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Objective: For selecting minimally invasive surgery (i.e. laparoscopic ovarian cystectomy) for treating ovarian tumours (OTs) in premenopausal patients, the pre-operative differentiation of benign ovarian tumours (Be-OTs) based on magnetic resonance imaging (MRI) interpretation is important. This paper describes the authors' 8-year experience of approximately 1000 OT cases, and provides information about a diagnostic algorithm to help other hospitals., Study Design: The medical records of 901 patients aged < 50 years with OTs from 1 January 2015-31 March 31 2023 were reviewed. First, the accuracy of pre-operative differentiation between Be-OTs and borderline/malignant ovarian tumours (Bo/Ma-OTs) was compared in each type of OT. Second, to identify the factors influencing differentiation between Be-OTs and Bo/Ma-OTs in 164 serous/mucinous ovarian tumours (SM-OTs), a multi-variate logistic regression analysis was performed to assess the effect of 13 factors, including MRI findings, OT size and tumour markers., Results: In the comparison of diagnostic accuracy of pre-operative MRI for each OT type, accuracy was found to be notably high for ovarian endometrial cyst (OEC) ( n  = 409), ovarian mature cystic teratoma (OMCT) ( n  = 308), ovarian endometrioid adenocarcinoma (OEA) ( n  = 6) and ovarian clear cell adenocarcinoma (OCCA) ( n  = 14). On the other hand, discrepancies between MRI and pathological findings often occurred in SM-OTs, including ovarian serous cystadenoma ( n  = 86), ovarian mucinous adenocarcinoma ( n  = 61), ovarian serous adenocarcinoma ( n  = 12) and ovarian mucinous adenocarcinoma ( n  = 5). In the multi-variate logistic regression analysis of the latter 164 patients, in addition to MRI findings, OT size and carbohydrate antigen 125 also had an effect to some extent. The combination of MRI interpretation and OT size may enhance differentiation of Be-OTs and Bo/Ma-OTs., Conclusions: Among four types of OTs (OEC, OMCT, OEA and OCCA), MRI interpretation was able to differentiate between Be-OTs and Bo/Ma-OTs almost perfectly. Additionally, to mitigate the difficulty in differentiating SM-OTs, OT size may be useful in combination with MRI findings, although further accumulation and analysis of OT cases is needed., 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 article., (© 2023 The Authors. Published by Elsevier B.V.)

Introduction: Academic stress (AS) is a prevalent challenge faced by university students, potentially affecting molecular indicators such as brain-derived neurotrophic factor (BDNF) and global DNA methylation (G-DNA-M). These indicators could illuminate the physiological ramifications of academic stress., Study Design and Methods: This research followed a quantitative, non-experimental, longitudinal panel design spanning two academic semesters, observing phenomena in their natural context. Students from the Medical Technology program at Universidad de Concepción, Chile were involved, with assessments at the beginning and during heightened academic stress periods., Sample: Of the total participants, 63.0% were females, with an average age of 21.14 years at baseline, and 36.92% were males, averaging 21.36 years. By the study's conclusion, female participants averaged 21.95 years, and males 22.13 years., Results: Significant differences were observed between initial and final assessments for the SISCO-II Inventory of Academic Stress and Beck Depression Inventory-II, notably in stressor scores, and physical, and psychological reactions. Gender differences emerged in the final physical and psychological reactions. No significant changes were detected between the two assessments in plasma BDNF or G-DNA-M values. A refined predictive model showcased that, on average, there was a 3.56% decrease in females' plasma BDNF at the final assessment and a 17.14% decrease in males. In the sample, the G-DNA-M percentage at the final assessment increased by 15.06% from the baseline for females and 18.96% for males., Conclusions: The study underscores the physiological impact of academic stress on university students, evidenced by changes in markers like BDNF and G-DNA-M. These findings offer an in-depth understanding of the intricate mechanisms regulating academic stress responses and highlight the need for interventions tailored to mitigate its physiological and psychological effects., Competing Interests: The authors declare there are no competing interests., (©2023 Castillo-Navarrete et al.)

