Salivary interleukin-17A and interleukin-18 levels in patients with celiac disease and periodontitis.

Background: An increased level of interleukin-17A and interleukin-18 in the serum and intestinal mucosa of celiac disease patients reflecting the severity of villous atrophy and inflammation was documented. Thus, the objective of this study was to evaluate the concentrations of salivary-17A, interleukin-1 beta, and interleukin-18 in patients with celiac disease who are on a gluten-free diet, both with and without periodontitis, and to compare these levels with those in healthy individuals. Methods: The study involved 23 participants with serologically confirmed celiac disease (CD) and 23 control subjects. The CD patients had been following a gluten-free diet (GFD) for a minimum of 1 year and had no other autoimmune disorders. The research involved collecting demographic data, conducting periodontal examinations, gathering unstimulated whole saliva, and performing enzyme-linked immunosorbent assays to measure salivary interleukin-17A, interleukin-1 beta, and interleukin-18 levels. Spearman's correlation analysis was utilized to explore the relationships between CD markers in patients on a GFD and their periodontal clinical findings. Results: The periodontal findings indicated significantly lower values in celiac disease patients adhering to a gluten-free diet compared to control subjects (p = 0.001). No significant differences were found in salivary IL-17A, IL-18, and IL-1B levels between celiac disease patients and control subjects. Nevertheless, the levels of all interleukins were elevated in periodontitis patients in both the celiac and control groups. The IL-1 Beta level was significantly higher in periodontitis patients compared to non-periodontitis patients in the control group (p = 0.035). Significant negative correlations were observed between serum IgA levels and plaque index (r = -0.460, p = 0.010), as well as gingival index (r = -0.396, p = 0.030) in CD patients on a gluten-free diet. Conclusion: Celiac disease patients on gluten-free diet exhibited better periodontal health compared to control subjects. However, increased levels of salivary IL-17A, IL-18 and IL-1B levels were associated with periodontitis. Additionally, serum IgA level was significantly inversely associated with periodontitis clinical manifestations and with salivary inflammatory mediators in CD patients on GFD.

Considerations for biomarker strategies in clinical trials investigating tau-targeting therapeutics for Alzheimer's disease.

The use of biomarker-led clinical trial designs has been transformative for investigating amyloid-targeting therapies for Alzheimer's disease (AD). The designs have ensured the correct selection of patients on these trials, supported target engagement and have been used to support claims of disease modification and clinical efficacy. Ultimately, this has recently led to approval of disease-modifying, amyloid-targeting therapies for AD; something that should be noted for clinical trials investigating tau-targeting therapies for AD. There is a clear overlap of the purpose of biomarker use at each stage of clinical development between amyloid-targeting and tau-targeting clinical trials. However, there are differences within the potential context of use and interpretation for some biomarkers in particular measurements of amyloid and utility of soluble, phosphorylated tau biomarkers. Given the complexities of tau in health and disease, it is paramount that therapies target disease-relevant tau and, in parallel, appropriate assays of target engagement are developed. Tau positron emission tomography, fluid biomarkers reflecting tau pathology and downstream measures of neurodegeneration will be important both for participant recruitment and for monitoring disease-modification in tau-targeting clinical trials. Bespoke design of biomarker strategies and interpretations for different modalities and tau-based targets should also be considered.

Markers of sensory dynamics in phenols-rich virgin olive oils under optimal storage conditions.

Early changes in sensory quality of phenols-rich virgin olive oil (VOO) and their relationship with the chemical changes are less studied in the literature. Therefore, the objective of this study was to propose a predictive model of dynamics of sensory changes based on specific chemical markers. The evolution of the sensory quality of phenol-rich VOOs from Tuscan cultivars stored under optimal storage conditions (i.e., absence of light, no O2 exposure, low temperature) was investigated using a multi-step methodological approach combining sensory (official sensory analysis (so-called Panel Test), Descriptive Analysis and Temporal Dominance of Sensation) and chemical measurements. The sensory map from descriptive data was related to the phenolic and volatile profiles, measured using HPLC-DAD and HS-SPME-GC-MS, respectively. A predictive model of the sensory changes over storage based on chemical compounds was developed. Results showed that very early changes involving phenolic and volatile compounds profiles occur in VOOs stored under optimal storage conditions, which turn in changes in sensory properties evaluated by the official panel test, the descriptive analysis and the temporal dominance of sensation. Furthermore, a chemical marker of sensory dynamics of oils during storage was identified as the ratio between two groups of secoiridoids. The proposed model, supported by the mentioned chemical marker, has the potential of improving the control of sensory changes in phenols-rich virgin olive oils during storage in optimal conditions.

