and transport of samples. These include: contamination of samples on collection (most commonly by dirt or faeces), short-term storage before returning to a field station, differences in processing and long-term storage methods (blotting onto filter paper, freezing, lyophilizing), and for frozen samples, transportation while keeping samples frozen. Such issues have been investigated for urine samples in particular with respect to their effects on steroid hormone metabolites, but there has been little investigation of their effects on UCP measurement. We collected samples from captive macaques, and undertook a series of experiments where we systematically manipulated samples and tested the effects on subsequent UCP measurements. We show that contamination of urine samples by faeces led to a decrease in UCP levels by >90%, but that contamination with dirt did not have substantial effects. Short-term storage (up to 12 hours) of samples on ice did not affect UCP levels significantly, but medium-term storage (up to 78 hours) did. Freezing and lyophilization for long-term storage did not affect UCP levels, but blotting onto filter paper did. A transportation simulation showed that transporting frozen samples packed in ice and insulated should be acceptable, but only if it can be completed within a period of a few days and if freeze-thaw can be avoided. We use our data to make practical recommendations for fieldworkers. [ABSTRACT FROM AUTHOR]

"Faster, higher, stronger" is the motto of any professional athlete. Does that apply to brain dynamics as well? In our paper, we performed a series of EEG experiments on Visually Evoked Potentials and a series of cognitive tests—reaction time and visual search, with professional eSport players in Counter-Strike: Global Offensive (CS:GO) and novices (control group) in order to find important differences between them. EEG data were studied in a temporal domain by Event-Related Potentials (ERPs) and in a frequency domain by Variational Mode Decomposition. The EEG analysis showed that the brain reaction of eSport players is faster (P300 latency is earlier on average by 20-70 ms, p < 0.005) and stronger (P300 peak amplitude is higher on average by 7-9 mkV, p < 0.01). Professional eSport players also exhibit stronger stimulus-locked alpha-band power. Besides, the Spearman correlation analysis showed a significant correlation between hours spend in CS:GO and mean amplitude of P200 and N200 for the professional players. The comparison of cognitive test results showed the superiority of the professional players to the novices in reaction time (faster) and choice reaction time—faster reaction, but similar correctness, while a significant difference in visual search skills was not detected. Thus, significant differences in EEG signals (in spectrograms and ERPs) and cognitive test results (reaction time) were detected between the professional players and the control group. Cognitive tests could be used to separate skilled players from novices, while EEG testing can help to understand the skilled player's level. The results can contribute to understanding the impact of eSport on a player's cognitive state and associating eSport with a real sport. Moreover, the presented results can be useful for evaluating eSport team members and making training plans. [ABSTRACT FROM AUTHOR]

With current imaging, discriminating tumor progression from treatment effect following immunotherapy or oncolytic virotherapy of glioblastoma (GBM) is challenging. A blood based diagnostic biomarker would therefore be helpful. Axl is a receptor tyrosine kinase that is highly expressed by many cancers including GBM. Axl expression is regulated through enzymatic cleavage of its extracellular domain. The resulting fragment can be detected in serum as soluble Axl (sAxl). sAxl levels can distinguish patients with melanoma, hepatocellular carcinoma, and pancreatic ductal adenocarcinoma from healthy controls. This is a pilot study to determine if sAxl is a candidate biomarker for GBM. The sAxl levels in the serum of 40 healthy volunteers and 20 GBM patients were determined using an enzyme-linked immunosorbent assay (ELISA). Pre- and post- operative sAxl levels were obtained. Volumetric MRI evaluation provided GBM tumor volume metrics. There was no significant difference in the sAxl levels of the volunteers (30.16±1.88 ng/ml) and GBM patients (30.74±1.96 ng/ml) p = 0.27. The postoperative sAxl levels were significantly higher than preoperative levels (32.32±2.26 ng/ml vs 30.74±1.96 ng/ml, p = 0.03). We found no correlation between tumor volume and sAxl levels. Axl expression was low or absent in 6 of 11 (55%) patient derived GBM cell lines. Given the wide range of Axl expression by GBM tumors, sAxl may not be a reliable indicator of GBM. However, given the small sample size in this study, a larger study may be considered. [ABSTRACT FROM AUTHOR]

Background: Alzheimer’s disease neuroimaging initiative (ADNI) is a landmark imaging and omics study in AD. ADNI research literature has increased substantially over the past decade, which poses challenges for effectively communicating information about the results and impact of ADNI-related studies. In this work, we employed advanced information visualization techniques to perform a comprehensive and systematic mapping of the ADNI scientific growth and impact over a period of 12 years. Methods: Citation information of ADNI-related publications from 01/01/2003 to 05/12/2015 were downloaded from the Scopus database. Five fields, including authors, years, affiliations, sources (journals), and keywords, were extracted and preprocessed. Statistical analyses were performed on basic publication data as well as journal and citations information. Science mapping workflows were conducted using the Science of Science (Sci2) Tool to generate geospatial, topical, and collaboration visualizations at the micro (individual) to macro (global) levels such as geospatial layouts of institutional collaboration networks, keyword co-occurrence networks, and author collaboration networks evolving over time. Results: During the studied period, 996 ADNI manuscripts were published across 233 journals and conference proceedings. The number of publications grew linearly from 2008 to 2015, so did the number of involved institutions. ADNI publications received much more citations than typical papers from the same set of journals. Collaborations were visualized at multiple levels, including authors, institutions, and research areas. The evolution of key ADNI research topics was also plotted over the studied period. Conclusions: Both statistical and visualization results demonstrate the increasing attention of ADNI research, strong citation impact of ADNI publications, the expanding collaboration networks among researchers, institutions and ADNI core areas, and the dynamic evolution of ADNI research topics. The visualizations presented here can help improve daily decision making based on a deep understanding of existing patterns and trends using proven and replicable data analysis and visualization methods. They have great potential to provide new insights and actionable knowledge for helping translational research in AD. [ABSTRACT FROM AUTHOR]

Many researchers try to understand a biological condition by identifying biomarkers. This is typically done using univariate hypothesis testing over a labeled dataset, declaring a feature to be a biomarker if there is a significant statistical difference between its values for the subjects with different outcomes. However, such sets of proposed biomarkers are often not reproducible – subsequent studies often fail to identify the same sets. Indeed, there is often only a very small overlap between the biomarkers proposed in pairs of related studies that explore the same phenotypes over the same distribution of subjects. This paper first defines the Reproducibility Score for a labeled dataset as a measure (taking values between 0 and 1) of the reproducibility of the results produced by a specified fixed biomarker discovery process for a given distribution of subjects. We then provide ways to reliably estimate this score by defining algorithms that produce an over-bound and an under-bound for this score for a given dataset and biomarker discovery process, for the case of univariate hypothesis testing on dichotomous groups. We confirm that these approximations are meaningful by providing empirical results on a large number of datasets and show that these predictions match known reproducibility results. To encourage others to apply this technique to analyze their biomarker sets, we have also created a publicly available website, https://biomarker.shinyapps.io/BiomarkerReprod/, that produces these Reproducibility Score approximations for any given dataset (with continuous or discrete features and binary class labels). [ABSTRACT FROM AUTHOR]

Sudden infant death syndrome (SIDS) is one of the primary causes of death of infants in the first year of life. According to the WHO's data, the global infant mortality rate is 0.64-2 per 1,000 live-born children. Molecular and cellular aspects of SIDS development have not been identified so far. The purpose of this paper is to verify and analyze the expression of melatonin 1 and 2 receptors, serotonin (as a melatonin precursor), and CD34 molecules (as hematopoietic and endothelial markers of cardiovascular damage) in the medulla, heart, and aorta in infants who died from SIDS. An immunohistochemical method was used to investigate samples of medulla, heart, and aorta tissues of infants 3 to 9 months of age who died from SIDS. The control group included children who died from accidents. It has been shown that the expression of melatonin receptors as well as serotonin and CD34 angiogenesis markers in tissues of the medulla, heart, and aorta of infants who died from SIDS is statistically lower as compared with their expression in the same tissues in children who died from accidents. The obtained data help to clarify in detail the role of melatonin and such signaling molecules as serotonin and CD34 in SIDS pathogenesis, which can open new prospects for devising novel methods for predictive diagnosis of development and targeted prophylaxis of SIDS., Competing Interests: The authors have declared that no competing interests exist.

