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Ovarian cancer has a poor cure rate and rates of relapse are high. Current recurrence detection is limited by non-specific methods such as blood testing and ultrasound. Based on reports that human epididymis four (HE4) / creatinine (CRE) ratios found in urine are elevated in ovarian cancers, we have developed a paper-based device that combines lateral flow technology and cell phone analysis to quantitatively measure HE4/CRE. Surrogate samples were used to test the performance over clinically expected HE4/CRE ratios. For HE4/CRE ratios of 2 to 47, the percent error was found to be 16.0% on average whether measured by a flatbed scanner or cell phone. There was not a significant difference between the results from the cell phone or scanner. Based on published studies, error in this method was less than the difference required to detect recurrence. This promising new tool, with further development, could be used at home or in low-resource settings to provide timely detection of ovarian cancer recurrence. [ABSTRACT FROM AUTHOR]

The sensitivity of localized surface plasmon resonance (LSPR) of metal nanostructures to adsorbates lends itself to a powerful class of label-free biosensors. Optical properties of plasmonic nanostructures are dependent on the geometrical features and the local dielectric environment. The exponential decay of the sensitivity from the surface of the plasmonic nanotransducer calls for the careful consideration in its design with particular attention to the size of the recognition and analyte layers. In this study, we demonstrate that short peptides as biorecognition elements (BRE) compared to larger antibodies as target capture agents offer several advantages. Using a bioplasmonic paper device (BPD), we demonstrate the selective and sensitive detection of the cardiac biomarker troponin I (cTnI). The smaller sized peptide provides higher sensitivity and a lower detection limit using a BPD. Furthermore, the excellent shelf-life and thermal stability of peptide-based LSPR sensors, which precludes the need for special storage conditions, makes it ideal for use in resource-limited settings. [ABSTRACT FROM AUTHOR]

Interstitial fluid (ISF) is a body fluid that fills, surrounds cells and contains various biomarkers, but it has been challenging to extract ISF in a reliable and sufficient amount with high speed. To address the issues, we developed the tilted microneedle ISF collecting system (TMICS) fabricated by 3D printing. In this system, the microneedle (MN) was inserted at 66° to the skin by TMICS so that the MN length could be extended within a safe range of skin penetration. Moreover, TMICS incorporating three MN patches created reliable ISF collecting conditions by penetrating the skin at consistent angle and force, 4.9 N. Due to the MN length increase and the patch number expansion, the surface area of the penetrated tissue was increased, thereby confirming that ISF extraction efficiency was improved. Skin ISF was collected into the paper reservoir on the patch, and the absorbed area was converted into a volume. ISF extraction from the rat skin in vivo by TMICS was well tolerated, and the 2.9 μL of ISF was obtained within 30 s. Therefore, TMICS is promising to apply in the diagnosis of multiple biomarkers in ISF with high speed and stability. [ABSTRACT FROM AUTHOR]

Any kidney dimension and volume variation can be a remarkable indicator of kidney disorders. Precise kidney segmentation in standard planes plays an undeniable role in predicting kidney size and volume. On the other hand, ultrasound is the modality of choice in diagnostic procedures. This paper proposes a convolutional neural network with nested layers, namely Fast-Unet++, promoting the Fast and accurate Unet model. First, the model was trained and evaluated for segmenting sagittal and axial images of the kidney. Then, the predicted masks were used to estimate the kidney image biomarkers, including its volume and dimensions (length, width, thickness, and parenchymal thickness). Finally, the proposed model was tested on a publicly available dataset with various shapes and compared with the related networks. Moreover, the network was evaluated using a set of patients who had undergone ultrasound and computed tomography. The dice metric, Jaccard coefficient, and mean absolute distance were used to evaluate the segmentation step. 0.97, 0.94, and 3.23 mm for the sagittal frame, and 0.95, 0.9, and 3.87 mm for the axial frame were achieved. The kidney dimensions and volume were evaluated using accuracy, the area under the curve, sensitivity, specificity, precision, and F1. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

Respiratory diseases (RD) are significant public health burdens and malignant diseases worldwide. However, the RD-related biological information and interconnection still need to be better understood. Thus, this study aims to detect common differential genes and potential hub genes (HubGs), emphasizing their actions, signaling pathways, regulatory biomarkers for diagnosing RD and candidate drugs for treating RD. In this paper we used integrated bioinformatics approaches (such as, gene ontology (GO) and KEGG pathway enrichment analysis, molecular docking, molecular dynamic simulation and network-based molecular interaction analysis). We discovered 73 common DEGs (CDEGs) and ten HubGs (ATAD2B, PPP1CB, FOXO1, AKT3, BCR, PDE4D, ITGB1, PCBP2, CD44 and SMARCA2). Several significant functions and signaling pathways were strongly related to RD. We recognized six transcription factor (TF) proteins (FOXC1, GATA2, FOXL1, YY1, POU2F2 and HINFP) and five microRNAs (hsa-mir-218-5p, hsa-mir-335-5p, hsa-mir-16-5p, hsa-mir-106b-5p and hsa-mir-15b-5p) as the important transcription and post-transcription regulators of RD. Ten HubGs and six major TF proteins were considered drug-specific receptors. Their binding energy analysis study was carried out with the 63 drug agents detected from network analysis. Finally, the five complexes (the PDE4D-benzo[a]pyrene, SMARCA2-benzo[a]pyrene, HINFP-benzo[a]pyrene, CD44-ketotifen and ATAD2B-ponatinib) were selected for RD based on their strong binding affinity scores and stable performance as the most probable repurposable protein-drug complexes. We believe our findings will give readers, wet-lab scientists, and pharmaceuticals a thorough grasp of the biology behind RD. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