Bibliometric Analysis of the Highly Cited Articles on Biomarker Discovery for Irritable Bowel Syndrome from 1985 to 2023.

BACKGROUND: With an increasing number of patients with irritable bowel syndrome (IBS) and availability of many pharmacological treatment options, there is an urgent need to develop and validate IBS biomarkers for prognostication and selection of patients for treatment and monitoring. The usage of investigations is limited because of its invasive nature, poor patient acceptability, and sampling variability. The bibliometric analysis of biomarker discovery of IBS articles will help in understanding current research trends of biomarkers study of IBS. AIM: To study the most highly cited articles from literature search on the biomarker for IBS to provide simple educational source. STUDY DESIGN: A bibliometric literature review-the electronic search by terms and keywords were searched in PubMed databases. The commonly cited article was searched from 1985 to 2023. The total citation number was received from knowledge search engines like Google Scholar. Articles were classified according to number of citations, year of publication, journal name, authors, publications from continents of countries of origin, research hotspots, and article title. The articles written in English and other languages were included in the analysis. RESULTS: Total number of articles found was 1,449. The mean number of citations per article was 177. The citation count ranged from 24 to 1,191. The articles with citations >30 were included in the study. The majority of articles (n = 175) were published between 2016 and 2023. Among the highly cited articles, the most prevalent topic of interest was biomarker discovery. Most of the articles were original articles. The continent of origin for most of the articles was the United States of America (n = 163), Europe (n = 145), United Kingdom (n = 23), Asia (n = 23), Australia (n = 14), etc. Conclusion: The analysis of articles on biomarker discovery for IBS will help in understanding the requirement for unmet need for the discovery of biomarker for IBS. The current bibliometric study has highlighted the work of authors with advanced knowledge about discovery of the biomarker for IBS. This study will help to identify the current trends in the biomarker discovery for IBS and help for the further evolution of the field.

Quantitative Detection of Multiple Cardiovascular Biomarkers by an Antibody Microarray-Based Metal-Enhanced Fluorescence Assay.

Accurate detection of multiple cardiovascular biomarkers is crucial for the timely screening of acute coronary syndrome (ACS) and differential diagnosis from acute aortic syndrome (AAS). Herein, an antibody microarray-based metal-enhanced fluorescence assay (AMMEFA) has been developed to quantitatively detect 7 cardiovascular biomarkers through the formation of a sandwich immunoassay on the poly(glycidyl methacrylate-co-2-hydroxyethyl methacrylate)-decorated GNR-modified slide (GNR@P(GMA-HEMA) slide). The AMMEFA exhibits high specificity and sensitivity, the linear ranges span 5 orders of magnitude, and the limits of detection (LODs) of cardiac troponin I (cTnI), heart-type fatty acid binding protein (H-FABP), C-reactive protein (CRP), copeptin, myoglobin, D-Dimer, and N-terminal pro-brain natriuretic peptide (NT-proBNP) reach 0.07, 0.2, 65.7, 0.6, 0.2, 8.3, and 0.3 pg mL-1, respectively. To demonstrate its practicability, the AMMEFA has been applied to quantitatively analyze 7 cardiovascular biomarkers in 140 clinical plasma samples. In addition, the expression levels of cardiovascular biomarkers were analyzed by the least absolute shrinkage and selector operator (LASSO) regression, and the area under receiver operator characteristic curves (AUCs) of healthy donors (HDs), ACS patients, and AAS patients are 0.99, 0.98, and 0.97, respectively.

Non-invasive biomarkers for sperm retrieval in non-obstructive patients: a comprehensive review.

