Critical Biomarkers Research Articles

Role of Artificial Intelligence in Identifying Vital Biomarkers with Greater Precision in Emergency Departments During Emerging Pandemics

The COVID-19 pandemic has accelerated advances in molecular biology and virology, enabling the identification of key biomarkers to differentiate between severe and mild cases. Furthermore, the use of artificial intelligence (AI) and machine learning (ML) to analyze large datasets has been crucial for rapidly identifying relevant biomarkers for disease prognosis, including COVID-19. This approach enhances diagnostics in emergency settings, allowing for more accurate and efficient patient management. This study demonstrates how machine learning algorithms in emergency departments can rapidly identify key biomarkers for the vital prognosis in an emerging pandemic using COVID-19 as an example by analyzing clinical, epidemiological, analytical, and radiological data. All consecutively admitted patients were included, and more than 89 variables were processed using the Random Forest (RF) algorithm. The RF model achieved the highest balanced accuracy at 92.61%. The biomarkers most predictive of mortality included procalcitonin (PCT), lactate dehydrogenase (LDH), and C-reactive protein (CRP). Additionally, the system highlighted the significance of interstitial infiltrates in chest X-rays and D-dimer levels. Our results demonstrate that RF is crucial in identifying critical biomarkers in emerging diseases, accelerating data analysis, and optimizing prognosis and personalized treatment, emphasizing the importance of PCT and LDH in high-risk patients.

Integrative multi-omics analysis for identifying novel therapeutic targets and predicting immunotherapy efficacy in lung adenocarcinoma

Aim: Lung adenocarcinoma (LUAD), the most prevalent subtype of non-small cell lung cancer (NSCLC), presents significant clinical challenges due to its high mortality and limited therapeutic options. The molecular heterogeneity and the development of therapeutic resistance further complicate treatment, underscoring the need for a more comprehensive understanding of its cellular and molecular characteristics. This study sought to delineate novel cellular subpopulations and molecular subtypes of LUAD, identify critical biomarkers, and explore potential therapeutic targets to enhance treatment efficacy and patient prognosis. Methods: An integrative multi-omics approach was employed to incorporate single-cell RNA sequencing (scRNA-seq), bulk transcriptomic analysis, and genome-wide association study (GWAS) data from multiple LUAD patient cohorts. Advanced computational approaches, including Bayesian deconvolution and machine learning algorithms, were used to comprehensively characterize the tumor microenvironment, classify LUAD subtypes, and develop a robust prognostic model. Results: Our analysis identified eleven distinct cellular subpopulations within LUAD, with epithelial cells predominating and exhibiting high mutation frequencies in Tumor Protein 53 (TP53) and Titin (TTN) genes. Two molecular subtypes of LUAD [consensus subtype (CS)1 and CS2] were identified, each showing distinct immune landscapes and clinical outcomes. The CS2 subtype, characterized by increased immune cell infiltration, demonstrated a more favorable prognosis and higher sensitivity to immunotherapy. Furthermore, a multi-omics-driven machine learning signature (MOMLS) identified ribonucleotide reductase M1 (RRM1) as a critical biomarker associated with chemotherapy response. Based on this model, several potential therapeutic agents targeting different subtypes were proposed. Conclusion: This study presents a comprehensive multi-omics framework for understanding the molecular complexity of LUAD, providing insights into cellular heterogeneity, molecular subtypes, and potential therapeutic targets. Differential sensitivity to immunotherapy among various cellular subpopulations was identified, paving the way for future immunotherapy-focused research.