Background: HOTAIR, a newly discovered long intergenic noncoding RNA (lincRNA), has been reported to be aberrantly expressed in many types of cancers. This meta-analysis summarizes its potential role as a biomarker in malignancy. Methods: A quantitative meta-analysis was performed through a systematic search in Pubmed, Medline and Web of Science for eligible papers on the prognostic impact of HOTAIR in cancer from inception to Feb. 28, 2014. Pooled hazard ratios (HRs) with 95% confidence interval (95% CI) were calculated to summarize the effect. Results: Nineteen studies were included in the study, with a total of 2033 patients. A significant association was observed between high HOTAIR expression and poor overall survival (OS) in patients with cancer (pooled HR 2.22, 95% CI: 1.68–2.93). Place of residence (Asian or Western countries), type of cancer (digestive or non-digestive disease), sample size (more or less than 100), and paper quality (score more or less than 85%) did not alter the significant predictive value of HOTAIR in OS from various kinds of cancer but preoperative status did. By combining HRs from Cox multivariate analyses, we found that HOTAIR expression was an independent prognostic factor for cancer patients (pooled HR 2.26, 95% CI: 1.62–3.15). Subgroup analysis showed that HOTAIR abundance was an independent prognostic factor for cancer metastasis (HR 3.90, 95% CI: 2.25–6.74). For esophageal carcinoma, high HOTAIR expression was significantly associated with TNM stage (III/IV vs. I/II: OR 6.90, 95% CI: 2.81–16.9) without heterogeneity. In gastric cancer, HOTAIR expression was found to be significantly associated with lymph node metastases (present vs. absent: OR 4.47, 95% CI: 1.88–10.63) and vessel invasion (positive vs. negative: OR 2.88, 95% CI: 1.38–6.04) without obvious heterogeneity. Conclusions: HOTAIR abundance may serve as a novel predictive factor for poor prognosis in different types of cancers in both Asian and Western countries. [ABSTRACT FROM AUTHOR]

Background: Our systematic review summarizes the evidence concerning the accuracy of serum diagnostic and prognostic tests for colorectal cancer (CRC). Methods: The databases MEDLINE and EMBASE were searched iteratively to identify the relevant literature for serum markers of CRC published from 1950 to August 2012. The articles that provided adequate information to meet the requirements of the meta-analysis of diagnostic and prognostic markers were included. A 2-by-2 table of each diagnostic marker and its hazard ratio (HR) and the confidence interval (CI) of each prognostic marker was directly or indirectly extracted from the included papers, and the pooled sensitivity and specificity of the diagnostic marker and the pooled HR and the CI of the prognostic marker were subsequently calculated using the extracted data. Results: In total, 104 papers related to the diagnostic markers and 49 papers related to the prognostic serum markers of CRC were collected, and only 19 of 92 diagnostic markers were investigated in more than two studies, whereas 21 out of 44 prognostic markers were included in two or more studies. All of the pooled sensitivities of the diagnostic markers with > = 3 repetitions were less than 50%, and the meta-analyses of the prognostic markers with more than 3 studies were performed, VEGF with highest (2.245, CI: 1.347–3.744) and MMP-7 with lowest (1.099, CI: 1.018–1.187)) pooled HRs are presented. Conclusions: The quality of studies addressing the diagnostic and prognostic accuracy of the tests was poor, and the results were highly heterogeneous. The poor characteristics indicate that these tests are of little value for clinical practice. [ABSTRACT FROM AUTHOR]

Background: Large-scale screening for atrial fibrillation (AF) requires reliable methods to identify at-risk populations. Using an experimental semi-quantitative biomarker assay, B-type natriuretic peptide (BNP) and fibroblast growth factor 23 (FGF23) were recently identified as the most suitable biomarkers for detecting AF in combination with simple morphometric parameters (age, sex, and body mass index [BMI]). In this study, we validated the AF model using standardised, high-throughput, high-sensitivity biomarker assays., Methods and Findings: For this study, 1,625 consecutive patients with either (1) diagnosed AF or (2) sinus rhythm with CHA2DS2-VASc score of 2 or more were recruited from a large teaching hospital in Birmingham, West Midlands, UK, between September 2014 and February 2018. Seven-day ambulatory ECG monitoring excluded silent AF. Patients with tachyarrhythmias apart from AF and incomplete cases were excluded. AF was diagnosed according to current clinical guidelines and confirmed by ECG. We developed a high-throughput, high-sensitivity assay for FGF23, quantified plasma N-terminal pro-B-type natriuretic peptide (NT-proBNP) and FGF23, and compared results to the previously used multibiomarker research assay. Data were fitted to the previously derived model, adjusting for differences in measurement platforms and known confounders (heart failure and chronic kidney disease). In 1,084 patients (46% with AF; median [Q1, Q3] age 70 [60, 78] years, median [Q1, Q3] BMI 28.8 [25.1, 32.8] kg/m2, 59% males), patients with AF had higher concentrations of NT-proBNP (median [Q1, Q3] per 100 pg/ml: with AF 12.00 [4.19, 30.15], without AF 4.25 [1.17, 15.70]; p < 0.001) and FGF23 (median [Q1, Q3] per 100 pg/ml: with AF 1.93 [1.30, 4.16], without AF 1.55 [1.04, 2.62]; p < 0.001). Univariate associations remained after adjusting for heart failure and estimated glomerular filtration rate, known confounders of NT-proBNP and FGF23. The fitted model yielded a C-statistic of 0.688 (95% CI 0.656, 0.719), almost identical to that of the derived model (C-statistic 0.691; 95% CI 0.638, 0.744). The key limitation is that this validation was performed in a cohort that is very similar demographically to the one used in model development, calling for further external validation., Conclusions: Age, sex, and BMI combined with elevated NT-proBNP and elevated FGF23, quantified on a high-throughput platform, reliably identify patients with AF., Trial Registration: Registry IRAS ID 97753 Health Research Authority (HRA), United Kingdom., Competing Interests: I have read the journal's policy and the authors of this manuscript have the following competing interests: LF has received institutional research grants and non-financial support from European Union, British Heart Foundation, Medical Research Council (UK), and DFG and Gilead. PK and LF are listed as inventors on two patents held by University of Birmingham (Atrial Fibrillation Therapy WO 2015140571, Markers for Atrial Fibrillation WO 2016012783). PKi has received additional support for research from the European Union, British Heart Foundation, Leducq Foundation, Medical Research Council (UK), and German Centre for Heart Research, from several drug and device companies active in atrial fibrillation, honoraria from several such companies. PKa is an employee of Roche Diagnostics GmbH. AZ is an employee of Roche Diagnostics Intl. All other authors have reported no relationships relevant to the contents of this paper to disclose.

Background: Survivin has been proposed as a promising prognostic marker in oral squamous cell carcinoma (OSCC), but the published data on survivin expression in patients with this condition are controversial. To address this we performed a meta-analysis systematically to assess the clinicopathological and prognostic significance of survivin expression in OSCC. Methods: We searched PubMed, Embase, Web of Science and Ovid databases for papers investigating the clinicopathological and prognostic significance of survivin expression in OSCC. The pooled odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were used to determine the relevance of survivin. Results: A total of 15 papers, including 1040 cases in which survivin expression was detected by immunohistochemistry (IHC) or reverse transcription polymerase chain reaction (RT-PCR), were included. A meta-analysis of clinicopathological variables revealed a correlation between survivin expression and lymph node metastasis (OR = 0.62, 95% CI = 0.44–0.88, p < 0.05) and clinical stage (OR = 0.63, 95% CI = 0.41–0.96, p < 0.05). However, no significant associations were found between survivin expression and tumor differentiation grade (OR = 0.72, 95%CI = 0.26–1.11, p > 0.05), depth of invasion (OR = 0.76, 95% CI = 0.50–1.14, p > 0.05), age (OR = 0.78, 95% CI = 0.48–1.29, p > 0.05) or gender (OR = 1.31, 95% CI = 0.86–2.01, p > 0.05). Subgroup analysis using stratified detection methods showed no significant associations between the expression of survivin protein and clinicopathological variables in OSCC. A correlation between survivin expression and poor prognosis of patients with OSCC (HR = 1.62, 95% CI = 1.23–2.01, p < 0.05) was demonstrated. Conclusion: Survivin is a potential prognostic marker of OSCC. Future studies with larger sample sizes and well-designed inclusion criteria will be needed to dissect the role of survivin expression in determining the clinicopathological features and/or prognosis of OSCC. [ABSTRACT FROM AUTHOR]