Recent advancements in reproductive medicine have guided novel strategies for addressing male infertility, particularly in cases of non-obstructive azoospermia (NOA). Two prominent invasive interventions, namely testicular sperm extraction (TESE) and microdissection TESE (micro-TESE), have emerged as key techniques to retrieve gametes for assisted reproduction technologies (ART). Both heterogeneity and complexity of NOA pose a multifaceted challenge to clinicians, as the invasiveness of these procedures and their unpredictable success underscore the need for more precise guidance. Seminal plasma can be aptly regarded as a liquid biopsy of the male reproductive tract, encompassing secretions from the testes, epididymides, seminal vesicles, bulbourethral glands, and prostate. This fluid harbors a variety of cell-free nucleic acids, microvesicles, proteins, and metabolites intricately linked to gonadal activity. However, despite numerous investigations exploring potential biomarkers from seminal fluid, their widespread inclusion into the clinical practice remains limited. This could be partially due to the complex interplay of diverse clinical and genetic factors inherent to NOA that likely contributes to the absence of definitive biomarkers for residual spermatogenesis. It is conceivable that the integration of clinical data with biomarkers could increase the potential in predicting surgical procedure outcomes and their choice in NOA cases. This comprehensive review addresses the challenge of sperm retrieval in NOA through non-invasive biomarkers. Moreover, we delve into promising perspectives, elucidating innovative approaches grounded in multi-omics methodologies, including genomics, transcriptomics and proteomics. These cutting-edge techniques, combined with the clinical and genetics features of patients, could improve the use of biomarkers in personalized medical approaches, patient counseling, and the decision-making continuum. Finally, Artificial intelligence (AI) holds significant potential in the realm of combining biomarkers and clinical data, also in the context of identifying non-invasive biomarkers for sperm retrieval.

Salivary Proteinase 3 as a Biomarker for Caries Severity in Children: A Cross-sectional Study.

AIM: This study aims to evaluate the relation between salivary proteinase 3 (PR3) concentration and caries severity in children. MATERIALS AND METHODS: Six-to-eight-year age group children, from the Outpatient Department of Pediatric and Preventive dentistry at PMS Dental College were selected for the study. From these children, three groups each consisting of 28 children were selected according to the dental caries severity. Three groups were: (1) No Dental Caries group, (2) Low Dental Caries group with DMFT/DEFT score of 1-4, and (3) High Dental Caries group with DMFT/DEFT score of 5-15. Thus, a total of 84 children who satisfied the inclusion criteria were selected. The concentration of PR3 in saliva of the donors were analyzed using an ELISA kit. One way ANOVA was used for finding the relation of salivary PR3 concentration with caries severity. Pairwise comparison of PR3 concentration and caries severity were analyzed using post hoc Tukey test. RESULTS: Severity of caries and concentration of salivary PR3 showed an inverse relation. As the caries severity increases there was a decrease in PR3 concentration and vice versa. CONCLUSION: The children with high caries severity showed lower concentration of PR3 in their saliva compared with those with lower caries severity which indicates that PR3 can be used as a biomarker for assessing caries severity and also paves way to use PR3 as a caries vaccine in future. Nowadays, interest toward noninvasive and personalized dentistry has been increased. Molecular assays using salivary biomarkers can be an effective tool in detecting the caries in earlier stages and assessing a patient's caries risk. CLINICAL SIGNIFICANCE: Salivary PR3 can be used as prognostic biomarker for assessing caries severity and after treatment the value of PR3 can be used as a assessment tool to confirm its relation with caries. How to cite this article: Karthika S, George S, Soman A, et al. Salivary Proteinase 3 as a Biomarker for Caries Severity in Children: A Cross-sectional Study. J Contemp Dent Pract 2024;25(3):236-240.

Considerations for using potential surrogate endpoints in cancer screening trials.

The requirement of large-scale expensive cancer screening trials spanning decades creates considerable barriers to the development, commercialisation, and implementation of novel screening tests. One way to address these problems is to use surrogate endpoints for the ultimate endpoint of interest, cancer mortality, at an earlier timepoint. This Review aims to highlight the issues underlying the choice and use of surrogate endpoints for cancer screening trials, to propose criteria for when and how we might use such endpoints, and to suggest possible candidates. We present the current landscape and challenges, and discuss lessons and shortcomings from the therapeutic trial setting. It is hugely challenging to validate a surrogate endpoint, even with carefully designed clinical studies. Nevertheless, we consider whether there are candidates that might satisfy the requirements defined by research and regulatory bodies.