Skin Microbiota: Mediator of Interactions Between Metabolic Disorders and Cutaneous Health and Disease

Metabolic disorders, including type 2 diabetes mellitus (T2DM), obesity, and metabolic syndrome, are systemic conditions that profoundly impact the skin microbiota, a dynamic community of bacteria, fungi, viruses, and mites essential for cutaneous health. Dysbiosis caused by metabolic dysfunction contributes to skin barrier disruption, immune dysregulation, and increased susceptibility to inflammatory skin diseases, including psoriasis, atopic dermatitis, and acne. For instance, hyperglycemia in T2DM leads to the formation of advanced glycation end products (AGEs), which bind to the receptor for AGEs (RAGE) on keratinocytes and immune cells, promoting oxidative stress and inflammation while facilitating Staphylococcus aureus colonization in atopic dermatitis. Similarly, obesity-induced dysregulation of sebaceous lipid composition increases saturated fatty acids, favoring pathogenic strains of Cutibacterium acnes, which produce inflammatory metabolites that exacerbate acne. Advances in metabolomics and microbiome sequencing have unveiled critical biomarkers, such as short-chain fatty acids and microbial signatures, predictive of therapeutic outcomes. For example, elevated butyrate levels in psoriasis have been associated with reduced Th17-mediated inflammation, while the presence of specific Lactobacillus strains has shown potential to modulate immune tolerance in atopic dermatitis. Furthermore, machine learning models are increasingly used to integrate multi-omics data, enabling personalized interventions. Emerging therapies, such as probiotics and postbiotics, aim to restore microbial diversity, while phage therapy selectively targets pathogenic bacteria like Staphylococcus aureus without disrupting beneficial flora. Clinical trials have demonstrated significant reductions in inflammatory lesions and improved quality-of-life metrics in patients receiving these microbiota-targeted treatments. This review synthesizes current evidence on the bidirectional interplay between metabolic disorders and skin microbiota, highlighting therapeutic implications and future directions. By addressing systemic metabolic dysfunction and microbiota-mediated pathways, precision strategies are paving the way for improved patient outcomes in dermatologic care.

Exploring the comorbidity mechanisms between atherosclerosis and hashimoto's thyroiditis based on microarray and single-cell sequencing analysis.

Atherosclerosis (AS) is a chronic vascular disease characterized by inflammation of the arterial wall and the formation of cholesterol plaques. Hashimoto's thyroiditis (HT) is an autoimmune disorder marked by chronic inflammation and destruction of thyroid tissue. Although previous studies have identified common risk factors between AS and HT, the specific etiology and pathogenic mechanisms underlying these associations remain unclear. We obtained relevant datasets for AS and HT from the Gene Expression Omnibus (GEO). By employing the Limma package, we pinpointed common differentially expressed genes (DEGs) and discerned co-expression modules linked to AS and HT via Weighted Gene Co-expression Network Analysis (WGCNA). We elucidated gene functions and regulatory networks across various biological scenarios through enrichment and pathway analysis using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Core genes were identified using Cytoscape software and further validated with external datasets. We also conducted immune infiltration analysis on these core genes utilizing the CIBERSORT method. Lastly, Single-cell analysis was instrumental in uncovering common diagnostic markers. Based on differential analysis and WGCNA, we identified 119 candidate genes within the cohorts for AS and HT. KEGG and GO enrichment analyses indicate that these genes are significantly involved in antigen processing and presentation, along with various immune-inflammatory pathways. Two pivotal genes, PTPRC and TYROBP, were identified using five algorithms from the cytoHubba plugin. Validation through external datasets confirmed their substantial diagnostic value for AS and HT. Moreover, the results of Gene Set Enrichment Analysis (GSEA) indicated that these core genes are significantly enriched in various receptor interactions and signaling pathways. Immune infiltration analysis revealed a strong association of lymphocytes and macrophages with the pathogenesis of AS and HT. Single-cell analysis demonstrated predominant expression of the core genes in macrophages, monocytes, T cells and Common Myeloid Progenitor (CMP). This study proposes that an aberrant immune response might represent a shared pathogenic mechanism in AS and HT. The genes PTPRC and TYROBP are identified as critical potential biomarkers and therapeutic targets for these comorbid conditions. Furthermore, the core genes and their interactions with immune cells could serve as promising targets for future diagnostic and therapeutic strategies.