Serum activities of alanine and aspartate aminotransferases (ALT and AST) are used as gold standard biomarkers for the diagnosis of hepatocellular injury. Since ALT and AST lack liver specificity, the diagnosis of the onset of hepatocellular injury in patients with underlying muscle impairments is severely limited. Thus, we evaluated the potential of glutamate dehydrogenase (GLDH) as a liver specific alternative biomarker of hepatocellular injury. In our study, serum GLDH in subjects with Duchene muscular dystrophy (DMD) was equivalent to serum GLDH in age matched healthy subjects, while serum ALT was increased 20-fold in DMD subjects. Furthermore, serum GLDH in 131 subjects with variety of muscle impairments was similar to serum GLDH of healthy subjects while serum ALT corelated with serum creatine kinase, a widely accepted biomarker of muscle impairment. In addition, significant elevations of ALT, AST, and CK were observed in a case of a patient with rhabdomyolysis, while serum GLDH stayed within the normal range until the onset of hypoxia-induced liver injury. In a mouse model of DMD (DMDmdx), serum GLDH but not serum ALT clearly correlated with the degree of acetaminophen-induced liver injury. Taken together, our data support the utility of serum GLDH as a liver-specific biomarker of liver injury that has a potential to improve diagnosis of hepatocellular injury in patients with underlying muscle impairments. In drug development, GLDH may have utility as a biomarker of drug induced liver injury in clinical trials of new therapies to treat muscle diseases such as DMD., Competing Interests: The project was funded by Pfizer Inc. in forms of research grants to K.J.J and R.W. (University of Michigan). Critical Path Institute provided salary support to J.L., A.C.P., and J-M.S. Pfizer Inc. provided support in the form of salaries for authors [S.J.S., J.O., T.L., and J.A.]. Critical Path Institute and Pfizer Inc reviewed the final manuscript, but the funding organizations did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section. None of the authors serve on the editorial board of PLOS ONE. None of the authors have acted as an expert witness in relevant legal proceedings. None of the authors have sat or currently sit on a committee for an organization that may benefit from publication of the paper. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Background and Purpose: Excess insulin resistance is considered the predominant pathophysiological mechanism in obese women who develop gestational diabetes (GDM). We hypothesised that obese women requiring differing treatment modalities for GDM may have diverse underlying metabolic pathways., Methods: In this secondary analysis of the UK pregnancies Better Eating and Activity Trial (UPBEAT) we studied women from the control arm with complete biochemical data at three gestational time points; at 15-18+6 and 27-28+6 weeks (before treatment), and 34-36+0 weeks (after treatment). A total of 89 analytes were measured (plasma/serum) using a targeted nuclear magnetic resonance (NMR) platform and conventional assays. We used linear regression with appropriate adjustment to model metabolite concentration, stratified by treatment group., Main Findings: 300 women (median BMI 35kg/m2; inter quartile range 32.8-38.2) were studied. 71 developed GDM; 28 received dietary treatment only, 20 metformin, and 23 received insulin. Prior to the initiation of treatment, multiple metabolites differed (p<0.05) between the diet and insulin-treated groups, especially very large density lipoprotein (VLDL) and high density lipoprotein (HDL) subclasses and constituents, with some differences maintained at 34-36 weeks' gestation despite treatment. Gestational lipid profiles of the diet treatment group were indicative of a lower insulin resistance profile, when compared to both insulin-treated women and those without GDM. At 28 weeks' the diet treatment group had lower plasma fasting glucose and insulin than women treated with insulin, yet similar to those without GDM, consistent with a glycaemic mechanism independent of insulin resistance., Conclusions/interpretation: This exploratory study suggests that GDM pathophysiological processes may differ amongst obese women who require different treatment modalities to achieve glucose control and can be revealed using metabolic profiling., Competing Interests: SMN and LP disclose receipt of lecture/other fees from Roche Diagnostics, outside the scope of this work. DAL discloses receipt of fees from Roche Diagnostics and Ferring Pharmaceuticals for research unrelated to this paper. All other authors declare that there is no declaration of interest associated with their contribution to this manuscript. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Diabetes is a large healthcare burden worldwide. There is substantial evidence that lifestyle modifications and drug intervention can prevent diabetes, therefore, an early identification of high risk individuals is important to design targeted prevention strategies. In this paper, we present an automatic tool that uses machine learning techniques to predict the development of type 2 diabetes mellitus (T2DM). Data generated from an oral glucose tolerance test (OGTT) was used to develop a predictive model based on the support vector machine (SVM). We trained and validated the models using the OGTT and demographic data of 1,492 healthy individuals collected during the San Antonio Heart Study. This study collected plasma glucose and insulin concentrations before glucose intake and at three time-points thereafter (30, 60 and 120 min). Furthermore, personal information such as age, ethnicity and body-mass index was also a part of the data-set. Using 11 OGTT measurements, we have deduced 61 features, which are then assigned a rank and the top ten features are shortlisted using minimum redundancy maximum relevance feature selection algorithm. All possible combinations of the 10 best ranked features were used to generate SVM based prediction models. This research shows that an individual's plasma glucose levels, and the information derived therefrom have the strongest predictive performance for the future development of T2DM. Significantly, insulin and demographic features do not provide additional performance improvement for diabetes prediction. The results of this work identify the parsimonious clinical data needed to be collected for an efficient prediction of T2DM. Our approach shows an average accuracy of 96.80% and a sensitivity of 80.09% obtained on a holdout set., Competing Interests: The authors have declared that no competing interests exist.

Background: Recent advances in targeted therapies have raised expectations that the clinical application of biomarkers would improve patient’s health outcomes and potentially save costs. However, the cost-effectiveness of biomarkers remains unclear irrespective of the cost-effectiveness of corresponding therapies. It is thus important to determine whether biomarkers for targeted therapies provide good value for money. This study systematically reviews economic evaluations of biomarkers for targeted therapies in metastatic colorectal cancer (mCRC) and assesses the cost-effectiveness of predictive biomarkers in mCRC. Methods: A literature search was performed using Medline, Embase, EconLit, NHSEED. Papers published from 2000 until June 2018 were searched. All economic evaluations assessing biomarker-guided therapies with companion diagnostics in mCRC were searched. To make studies more comparable, cost-effectiveness results were synthesized as per biomarker tests and corresponding therapies. Methodological quality was assessed using the Quality of Health Economic Studies (QHES) instrument. Results: Forty-six studies were included in this review. Of these, 17 studies evaluated the intrinsic value of cancer biomarkers, whereas the remaining studies focused on assessing the cost-effectiveness of corresponding drugs. Most studies indicated favourable cost-effectiveness of biomarkers for targeted therapies in mCRC. Some studies reported that biomarkers were cost-effective, while their corresponding therapies were not cost-effective. A considerable number of economic evaluations were conducted in pre-defined genetic populations and thus, often failed to fully capture the biomarker’s clinical and economic values. The average QHES score was 73.6. Conclusion: Cancer biomarkers for targeted therapies in mCRC were mostly found to be cost-effective; otherwise, they at least improved the cost-effectiveness of targeted therapies by saving some costs. However, this did not necessarily make their corresponding therapies cost-effective. While companion biomarkers reduced therapy costs, the savings were not sufficient to make the corresponding agents cost-effective. Evaluation of biomarkers was often restricted to the cost of tests and did not consider their clinical values or biomarker prevalence. [ABSTRACT FROM AUTHOR]