Sepsis is the uncontrolled reaction of the body to infection-induced inflammation, which results in life-threatening multiple-organ dysfunction (MODS). Although the research on sepsis has advanced significantly in recent years, its pathophysiology remains entirely unknown. Ferroptosis is a new-fashioned type of programmed cell death that may have an impact on sepsis development. However, the precise mechanism still needs to be explored. In this paper, Four pediatric sepsis datasets [training datasets (GSE26378 and GSE26440) and validation datasets (GSE11755 and GSE11281)] were chosen through the GEO (Gene Expression Omnibus) database, and 63 differentially expressions of ferroptosis-relation-genes (DE-FRGs) were eventually discovered using bioinformatics investigation. Functional annotation was performed using GO and KEGG pathway enrichment analysis. Then, four Core-FRGs (FTH1, GPX4, ACSL1, and ACSL6) were extracted after the construction of the protein–protein interaction (PPI) network and the research of the MCODE module. Consequently, Hub-FRG (GPX4) was found using the validation datasets, and correlation exploration of immunity populations (neutrophils, r = − 0.52; CD8 T-cells, r = 0.43) and immunity checkpoints (CD274, r = − 0.42) was implemented. The usefulness of GPX4 as a marker in sepsis was assessed in a mouse model of sepsis. The findings demonstrate that GPX4 is a crucial biomarker and a new latent immunotherapy target for the prediction and therapy of pediatric sepsis. [ABSTRACT FROM AUTHOR]

The scope of the work undertaken in this paper was to explore the feasibility and reliability of using the Raman signature of aromatic amino acids as a marker in the detection of the presence of breast cancer and perhaps, even the prediction of cancer development in very early stages of cancer onset. To be able to assess this hypothesis, we collected most recent and relevant literature in which Raman spectroscopy was used as an analytical tool in the evaluation of breast cell lines and breast tissue, re-analyzed all the Raman spectra, and extracted all spectral bands from each spectrum that were indicative of aromatic amino acids. The criteria for the consideration of the various papers for this study, and hence, the inclusion of the data that they contained were two-fold: (1) The papers had to focus on the characterization of breast tissue with Raman spectroscopy, and (2) the spectra provided within these papers included the spectral range of 500–1200 cm−1, which constitutes the characteristic region for aromatic amino acid vibrational modes. After all the papers that satisfied these criteria were collected, the relevant spectra from each paper were extracted, processed, normalized. All data were then plotted without bias in order to decide whether there is a pattern that can shed light on a possible diagnostic classification. Remarkably, we have been able to demonstrate that cancerous breast tissues and cells decidedly exhibit overexpression of aromatic amino acids and that the difference between the extent of their presence in cancerous cells and healthy cells is overwhelming. On the basis of this analysis, we conclude that it is possible to use the signature Raman bands of aromatic amino acids as a biomarker for the detection, evaluation and diagnosis of breast cancer. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

Immune checkpoint inhibitors have been approved in the USA for tumours exhibiting mismatch repair deficiency (dMMR), microsatellite instability (MSI), or high tumour mutational burden (TMB), with regulatory and reimbursement applications in multiple other countries underway. As the estimated budget impacts of future reimbursements depend on the size of the potential target population, we performed a scoping review and meta-analysis of the prevalence of these pan-tumour biomarkers in different cancers. We systematically searched Medline/Embase and included studies reporting the prevalence of dMMR/MSI/high TMB in solid tumours published 01/01/2018–31/01/2021. Meta-analyses were performed separately for the pan-cancer prevalence of each biomarker, and by cancer type and stage where possible. The searches identified 3890 papers, with 433 prevalence estimates for 32 different cancer types from 201 studies included in meta-analyses. The pooled overall prevalence of dMMR, MSI and high TMB (≥ 10 mutations/Mb) in pan-cancer studies was 2.9%, 2.7% and 14.0%, respectively. The prevalence profiles of dMMR/MSI and high TMB differed across cancer types. For example, endometrial, colorectal, small bowel and gastric cancers showed high prevalence of both dMMR and MSI (range: 8.7–26.8% and 8.5–21.9%, respectively) and high TMB (range: 8.5–43.0%), while cervical, esophageal, bladder/urothelial, lung and skin cancers showed low prevalence of dMMR and MSI (< 5%), but high prevalence of high TMB (range: 23.7–52.6%). For other cancer types, prevalence of all three biomarkers was generally low (< 5%). This structured review of dMMR/MSI/high TMB prevalence across cancers and for specific cancer types and stages provide timely evidence to inform budget impact forecasts in health technology assessments for drug approvals based on these pan-tumour biomarkers. [ABSTRACT FROM AUTHOR]

To predict the health effects of accidental or therapeutic radiation exposure, one must estimate the radiation dose that person received. A well-known ionising radiation biomarker, phosphorylated γ -H2AX protein, is used to evaluate cell damage and is thus suitable for the dose estimation process. In this paper, we present new Bayesian methods that, in contrast to approaches where estimation is carried out at predetermined post-irradiation times, allow for uncertainty regarding the time since radiation exposure and, as a result, produce more precise results. We also use the Laplace approximation method, which drastically cuts down on the time needed to get results. Real data are used to illustrate the methods, and analyses indicate that the models might be a practical choice for the γ -H2AX biomarker dose estimation process. [ABSTRACT FROM AUTHOR]

Biomarker selection and cancer classification play an important role in knowledge discovery using genomic data. Successful identification of gene biomarkers and biological pathways can significantly improve the accuracy of diagnosis and help machine learning models have better performance on classification of different types of cancer. In this paper, we proposed a LogSum + L2 penalized logistic regression model, and furthermore used a coordinate decent algorithm to solve it. The results of simulations and real experiments indicate that the proposed method is highly competitive among several state-of-the-art methods. Our proposed model achieves the excellent performance in group feature selection and classification problems. [ABSTRACT FROM AUTHOR]