Identification of critical biomarkers and immune landscape patterns in glioma based on multi-database

PurposeGlioma is the most prevalent tumor of the central nervous system. The poor clinical outcomes and limited therapeutic efficacy underscore the urgent need for early diagnosis and an optimized prognostic approach for glioma. Therefore, the aim of this study was to identify sensitive biomarkers for glioma.Patients and methodsDifferentially expressed genes (DEGs) of glioma were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The potential biomarkers were identified using weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) regression. The prognostic ability of the potential biomarkers was evaluated by Cox regression and survival curve. CellMiner was used to access the correlation between the expression of potential biomarkers and anticancer drug sensitivity. We then explored the association of potential biomarkers and tumor immune infiltration by single-sample GSEA (ssGSEA) and CIBERSORT. Immune staining in glioma patient samples and cell experiments of potential biomarkers further verified their expression and function.ResultsUltimately, we identified three potential biomarkers: SLC8A2, ATP2B3, and SRCIN1. These 3 genes were found significantly correlated with clinicopathological features (age, WHO grade, IDH mutation status, 1p19q codeletion status). Furthermore, the overall survival (OS), disease-specific survival (DSS), and progression-free survival (PFS) were found to be positively correlated with high expression of these 3 potential biomarkers. Besides, there was a substantial relationship between the sensitivity of anticancer drugs and these biomarkers expression. More importantly, the negative association between the 3 genes with most tumor immune cells was also established. Moreover, the decreased expression of the biomarkers was also verified in glioma patient samples. Finally, we confirmed that these 3 genes might promotes glioma proliferation and migration in vitro.ConclusionSLC8A2, ATP2B3, and SRCIN1 were identified as underlying biomarkers for glioma associated with prognosis assessments and personal immunotherapy.Graphical

The Patterns of P53, E-Cadherin, β-Catenin, CXCR4 and Podoplanin Expression in Oral Squamous Cell Carcinoma Suggests a Hybrid Invasion Model: an Immunohistochemical Study on Tissue Microarrays.

Oral squamous cell carcinoma (OSCC) is a significant public health challenge associated with high mortality rates primarily due to its invasive and metastatic behavior. This study aimed to evaluate the expression patterns of five critical biomarkers: β-catenin, E-cadherin, podoplanin (PDPN), CXCR4, and p53 in OSCC tissues and to investigate their correlations with clinicopathologic features and patient outcomes. We conducted an immunohistochemical analysis utilizing tissue microarrays (TMAs) from 95 patients diagnosed with primary OSCC. The expression levels of the five biomarkers were quantified using H-scores. Statistical analyses, including Kruskal-Wallis tests, Dunn's post-hoc tests, and correlation analyses, were performed to explore the associations between biomarker expression, clinicopathologic parameters, and overall patient survival. The study found that loss of E-cadherin and β-catenin expression was significantly associated with increased tumor depth and lymphatic invasion, corroborating their role in the process of epithelial-mesenchymal transition (EMT). High levels of PDPN were noted in both early and late-stage OSCC, indicating its potential involvement in initiating invasive behaviors. Notably, CXCR4 expression exhibited positive correlations with E-cadherin and β-catenin, suggesting a hybrid invasion phenotype incorporating both EMT and collective invasion strategies. Although Cox regression analysis did not reveal significant associations between biomarker expression and overall survival (OS) or disease-specific survival (DSS), factors such as alcohol consumption, tumor size, lymph node involvement, and advanced clinical stage emerged as significant negative predictors of both OS and DSS. The expression profiles of β-catenin, E-cadherin, PDPN, CXCR4, and p53 in OSCC tissues provide valuable insights into a hybrid model of invasion that integrates mechanisms of EMT with an important rule in the tumor invasion. This nuanced understanding of OSCC progression highlights the potential of PDPN and CXCR4 as novel therapeutic targets, emphasizing the need for further investigation into their roles in OSCC biology and the development of targeted treatments that could improve patient outcomes and survival rates.

Application and Development of SERS Technology in VOCs Gas Detection

Volatile organic compounds (VOCs), recognized as critical environmental contaminants and cancer biomarkers, highlight the significance of their trace detection for environmental conservation and human health. Surface-enhanced Raman spectroscopy (SERS) technology...