In brain-based communication, voluntarily modulated brain signals (instead of motor output) are utilized to interact with the outside world. The possibility to circumvent the motor system constitutes an important alternative option for severely paralyzed. Most communication brain-computer interface (BCI) paradigms require intact visual capabilities and impose a high cognitive load, but for some patients, these requirements are not given. In these situations, a better-suited, less cognitively demanding information-encoding approach may exploit auditorily-cued selective somatosensory attention to vibrotactile stimulation. Here, we propose, validate and optimize a novel communication-BCI paradigm using differential fMRI activation patterns evoked by selective somatosensory attention to tactile stimulation of the right hand or left foot. Using cytoarchitectonic probability maps and multi-voxel pattern analysis (MVPA), we show that the locus of selective somatosensory attention can be decoded from fMRI-signal patterns in (especially primary) somatosensory cortex with high accuracy and reliability, with the highest classification accuracy (85.93%) achieved when using Brodmann area 2 (SI-BA2) at a probability level of 0.2. Based on this outcome, we developed and validated a novel somatosensory attention-based yes/no communication procedure and demonstrated its high effectiveness even when using only a limited amount of (MVPA) training data. For the BCI user, the paradigm is straightforward, eye-independent, and requires only limited cognitive functioning. In addition, it is BCI-operator friendly given its objective and expertise-independent procedure. For these reasons, our novel communication paradigm has high potential for clinical applications., Competing Interests: Declaration of Competing Interest 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 © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Cocos nucifera (L.) is an important plantation crop with immense but untapped nutraceutical potential. Despite its bioactive potential, the biochemical features of testa oils of various coconut genotypes are poorly understood. Hence, in this study, the physicochemical characteristics of testa oils extracted from six coconut genotypes - namely West Coast Tall (WCT), Federated Malay States Tall (FMST), Chowghat Orange Dwarf (COD), Malayan Yellow Dwarf (MYD), and two Dwarf × Dwarf (D × D hybrids) viz., Cameroon Red Dwarf (CRD) × Ganga Bondam Green Dwarf (GBGD) and MYD × Chowghat Green Dwarf (CGD) - were analyzed., Results: The proportion of testa in the nuts (fruits) (1.29-3.42%), the proportion of oil in the testa (40.97-50.56%), and biochemical components in testa oils - namely proxidant elements Fe (34.17-62.48 ppm) and Cu (1.63-2.77 ppm), and the total phenolic content (6.84-8.67 mg GAE/100 g), and phytosterol content (54.66-137.73 mg CE/100 g) varied depending on the coconut genotypes. The saturated fatty acid content of testa oils (67.75 to 78.78%) was lower in comparison with that of coconut kernel oils. Similarly, the lauric acid (26.66-32.04%), myristic (18.31-19.60%), and palmitic acid (13.43-15.71%,) content of testa oils varied significantly in comparison with the coconut kernel oils (32-51%, 17-21% and 6.9-14%, respectively). Liquid chromatography-mass spectrometry (LC-MS) analysis revealed the presence of 18 phenolic acids in coconut testa oil. Multivariate analysis revealed the biochemical attributes that defined the principal components loadings. Hierarchical clustering analysis of the genotypes showed two distinct clusters., Conclusion: This study reveals the genotypic variations in the nutritionally important biochemical components of coconut testa oils. The relatively high concentration of polyunsaturated fatty acids (PUFA) and polyphenol content in testa oils warrant further investigation to explore their nutraceutical potential. © 2022 Society of Chemical Industry., (© 2022 Society of Chemical Industry.)

In the current aging society, there has been a marked increase in the incidence of cervical spinal cord injury (CSCI) without major bone injury. This multi-center study aimed to identify predictors of neurological improvement in elderly patients with CSCI without major bone injury. The participants were 591 patients aged ≥65 years with CSCI without major bone injury and a minimum follow-up period of three months. Neurologic status was defined using the American Spinal Injury Association (ASIA) impairment scale (AIS). Univariate and multi-variate analyses were performed to identify prognostic factors for walking recovery in AIS A-C cases and full upper extremity motor recovery in AIS D cases. In AIS A-C cases, body mass index (odds ratio (OR): 1.112), magnetic resonance imaging signal change (OR: 0.240), AIS on admission (OR: 3.497), comorbidity of dementia/delirium (OR: 0.365), and post-injury pneumonia (OR: 0.194) were identified as independent prognostic factors for walking recovery. The prevalence of ossification of the posterior longitudinal ligament (OR: 0.494) was also found to be an independent prognostic factor in AIS B and C cases only. In AIS D cases, age (OR: 0.937), upper extremity ASIA motor score on admission (OR: 1.230 [per 5 scores]), and operation (OR: 0.519) were independent prognostic factors for full motor recovery. The severity of AIS at admission was the strongest predictor of functional outcomes. Promoting rehabilitation, however, through measures to reduce cognitive changes, post-injury pneumonia, and unhealthy body weight changes can contribute to greater neurological improvement in AIS A-C cases.