The noninvasive diagnosis and monitoring of high prevalence diseases such as cardiovascular diseases, cancers and chronic respiratory diseases are currently priority objectives in the area of health. In this regard, the analysis of volatile organic compounds (VOCs) has been identified as a potential noninvasive tool for the diagnosis and surveillance of several diseases. Despite the advantages of this strategy, it is not yet a routine clinical tool. The lack of reproducible protocols for each step of the biomarker discovery phase is an obstacle of the current state. Specifically, this issue is present at the data preprocessing step. Thus, an open source workflow for preprocessing the data obtained by the analysis of exhaled breath samples using gas chromatography coupled with single quadrupole mass spectrometry (GC/MS) is presented in this paper. This workflow is based on the connection of two approaches to transform raw data into a useful matrix for statistical analysis. Moreover, this workflow includes matching compounds from breath samples with a spectral library. Three free packages (xcms, cliqueMS and eRah) written in the language R are used for this purpose. Furthermore, this paper presents a suitable protocol for exhaled breath sample collection from infants under 2 years of age for GC/MS. [ABSTRACT FROM AUTHOR]

In bioinformatics, the rapid development of gene sequencing technology has produced an increasing amount of microarray data. This type of data shares the typical characteristics of small sample size and high feature dimensions. Searching for biomarkers from microarray data, which expression features of various diseases, is essential for the disease classification. feature selection has therefore became fundemental for the analysis of microarray data, which designs to remove irrelevant and redundant features. There are a large number of redundant features and irrelevant features in microarray data, which severely degrade the classification effectiveness. We propose an innovative feature selection method with the goal of obtaining feature dependencies from a priori knowledge and removing redundant features using spectral clustering. In this paper, the graph structure is firstly constructed by using the gene interaction network as a priori knowledge, and then a link prediction method based on graph neural network is proposed to enhance the graph structure data. Finally, a feature selection method based on spectral clustering is proposed to determine biomarkers. The classification accuracy on DLBCL and Prostate can be improved by 10.90% and 16.22% compared to traditional methods. Link prediction provides an average classification accuracy improvement of 1.96% and 1.31%, and is up to 16.98% higher than the published method. The results show that the proposed method can have full use of a priori knowledge to effectively select disease prediction biomarkers with high classification accuracy. [ABSTRACT FROM AUTHOR]

Tumour progression within the tissue microenvironment is accompanied by complex biomechanical alterations of the extracellular environment. While histopathology images provide robust biochemical markers for tumor progression in clinical settings, a quantitative single cell score using nuclear morphology and chromatin organization integrated with the long range mechanical coupling within the tumor microenvironment is missing. We propose that the spatial chromatin organization in individual nuclei characterises the cell state and their alterations during tumor progression. In this paper, we first built an image analysis pipeline and implemented it to classify nuclei from patient derived breast tissue biopsies of various cancer stages based on their nuclear and chromatin features. Replacing H&E with DNA binding dyes such as Hoescht stained tissue biopsies, we improved the classification accuracy. Using the nuclear morphology and chromatin organization features, we constructed a pseudo-time model to identify the chromatin state changes that occur during tumour progression. This enabled us to build a single-cell mechano-genomic score that characterises the cell state during tumor progression from a normal to a metastatic state. To gain further insights into the alterations in the local tissue microenvironments, we also used the nuclear orientations to identify spatial neighbourhoods that have been posited to drive tumor progression. Collectively, we demonstrate that image-based single cell chromatin and nuclear features are important single cell biomarkers for phenotypic mapping of tumor progression. [ABSTRACT FROM AUTHOR]

This paper addresses the development of predictive models for distinguishing pre-symptomatic infections from uninfected individuals. Our machine learning experiments are conducted on publicly available challenge studies that collected whole-blood transcriptomics data from individuals infected with HRV, RSV, H1N1, and H3N2. We address the problem of identifying discriminatory biomarkers between controls and eventual shedders in the first 32 h post-infection. Our exploratory analysis shows that the most discriminatory biomarkers exhibit a strong dependence on time over the course of the human response to infection. We visualize the feature sets to provide evidence of the rapid evolution of the gene expression profiles. To quantify this observation, we partition the data in the first 32 h into four equal time windows of 8 h each and identify all discriminatory biomarkers using sparsity-promoting classifiers and Iterated Feature Removal. We then perform a comparative machine learning classification analysis using linear support vector machines, artificial neural networks and Centroid-Encoder. We present a range of experiments on different groupings of the diseases to demonstrate the robustness of the resulting models. [ABSTRACT FROM AUTHOR]

This paper demonstrates the design, synthesis, simulation, and testing of three distinct geometries of plasmonic gold nanoparticles for on-chip DNA screening towards liquid biopsy. By employing a seed-mediated growth method, we have synthesized gold nanospheres, nanorods, and nanobipyramids. In parallel, we developed numerical simulations to understand the effects of nanoparticle geometry on the resonance features and refractive index sensitivity. Both experimental and simulation results were compared through a series of studies including in-solution and on-chip tests. We have thoroughly characterized the impact of nanoparticle geometry on the sensitivity to circulating tumor DNA, with immediate implications for liquid biopsy. The results agree well with theoretical predictions and simulations, including both bulk refractive index sensitivity and thin film sensitivity. Importantly, this work quantitatively establishes the link between nanoparticle geometry and efficacy in detecting rare circulating biomarkers. The nanobipyramids provided the highest sensitivity, approximately doubling the sensitivity compared to nanorods. To the best of our knowledge this is the first report carrying through geometric effects of simulation to clinically relevant biosensing. We put forth here synthesis and testing of three nanoparticle geometries, and a framework for both experimental and theoretical validation of plasmonic sensitivities towards liquid biopsy. [ABSTRACT FROM AUTHOR]