Simultaneous Imaging of pH and Peroxynitrite in the Endoplasmic Reticulum and Mitochondria: Revealing Organelle Interactions in Alzheimer's Disease Pathogenesis.

pH and peroxynitrite (ONOO-) are two critical biomarkers to unveil the corresponding status of endoplasmic reticulum (ER) stress and mitochondrial dysfunction, which are closely related to Alzheimer's disease (AD). Simultaneously monitoring pH and ONOO- fluctuations in the ER and mitochondria during AD progression is pivotal for clarifying the interplay between the disorders of the two organelles and revealing AD pathogenesis. Herein, we designed and synthesized a dual-channel fluorescent probe (DCFP) to visualize pH and ONOO- in the ER and mitochondria. DCFP possessed excellent sensitivity and selectivity to pH and ONOO- without spectral crosstalk and was utilized in monitoring the two analytes within AD model cells and larval zebrafish. Importantly, DCFP could preferentially target mitochondria in normal cells and be enriched in the ER after mitochondrial depolarization. With the aid of DCFP, the slower acidification rate of the ER than that of mitochondria induced by Aβ oligomers (AβOs) was first identified, which could be ascribed to the relief of the AβOs-triggered ER stress through the Ca2+ migration from the ER to mitochondria. Moreover, continuous exposure to AβOs led to mitochondrial Ca2+ overload, accelerating the acidification and ONOO- overproduction within mitochondria. As a result, intracellular oxidative stress levels were elevated, further exacerbating ER stress and aggravating ER acidification in turn. The advanced understanding of the potential interplay between the ER and mitochondria in this work may offer new insights and methodologies for studying AD pathogenesis. The DCFP developed in this work could also be employed to study other diseases related to ER stress and mitochondrial dysfunction.

Ammonium Sensing Patch with Ultrawide Linear Range and Eliminated Interference for Universal Body Fluids Analysis

Ammonium level in body fluids serves as one of the critical biomarkers for healthcare, especially those relative to liver diseases. The continuous and real-time monitoring in both invasive and non-invasive manners is highly desired, while the ammonium concentrations vary largely in different body fluids. Besides, the sensing reliability based on ion-selective biosensors can be significantly interfered by potassium ions. To tackle these challenges, a flexible and biocompatible sensing patch for wireless ammonium level sensing was reported with an ultrawide linear range for universal body fluids including blood, tears, saliva, sweat and urine. The as-prepared biocompatible sensors deliver a reliable sensitivity of 58.7mV decade-1 in the range of 1-100mM and a desirable selectivity coefficient of 0.11 in the interference of potassium ions, attributed to the cross-calibration within the sensors array. The sensor's biocompatibility was validated by the cell growth on the sensor surface (> 80%), hemolysis rates (< 5%), negligible cellular inflammatory responses and weight changes of the mice with implanted sensors. Such biocompatible sensors with ultrawide linear range and desirable selectivity open up new possibility of highly compatible biomarker analysis via different body fluids in versatile approaches.

Transcriptomic Profiling of Carboplatin- and Paclitaxel-Resistant Lung Adenocarcinoma Cells Reveals CSF3 as a Potential Biomarker for the Carboplatin Plus Paclitaxel Doublet Regimens.