Traumatic brain injury (TBI) is a major public health problem. Despite considerable research deciphering injury pathophysiology, precision therapies remain elusive. Here, we present large-scale data sharing and machine intelligence approaches to leverage TBI complexity. The Open Data Commons for TBI (ODC-TBI) is a community-centered repository emphasizing Findable, Accessible, Interoperable, and Reusable data sharing and publication with persistent identifiers. Importantly, the ODC-TBI implements data sharing of individual subject data, enabling pooling for high-sample-size, feature-rich data sets for machine learning analytics. We demonstrate pooled ODC-TBI data analyses, starting with descriptive analytics of subject-level data from 11 previously published articles ( N  = 1250 subjects) representing six distinct pre-clinical TBI models. Second, we perform unsupervised machine learning on multi-cohort data to identify persistent inflammatory patterns across different studies, improving experimental sensitivity for pro- versus anti-inflammation effects. As funders and journals increasingly mandate open data practices, ODC-TBI will create new scientific opportunities for researchers and facilitate multi-data-set, multi-dimensional analytics toward effective translation., Competing Interests: No competing financial interests exist., (© Austin Chou et al., 2022; Published by Mary Ann Liebert, Inc.)

Here we demonstrate a facile, two step formation of silver core - gold shell (Ag-Au) nanostructures using microelectrodes and assess their performance as surface-enhanced Raman scattering (SERS) substrates to detect and quantify toxicants. Ag nanostructures, serving as the scaffolds for the bimetallic structures, were grown first by using electrochemical deposition on the edges of microelectrodes functionalized with the alkanethiol, 11-mercaptoundecanoic acid. Subsequently, different concentrations of HAuCl 4 were used to perform a galvanic reaction on the surfaces of the Ag nanostructures with aqueous droplets being placed on the microelectrodes for 10 min before the substrate was rinsed and dried. Lower HAuCl 4 concentrations were found to better preserve the fractal morphology of the formed Ag-Au nanostructures, while higher concentrations resulted in Ag-Au fragments. The SERS enhancement factor for the Ag-Au nanostructures was estimated to have a max value of 6.51 x 10 5 . Combining a data reduction technique with a linear classifier, both identification and quantification were demonstrated with 100% success. The toxicants thiram, thiabendazole, malachite green and biphenyl-4-thiol were all detected and identified at 1 ppm. Lastly, as a proof of concept, the Ag-Au nanostructures were transferred to a PDMS film resulting in a flexible SERS substrate capable of direct detection of thiram on an apple peel without any additional sample pre-treatment., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Background: The COVID-19 pandemic has led to an unprecedented demand for testing - for diagnosis and prognosis - as well as for investigation into the impact of the disease on the host metabolism. Sebum sampling has the potential to support both needs by looking at what the virus does to us, rather than looking for the virus itself., Methods: In this pilot study, sebum samples were collected from 67 hospitalised patients (30 COVID-19 positive and 37 COVID-19 negative) by gauze swab. Lipidomics analysis was carried out using liquid chromatography mass spectrometry, identifying 998 reproducible features. Univariate and multivariate statistical analyses were applied to the resulting feature set., Findings: Lipid levels were depressed in COVID-19 positive participants, indicative of dyslipidemia; p -values of 0·022 and 0·015 were obtained for triglycerides and ceramides respectively, with effect sizes of 0·44 and 0·57. Partial Least Squares-Discriminant Analysis showed separation of COVID-19 positive and negative participants with sensitivity of 57% and specificity of 68%, improving to 79% and 83% respectively when controlled for confounding comorbidities., Interpretation: COVID-19 dysregulates many areas of metabolism; in this work we show that the skin lipidome can be added to the list. Given that samples can be provided quickly and painlessly, we conclude that sebum is worthy of future consideration for clinical sampling., Funding: The authors acknowledge funding from the EPSRC Impact Acceleration Account for sample collection and processing, as well as EPSRC Fellowship Funding EP/R031118/1, the University of Surrey and BBSRC BB/T002212/1. Mass Spectrometry was funded under EP/P001440/1., Competing Interests: Dr Barran and Dr Trivedi report patent PCT/GB2019/052169. The other authors have no interests to declare., (© 2021 The Authors.)