Background: Despite elimination efforts, the number of Mycobacterium leprae (M. leprae) infected individuals who develop leprosy, is still substantial. Solid evidence exists that individuals living in close proximity to patients are at increased risk to develop leprosy. Early diagnosis of leprosy in endemic areas requires field-friendly tests that identify individuals at risk of developing the disease before clinical manifestation. Such assays will simultaneously contribute to reduction of current diagnostic delay as well as transmission. Antibody (Ab) levels directed against the M.leprae-specific phenolic glycolipid I (PGL-I) represents a surrogate marker for bacterial load. However, it is insufficiently defined whether anti-PGL-I antibodies can be utilized as prognostic biomarkers for disease in contacts. Particularly, in Bangladesh, where paucibacillary (PB) patients form the majority of leprosy cases, anti-PGL-I serology seems an inadequate method for leprosy screening in contacts as a directive for prophylactic treatment. Methods: Between 2002 and 2009, fingerstick blood from leprosy patients’ contacts without clinical signs of disease from a field-trial in Bangladesh was collected on filter paper at three time points covering six years of follow-up per person. Analysis of anti-PGL-I Ab levels for 25 contacts who developed leprosy during follow-up and 199 contacts who were not diagnosed with leprosy, was performed by ELISA after elution of bloodspots from filter paper. Results: Anti-PGL-I Ab levels at intake did not significantly differ between contacts who developed leprosy during the study and those who remained free of disease. Moreover, anti-PGL-I serology was not prognostic in this population as no significant correlation was identified between anti-PGL-I Ab levels at intake and the onset of leprosy. Conclusion: In this highly endemic population in Bangladesh, no association was observed between anti-PGL-I Ab levels and onset of disease, urging the need for an extended, more specific biomarker signature for early detection of leprosy in this area. Trial registration: ClinicalTrials.gov [ABSTRACT FROM AUTHOR]

Nutrient recommendations in use today are often derived from relatively old data of few studies with few individuals. However, for many nutrients, including vitamin B-12, extensive data have now become available from both observational studies and randomized controlled trials, addressing the relation between intake and health-related status biomarkers. The purpose of this article is to provide new methodology for dietary planning based on dose-response data and meta-analysis. The methodology builds on existing work, and is consistent with current methodology and measurement error models for dietary assessment. The detailed purposes of this paper are twofold. Firstly, to define a Population Nutrient Level (PNL) for dietary planning in groups. Secondly, to show how data from different sources can be combined in an extended meta-analysis of intake-status datasets for estimating PNL as well as other nutrient intake values, such as the Average Nutrient Requirement (ANR) and the Individual Nutrient Level (INL). For this, a computational method is presented for comparing a bivariate lognormal distribution to a health criterion value. Procedures to meta-analyse available data in different ways are described. Example calculations on vitamin B-12 requirements were made for four models, assuming different ways of estimating the dose-response relation, and different values of the health criterion. Resulting estimates of ANRs and less so for INLs were found to be sensitive to model assumptions, whereas estimates of PNLs were much less sensitive to these assumptions as they were closer to the average nutrient intake in the available data. [ABSTRACT FROM AUTHOR]

Identifying biomarkers that are associated with different types of cancer is an important goal in the field of bioinformatics. Different researcher groups have analyzed the expression profiles of many genes and found some certain genetic patterns that can promote the improvement of targeted therapies, but the significance of some genes is still ambiguous. More reliable and effective biomarkers identification methods are then needed to detect candidate cancer-related genes. In this paper, we proposed a novel method that combines the infinite latent feature selection (ILFS) method with the functional interaction (FIs) network to rank the biomarkers. We applied the proposed method to the expression data of five cancer types. The experiments indicated that our network-constrained ILFS (NCILFS) provides an improved prediction of the diagnosis of the samples and locates many more known oncogenes than the original ILFS and some other existing methods. We also performed functional enrichment analysis by inspecting the over-represented gene ontology (GO) biological process (BP) terms and applying the gene set enrichment analysis (GSEA) method on selected biomarkers for each feature selection method. The enrichments analysis reports show that our network-constraint ILFS can produce more biologically significant gene sets than other methods. The results suggest that network-constrained ILFS can identify cancer-related genes with a higher discriminative power and biological significance. [ABSTRACT FROM AUTHOR]

In this paper, we explore the utility of resting-state EEG measures as potential biomarkers for the detection and assessment of cognitive decline in mild cognitive impairment (MCI) and Alzheimer's disease (AD). Neurophysiological biomarkers of AD derived from EEG and FDG-PET, once characterized and validated, would expand the set of existing diagnostic molecular biomarkers of AD pathology with associated biomarkers of disease progression and neural dysfunction. Since symptoms of AD often begin to appear later in life, successful identification of EEG-based biomarkers must account for age-related neurophysiological changes that occur even in healthy individuals. To this end, we collected EEG data from individuals with AD (n = 26), MCI (n = 53), and cognitively normal healthy controls stratified by age into three groups: 18–40 (n = 129), 40–60 (n = 62) and 60–90 (= 55) years old. For each participant, we computed power spectral density at each channel and spectral coherence between pairs of channels. Compared to age matched controls, in the AD group, we found increases in both spectral power and coherence at the slower frequencies (Delta, Theta). A smaller but significant increase in power of slow frequencies was observed for the MCI group, localized to temporal areas. These effects on slow frequency spectral power opposed that of normal aging observed by a decrease in the power of slow frequencies in our control groups. The AD group showed a significant decrease in the spectral power and coherence in the Alpha band consistent with the same effect in normal aging. However, the MCI group did not show any significant change in the Alpha band. Overall, Theta to Alpha ratio (TAR) provided the largest and most significant differences between the AD group and controls. However, differences in the MCI group remained small and localized. We proposed a novel method to quantify these small differences between Theta and Alpha bands' power using empirically derived distributions of spectral power across the time domain as opposed to averaging power across time. We defined Power Distribution Distance Measure (PDDM) as a distance measure between probability distribution functions (pdf) of Theta and Alpha power. Compared to average TAR, using PDDF enhanced the statistical significance, the effect size, and the spatial distribution of significant effects in the MCI group. We designed classifiers for differentiating individual MCI and AD participants from age-matched controls. The classification performance measured by the area under ROC curve after cross-validation were AUC = 0.85 and AUC = 0.6, for AD and MCI classifiers, respectively. Posterior probability of AD, TAR, and the proposed PDDM measure were all significantly correlated with MMSE score and neuropsychological tests in the AD group. [ABSTRACT FROM AUTHOR]

Subjective tinnitus, the perception of sound in the absence of any sound source, is routinely assessed using questionnaires. The subjective nature of these tools hampers objective evaluation of tinnitus presence, severity and treatment effects. Late auditory evoked potentials (LAEPs) might be considered as a potential biomarker for assessing tinnitus complaints. Using a multivariate meta-analytic model including data from twenty-one studies, we determined the LAEP components differing systematically between tinnitus patients and controls. Results from this model indicate that amplitude of the P300 component is lower in tinnitus patients (standardized mean difference (SMD) = -0.83, p < 0.01), while latency of this component is abnormally prolonged in this population (SMD = 0.97, p < 0.01). No other investigated LAEP components were found to differ between tinnitus and non-tinnitus subjects. Additional sensitivity analyses regarding differences in experimental conditions confirmed the robustness of these results. Differences in age and hearing levels between the two experimental groups might have a considerable impact on LAEP outcomes and should be carefully considered in future studies. Although we established consistent differences in the P300 component between tinnitus patients and controls, we could not identify any evidence that this component might covary with tinnitus severity. We conclude that out of several commonly assessed LAEP components, only the P300 can be considered as a potential biomarker for subjective tinnitus, although more research is needed to determine its relationship with subjective tinnitus measures. Future trials investigating experimental tinnitus therapies should consider including P300 measurements in the evaluation of treatment effect. [ABSTRACT FROM AUTHOR]

Metabolomics uses advanced analytical chemistry techniques to comprehensively measure large numbers of small molecule metabolites in cells, tissues and biofluids. The ability to rapidly detect and quantify hundreds or even thousands of metabolites within a single sample is helping scientists paint a far more complete picture of system-wide metabolism and biology. Metabolomics is also allowing researchers to focus on measuring the end-products of complex, hard-to-decipher genetic, epigenetic and environmental interactions. As a result, metabolomics has become an increasingly popular “omics” approach to assist with the robust phenotypic characterization of humans, crop plants and model organisms. Indeed, metabolomics is now routinely used in biomedical, nutritional and crop research. It is also being increasingly used in livestock research and livestock monitoring. The purpose of this systematic review is to quantitatively and objectively summarize the current status of livestock metabolomics and to identify emerging trends, preferred technologies and important gaps in the field. In conducting this review we also critically assessed the applications of livestock metabolomics in key areas such as animal health assessment, disease diagnosis, bioproduct characterization and biomarker discovery for highly desirable economic traits (i.e., feed efficiency, growth potential and milk production). A secondary goal of this critical review was to compile data on the known composition of the livestock metabolome (for 5 of the most common livestock species namely cattle, sheep, goats, horses and pigs). These data have been made available through an open access, comprehensive livestock metabolome database (LMDB, available at ). The LMDB should enable livestock researchers and producers to conduct more targeted metabolomic studies and to identify where further metabolome coverage is needed. [ABSTRACT FROM AUTHOR]