This study aimed to generate Car- and Pac-resistant cell lines from the human lung adenocarcinoma H1792 cell line, designated as H1792/Car and H1792/Pac, and perform transcriptome sequencing to identify potential targets. Common differentially expressed genes (Co-DEGs) in both resistant cell lines were identified, followed by hub gene identification. Online validation was conducted through GEPIA and Kaplan-Meier Plotter platforms, with experimental validation performed using real-time quantitative PCR (RT-qPCR). After six months, the H1792/Car and H1792/Pac cell lines exhibited a 10.7-fold and 5.6-fold increase in resistance to Car and Pac, respectively. Flow cytometry analysis demonstrated that both resistant cell lines were resistant to cell cycle arrest and apoptosis induced by Car or Pac. Transcriptomic sequencing identified 123 Co-DEGs, including 72 upregulated and 51 downregulated genes, consistently expressed in both H1792/Car and H1792/Pac cell lines. Among these, 13 hub genes were identified, with colony-stimulating factor 3 (CSF3) uniquely associated with post-progression survival (PPS) in adenocarcinoma patients undergoing chemotherapy. Notably, CSF3 expression was significantly elevated in both H1792/Car and H1792/Pac compared to parental cells. These findings underscore the value of drug-resistant models in uncovering critical biomarkers. CSF3 emerges as a promising guiding marker or potential molecular target for optimizing Car- and Pac-based doublet regimens.

Predictive Factors of Antibody-Drug Conjugate Treatment in Metastatic Breast Cancer: A Narrative Review.

Antibody-drug conjugates (ADCs) have revolutionized the treatment landscape for metastatic breast cancer, offering targeted delivery of cytotoxic agents with improved efficacy and tolerability compared to conventional chemotherapy. This narrative review explores key predictive factors influencing the efficacy of ADCs, focusing on HER2-targeted therapies, such as trastuzumab emtansine and trastuzumab deruxtecan, as well as sacituzumab govitecan for triple-negative breast cancer. HER2 expression, TROP-2 levels, hormone receptor status, and the tumor microenvironment emerge as critical biomarkers for patient selection and therapeutic outcomes. Additionally, we discuss resistance mechanisms, such as antigen loss, impaired drug internalization, and the role of circulating tumor DNA in predicting ADC response. Finally, future perspectives on the sequential use of ADCs and potential combination therapies are highlighted, along with emerging agents targeting alternative antigens like HER3 and LIV-1. Overall, identifying predictive biomarkers and overcoming resistance mechanisms are essential for optimizing the use of ADCs in metastatic breast cancer, thereby improving patient outcomes.

Biomarkers and potential therapeutic targets driving progression of non-alcoholic steatohepatitis to hepatocellular carcinoma predicted through transcriptomic analysis.

Non-alcoholic steatohepatitis (NASH) is the most prevalent chronic liver condition globally, with potential progression to cirrhosis, and even hepatocellular carcinoma (HCC). The increasing prevalence of NASH underscores the urgent need for advanced diagnostic and therapeutic strategies. Despite its widespread impact, effective treatments to prevent the progression of NASH remain elusive, highlighting the critical importance of innovative molecular techniques in both the diagnosis and management of this disease. Six microarray datasets available in GEO were used to perform Robust Rank Aggregation (RRA) to identify differentially expressed genes (DEGs).We identified 62 robust upregulated genes and 24 robust downregulated genes. These genes were undergone Gene Ontology enrichment analysis and further examination for expression correlation with NAS score. Molecular subtypes were generated using "ConsensusClusterPlus" on identified genes, which were further assessed for tumor stage relevance, expression differences in adjacent and tumor tissues, and impact on survival in TCGA liver cancer patients. Single-cell analysis was then used to explore the genes across different cell types and subgroups as well as cell-type interactions. The clinical utility of predicted core genes was highlighted through decision curve analysis, with emphasis on HCC prognosis. The GDSC database was used to evaluate the relationship between the predicted core genes and drug sensitivity, while the TIDE database was used to evaluate their relationship with immunotherapy. Four core genes, TREM2, GDF15, TTC39A, and ANXA2, were identified as key to influencing HCC prognosis and therapy responsiveness, especially immune treatment efficacy in NASH-associated HCC. The core genes may act as critical biomarkers driving the progression of NASH to HCC. They are potential novel targets for the diagnosis and treatment of NASH progression, offering innovative perspectives for its clinical management.

Molecular Biomarkers in Cutaneous Photodynamic Therapy: A Comprehensive Review.