This work aims to understand the properties of co-formers that form co-amorphous pharmaceutical materials and to predict co-amorphous system formation. A partial least square - discriminant analysis (PLS-DA) was performed using known co-amorphous systems described by 36 variables based on the properties of the co-former and the binding energy of the system. The PLS-DA investigated the propensity to form co-amorphous material of the active pharmaceutical ingredients: mebendazole, carvedilol, indomethacin, simvastatin, carbamazepine and furosemide in combination with 20 amino acid co-formers. The variables that were found to favour the propensity to form co-amorphous systems appear to be a relatively large value for average molecular weight and the sum of the difference between hydrogen bond donors and hydrogen bond acceptors for both components, and a relatively small or negative value for excess enthalpy of mixing, excess enthalpy of hydrogen bonding and the difference in the Hansen parameter for hydrogen bonding of the coformer and the active pharmaceutical ingredient (API). To test the predictive power of this model, 29 potential co-formers were used to form either co-amorphous or crystalline two-component materials with mebendazole. Of these 29 two-component systems, the co-amorphous nature of a total of 26 materials was correctly predicted by the model, giving a predictive hit rate of 90 %., (Copyright © 2020. Published by Elsevier B.V.)

Background: There is an urgent need to rapidly distinguish COVID-19 from other respiratory conditions, including influenza, at first-presentation. Point-of-care tests not requiring laboratory- support will speed diagnosis and protect health-care staff. We studied the feasibility of using breath-analysis to distinguish these conditions with near-patient gas chromatography-ion mobility spectrometry (GC-IMS)., Methods: Independent observational prevalence studies at Edinburgh, UK, and Dortmund, Germany, recruited adult patients with possible COVID-19 at hospital presentation. Participants gave a single breath-sample for VOC analysis by GC-IMS. COVID-19 infection was identified by transcription polymerase chain reaction (RT- qPCR) of oral/nasal swabs together with clinical-review. Following correction for environmental contaminants, potential COVID-19 breath-biomarkers were identified by multi-variate analysis and comparison to GC-IMS databases. A COVID-19 breath-score based on the relative abundance of a panel of volatile organic compounds was proposed and tested against the cohort data., Findings: Ninety-eight patients were recruited, of whom 21/33 (63.6%) and 10/65 (15.4%) had COVID-19 in Edinburgh and Dortmund, respectively. Other diagnoses included asthma, COPD, bacterial pneumonia, and cardiac conditions. Multivariate analysis identified aldehydes (ethanal, octanal), ketones (acetone, butanone), and methanol that discriminated COVID-19 from other conditions. An unidentified-feature with significant predictive power for severity/death was isolated in Edinburgh, while heptanal was identified in Dortmund. Differentiation of patients with definite diagnosis (25 and 65) of COVID-19 from non-COVID-19 was possible with 80% and 81.5% accuracy in Edinburgh and Dortmund respectively (sensitivity/specificity 82.4%/75%; area-under-the-receiver- operator-characteristic [AUROC] 0.87 95% CI 0.67 to 1) and Dortmund (sensitivity / specificity 90%/80%; AUROC 0.91 95% CI 0.87 to 1)., Interpretation: These two studies independently indicate that patients with COVID-19 can be rapidly distinguished from patients with other conditions at first healthcare contact. The identity of the marker compounds is consistent with COVID-19 derangement of breath-biochemistry by ketosis, gastrointestinal effects, and inflammatory processes. Development and validation of this approach may allow rapid diagnosis of COVID-19 in the coming endemic flu seasons., Funding: MR was supported by an NHS Research Scotland Career Researcher Clinician award. DMR was supported by the University of Edinburgh ref COV&#95;29., Competing Interests: DMR and CLPT report a grant from University of Edinburgh. TW reports personal fees from G.A.S. Gesellschaft für analytische Sensorsysteme mbH,  outside the submitted work;  In addition, Dr. Wortelmann has a patent PCT/EP2014/075236 pending.Dr. Wortelmann reports personal fees from G.A.S. Gesellschaft für analytische Sensorsysteme mbH,  outside the submitted work;  In addition, Dr. Wortelmann has a patent PCT/EP2014/075236 pending, (© 2020 The Authors.)