Identifying temporal pathways using biomarkers in the presence of latent non-Gaussian components.

Time-series data collected from a network of random variables are useful for identifying temporal pathways among the network nodes. Observed measurements may contain multiple sources of signals and noises, including Gaussian signals of interest and non-Gaussian noises, including artifacts, structured noise, and other unobserved factors (eg, genetic risk factors, disease susceptibility). Existing methods, including vector autoregression (VAR) and dynamic causal modeling do not account for unobserved non-Gaussian components. Furthermore, existing methods cannot effectively distinguish contemporaneous relationships from temporal relations. In this work, we propose a novel method to identify latent temporal pathways using time-series biomarker data collected from multiple subjects. The model adjusts for the non-Gaussian components and separates the temporal network from the contemporaneous network. Specifically, an independent component analysis (ICA) is used to extract the unobserved non-Gaussian components, and residuals are used to estimate the contemporaneous and temporal networks among the node variables based on method of moments. The algorithm is fast and can easily scale up. We derive the identifiability and the asymptotic properties of the temporal and contemporaneous networks. We demonstrate superior performance of our method by extensive simulations and an application to a study of attention-deficit/hyperactivity disorder (ADHD), where we analyze the temporal relationships between brain regional biomarkers. We find that temporal network edges were across different brain regions, while most contemporaneous network edges were bilateral between the same regions and belong to a subset of the functional connectivity network.

Assessing Rat Diaphragm Motor Unit Connectivity Outcome Measures as Quantitative Biomarkers of Phrenic Motor Neuron Degeneration and Compensation.

Loss of ventilatory muscle function is a consequence of motor neuron injury and neurodegeneration (e.g., cervical spinal cord injury and amyotrophic lateral sclerosis, respectively). Phrenic motor neurons are the final link between the central nervous system and muscle, and their respective motor units (groups of muscle fibers innervated by a single motor neuron) represent the smallest functional unit of the neuromuscular ventilatory system. Compound muscle action potential (CMAP), single motor unit potential (SMUP), and motor unit number estimation (MUNE) are established electrophysiological approaches that enable the longitudinal assessment of motor unit integrity in animal models over time but have mostly been applied to limb muscles. Therefore, the objectives of this study are to describe an approach in preclinical rodent studies that can be used longitudinally to quantify the phrenic MUNE, motor unit size (represented as SMUP), and CMAP, and then to demonstrate the utility of these approaches in a motor neuron loss model. Sensitive, objective, and translationally relevant biomarkers for neuronal injury, degeneration, and regeneration in motor neuron injury and diseases can significantly aid and accelerate experimental research discoveries to clinical testing.

Inflammatory subphenotypes previously identified in ARDS are associated with mortality at intensive care unit discharge: a secondary analysis of a prospective observational study.