Photodynamic therapy (PDT) is widely utilized in dermatology for the treatment of various skin conditions. Despite its effectiveness, the exact biomolecular changes underlying therapeutic outcomes remain only partially understood. This review, through a transversal approach, aims to provide an in-depth exploration of molecular biomarkers involved in PDT, evaluate its underlying mechanisms, and examine how these insights can contribute to enhanced treatment protocols and personalized therapy approaches. A narrative review of the literature was conducted, targeting peer-reviewed articles and clinical trials that focus on PDT and its molecular biomarker effects on dermatological conditions. The databases searched included PubMed, Scopus, and Web of Science, and the inclusion criteria encompassed original research articles, systematic reviews, and meta-analyses in English. PDT effectively reduces the expression of critical biomarkers such as p53, Cyclin D1, and Ki-67 in AK and other cancerous lesions, leading to reduced cell proliferation and increased apoptosis. Additionally, PDT promotes extracellular matrix remodeling and stimulates collagen production, which has a rejuvenating effect on the skin and a promising role in the treatment of chronic wounds. PDT represents a powerful and versatile treatment option for various dermatological conditions due to its ability to target cellular pathways involved in proliferation and apoptosis. Further research into optimizing treatment parameters and combining PDT with other targeted therapies may enhance patient outcomes, reduce resistance, and pave the way for more individualized therapeutic approaches in dermatology.

Myocardial Disorders in BDNF-Deficient Rats: Limited Recovery Post-Moderate Endurance Training.

The study aimed to determine whether heterozygous BDNF-deficient (BDNF-knockout, SD-BDNF) rats exhibit pathological changes in the myocardium and to assess whether a 5-week moderate-intensity endurance training program can reverse adverse changes in the heart muscle. Experiments were conducted on four groups of rats: control wild-type, control BDNF knockout, trained wild-type and trained BDNF knockout. Knockout rats were selected due to the presence of symptoms resembling metabolic syndrome in serum and liver while 5-week moderate endurance training was used as an intervention targeted at restoring heart function. Measurements of BDNF/Trk-B concentrations and molecules levels and activities, such as cardiac specific enzymes like creatine kinase and creatine kinase myocardial band, lipids as total cholesterol, low-density lipoprotein and triglycerides, metabolic enzymes including alanine aminotransferase, aspartate aminotransferase, gamma-glutamyl transferase and lactate dehydrogenase and interleukin-1 were carried out in myocardium homogenates. In BDNF-deficient rats, the myocardium showed significantly reduced lipid concentrations, decreased metabolic and cardiac enzyme activity, and elevated Trk-B levels, all of which are indicative of myocardial ischemia or hypoxia. These changes in critical biomarkers were consistent with those earlier observed in the livers of BDNF-deficient rats, suggesting a link between the liver and cardiac function. Moderate endurance training led to an increase in creatine kinase activity in the myocardium of trained rats, suggesting increased production and utilization of energy required for myocardial contraction in trained wild-type and knockout populations of rats. BDNF-deficient rats exhibited numerous myocardial abnormalities, most of which were not reversible after moderate-intensity endurance training. These findings provide a basis for a deeper understanding of the mechanisms underlying myocardial disorders in BDNF-deficient rats, which appear to be a suitable model for studying various aspects of metabolic disorders.

Inferring microarray datasets reveals critical biomarkers and potential drug targets of Parkinson’s disease

Parkinson’s disease (PD) is a critical neurological disorder characterized by loss of voluntary motor control and substantial slowing of movement. While traditionally attributed to environmental factors, recent studies underscore the significant role of genetics in the onset and progression of PD. This study aimed to identify differentially expressed genes (DEGs) and relevant pathways in PD by analyzing gene expression data from four datasets (83 PD and 53 control substantia nigra samples) sourced from the Gene Expression Omnibus (GEO) database. Using GEO2R, we identified common DEGs and performed functional annotation and KEGG pathway enrichment analysis through Enrichr. We constructed a protein–protein interaction (PPI) network using StringDB and identified hub genes via CytoHubba. Results revealed 18 critical DEGs enriched in pathways such as dopaminergic synapse and cocaine addiction. Key hub genes included Tyrosine Hydroxylase (TH), Solute Carrier Family 18 Member A2 (SLC18A2), and Potassium Inwardly Rectifying Channel Subfamily J Member 6 (KCNJ6). These findings provide insights into the molecular mechanisms of PD, highlighting potential biomarkers and therapeutic targets. This study offers a robust framework for future research and the development of effective treatment strategies for Parkinson’s disease.