Biofilms are communities of bacteria living embedded in a highly hydrated matrix composed of polysaccharides, proteins, and extracellular DNA. This life style confers numerous advantages to bacteria including protection against external threats. However, they also contribute to increase bacterial resistance against antimicrobials, an issue particularly relevant in dangerous infections. Due to the complexity of the matrix, few information is present in the literature on details of its architecture including the spatial distribution of the macromolecular components which might give hints on the way the biofilm scaffold is built up by bacteria. In this study, we investigated the possibility to combine well-established microbiological procedures with advanced microscopies to get information on composition and distribution of the macromolecular components of biofilm matrices. To this, confocal microscopy, diffraction-limited infrared (IR) spectral imaging, and atomic force microscopy (AFM) were used to explore biofilm produced by a clinical strain of Klebsiella pneumoniae. IR imaging permitted to have clues on how the biofilm grows and spreads on surfaces, and the local distribution of the components within it. Through the analysis of the pure component spectra, it was possible to assess the chemical and structural composition of the saccaridic matrix, confirming the data obtained by NMR. It was also possible to follow the time course of biofilm from 6 up to 48 h when the biofilm grew into a 3-dimensional multi-layered structure, characteristic of colonies of bacteria linked together by a complex matrix. In addition, nanoFTIR and AFM investigations allowed the estimation of biofilm growth in the vertical direction and the morphological analysis of bacterial colonies at different time points and the evaluation of the chemical composition at the nanoscale.

Non-alcoholic fatty liver disease is a highly prevalent condition worldwide that increases the risk to develop liver fibrosis, cirrhosis, and hepatocellular carcinoma. Thus, it is imperative to develop novel diagnostic tools that together with liver biopsy help to differentiate mild and advanced degrees of steatosis. Ex-vivo liver samples were collected from mice fed a methionine-choline deficient diet for two or eight weeks, and from a control group. The degree of hepatic steatosis was histologically evaluated, and fat content was assessed by Oil-Red O staining. On the other hand, fluorescence spectroscopy was used for the assessment of the steatosis progression. Fluorescence spectra were recorded at excitation wavelengths of 330, 365, 385, 405, and 415 nm by establishing surface contact of the fiber optic probe with the liver specimens. A multi-variate statistical approach based on principal component analysis followed by quadratic discriminant analysis was applied to spectral data to obtain classifiers able to distinguish mild and moderate stages of steatosis at the different excitation wavelengths. Receiver Operating Characteristic (ROC) curves were computed to compare classifier's performances for each one of the five excitation wavelengths and steatosis stages. Optimal sensitivity and specificity were calculated from the corresponding ROC curves using the Youden index. Intensity in the endogenous fluorescence spectra at the given wavelengths progressively increased according to the time of exposure to diet. The area under the curve of the spectra was able to discriminate control liver samples from those with steatosis and differentiate among the time of exposure to the diet for most of the used excitation wavelengths. High specificities and sensitivities were obtained for every case; however, fluorescence spectra obtained by exciting with 405 nm yielded the best results distinguishing between the mentioned classes with a total classification error of 1.5% and optimal sensitivities and specificities better than 98.6% and 99.3%, respectively.

Confocal Raman microspectral imaging (CRMI) is an advanced cell-imaging method that maps endogenous molecular compositions with their unique spectral fingerprint indicators. The aim of this work was to provide a visualized understanding of subcellular features of live osteosarcoma cells using a 532-nm laser excitation without the use of dyes or molecular probes. Both malignant osteoblast and spindle osteosarcoma cells derived from the BALB/c mouse osteosarcoma cell line K7M2 were investigated in this work. After preprocessing the obtained spectral dataset, K-means cluster analysis (KCA) is employed to reconstruct Raman spectroscopic maps of single biological cells by identifying regions of the cellular membrane, cytoplasm, organelles, and nucleus with their corresponding mean spectra. Principal component analysis (PCA) was further employed to indicate variables of significant influence on the separation of the spectra of each cellular component. The biochemical components of the two cell types were then extracted by showing the spectral and distribution features attributed to proteins, lipids, and DNA. Using this standardized CRMI technique and multivariate analysis approaches, the results obtained could be a sound foundation for a typical Raman imaging protocol of live cellular biomedical analysis.