BACKGROUND: Intensive care unit (ICU)-survivors have an increased risk of mortality after discharge compared to the general population. On ICU admission subphenotypes based on the plasma biomarker levels of interleukin-8, protein C and bicarbonate have been identified in patients admitted with acute respiratory distress syndrome (ARDS) that are prognostic of outcome and predictive of treatment response. We hypothesized that if these inflammatory subphenotypes previously identified among ARDS patients are assigned at ICU discharge in a more general critically ill population, they are associated with short- and long-term outcome. METHODS: A secondary analysis of a prospective observational cohort study conducted in two Dutch ICUs between 2011 and 2014 was performed. All patients discharged alive from the ICU were at ICU discharge adjudicated to the previously identified inflammatory subphenotypes applying a validated parsimonious model using variables measured median 10.6 h [IQR, 8.0-31.4] prior to ICU discharge. Subphenotype distribution at ICU discharge, clinical characteristics and outcomes were analyzed. As a sensitivity analysis, a latent class analysis (LCA) was executed for subphenotype identification based on plasma protein biomarkers at ICU discharge reflective of coagulation activation, endothelial cell activation and inflammation. Concordance between the subphenotyping strategies was studied. RESULTS: Of the 8332 patients included in the original cohort, 1483 ICU-survivors had plasma biomarkers available and could be assigned to the inflammatory subphenotypes. At ICU discharge 6% (n = 86) was assigned to the hyperinflammatory and 94% (n = 1397) to the hypoinflammatory subphenotype. Patients assigned to the hyperinflammatory subphenotype were discharged with signs of more severe organ dysfunction (SOFA scores 7 [IQR 5-9] vs. 4 [IQR 2-6], p < 0.001). Mortality was higher in patients assigned to the hyperinflammatory subphenotype (30-day mortality 21% vs. 11%, p = 0.005; one-year mortality 48% vs. 28%, p < 0.001). LCA deemed 2 subphenotypes most suitable. ICU-survivors from class 1 had significantly higher mortality compared to class 2. Patients belonging to the hyperinflammatory subphenotype were mainly in class 1. CONCLUSIONS: Patients assigned to the hyperinflammatory subphenotype at ICU discharge showed significantly stronger anomalies in coagulation activation, endothelial cell activation and inflammation pathways implicated in the pathogenesis of critical disease and increased mortality until one-year follow up.

ADHD classification with cross-dataset feature selection for biomarker consistency detection.

Objective.Attention deficit hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder in children. While numerous intelligent methods are applied for its subjective diagnosis, they seldom consider the consistency problem of ADHD biomarkers. In practice, these data-driven approaches lead to varying learned features for ADHD classification across diverse ADHD datasets. This phenomenon significantly undermines the reliability of identified biomarkers and hampers the interpretability of these methods.Approach.In this study, we propose a cross-dataset feature selection (FS) module using a grouped SVM-based recursive feature elimination approach (G-SVM-RFE) to enhance biomarker consistency across multiple datasets. Additionally, we employ connectome gradient data for ADHD classification. In details, we introduce the G-SVM-RFE method to effectively concentrate gradient components within a few brain regions, thereby increasing the likelihood of identifying these regions as ADHD biomarkers. The cross-dataset FS module is integrated into an existing binary hypothesis testing (BHT) framework. This module utilizes external datasets to identify global regions that yield stable biomarkers. Meanwhile, given a dataset which waits for implementing the classification task as local dataset, we learn its own specific regions to further improve the performance of accuracy on this dataset.Main results.By employing this module, our experiments achieve an average accuracy of 96.7% on diverse datasets. Importantly, the discriminative gradient components primarily originate from the global regions, providing evidence for the significance of these regions. We further identify regions with the high appearance frequencies as biomarkers, where all the used global regions and one local region are recognized.Significance.These biomarkers align with existing research on impaired brain regions in children with ADHD. Thus, our method demonstrates its validity by providing enhanced biological explanations derived from ADHD mechanisms.

Machine learning derived serum creatinine trajectories in acute kidney injury in critically ill patients with sepsis.

BACKGROUND: Current classification for acute kidney injury (AKI) in critically ill patients with sepsis relies only on its severity-measured by maximum creatinine which overlooks inherent complexities and longitudinal evaluation of this heterogenous syndrome. The role of classification of AKI based on early creatinine trajectories is unclear. METHODS: This retrospective study identified patients with Sepsis-3 who developed AKI within 48-h of intensive care unit admission using Medical Information Mart for Intensive Care-IV database. We used latent class mixed modelling to identify early creatinine trajectory-based classes of AKI in critically ill patients with sepsis. Our primary outcome was development of acute kidney disease (AKD). Secondary outcomes were composite of AKD or all-cause in-hospital mortality by day 7, and AKD or all-cause in-hospital mortality by hospital discharge. We used multivariable regression to assess impact of creatinine trajectory-based classification on outcomes, and eICU database for external validation. RESULTS: Among 4197 patients with AKI in critically ill patients with sepsis, we identified eight creatinine trajectory-based classes with distinct characteristics. Compared to the class with transient AKI, the class that showed severe AKI with mild improvement but persistence had highest adjusted risks for developing AKD (OR 5.16; 95% CI 2.87-9.24) and composite 7-day outcome (HR 4.51; 95% CI 2.69-7.56). The class that demonstrated late mild AKI with persistence and worsening had highest risks for developing composite hospital discharge outcome (HR 2.04; 95% CI 1.41-2.94). These associations were similar on external validation. CONCLUSIONS: These 8 classes of AKI in critically ill patients with sepsis, stratified by early creatinine trajectories, were good predictors for key outcomes in patients with AKI in critically ill patients with sepsis independent of their AKI staging.