The aim of this paper was to identify DNA methylation based biomarkers for predicting overall survival (OS) of stage I–II lung adenocarcinoma (LUAD) patients. Methylation profile data of patients with stage I–II LUAD from The Cancer Genome Atlas (TCGA) database was used to determine methylation sites-based hallmark for stage I–II LUAD patients' OS. The patients were separated into training and validation datasets by using median risk score as cutoff. Univariate Cox, least absolute shrinkage and selection operator (LASSO) and multivariate Cox analyses were employed to develop a DNA methylation signature for OS of patients with stage I–II LUAD. As a result, an 11-DNA methylation signature was determined to be critically associated with the OS of patients with stage I–II LUAD. Analysis of receiver operating characteristics (ROC) suggested a high prognostic effectiveness of the 11-DNA methylation signature in patients with stage I–II LUAD (AUC at 1, 3, 5 years in training set were (0.849, 0.879, 0.831, respectively), validation set (0.742, 0.807, 0.904, respectively), entire TCGA dataset (0.747, 0.818, 0.870, respectively). Kaplan–Meier survival analyses exhibited that survival was significantly longer in the low-risk cohort compared to the high-risk cohort in the training dataset (P = 7e − 07), in the validation dataset (P = 1e − 08), and in the all-cohort dataset (P = 6e − 14). In addition, a nomogram was developed based on molecular factor (methylation risk score) as well as clinical factors (age and cancer status) (AUC at 1, 3, 5 years entire TCGA dataset were 0.770, 0.849, 0.979, respectively). The result verified that our methylomics-associated nomogram had a strong robustness for predicting stage I–II LUAD patients' OS. Furthermore, the nomogram combined clinical and molecular factors to determine an individualized probability of recurrence for patients with stage I–II LUAD, which stood for a major advance in the field of personalized medicine for pulmonary oncology. Collectively, we successfully identified a DNA methylation biomarker and a DNA methylation-based nomogram to predict the OS of patients with stage I–II LUAD. [ABSTRACT FROM AUTHOR]

Recent studies have shown that extracellular vesicles (EVs) can be utilized as appropriate and highly specific biomarkers in liquid biopsy for the diagnosis and prognosis of serious illness. However, there are few methods that can collect and isolate miRNA in EVs simply, quickly and efficiently using general equipment such as a normal centrifuge. In this paper, we developed an advanced glass membrane column (AGC) device incorporating a size-controlled macro-porous glass (MPG) membrane with a co-continuous structure to overcome the limitations of conventional EV collection and miRNA extraction from the EVs. The size of macro-pores in the MPG membrane could be accurately controlled by changing the heating temperature and time on the basis of spinodal decomposition of B2O3, Na2O, and SiO2 in phase separation. The AGC device with an MPG membrane could collect the EVs simply and quickly (< 10 min) from cell culture supernatant, serum and urine. This AGC device could extract miRNA from the EVs captured in the MPG membrane with high efficiency when combined with a miRNA extraction solution. We suggest that the AGC device with an MPG membrane can be useful for the diagnosis and prognosis of serious illness using of EVs in various kinds of body fluids. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

Since the world population is ageing, dementia is going to be a growing concern. Alzheimer's disease is the most common form of dementia. The pathogenesis of Alzheimer's disease is extensively studied, yet unknown remains. Therefore, we aimed to extract new knowledge from existing data. We analysed about 2700 upregulated genes and 2200 downregulated genes from three studies on the CA1 of the hippocampus of brains with Alzheimer's disease. We found that only the calcium signalling pathway enriched by 48 downregulated genes was consistent between all three studies. We predicted miR-129 to target nine out of 48 genes. Then, we validated miR-129 to regulate six out of nine genes in HEK cells. We noticed that four out of six genes play a role in synaptic plasticity. Finally, we confirmed the upregulation of miR-129 in the hippocampus of brains of rats with scopolamine-induced amnesia as a model of Alzheimer's disease. We suggest that future research should investigate the possible role of miR-129 in synaptic plasticity and Alzheimer's disease. This paper presents a novel framework to gain insight into potential biomarkers and targets for diagnosis and treatment of diseases. [ABSTRACT FROM AUTHOR]

Deconvolution of bulk transcriptomics data from mixed cell populations is vital to identify the cellular mechanism of complex diseases. Existing deconvolution approaches can be divided into two major groups: supervised and unsupervised methods. Supervised deconvolution methods use cell type-specific prior information including cell proportions, reference cell type-specific gene signatures, or marker genes for each cell type, which may not be available in practice. Unsupervised methods, such as non-negative matrix factorization (NMF) and Convex Analysis of Mixtures (CAM), in contrast, completely disregard prior information and thus are not efficient for data with partial cell type-specific information. In this paper, we propose a semi-supervised deconvolution method, semi-CAM, that extends CAM by utilizing marker information from partial cell types. Analysis of simulation and two benchmark data have demonstrated that semi-CAM outperforms CAM by yielding more accurate cell proportion estimations when markers from partial/all cell types are available. In addition, when markers from all cell types are available, semi-CAM achieves better or similar accuracy compared to the supervised method using signature genes, CIBERSORT, and the marker-based supervised methods semi-NMF and DSA. Furthermore, analysis of human chlamydia-infection data with bulk expression profiles from six cell types and prior marker information of only three cell types suggests that semi-CAM achieves more accurate cell proportion estimations than CAM. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) is the third most common cancer affecting people. The discovery of new, non-invasive, specific, and sensitive molecular biomarkers for CRC may assist in the diagnosis and support therapeutic decision making. Exosomal miRNAs have been demonstrated in carcinogenesis and CRC development, which makes these miRNAs strong biomarkers for CRC. Deep sequencing allows a robust high-throughput informatics investigation of the types and abundance of exosomal miRNAs. Thus, exosomal miRNAs can be efficiently examined as diagnostic biomarkers for disease screening. In the present study, a number of 660 mature miRNAs were detected in patients diagnosed with CRC at different stages. Of which, 29 miRNAs were differentially expressed in CRC patients compared with healthy controls. Twenty-nine miRNAs with high abundance levels were further selected for subsequent analysis. These miRNAs were either highly up-regulated (e.g., let-7a-5p, let-7c-5p, let-7f-5p, let-7d-3p, miR-423-5p, miR-3184-5p, and miR-584) or down-regulated (e.g., miR-30a-5p, miR-99-5p, miR-150-5p, miR-26-5p and miR-204-5p). These miRNAs influence critical genes in CRC, leading to either tumor growth or suppression. Most of the reported diagnostic exosomal miRNAs were shown to be circulating in blood serum. The latter is a novel miRNA that was found in exosomal profile of blood serum. Some of the predicted target genes of highly expressed miRNAs participate in several cancer pathways, including CRC pathway. These target genes include tumor suppressor genes, oncogenes and DNA repair genes. Main focus was given to multiple critical signaling cross-talking pathways including transforming growth factor β (TGFβ) signaling pathways that are directly linked to CRC. In conclusion, we recommend further analysis in order to experimentally confirm exact relationships between selected differentially expressed miRNAs and their predicted target genes and downstream functional consequences. [ABSTRACT FROM AUTHOR]