Lung cancer, primarily non-small-cell lung cancer (NSCLC), is the leading cause of cancer deaths in the United States and worldwide. While early detection significantly improves five-year survival, there are no reliable diagnostic tools for early detection. Several exosomal microRNAs (miRs) are overexpressed in NSCLC, and have been suggested as potential biomarkers for early detection. The present paper develops a mathematical model for early stage of NSCLC with emphasis on the role of the three highest overexpressed miRs, namely miR-21, miR-205 and miR-155. Simulations of the model provide quantitative relationships between the tumor volume and the total mass of each of the above miRs in the tumor. Because of the positive correlation between these miRs in the tumor tissue and in the blood, the results of the paper may be viewed as a first step toward establishing a combination of miRs 21, 205, 155 and possibly other miRs as serum biomarkers for early detection of NSCLC. [ABSTRACT FROM AUTHOR]

The subset of pro-inflammatory B cells, called late memory, tissue-like or double negative (DN), accumulates in the blood of elderly individuals. Here we show that DN B cells do not proliferate and do not make antibodies to influenza antigens, but they secrete antibodies with autoimmune reactivity, in agreement with their membrane phenotype (CD95+CD21-CD11c+) and their spontaneous expression of the transcription factor T-bet. These cells also increase in the blood of individuals with obesity and autoimmune diseases, but causative mechanisms and signaling pathways involved are known only in part. In the present paper we compare frequencies and metabolic requirements of these cells in the blood of healthy individuals of different ages and in the blood and the subcutaneous adipose tissue (SAT) of individuals with obesity. Results show that DN B cells from young individuals have minimal metabolic requirements, DN B cells from elderly and obese individuals utilize higher amounts of glucose to perform autoimmune antibody production and enroll in aerobic glycolysis to support their function. DN B cells from the SAT have the highest metabolic requirements as they activate oxidative phosphorylation, aerobic glycolysis and fatty acid oxidation. DN B cells from the SAT also show the highest levels of ROS and the highest levels of phosphorylated AMPK (5'-AMP activated kinase) and Sestrin 1, both able to mitigate stress and cell death. This metabolic advantage drives DN B cell survival and function (secretion of autoimmune antibodies)., Competing Interests: The authors have declared that no competing interests exist.

Honey bee (Apis mellifera) health has been severely impacted by multiple environmental stressors including parasitic infection, pesticide exposure, and poor nutrition. The decline in bee health is therefore a complex multifactorial problem which requires a holistic investigative approach. Within the exposome paradigm, the combined exposure to the environment, drugs, food, and individuals' internal biochemistry affects health in positive and negative ways. In the context of the exposome, honey bee hive infection with parasites such as Nosema ceranae is also a form of environmental exposure. In this study, we hypothesized that exposure to xenobiotic pesticides and other environmental chemicals increases susceptibility to N. ceranae infection upon incidental exposure to the parasite. We further queried whether these exposures could be linked to changes in conserved metabolic biological pathways. From 30 hives sampled across 10 sites, a total of 2,352 chemical features were found via gas chromatography-time of flight mass spectrometry (GC-TOF) in extracts of honey bees collected from each hive. Of these, 20 pesticides were identified and annotated, and found to be significantly associated with N. ceranae infection. We further determined that infected hives were linked to a greater number of xenobiotic exposures, and the relative concentration of the exposures were not linked to the presence of a N. ceranae infection. In the exposome profiles of the bees, we also found chemicals inherent to known biological metabolic pathways of Apis mellifera and identified 9 dysregulated pathways. These findings have led us to posit that for hives exposed to similar chemicals, those that incur multiple, simultaneous xenobiotic stressors have a greater incidence of infection with N. ceranae. Mechanistically, our results suggests the overwhelming nature of these exposures negatively affects the biological functioning of the bee, and could explain how the decline in bee populations is associated with pesticide exposures., Competing Interests: We have the following interests: Agilent Technologies provided financial support to HW. Anthony Macherone is employed by Agilent Technologies. There are no patents, products in development or marketed products to declare. This does not alter our adherence to all the PLOS ONE policies on sharing data and materials. The views expressed in this paper are those of the authors and do not necessarily reflect the views or policies of Agilent Technologies. The work presented herein is for research use only and not for use in diagnostic procedures. Mention of trade names or commercial products do not constitute endorsement or recommendation for use.

The chemokine IP-10 (CXCL10) is a candidate marker for hepatitis C virus (HCV) fibrosis monitoring. The aim of this proof-of-concept study is to assess if IP-10 measurements from dried plasma spots (DPS) are accurate in HCV-infected patients with either minimal or significant fibrosis. We measured IP-10 levels in plasma and DPS of 21 HCV-infected patients with cirrhosis and 19 patients with no/little fibrosis (determined with FibroScan). Cirrhotic patients had significantly higher levels of IP-10 compared to patients with minimal fibrosis. DPS and plasma measurements of IP-10 are comparable and the correlation was excellent (r² = 0.97, p,0.0001). The DPS based method for IP-10 detection performs well in HCV-infected patients with either minimal or significant fibrosis. [ABSTRACT FROM AUTHOR]

Activation of the transcription factor NF-&kgr;B appears to be involved in different stages of atherogenesis. In this paper we investigate the role of NF-&kgr;B inhibitor I&kgr;Bα in atherosclerosis. Myeloid-specific deletion of I&kgr;Bα results in larger and more advanced lesions in LDL-R-deficient mice without affecting the compositional phenotype of the plaques or systemic inflammatory markers in the plasma. We show that IkBa-deleted macrophages display enhanced adhesion to an in vitro endothelial cell layer, coinciding with an increased expression of the chemokine CCL5. Also, in vivo we found that IkBαdel mice had more leukocytes adhering to the luminal side of the endothelial cell layers that cover the atherosclerotic plaques. Moreover, we introduce ER-MP58 in this paper as a new immunohistochemical tool for quantifying newly recruited myeloid cells in the atherosclerotic lesion. This staining confirms that in IkBαdel mice more leukocytes are attracted to the plaques. In conclusion, we show that I&kgr;Bα deletion in myeloid cells promotes atherogenesis, probably through an induced leukocyte recruitment to plaques. [ABSTRACT FROM AUTHOR]

Background: The collection of accurate data on adherence and sexual behaviour is crucial in microbicide (and other HIVrelated) research. In the absence of a ''gold standard'' the collection of such data relies largely on participant self-reporting. After reviewing available methods, this paper describes a mixed method/triangulation model for generating more accurate data on adherence and sexual behaviour in a multi-centre vaginal microbicide clinical trial. In a companion paper some of the results from this model are presented [1]. Methodology/Principal Findings: Data were collected from a random subsample of 725 women (7.7% of the trial population) using structured interviews, coital diaries, in-depth interviews, counting returned gel applicators, focus group discussions, and ethnography. The core of the model was a customised, semi-structured in-depth interview. There were two levels of triangulation: first, discrepancies between data from the questionnaires, diaries, in-depth interviews and applicator returns were identified, discussed with participants and, to a large extent, resolved; second, results from individual participants were related to more general data emerging from the focus group discussions and ethnography. A democratic and equitable collaboration between clinical trialists and qualitative social scientists facilitated the success of the model, as did the preparatory studies preceding the trial. The process revealed some of the underlying assumptions and routinised practices in ''clinical trial culture'' that are potentially detrimental to the collection of accurate data, as well as some of the shortcomings of large qualitative studies, and pointed to some potential solutions. Conclusions/Significance: The integration of qualitative social science and the use of mixed methods and triangulation in clinical trials are feasible, and can reveal (and resolve) inaccuracies in data on adherence and sensitive behaviours, as well as illuminating aspects of ''trial culture'' that may also affect data accuracy. [ABSTRACT FROM AUTHOR]