Droplet Microfluidics with Capillary Electrophoresis for Glycan Biomarker Analysis.

Several glycoproteins are validated biomarkers of various diseases such as cancer, cardiovascular diseases, chronic alcohol abuse, or congenital disorders of glycosylation (CDG). In particular, CDG represent a group of more than 150 inherited diseases with varied symptoms affecting multiple organs. The distribution of glycans from target glycoprotein(s) can be used to extract information to help the diagnosis and possibly differentiate subtypes of CDG. Indeed, depending on the glycans and the proteins to which they are attached, glycans can play a very broad range of roles in both physical and biological properties of glycoproteins. For glycans in general, capillary electrophoresis with laser-induced fluorescence detection (CE-LIF) has become a staple. Analysis of glycans with CE-LIF requires several sample preparation steps, including release of glycans from the target glycoprotein, fluorescent labeling of glycans, and purification of labeled glycans. Here, we describe the protocol for glycan sample treatment in a microfluidic droplet system prior to CE-LIF of labeled glycans. The microfluidic droplet approach offers full automation, sample, and reagent volume reduction and elimination of contamination from external environment.

Novel point-of-care cytokine biomarker lateral flow test for the screening for sexually transmitted infections and bacterial vaginosis: study protocol of a multicentre multidisciplinary prospective observational clinical study to evaluate the performance and feasibility of the Genital InFlammation Test (GIFT).

INTRODUCTION: A prototype lateral flow device detecting cytokine biomarkers interleukin (IL)-1α and IL-1ß has been developed as a point-of-care test-called the Genital InFlammation Test (GIFT)-for detecting genital inflammation associated with sexually transmitted infections (STIs) and/or bacterial vaginosis (BV) in women. In this paper, we describe the rationale and design for studies that will be conducted in South Africa, Zimbabwe and Madagascar to evaluate the performance of GIFT and how it could be integrated into routine care. METHODS AND ANALYSIS: We will conduct a prospective, multidisciplinary, multicentre, cross-sectional and observational clinical study comprising two distinct components: a biomedical ('diagnostic study') and a qualitative, modelling and economic ('an integration into care study') part. The diagnostic study aims to evaluate GIFT's performance in identifying asymptomatic women with discharge-causing STIs (Chlamydia trachomatis (CT), Neisseria gonorrhoeae (NG), Trichomonas vaginalis (TV) and Mycoplasma genitalium (MG)) and BV. Study participants will be recruited from women attending research sites and family planning services. Several vaginal swabs will be collected for the evaluation of cytokine concentrations (ELISA), STIs (nucleic acid amplification tests), BV (Nugent score) and vaginal microbiome characteristics (16S rRNA gene sequencing). The first collected vaginal swab will be used for the GIFT assay which will be performed in parallel by a healthcare worker in the clinic near the participant, and by a technician in the laboratory. The integration into care study aims to explore how GIFT could be integrated into routine care. Four activities will be conducted: user experiences and/or perceptions of the GIFT device involving qualitative focus group discussions and in-depth interviews with key stakeholders; discrete choice experiments; development of a decision tree classification algorithm; and economic evaluation of defined management algorithms. ETHICS AND DISSEMINATION: Findings will be reported to participants, collaborators and local government for the three sites, presented at national and international conferences, and disseminated in peer-reviewed publications.The protocol and all study documents such as informed consent forms were reviewed and approved by the University of Cape Town Human Research Ethics Committee (HREC reference 366/2022), Medical Research Council of Zimbabwe (MRCZ/A/2966), Comité d'Ethique pour la Recherche Biomédicale de Madagascar (N° 143 MNSAP/SG/AMM/CERBM) and the London School of Hygiene and Tropical Medicine ethics committee (LSHTM reference 28046).Before the start, this study was submitted to the Clinicaltrials.gov public registry (NCT05723484). TRIAL REGISTRATION NUMBER: NCT05723484.