Cerebral amyloid angiopathy (CAA) is a prevalent vascular dementia and common comorbidity of Alzheimer's disease (AD). While it is known that vascular fibrillar amyloid β (Aβ) deposits leads to vascular deterioration and can drive parenchymal CAA related inflammation (CAA-ri), underlying mechanisms of CAA pathology remain poorly understood. Here, we conducted brain regional proteomic analysis of early and late disease stages in the rTg-DI CAA rat model to gain molecular insight to mechanisms of CAA/CAA-ri progression and identify potential brain protein markers of CAA/CAA-ri. Longitudinal brain regional proteomic analysis revealed increased differentially expressed proteins (DEP) including ANXA3, HTRA1, APOE, CST3, and CLU, shared between the cortex, hippocampus, and thalamus, at both stages of disease in rTg-DI rats. Subsequent pathway analysis indicated pathway enrichment and predicted activation of TGF-β1, which was confirmed by immunolabeling and ELISA. Further, we identified numerous CAA related DEPs associate with astrocytes (HSPB1 and MLC1) and microglia (ANXA3, SPARC, TGF-β1) not previously associated with astrocytes or microglia in other AD models, possibly indicating that they are specific to CAA-ri. Thus, the data presented here identify several potential brain protein biomarkers of CAA/CAA-ri while providing novel molecular and mechanistic insight to mechanisms of CAA and CAA-ri pathological progression and glial cell mediated responses. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) and post-stroke cognitive impairment (PSCI) are the leading causes of progressive dementia related to neurodegenerative and cerebrovascular injuries in elderly populations. Despite decades of research, patients with these conditions still lack minimally invasive, low-cost, and effective diagnostic and treatment methods. MicroRNAs (miRNAs) play a vital role in AD and PSCI pathology. As they are easily obtained from patients, miRNAs are promising candidates for the diagnosis and treatment of these two disorders. In this study, we performed complete sequencing analysis of miRNAs from 24 participants, split evenly into the PSCI, post-stroke non-cognitive impairment (PSNCI), AD, and normal control (NC) groups. To screen for differentially expressed miRNAs (DE-miRNAs) in patients, we predicted their target genes using bioinformatics analysis. Our analyses identified miRNAs that can distinguish between the investigated disorders; several of them were novel and never previously reported. Their target genes play key roles in multiple signaling pathways that have potential to be modified as a clinical treatment. In conclusion, our study demonstrates the potential of miRNAs and their key target genes in disease management. Further in-depth investigations with larger sample sizes will contribute to the development of precise treatments for AD and PSCI. [ABSTRACT FROM AUTHOR]

Central retinal artery occlusion (CRAO) is an acute retinal ischaemic disease, but early diagnosis is challenging due to a lack of biomarkers. Blood samples were collected from CRAO patients and cataract patients. Gene expression profiles were distinct between arterial/venous CRAO blood (A–V group) and venous CRAO/control blood (V–C group) samples. Differentially expressed genes (DEGs) were subjected to GO and KEGG enrichment analyses. Hub genes were identified by Cytoscape and used to predict gene interactions via GeneMANIA. Immune cell infiltration was analysed by CIBERSORT. More than 1400 DEGs were identified in the A–V group and 112 DEGs in the V–C group compared to controls. The DEGs in both groups were enriched in the ribosome pathway, and those in the V–C group were also enriched in antigen processing/MHC pathways. Network analysis identified ribosomal proteins (RPS2 and RPS5) as the core genes of the A–V group and MHC genes (HLA-F) as the core genes of the V–C group. Coexpression networks showed ribosomal involvement in both groups, with additional immune responses in the V–C group. Immune cell analysis indicated increased numbers of neutrophils and T cells. Ribosomal and MHC-related genes were identified as potential CRAO biomarkers, providing research directions for prevention, diagnosis, treatment and prognosis. [ABSTRACT FROM AUTHOR]