Large-scale genome wide association studies (GWASs) have led to discovery of many genetic risk factors in Alzheimer's disease (AD), such as APOE, TOMM40 and CLU. Despite the significant progress, it remains a major challenge to functionally validate these genetic findings and translate them into targetable mechanisms. Integration of multiple types of molecular data is increasingly used to address this problem. In this paper, we proposed a modularity-constrained Lasso model to jointly analyze the genotype, gene expression and protein expression data for discovery of functionally connected multi-omic biomarkers in AD. With a prior network capturing the functional relationship between SNPs, genes and proteins, the newly introduced penalty term maximizes the global modularity of the subnetwork involving selected markers and encourages the selection of multi-omic markers with dense functional connectivity, instead of individual markers. We applied this new model to the real data collected in the ROS/MAP cohort where the cognitive performance was used as disease quantitative trait. A functionally connected subnetwork involving 276 multi-omic biomarkers, including SNPs, genes and proteins, were identified to bear predictive power. Within this subnetwork, multiple trans-omic paths from SNPs to genes and then proteins were observed. This suggests that cognitive performance deterioration in AD patients can be potentially a result of genetic variations due to their cascade effect on the downstream transcriptome and proteome level. [ABSTRACT FROM AUTHOR]

Background: Blood-based or urinary biomarkers may play a role in quantifying the future risk of type 2 diabetes (T2D) and in understanding possible aetiological pathways to disease. However, no systematic review has been conducted that has identified and provided an overview of available biomarkers for incident T2D. We aimed to systematically review the associations of biomarkers with risk of developing T2D and to highlight evidence gaps in the existing literature regarding the predictive and aetiological value of these biomarkers and to direct future research in this field. Methods and Findings: We systematically searched PubMed MEDLINE (January 2000 until March 2015) and Embase (until January 2016) databases for observational studies of biomarkers and incident T2D according to the 2009 PRISMA guidelines. We also searched availability of meta-analyses, Mendelian randomisation and prediction research for the identified biomarkers. We reviewed 3910 titles (705 abstracts) and 164 full papers and included 139 papers from 69 cohort studies that described the prospective relationships between 167 blood-based or urinary biomarkers and incident T2D. Only 35 biomarkers were reported in large scale studies with more than 1000 T2D cases, and thus the evidence for association was inconclusive for the majority of biomarkers. Fourteen biomarkers have been investigated using Mendelian randomisation approaches. Only for one biomarker was there strong observational evidence of association and evidence from genetic association studies that was compatible with an underlying causal association. In additional search for T2D prediction, we found only half of biomarkers were examined with formal evidence of predictive value for a minority of these biomarkers. Most biomarkers did not enhance the strength of prediction, but the strongest evidence for prediction was for biomarkers that quantify measures of glycaemia. Conclusions: This study presents an extensive review of the current state of the literature to inform the strategy for future interrogation of existing and newly described biomarkers for T2D. Many biomarkers have been reported to be associated with the risk of developing T2D. The evidence of their value in adding to understanding of causal pathways to disease is very limited so far. The utility of most biomarkers remains largely unknown in clinical prediction. Future research should focus on providing good genetic instruments across consortia for possible biomarkers in Mendelian randomisation, prioritising biomarkers for measurement in large-scale cohort studies and examining predictive utility of biomarkers for a given context. [ABSTRACT FROM AUTHOR]

Background: To allow early diagnosis and monitoring of disease progression, there is a need for biomarkers in amyotrophic lateral sclerosis (ALS). Neurofilaments (NF) are emerging protein biomarkers in other neurological diseases, and are of possible use in ALS. Objective: The aim of this study is to evaluate the utility of NF levels as blood or cerebrospinal fluid (CSF) biomarker in patients with ALS. Methods: A systematic search of Pubmed, Embase and Scopus was performed. Methodological quality assessment was applied to refine the final search results. Meta-analysis of the data was performed. Results: Level of NF heavy chain and light chains were significantly elevated in the CSF of ALS patients compared to healthy controls/controls without parenchymal central nervous system (CNS) involvement and ALS mimic disease patients. NF light chain level in CSF was higher in ALS patients than in neurological patients with CNS involvement (SMD = 1.352, P = 0.01). NF light chain concentration in blood was higher in ALS patients than healthy controls/controls without CNS involvement (SMD = 1.448, P<0.0001). NF heavy chain levels in CSF were negatively correlated disease duration and ALSFRS-R ((r = -0.447, P<0.0001; r = -0.486, P<0.0001). NF light chain levels in CSF were negatively correlated with disease duration (r = -0.273, P = 0.011). Conclusion: NF heavy and light chain levels have potential use as a marker of neural degeneration in ALS, but are not specific for the disease, and are more likely to be used as measures of disease progression. [ABSTRACT FROM AUTHOR]

Purpose: Many studies have reported that carbohydrate antigen 153 (CA153) in breast secretions (BS) can discriminate breast cancer (BC) patients from healthy individuals, indicating CA153 in BS as a potential index for BC. This meta-analysis aimed to evaluate the actual diagnostic value of CA153 in BS. Methods: Related papers were obtained from Pubmed, Embase, Scopus, Ovid, Sciverse, the Cochrane library, Chinese Biomedical literature Database (CBM), Technology of Chongqing (VIP), Wan Fang Data, and Chinese National Knowledge Infrastructure (CNKI). Pooled sensitivity, specificity, and diagnostic odds ratio (DOR) of CA153 in BS for BC diagnosis were analyzed with the random effect model. SROC and the area under the curve (AUC) were applied to assess overall diagnostic efficiency. Results: This meta-analysis included five studies with a total of 329 BC patients and 381 healthy subjects. For CA153 in BS, the summary sensitivity, specificity, and DOR to diagnose BC were 0.63 (95% confidence interval (CI): 0.57∼0.68), 0.82 (95% CI: 0.78∼0.86), and 9.18 (95% CI: 4.22∼19.95), respectively. Furthermore, the AUC of BS CA153 in the diagnosis of BC was 0.8614. Conclusions: CA153 in BS is a valuable molecular marker in diagnosing BC and should be applied in standard clinical practices of BC screening upon confirmation of our findings in a larger prospective study. [ABSTRACT FROM AUTHOR]

Background: Tumor proliferation speed, most commonly assessed by counting of mitotic figures in histological slide preparations, is an important biomarker for breast cancer. Although mitosis counting is routinely performed by pathologists, it is a tedious and subjective task with poor reproducibility, particularly among non-experts. Inter- and intraobserver reproducibility of mitosis counting can be improved when a strict protocol is defined and followed. Previous studies have examined only the agreement in terms of the mitotic count or the mitotic activity score. Studies of the observer agreement at the level of individual objects, which can provide more insight into the procedure, have not been performed thus far. Methods: The development of automatic mitosis detection methods has received large interest in recent years. Automatic image analysis is viewed as a solution for the problem of subjectivity of mitosis counting by pathologists. In this paper we describe the results from an interobserver agreement study between three human observers and an automatic method, and make two unique contributions. For the first time, we present an analysis of the object-level interobserver agreement on mitosis counting. Furthermore, we train an automatic mitosis detection method that is robust with respect to staining appearance variability and compare it with the performance of expert observers on an “external” dataset, i.e. on histopathology images that originate from pathology labs other than the pathology lab that provided the training data for the automatic method. Results: The object-level interobserver study revealed that pathologists often do not agree on individual objects, even if this is not reflected in the mitotic count. The disagreement is larger for objects from smaller size, which suggests that adding a size constraint in the mitosis counting protocol can improve reproducibility. The automatic mitosis detection method can perform mitosis counting in an unbiased way, with substantial agreement with human experts. [ABSTRACT FROM AUTHOR]