Meta-analysis identifying gut microbial biomarkers of Qinghai-Tibet Plateau populations and the functionality of microbiota-derived butyrate in high-altitude adaptation.

The extreme environmental conditions of a plateau seriously threaten human health. The relationship between gut microbiota and human health at high altitudes has been extensively investigated. However, no universal gut microbiota biomarkers have been identified in the plateau population, limiting research into gut microbiota and high-altitude adaptation. 668 16s rRNA samples were analyzed using meta-analysis to reduce batch effects and uncover microbiota biomarkers in the plateau population. Furthermore, the robustness of these biomarkers was validated. Mendelian randomization (MR) results indicated that Tibetan gut microbiota may mediate a reduced erythropoietic response. Functional analysis and qPCR revealed that butyrate may be a functional metabolite in high-altitude adaptation. A high-altitude rat model showed that butyrate reduced intestinal damage caused by high altitudes. According to cell experiments, butyrate may downregulate hypoxia-inducible factor-1α (HIF-1α) expression and blunt cellular responses to hypoxic stress. Our research found universally applicable biomarkers and investigated their potential roles in promoting human health at high altitudes.

Diagnostic biomarker discovery from brain EEG data using LSTM, reservoir-SNN, and NeuCube methods in a pilot study comparing epilepsy and migraine.

The study introduces a new online spike encoding algorithm for spiking neural networks (SNN) and suggests new methods for learning and identifying diagnostic biomarkers using three prominent deep learning neural network models: deep BiLSTM, reservoir SNN, and NeuCube. EEG data from datasets related to epilepsy, migraine, and healthy subjects are employed. Results reveal that BiLSTM hidden neurons capture biological significance, while reservoir SNN activities and NeuCube spiking dynamics identify EEG channels as diagnostic biomarkers. BiLSTM and reservoir SNN achieve 90 and 85% classification accuracy, while NeuCube achieves 97%, all methods pinpointing potential biomarkers like T6, F7, C4, and F8. The research bears implications for refining online EEG classification, analysis, and early brain state diagnosis, enhancing AI models with interpretability and discovery. The proposed techniques hold promise for streamlined brain-computer interfaces and clinical applications, representing a significant advancement in pattern discovery across the three most popular neural network methods for addressing a crucial problem. Further research is planned to study how early can these diagnostic biomarkers predict an onset of brain states.

High-throughput proteomic analysis of extracellular vesicles from saliva by chemical probe-based array.

Extracellular vesicles (EVs) are cell-released, nucleus-free particles with a double-membrane structure that effectively prevents degradation of internal components by a variety of salivary enzymes. Saliva is an easily accessible biofluid that contains a wealth of valuable information for disease diagnosis and monitoring and especially reflect respiratory and digestive tract diseases. However, the lack of efficient and high-throughput methods for proteomic analysis of salivary biomarkers poses a significant challenge. Herein, we designed a salivary EV amphiphile-dendrimer supramolecular probe (SEASP) array which enables efficient enrichment and in situ detection of EVs protein biomarkers. Detergent Tween-20 washing of SEASP arrays removes high abundance of heteroproteins from saliva well. This array shows good analytical performance in the linear range of 10 µL-150 µL (LOD = 0.4 µg protein, or 10 µL saliva), exhibiting a good recovery (80.0 %). Compared to ultracentrifugation (UC), this procedure provides simple and convenient access to high-purity EVs (1.3 × 109 particles per mg protein) with good physiological status and structure. Coupling with mass spectrometry based proteomic analysis, differentially expressed proteins as selected asthma biomarkers have been screened. Then, we validated the proteomics primary screening results through clinical samples (100 µL each) using the SEASP array. Utilizing the dual antibody fluorescence technology, SEASP enables the simultaneous high-throughput detection of two proteins. Therefore, the EVs marker protein CD81 could be used as an internal standard to normalize the number of EVs, which was stably expressed in EVs. Proteomics and array results suggested that HNRNPU (P = 4.9 * 10-6) and MUC5B (P = 4.7 * 10-11) are promising protein biomarkers for infantile asthma. HNRNPU and MUC5B may be associated with disease onset and subtypes. The SEASP arrays provide a significant advancement in the field of salivary biomarker. The array enables high-throughput in situ protein detection for highly viscous and complex biological samples. It provides a rapid, low-cost, highly specific screening procedure and experimental basis for early disease screening and diagnosis in the field of liquid biopsy.