Cardiac Troponin I (cTnI) could be used to identify individuals at elevated risk of cardiac death in lung cancer (LC) screening settings. In a population-based, randomized LC screening trial in Germany ("LUSI" study) serum cTnI was measured by high-sensitivity assay in blood samples collected at baseline, and categorized into unquantifiable/low (< 6 ng/L), intermediate (≥ 6–15 ng/L), and elevated (≥ 16 ng/L). Cox proportional-hazard models were used to estimate risk of all-cause and cardiac mortality with cTnI levels. After exclusion criteria, 3653 participants were included for our analyses, of which 82.4% had low, 12.8% intermediate and 4.8% elevated cTnI, respectively. Over a median follow up of 11.87 years a total of 439 deaths occurred, including 67 caused by cardiac events. Within the first 5 years after cTnI measurement, intermediate or elevated cTnI levels showed approximately 1.7 (HR = 1.69 [95% CI 0.57–5.02) and 4.7-fold (HR = 4.66 [1.73–12.50]) increases in risk of cardiac death relative to individuals with unquantifiable/low cTnI, independently of age, sex, smoking and other risk factors. Within this time interval, a risk model based on age, sex, BMI, smoking history and cTnI showed a combined area under the ROC curve (AUC) of 73.6 (58.1–87.3), as compared to 70.4 (53.3–83.5) for a model without cTnI. Over the time interval of > 5–10 years after blood donation, the relative risk associations with cTnI and were weaker. cTnI showed no association with mortality from any other (non-cardiac) cause. Our findings show that cTnI may be of use for identifying individuals at elevated risk specifically of short-term cardiac mortality in the context of LC screening. [ABSTRACT FROM AUTHOR]

Lung cancer remains the leading cause of cancer mortality worldwide. Small cell lung cancer (SCLC) accounts for 10–15% of cases and has an overall 5-years survival rate of only 15%. Neuron-specific enolase (NSE) has been identified as a useful biomarker for early SCLC diagnosis and therapeutic monitoring. This work reports an electrochemical immunosensing platform based on a graphene-graphitic carbon nitride (g-C3N4) nanocomposite for ultrasensitive NSE detection. The g-C3N4 nanosheets and graphene nanosheets were synthesized via liquid exfoliation and integrated through self-assembly to form the nanocomposite. This nanocomposite was used to modify screen-printed carbon electrodes followed by covalent immobilization of anti-NSE antibodies. The unique properties of the graphene-g-C3N4 composite facilitated efficient antibody loading while also enhancing electron transfer efficiency and electrochemical response. Systematic optimization of experimental parameters was performed. The immunosensor exhibited a wide linear detection range of 10 pg/mL to 100 ng/mL and low limit of detection of 3 pg/mL for NSE along with excellent selectivity against interferences. Real serum matrix analysis validated the applicability of the developed platform for sensitive and accurate NSE quantifica-tion at clinically relevant levels. This novel graphene-g-C3N4 nanocomposite based electro-chemical immunoassay demonstrates great promise for early diagnosis of SCLC. [ABSTRACT FROM AUTHOR]

The SARS-CoV-2 pandemic has emphasized the need to explore how variations in the immune system relate to the severity of the disease. This study aimed to explore inter-individual variation in response to SARS-CoV-2 infection by comparing T cell, B cell, and innate cell immune subsets among primary infected children and adults (i.e., those who had never experienced SARS-CoV-2 infection nor received vaccination previously), with varying disease severity after infection. We also examined immune subset kinetics in convalescent individuals compared to those with persistent infection to identify possible markers of immune dysfunction. Distinct immune subset differences were observed between infected adults and children, as well as among adult cases with mild, moderate, and severe disease. IgM memory B cells were absent in moderate and severe cases whereas frequencies of B cells with a lack of surface immunoglobulin expression were significantly higher in severe cases. Interestingly, these immune subsets remained stable during recovery implying that these subsets could be associated with underlying baseline immune variation. Our results offer insights into the potential immune markers associated with severe COVID-19 and provide a foundation for future research in this area. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

Melanoma is a malignant skin tumor. This study aimed to explore and assess the effect of novel biomarkers on the progression of melanoma. Differently expressed genes (DEGs) were screened from GSE3189 and GSE46517 datasets of Gene Expression Omnibus database using GEO2R. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses were conducted based on the identified DEGs. Hub genes were identified and assessed using protein–protein interaction networks, principal component analysis, and receiver operating characteristic curves. Quantitative real-time polymerase chain reaction was employed to measure the mRNA expression levels. TIMER revealed the association between aldehyde dehydrogenase 2 (ALDH2) and tumor immune microenvironment. The viability, proliferation, migration, and invasion were detected by cell counting kit-8, 5-ethynyl-2′-deoxyuridine, wound healing, and transwell assays. Total 241 common DEGs were screened out from GSE3189 and GSE46517 datasets. We determined 6 hub genes with high prediction values for melanoma, which could distinguish tumor samples from normal samples. ALDH2, ADH1B, ALDH3A2, DPT, EPHX2, and GATM were down-regulated in A375 and SK-MEL-2 cells, compared with the human normal melanin cell line (PIG1 cells). ALDH2 was selected as the candidate gene in this research, presenting a high diagnostic and predictive value for melanoma. ALDH2 had a positive correlation with the infiltrating levels of immune cells in melanoma microenvironment. Overexpression of ALDH2 inhibited cell viability, proliferation, migration, and invasion of A375/SK-MEL-2 cells. ALDH2 is a new gene biomarker of melanoma, which exerts an inhibitory effect on melanoma. [ABSTRACT FROM AUTHOR]

We present the first unambiguous experimental method enabling single-fluorophore sensitivity in a flow cytometer using quantum properties of single-photon emitters. We use a quantum measurement based on the second-order coherence function to prove that the optical signal is produced by individual biomarkers traversing the interrogation volume of the flow cytometer from the first principles. This observation enables the use of the quantum toolbox for rapid detection, enumeration, and sorting of single fluorophores in large cell populations as well as a 'photons-to-moles' calibration of this measurement modality. [ABSTRACT FROM AUTHOR]