Previously, we have established two distinct progressive multiple sclerosis (MS) models by induction of experimental autoimmune encephalomyelitis (EAE) with myelin oligodendrocyte glycoprotein (MOG) in two mouse strains. A.SW mice develop ataxia with antibody deposition, but no T cell infiltration, in the central nervous system (CNS), while SJL/J mice develop paralysis with CNS T cell infiltration. In this study, we determined biomarkers contributing to the homogeneity and heterogeneity of two models. Using the CNS and spleen microarray transcriptome and cytokine data, we conducted computational analyses. We identified up-regulation of immune-related genes, including immunoglobulins, in the CNS of both models. Pro-inflammatory cytokines, interferon (IFN)-γ and interleukin (IL)-17, were associated with the disease progression in SJL/J mice, while the expression of both cytokines was detected only at the EAE onset in A.SW mice. Principal component analysis (PCA) of CNS transcriptome data demonstrated that down-regulation of prolactin may reflect disease progression. Pattern matching analysis of spleen transcriptome with CNS PCA identified 333 splenic surrogate markers, including Stfa2l1 , which reflected the changes in the CNS. Among them, we found that two genes ( PER1 / MIR6883 and FKBP5 ) and one gene ( SLC16A1/MCT1 ) were also significantly up-regulated and down-regulated, respectively, in human MS peripheral blood, using data mining.

We used pattern classifiers to extract features related to recognition memory retrieval from the temporal information in single-trial electroencephalography (EEG) data during attempted memory retrieval. Two-class classification was conducted on correctly remembered trials with accurate context (or source) judgments vs. correctly rejected trials. The average accuracy for datasets recorded in a single session was 61% while the average accuracy for datasets recorded in two separate sessions was 56%. To further understand the basis of the classifier's performance, two other pattern classifiers were trained on different pairs of behavioral conditions. The first of these was designed to use information related to remembering the item and the second to use information related to remembering the contextual information (or source) about the item. Mollison and Curran (2012) had earlier shown that subjects' familiarity judgments contributed to improved memory of spatial contextual information but not of extrinsic associated color information. These behavioral results were similarly reflected in the event-related potential (ERP) known as the FN400 (an early frontal effect relating to familiarity) which revealed differences between correct and incorrect context memories in the spatial but not color conditions. In our analyses we show that a classifier designed to distinguish between correct and incorrect context memories, more strongly involves early activity (400-500 ms) over the frontal channels for the location distinctions, than for the extrinsic color associations. In contrast, the classifier designed to classify memory for the item (without memory for the context), had more frontal channel involvement for the color associated experiments than for the spatial experiments. Taken together these results argue that location may be bound more tightly with the item than an extrinsic color association. The multivariate classification approach also showed that trial-by-trial variation in EEG corresponding to these ERP components were predictive of subjects' behavioral responses. Additionally, the multivariate classification approach enabled analysis of error conditions that did not have sufficient trials for standard ERP analyses. These results suggested that false alarms were primarily attributable to item memory (as opposed to memory of associated context), as commonly predicted, but with little previous corroborating EEG evidence.

Paddy rice is one of the most important cereal crops worldwide, so it is very important to accurately monitor its growth status and photosynthetic efficiency. The nitrogen (N) level is a key factor closely related to crop growth. In this study, laser-induced fluorescence (LIF) technology combined with multi-variate analysis was applied to investigate the effect of paddy rice variety on N fertilizer level monitoring. Principal components analysis was conducted to extract the variables of the main fluorescence characteristics to identify N levels. Experimental results demonstrated that no nitrogen fertilizer can be completely identified for each paddy rice variety. In addition, other N levels can also be well classified based on the fluorescence characteristics. The relationship between the fluorescence ratio (F735/F685 : F735, and F685 denote the fluorescence intensity at 735 nm, and 685 nm, respectively) and leaf N content of different paddy rice varieties is also discussed. Experimental results revealed that LIF technology is an effective method of monitoring the N fertilizer and leaf biochemical components of paddy rice., Competing Interests: We declare we have no competing interests.