Prospective associations between breast feeding, metabolic health, inflammation and bone density in women with prior gestational diabetes mellitus.

INTRODUCTION: The aim of the study is to investigate prospective associations between breastfeeding and metabolic outcomes, inflammation, and bone density in women with prior gestational diabetes mellitus (GDM). RESEARCH DESIGN AND METHODS: We prospectively included 171 women with GDM from the MySweetheart trial. Women were followed during pregnancy (from 24 up to 32 weeks' gestational age) up to 1 year postpartum. Outcomes included weight, weight retention, body composition, insulin resistance and secretion indices, C reactive protein (CRP), and bone density. We compared differences in the associations between breastfeeding and health outcomes between women who breast fed <6 months vs ≥6 months. Analyses were adjusted for potential medical and sociodemographic confounders. RESULTS: Breastfeeding initiation was 94.2% (n=161) and mean breastfeeding duration was 6.6 months. Breastfeeding duration was independently associated with lower weight, weight retention, body fat, visceral adipose tissue, lean mass, CRP, insulin resistance (Homeostatic Model Assessment for Insulin Resistance), and insulin secretion (Homeostatic Model Assessment of ß-cell index) at 1 year postpartum (all p≤0.04) after adjusting for confounders. Breastfeeding was associated with higher insulin resistance-adjusted insulin secretion (Insulin Secretion-Sensitivity Index-2) in the unadjusted analyses only. There was no association between breastfeeding duration and bone density. Compared with <6 months, breastfeeding duration ≥6 months was associated with lower weight, weight retention, body fat, fat-free mass as well as lower CRP at 1 year postpartum (all p<0.05) after adjusting for confounders. CONCLUSIONS: Longer breastfeeding duration among women with prior GDM was associated with lower insulin resistance, weight, weight retention, body fat and inflammation, but not lower bone density at 1 year postpartum. Breastfeeding for ≥6 months after GDM can help to improve cardiometabolic health outcomes 1 year after delivery.

Severe dawn phenomenon predicts long-term risk of all-cause mortality in patients with type 2 diabetes.

AIMS: The dawn phenomenon (DP) is an abnormal early morning blood glucose rise without nocturnal hypoglycaemia, which can be more easily and precisely assessed with continuous glucose monitoring (CGM). This prospective study aimed to explore the association between DP and the risk of all-cause mortality in patients with type 2 diabetes. MATERIALS AND METHODS: A total of 5542 adult inpatients with type 2 diabetes in a single centre were analysed. The magnitude of DP (ΔG) was defined as the increment in the CGM-determined glucose value from nocturnal nadir (after 24:00) to prebreakfast. Participants were stratified into four groups by ΔG: ≤1.11, 1.12-3.33, 3.34-5.55, and >5.55 mmol/L. Cox proportional hazard regression models were used to evaluate the impact of DP on all-cause mortality risk. RESULTS: During a median follow-up of 9.4 years, 1083 deaths were identified. The restricted cubic spline revealed a nonlinear (p for nonlinearity = 0.002) relationship between ΔG and the risk of all-cause mortality. A multivariate-adjusted Cox regression model including glycated haemoglobin A1c (HbA1c) showed that ΔG > 5.55 mmol/L was associated with 30% (95% CI, 1.01-1.66) higher risk of all-cause mortality, as compared with ΔG 1.12-3.33 mmol/L. CONCLUSIONS: Higher ΔG is significantly related to an increased risk of all-cause mortality in type 2 diabetes, suggesting that severe DP should be given more attention as a part of glucose management to reduce the risk of long-term adverse outcomes.