To date, publications have shown that compositions of oral microbiota differ depending on their habitats (e.g. tongue, tonsils, pharynx). The absence of set standards for the choice of the areas and conditions of material collection makes the oral microbiome one of the most difficult environments for a comparative analysis with other researchers, which is a meaningful limitation during an assessment of the potential effects of microorganisms as biomarkers in the courses of various human diseases. Therefore, standardisation of basic conditions of a dental examination and collection of material for the next generation sequencing (NGS) is worth attempting. The standardisation of the dental exam and collection of the clinical materials: saliva, swab from the tongue ridge, hard palate, palatine tonsils and oropharynx, supragingival plaque and subgingival plaque. Protocol involved the patients (n = 60), assigned to 3 groups: I—COVID-19 convalescents who received antibiotics, n = 17, II—COVID-19 convalescents, n = 23 and III—healthy individuals, n = 20. The collected biological samples were used to conduct NGS (16S rRNA). The conditions of patient preparation for collecting biological materials as well as the schedule of dental examination, were proposed. Based on the research conducted, we have indicated the dental indicators that best differentiate the group of COVID-19 patients (groups I and II) from healthy people (group III). These include the DMFT, D and BOP indices. The use of alpha and beta diversity analysis provided an overall insight into the diversity of microbial communities between specific niches and patient groups. The most different diversity between the studied group of patients (group II) and healthy people (group III) was noted in relation to the supragingival plaque. The order of activities during the dental exam as well as while collecting and securing clinical materials is particularly important to avoid technical errors and material contamination which may result in erroneous conclusions from the analyses of the results of sensitive tests such as the NGS. It has been shown that the dental indices: DMFT, D number, PI and BOP are the best prognostic parameters to assess the oral health. Based on beta diversity the most sensitive niche and susceptible to changes in the composition of the microbiota is the supragingival plaque. The procedures developed by our team can be applied as ready-to-use forms in studies conducted by other researchers. [ABSTRACT FROM AUTHOR]

The COVID-19 influenza emerged and proved to be fatal, causing millions of deaths worldwide. Vaccines were eventually discovered, effectively preventing the severe symptoms caused by the disease. However, some of the population (elderly and patients with comorbidities) are still vulnerable to severe symptoms such as breathlessness and chest pain. Identifying these patients in advance is imperative to prevent a bad prognosis. Hence, machine learning and deep learning algorithms have been used for early COVID-19 severity prediction using clinical and laboratory markers. The COVID-19 data was collected from two Manipal hospitals after obtaining ethical clearance. Multiple nature-inspired feature selection algorithms are used to choose the most crucial markers. A maximum testing accuracy of 95% was achieved by the classifiers. The predictions obtained by the classifiers have been demystified using five explainable artificial intelligence techniques (XAI). According to XAI, the most important markers are c-reactive protein, basophils, lymphocytes, albumin, D-Dimer and neutrophils. The models could be deployed in various healthcare facilities to predict COVID-19 severity in advance so that appropriate treatments could be provided to mitigate a severe prognosis. The computer aided diagnostic method can also aid the healthcare professionals and ease the burden on already suffering healthcare infrastructure. [ABSTRACT FROM AUTHOR]

The gut microbiome plays a significant role in the development of Type 2 Diabetes Mellitus (T2DM), but the functional mechanisms behind this association merit deeper investigation. Here, we used the nanopore sequencing technology for metagenomic analyses to compare the gut microbiome of individuals with T2DM from the United Arab Emirates (n = 40) with that of control (n = 44). DMM enterotyping of the cohort resulted concordantly with previous results, in three dominant groups Bacteroides (K1), Firmicutes (K2), and Prevotella (K3) lineages. The diversity analysis revealed a high level of diversity in the Firmicutes group (K2) both in terms of species richness and evenness (Wilcoxon rank-sum test, p value < 0.05 vs. K1 and K3 groups), consistent with the Ruminococcus enterotype described in Western populations. Additionally, functional enrichment analyses of KEGG modules showed significant differences in abundance between individuals with T2DM and controls (FDR < 0.05). These differences include modules associated with the degradation of amino acids, such as arginine, the degradation of urea as well as those associated with homoacetogenesis. Prediction analysis with the Predomics approach suggested potential biomarkers for T2DM, including a balance between a depletion of Enterococcus faecium and Blautia lineages with an enrichment of Absiella spp or Eubacterium limosum in T2DM individuals, highlighting the potential of metagenomic analysis in predicting predisposition to diabetic cardiomyopathy in T2DM patients. [ABSTRACT FROM AUTHOR]

The potentially problematic use of the Internet is a growing concern worldwide, which causes and consequences are not completely understood yet. The neurobiology of Internet addiction (IA) has attracted much attention in scientific research, which is now focusing on identifying measurable biological markers. Aim of this study was to investigate epigenetic and genetic regulation of oxytocin receptor (OXTR), dopamine transporter (DAT1) and serotonin transporter (SERT) genes using DNA obtained from saliva samples of young university students: the Internet Addiction Test (IAT) was administered to evaluate the potential existence and intensity of IA. Significant changes in DNA methylation levels at OXTR, DAT1 and SERT genes were observed in the 30 < IAT < 49 group (mild-risk internet users) compared to the IAT < 29 subjects (complete control of internet use) and IAT > 50 subjects (considered as moderately addicted). Moreover, epigenetic markers were significantly correlated, either directly (for OXTR and DAT1) or inversely (OXTR and DAT1 versus SERT), to the psychometric properties. Our data confirmed the association of OXTR, DAT1 and SERT genes in processes related to behavioural addictions and might be of relevance to suggest possible biological predictors of altered behaviours and the eventual vulnerability to develop an IA. Different other genetic pathways have been suggested to play a role in IA and research is ongoing to better define them, in order to help in the early diagnosis as well as in the development of new potential treatments. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