Purpose: Quantitative image analysis in previous research in obstructive sleep apnea syndrome (OSAS) has focused on the upper airway or several objects in its immediate vicinity and measures of object size. In this paper, we take a more general approach of considering all major objects in the upper airway region and measures pertaining to their individual morphological properties, their tissue characteristics revealed by image intensities, and the 3D architecture of the object assembly. We propose a novel methodology to select a small set of salient features from this large collection of measures and demonstrate the ability of these features to discriminate with very high prediction accuracy between obese OSAS and obese non-OSAS groups. Materials and Methods: Thirty children were involved in this study with 15 in the obese OSAS group with an apnea-hypopnea index (AHI) = 14.4 ± 10.7) and 15 in the obese non-OSAS group with an AHI = 1.0 ± 1.0 (p<0.001). Subjects were between 8–17 years and underwent T1- and T2-weighted magnetic resonance imaging (MRI) of the upper airway during wakefulness. Fourteen objects in the vicinity of the upper airways were segmented in these images and a total of 159 measurements were derived from each subject image which included object size, surface area, volume, sphericity, standardized T2-weighted image intensity value, and inter-object distances. A small set of discriminating features was identified from this set in several steps. First, a subset of measures that have a low level of correlation among the measures was determined. A heat map visualization technique that allows grouping of parameters based on correlations among them was used for this purpose. Then, through T-tests, another subset of measures which are capable of separating the two groups was identified. The intersection of these subsets yielded the final feature set. The accuracy of these features to perform classification of unseen images into the two patient groups was tested by using logistic regression and multi-fold cross validation. Results: A set of 16 features identified with low inter-feature correlation (< 0.36) yielded a high classification accuracy of 96% with sensitivity and specificity of 97.8% and 94.4%, respectively. In addition to the previously observed increase in linear size, surface area, and volume of adenoid, tonsils, and fat pad in OSAS, the following new markers have been found. Standardized T2-weighted image intensities differed between the two groups for the entire neck body region, pharynx, and nasopharynx, possibly indicating changes in object tissue characteristics. Fat pad and oropharynx become less round or more complex in shape in OSAS. Fat pad and tongue move closer in OSAS, and so also oropharynx and tonsils and fat pad and tonsils. In contrast, fat pad and oropharynx move farther apart from the skin object. Conclusions: The study has found several new anatomic bio-markers of OSAS. Changes in standardized T2-weighted image intensities in objects may imply that intrinsic tissue composition undergoes changes in OSAS. The results on inter-object distances imply that treatment methods should respect the relationships that exist among objects and not just their size. The proposed method of analysis may lead to an improved understanding of the mechanisms underlying OSAS. [ABSTRACT FROM AUTHOR]

The Database of Human Gastric Cancer (DBGC) is a comprehensive database that integrates various human gastric cancer-related data resources. Human gastric cancer-related transcriptomics projects, proteomics projects, mutations, biomarkers and drug-sensitive genes from different sources were collected and unified in this database. Moreover, epidemiological statistics of gastric cancer patients in China and clinicopathological information annotated with gastric cancer cases were also integrated into the DBGC. We believe that this database will greatly facilitate research regarding human gastric cancer in many fields. DBGC is freely available at [ABSTRACT FROM AUTHOR]

Every 15 minutes in the US, an infant exposed to opioids is born. Approximately 50% of these newborns will develop Neonatal Opioid Withdrawal Syndrome (NOWS) within 5 days of birth. It is not known which infants will develop NOWS, therefore, the current hospital standard-of-care dictates a 96-hour observational hold. Understanding which infants will develop NOWS soon after birth could reduce hospital stays for infants who do not develop NOWS and decrease burdens on hospitals and clinicians. We propose noninvasive clinical indicators of NOWS, including newborn neurobehavior, autonomic biomarkers, prenatal substance exposures, and socioeconomic environments. The goals of this protocol are to use these indicators shortly after birth to differentiate newborns who will be diagnosed with NOWS from those who will have mild/no withdrawal, to determine if the indicators predict development at 6 and 18 months of age, and to increase NOWS diagnostic sensitivity for earlier, more accurate diagnoses. [ABSTRACT FROM AUTHOR]

Objective: Approximately 50 million people worldwide have epilepsy and 8–17% of the deaths in patients with epilepsy are attributed to sudden unexpected death in epilepsy (SUDEP). The goal of the present work was to establish a biomarker for SUDEP so that preventive treatment can be instituted. Approach: Seizure activity in patients with SUDEP and non-SUDEP was analyzed, specifically, the scalp EEG extracted muscle activity (SMA) and the average wavelet phase coherence (WPC) during seizures was computed for two frequency ranges (1–12 Hz, 13–30 Hz) to identify differences between the two groups. Main results: Ictal SMA in SUDEP patients showed a statistically higher average WPC value when compared to non-SUDEP patients for both frequency ranges. Area under curve for a cross-validated logistic classifier was 81%. Significance: Average WPC of ictal SMA is a candidate biomarker for early detection of SUDEP. [ABSTRACT FROM AUTHOR]

Background: The grim (<10% 5-year) survival rates for pancreatic ductal adenocarcinoma (PDAC) are attributed to its complex intrinsic biology and most often late-stage detection. The overlap of symptoms with benign gastrointestinal conditions in early stage further complicates timely detection. The suboptimal diagnostic performance of carbohydrate antigen (CA) 19–9 and elevation in benign hyperbilirubinaemia undermine its reliability, leaving a notable absence of accurate diagnostic biomarkers. Using a selected patient cohort with benign pancreatic and biliary tract conditions we aimed to develop a data analysis protocol leading to a biomarker signature capable of distinguishing patients with non-specific yet concerning clinical presentations, from those with PDAC. Methods: 539 patient serum samples collected under the Accelerated Diagnosis of neuro Endocrine and Pancreatic TumourS (ADEPTS) study (benign disease controls and PDACs) and the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS, healthy controls) were screened using the Olink Oncology II panel, supplemented with five in-house markers. 16 specialized base-learner classifiers were stacked to select and enhance biomarker performances and robustness in blinded samples. Each base-learner was constructed through cross-validation and recursive feature elimination in a discovery set comprising approximately two thirds of the ADEPTS and UKCTOCS samples and contrasted specific diagnosis with PDAC. Results: The signature which was developed using diagnosis-specific ensemble learning demonstrated predictive capabilities outperforming CA19-9, the only biomarker currently accepted by the FDA and the National Comprehensive Cancer Network guidelines for pancreatic cancer, and other individual biomarkers and combinations in both discovery and held-out validation sets. An AUC of 0.98 (95% CI 0.98–0.99) and sensitivity of 0.99 (95% CI 0.98–1) at 90% specificity was achieved with the ensemble method, which was significantly larger than the AUC of 0.79 (95% CI 0.66–0.91) and sensitivity 0.67 (95% CI 0.50–0.83), also at 90% specificity, for CA19-9, in the discovery set (p = 0.0016 and p = 0.00050, respectively). During ensemble signature validation in the held-out set, an AUC of 0.95 (95% CI 0.91–0.99), sensitivity 0.86 (95% CI 0.68–1), was attained compared to an AUC of 0.80 (95% CI 0.66–0.93), sensitivity 0.65 (95% CI 0.48–0.56) at 90% specificity for CA19-9 alone (p = 0.0082 and p = 0.024, respectively). When validated only on the benign disease controls and PDACs collected from ADEPTS, the diagnostic-specific signature achieved an AUC of 0.96 (95% CI 0.92–0.99), sensitivity 0.82 (95% CI 0.64–0.95) at 90% specificity, which was still significantly higher than the performance for CA19-9 taken as a single predictor, AUC of 0.79 (95% CI 0.64–0.93) and sensitivity of 0.18 (95% CI 0.03–0.69) (p = 0.013 and p = 0.0055, respectively). Conclusion: Our ensemble modelling technique outperformed CA19-9, individual biomarkers and indices developed with prevailing algorithms in distinguishing patients with non-specific but concerning symptoms from those with PDAC, with implications for improving its early detection in individuals at risk. Author summary: Pancreatic ductal adenocarcinoma (PDAC) has one of the lowest 5-year survival rates among cancers, primarily due to its complex biology and late-stage diagnosis. Early symptoms often mimic benign gastrointestinal conditions, complicating timely detection. The standard biomarker, carbohydrate antigen (CA) 19–9, is not reliable due to its suboptimal performance and elevation in benign conditions, highlighting the need for better diagnostic tools. In our study, we aimed to develop a biomarker signature to distinguish between benign pancreatic/biliary conditions and PDAC using serum samples from the Accelerated Diagnosis of neuro Endocrine and Pancreatic Tumours and UK Collaborative Trial of Ovarian Cancer Screening studies. We screened 539 patient serum samples with state-of-the-art biomarker panels and developed a robust predictive signature by applying specialized machine learning methods. This new signature significantly outperformed CA19-9 and other panels reported in the literature. Our findings suggest that this new biomarker signature can improve early detection of PDAC in patients with ambiguous clinical symptoms, potentially leading to better outcomes for those at risk. [ABSTRACT FROM AUTHOR]