Background: During the last years, fluorescence spectroscopy has been used as a potential tool for the evaluation and characterization of tissues with different disease conditions due to its low cost, high sensitivity, and minimally or noninvasive character., Objective: In this study, fluorescence spectroscopy was used to study 19 paraffin blocks containing human liver tissue from biopsies., Methods and Results: All samples were previously analyzed by two senior pathologists in a single-blind trial. After their evaluation, four liver samples were classified as nonfibrosis (F0), four as initial fibrosis (F1-F2), four as advanced fibrosis (F3), and six as cirrhosis (F4). The fluorescence was induced at different wavelengths as follows: 330, 365, and 405 nm using a portable fiber-optic system. The fluorescence spectra were recorded in the range of 400-750 nm. A distinctive correlation between the shape of each spectrum and the level of fibrosis in the liver sample was detected. A multi-variate statistical analysis based on principal component analysis followed by linear discrimination analysis was applied to develop algorithms able to distinguish different stages of fibrosis based on the characteristics of fluorescence spectra. Pairwise comparisons were performed: F0 versus F1-F2, F1-F2 versus F3, F3 versus F4, and F1-F2 versus F4. The algorithms applied to each set of data yielded values of sensitivity and specificity that were higher than 90% and 95%, respectively, in all the analyzed cases., Conclusions: With this study, it is concluded that fluorescence spectroscopy can be used as a complementary tool for the assessment of liver fibrosis in liver tissue samples, which sets the stage for subsequent clinical trials.

Aims: To measure and model Australian, Cypriot and Italian nurses' beliefs about what care is missed and how frequently it occurs within their settings., Background: This study expands on previous MISSCARE research but now applies and predicts missed care within three countries., Methods: Multivariate analysis was performed to estimate 1,896 nurses' consensus scores about missed care activities based on Alfaro-Lefevre's conceptual framework of care priorities., Results: Five latent variables have direct predictor effects on missed care frequencies. Another four variables including the nurses' age, highest qualifications, absenteeism rate and workplace type, contributed to explaining the overall variance of missed care scores. The nurses' gender had no influence on missed care., Conclusion: Cross country comparisons of missed nursing care allow for a more refined identification of strategies for remediation for both managers and clinicians., Implications for Nursing Management: Reliable consensus estimates about the types and frequencies of missed care can be scaled with variables identified to predict missed care across three different countries. Comparative international studies build on the foundations for understanding missed care in terms of nursing practices, policies and related social policies., (© 2017 John Wiley & Sons Ltd.)

Clinically Isolated Syndrome (CIS) is often considered to be the first neurological episode associated with Multiple sclerosis (MS). At an early stage the inflammatory demyelination occurring in the CNS can manifest as a change in neuronal metabolism, with multiple asymptomatic white matter lesions detected in clinical MRI. Such damage may induce topological changes of brain networks, which can be captured by advanced functional MRI (fMRI) analysis techniques. We test this hypothesis by capturing the effective relationships of 90 brain regions, defined in the Automated Anatomic Labeling (AAL) atlas, using a large-scale Granger Causality (lsGC) framework. The resulting networks are then characterized using graph-theoretic measures that quantify various network topology properties at a global as well as at a local level. We study for differences in these properties in network graphs obtained for 18 subjects (10 male and 8 female, 9 with CIS and 9 healthy controls). Global network properties captured trending differences with modularity and clustering coefficient ( p <0.1). Additionally, local network properties, such as local efficiency and the strength of connections, captured statistically significant ( p <0.01) differences in some regions of the inferior frontal and parietal lobe. We conclude that multivariate analysis of fMRI time-series can reveal interesting information about changes occurring in the brain in early stages of MS.

Raman spectroscopy was used on 95 samples comprising mainly of uranium ore concentrates as well as some UF4 and UO2 samples, in order to classify uranium compounds for nuclear forensic purposes, for the first time. This technique was selected as it is non-destructive and rapid. The spectra obtained from 9 different classes of chemical compounds were subjected to multivariate data analysis such as principal component analysis (PCA), partial least square-discriminant analysis (PLS-DA) and Fisher Discriminant Analysis (FDA). These classes were ammonium diuranate (ADU), sodium diuranate (SDU), ammonium uranyl carbonate (AUC), uranyl hydroxide (UH), UO2, UO3, UO4, U3O8 and UF4. Unsupervised PCA of full spectra shows fairly good distinction among the classes with some overlaps observed with ADU and UH. These overlaps are also reflected in the poorer specificities determined by PLS-DA. Higher values of sensitivities and specificities of remaining compounds were obtained. Supervised FDA based on reduced dataset of only 40 variables shows similar results to that of PCA but with closer clustering of ADU, UH, SDU, AUC. As a rapid and non-destructive technique, Raman spectroscopy is useful and complements existing techniques in multi-faceted nuclear forensics., (Copyright © 2015 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.)