Cardiac myxoma (CM) is a potentially life-threatening disease because frequently asymptomatic or debuts with aspecific manifestations. Definitive diagnosis is established by histopathological assessment including tumor and endothelial cell markers. To derive a specific panel of circulating cells antigenically detectable, pre-surgery peripheral blood samples of CM patients were analyzed. Pre-surgery peripheral blood samples from patients with CM were simultaneously analyzed for Circulating tumor cells (CTCs) and circulating endothelial cells (CECs) that were matched with tumor tissue profiles and with patient-derived xenografts (PDXs) distinguishing tumor regions. Moreover, CECs values in CM patients were further matched with CEC's levels in cardiovascular disease and control subjects. The blood-derived cytological specimens detected at least 1–3 CTCs/ml in 10 tested CM samples (p = 0.0001) showing specific CM features preserved in the central zones of the tumor. The central zone of the primary tumor, supported by a vessel density rate (55 ± 7%), with a proliferative profile of 32 ± 3% and a percentage of Calretininpos cells (p = 0.03), is the principal site of CTCs (r = 00) dissemination. The subsets of endothelial cells recognized in the blood were indifferent to their topological distribution within the tumor and corresponding PDXs. With further refinement and validation in large cohorts, multiparametric liquid biopsies can optimally integrate clinically informative datasets and maximize their utility in pre-surgery evaluation of CM patients. Blood-derived culture's protocol provides a versatile method capable of viable analysis of CTCs of non-hematological rare tumors which conventional antibody-mediated analytical platform is unable to perform. Distinctive blood- based cell phenotype contributes to differentiate CM from other differentials assuring its prompt surgical resection by combining blood-based cell biomarkers integrated with clinically informative datasets. [ABSTRACT FROM AUTHOR]

Large-artery atherosclerosis (LAA) is a leading cause of cerebrovascular disease. However, LAA diagnosis is costly and needs professional identification. Many metabolites have been identified as biomarkers of specific traits. However, there are inconsistent findings regarding suitable biomarkers for the prediction of LAA. In this study, we propose a new method integrates multiple machine learning algorithms and feature selection method to handle multidimensional data. Among the six machine learning models, logistic regression (LR) model exhibited the best prediction performance. The value of area under the receiver operating characteristic curve (AUC) was 0.92 when 62 features were incorporated in the external validation set for the LR model. In this model, LAA could be well predicted by clinical risk factors including body mass index, smoking, and medications for controlling diabetes, hypertension, and hyperlipidemia as well as metabolites involved in aminoacyl-tRNA biosynthesis and lipid metabolism. In addition, we found that 27 features were present among the five adopted models that could provide good results. If these 27 features were used in the LR model, an AUC value of 0.93 could be achieved. Our study has demonstrated the effectiveness of combining machine learning algorithms with recursive feature elimination and cross-validation methods for biomarker identification. Moreover, we have shown that using shared features can yield more reliable correlations than either model, which can be valuable for future identification of LAA. [ABSTRACT FROM AUTHOR]

Sepsis is a time dependent condition. Screening tools based on clinical parameters have been shown to increase the identification of sepsis. The aim of current study was to evaluate the additional predictive value of immunological molecular markers to our previously developed prehospital screening tools. This is a prospective cohort study of 551 adult patients with suspected infection in the ambulance setting of Stockholm, Sweden between 2017 and 2018. Initially, 74 molecules and 15 genes related to inflammation were evaluated in a screening cohort of 46 patients with outcome sepsis and 50 patients with outcome infection no sepsis. Next, 12 selected molecules, as potentially synergistic predictors, were evaluated in combination with our previously developed screening tools based on clinical parameters in a prediction cohort (n = 455). Seven different algorithms with nested cross-validation were used in the machine learning of the prediction models. Model performances were compared using posterior distributions of average area under the receiver operating characteristic (ROC) curve (AUC) and difference in AUCs. Model variable importance was assessed by permutation of variable values, scoring loss of classification as metric and with model-specific weights when applicable. When comparing the screening tools with and without added molecular variables, and their interactions, the molecules per se did not increase the predictive values. Prediction models based on the molecular variables alone showed a performance in terms of AUCs between 0.65 and 0.70. Among the molecular variables, IL-1Ra, IL-17A, CCL19, CX3CL1 and TNF were significantly higher in septic patients compared to the infection non-sepsis group. Combing immunological molecular markers with clinical parameters did not increase the predictive values of the screening tools, most likely due to the high multicollinearity of temperature and some of the markers. A group of sepsis patients was consistently miss-classified in our prediction models, due to milder symptoms as well as lower expression levels of the investigated immune mediators. This indicates a need of stratifying septic patients with a priori knowledge of certain clinical and molecular parameters in order to improve prediction for early sepsis diagnosis. Trial registration: NCT03249597. Registered 15 August 2017. [ABSTRACT FROM AUTHOR]

Gastric cancer (GC) is the fifth most frequent malignancy worldwide and has a high mortality rate related to late diagnosis. Although the gold standard for the GC diagnosis is endoscopy with biopsy, nonetheless, it is not cost-effective and is invasive for the patient. The Human leukocyte antigen G (HLA-G) molecule is a checkpoint of the immune response. Its overexpression in cancer is associated with immune evasion, metastasis, poor prognosis, and lower overall survival. We evaluate the plasma levels of soluble HLA-G, (sHLA-G) in patients with GC and benign gastric pathologies using an ELISA test. A higher concentration of sHLA-G in patients with GC than in those with benign pathologies, higher levels of plasma sHLA-G in women with GC compared with men and significant differences in the sHLA-G levels between the benign gastric pathologies evaluated, was our main findings. As no significant differences were found between the GC assessed stages in our study population, we suggest that sHLA-G is not an adequate marker for staging GC, but it does have diagnostic potential. In addition to providing information on the potential of sHLA-G as a diagnostic marker for GC, our study demonstrate that HLA-G molecules can be found in the membrane of exosomes, which highlights the need to perform studies with a larger number of samples to explore the functional implications of HLA-G positive exosomes in the context of gastric cancer, and to determine the clinical significance and possible applications of these findings in the development of non-invasive diagnostic methods. [ABSTRACT FROM AUTHOR]