The lung is recognized as a site for initiating the formation of self-antigen and autoimmune responses in rheumatoid arthritis (RA). We aimed to investigate the association of upper respiratory microbiota with RA, autoantibody production, and disease activity. Forty-six patients with RA and 17 controls were examined. Nasopharyngeal swab samples were sequenced for microbiome profiling using the V3–V4 region of the 16S rRNA gene. The microbial diversity and relative abundance were compared between RA patients and controls. Correlation analyses were conducted to evaluate the relationship between microbial abundance and clinical markers such as autoantibodies and disease activity. Microbial diversity analysis revealed no major differences between RA patients and healthy controls. However, beta diversity analysis indicated a subtle distinction in microbial composition (unweighted UniFrac distance) between the two groups (P = 0.03), hinting at a minor subset of microbiota associated with disease status. Differential abundance analysis uncovered specific taxa at various taxonomic levels, including Saccharibacteria (TM7) [O-1] (PFDR = 2.53 × 10−2), TM7 [F-1] (PFDR = 5.20 × 10−3), Microbacterium (PFDR = 3.37 × 10−4), and Stenotrophomonas (PFDR = 2.57 × 10−3). The relative abundance of ten genera correlated significantly with anti-cyclic citrullinated peptide (anti-CCP) antibody levels (PFDR < 0.05) and 11 genera were significantly associated with disease activity markers, including ESR, CRP, DAS28-ESR, and DAS-CRP (PFDR < 0.05). In particular, Saccharibacteria TM7 [G-3] and Peptostreptococcaceae [XI] [G-1] were correlated with all disease activity biomarkers. Dysbiosis in the upper respiratory mucosa is associated with RA, anti-CCP antibody levels, and disease activity. [ABSTRACT FROM AUTHOR]

Objectives: This paper aims to evaluate the association between serum uric acid (SUA) levels, arterial hypertension (HT) prevalence, blood pressure values control, kidney function and intima media thickness (IMT), as a surrogate marker of early atherosclerosis, in a representative group of Romanian adult population., Materials and Methods: The study sample consists in 1920 adults included in SEPHAR III (Study for the Evaluation of Prevalence of Hypertension and cArdiovascular Risk in Romania) survey (mean age 48.63 years, 52.76% females) collecting data for SUA levels, blood pressure (BP) measurements, kidney function by estimated glomerular filtration rate (eGFR) and carotid IMT. SUA levels between 2,40-5,70mg/dl in females and 3,40-7,00mg/dl in males respectively were considered normal. HT and HT control were defined according to the current guidelines. IMT evaluation was assessed by B-mode Doppler ultrasound evaluation., Results: Hypertensive subjects had significantly higher values of SUA compared with normotensive subjects, hypertensive patients were 1.713 times more likely to have higher values of SUA. Among treated hypertensive patients, those without optimal BP control had significantly higher SUA levels compared with those with optimal BP control, the presence of hyperuricemia increasing the odds of suboptimal BP control by 1.023. Hyperuricemic subjects had significantly lower eGFR values compared with normouricemic ones, on an average with 14.28ml/min/1.73m2 by Modification of Diet in Renal Disease formula (MDRD) and with 16.64ml/min/1.73m2 by Chronic Kidney Disease Epidemiology Collaboration formula (CKD-EPI), with an indirect association between SUA levels and eGFR values (rs = -0.319 / -0.347), independent of age. IMT values recorded in hyperuricemic subjects were significantly increased, on an average with 0.08mm, compared with normouricemic subjects, with a direct association between SUA levels and IMT values (rs = 0.263), independent of BP values., Conclusion: The results of our study offers support that increased SUA levels are associated with arterial hypertension and with suboptimal BP control in treated hypertensive subjects. The decline in kidney function, independent of age, and also increased IMT values as a marker of atherosclerosis, were also correlated with elevated SUA values. Hyperuricemia screening may have a role in identifying patients at risk of developing HT and lowering SUA levels may improve not only BP control in treated HT patients but also decrease total cardiovascular mortality by slowing the progression of atherosclerosis and renal failure in hypertensive patients., Competing Interests: The authors have declared that no competing interests exist.

Background: Due to the surrounding bone, the human inner ear is relatively inaccessible and difficult to reach for cellular and molecular analyses. However, these types of investigations are needed to better understand the etiology, pathophysiology and progression of several inner ear disorders. Moreover, the fluid from the inner ear cannot be sampled for micro-chemical analyses from healthy individuals in vivo. Therefore, in the present paper, we studied patients with vestibular schwannoma (VS) undergoing trans-labyrinthine surgery (TLS). Our primary aim was to identify perilymph proteins in patients with VS on an individual level. Our second aim was to investigate the proteins identified at a functional level and our final aim was to search for biological markers for tumor-associated hearing loss and tumor diameter., Methods and Findings: Sixteen patients underwent TLS for sporadic VS. Perilymph was aspirated through the round window before opening the labyrinth. One sample was contaminated and excluded resulting in 15 usable samples. Perilymph samples were analyzed with an online tandem LTQ-Orbitrap mass spectrometer. Data were analyzed with MaxQuant software to identify the total number of proteins and to quantify proteins in individual samples. Protein function was analyzed using the PANTHER Overrepresentation tool. Associations between perilymph protein content, clinical parameters, tumor-associated hearing loss and tumor diameter were assessed using Random Forest and Boruta. In total, 314 proteins were identified; 60 in all 15 patients and 130 proteins only once in 15 patients. Ninety-one proteins were detected in at least 12 out of 15 patients. Random Forest followed by Boruta analysis confirmed that alpha-2-HS-glycoprotein (P02765) was an independent variable for tumor-associated hearing loss. In addition, functional analysis showed that numerous processes were significantly increased in the perilymph. The top three enriched biological processes were: 1) secondary metabolic processes; 2) complement activation and 3) cell recognition., Conclusions: The proteome of perilymph in patients with vestibular schwannoma has an inter-individual stable section. However, even in a cohort with homogenous disease, the variation between individuals represented the majority of the detected proteins. Alpha-2-HS-glycoprotein, P02765, was shown to be an independent variable for tumor-associated hearing loss, a finding that needs to be verified in other studies. In pathway analysis perilymph had highly enriched functions, particularly in terms of increased immune and metabolic processes., Competing Interests: The authors have declared that no competing interests exist.

This paper addresses the problem of quantifying biomarkers in multi-stained tissues based on the color and spatial information of microscopy images of the tissue. A deep learning-based method that can automatically localize and quantify the regions expressing biomarker(s) in any selected area on a whole slide image is proposed. The deep learning network, which we refer to as Whole Image (WI)-Net, is a fully convolutional network whose input is the true RGB color image of a tissue and output is a map showing the locations of each biomarker. The WI-Net relies on a different network, Nuclei (N)-Net, which is a convolutional neural network that classifies each nucleus separately according to the biomarker(s) it expresses. In this study, images of immunohistochemistry (IHC)-stained slides were collected and used. Images of nuclei (4679 RGB images) were manually labeled based on the expressing biomarkers in each nucleus (as p16 positive, Ki-67 positive, p16 and Ki-67 positive, p16 and Ki-67 negative). The labeled nuclei images were used to train the N-Net (obtaining an accuracy of 92% in a test set). The trained N-Net was then extended to WI-Net that generated a map of all biomarkers in any selected sub-image of the whole slide image acquired by the scanner (instead of classifying every nucleus image). The results of our method compare well with the manual labeling by humans (average F-score of 0.96). In addition, we carried a layer-based immunohistochemical analysis of cervical epithelium, and showed that our method can be used by pathologists to differentiate between different grades of cervical intraepithelial neoplasia by quantitatively assessing the percentage of proliferating cells in the different layers of HPV positive lesions.