Electrochemical determination of 5-heneicosylresorcinol using a disposable molecularly imprinted polymer-modified screen-printed electrode

Alkylresorcinols serve as critical biomarkers for whole-wheat grains, and their precise analysis is essential for authenticating and monitoring the quality of whole-wheat products. In this study, we developed a novel electrochemical sensor by using a disposable screen-printed electrode for the sensitive and selective detection of 5-heneicosylresorcinol (AR21). To enhance the sensor's sensitivity and selectivity, multilayered Ti3C2Tx (m-Ti3C2Tx) nanomaterials were incorporated into the electrode in conjunction with molecularly imprinted polymers. Under optimized conditions, the sensor exhibited a linear detection range of 0.005–100 mg L−1, with a detection limit of 3.98 μg L−1 for AR21. Furthermore, the sensing platform demonstrated remarkable selectivity for AR21 when compared to structurally similar compounds, along with excellent reproducibility and stability. Validation studies with real wheat food samples confirmed the effectiveness of the proposed method, underscoring its potential as a rapid and portable solution for the assessment of whole-grain foods.

Advanced Wearable Devices for Monitoring Sweat Biochemical Markers in Athletic Performance: A Comprehensive Review.

Wearable technology has advanced significantly, offering real-time monitoring of athletes' physiological parameters and optimizing training and recovery strategies. Recent developments focus on biosensor devices capable of monitoring biochemical parameters in addition to physiological ones. These devices employ noninvasive methods such as sweat analysis, which reveals critical biomarkers like glucose, lactate, electrolytes, pH, and cortisol. These biomarkers provide valuable insights into an athlete's energy use, hydration status, muscle function, and stress levels. Current technologies utilize both electrochemical and colorimetric methods for sweat analysis, with electrochemical methods providing higher precision despite potential signal interference. Wearable devices such as epidermal patches, temporary tattoos, and fabric-based sensors are preferred for their flexibility and unobtrusive nature compared to more rigid conventional wearables. Such devices leverage advanced materials and transmit real-time data to computers, tablets, or smartphones. These data would aid coaches and sports medical personnel in monitoring athletes' health, optimizing diets, and developing training plans to enhance performance and reduce injuries.

Stimulus responsive wireless sensor integrated smart urine bag for early detection of catheter-associated infections

Urinary catheterization is a routine intervention in intensive care unit (ICU) patients with prolonged immobility. However, extended use of urinary catheters significantly increases the risk of urinary tract infections (UTIs), posing serious healthcare concerns. Early detection of UTIs can effectively prevent disease progression and mitigate additional health complications. Most conventional diagnostic methods rely on periodic urine sample collection and laboratory analysis, which are time-consuming and lack the capacity for continuous, real-time assessment. To address these limitations, we developed a Smart Urinary Bag (SUB) system with an integrated sensor platform designed for real-time monitoring of UTI-associated biomarkers in urine directly within the urinary bag. The sensor platform incorporates two interdigitated electrode (IDE) patterns coated with stimuli-responsive polymers to selectively detect elevations in pH and xanthine oxidase (XOD) levels—two critical biomarkers for urinary infections. These IDEs connect to spiral planar coils, forming sensor tags that enable battery-free, wireless monitoring of urine conditions through near-field inductive coupling with an external reader. The sensor tags, measuring 10 cm × 6 cm, and the coating thickness were optimized using High-Frequency Structure Simulator (HFSS) software to ensure effective wireless readability and sensitivity for practical miniaturization and integration into standard urinary bags. Systematic characterization of the functional coatings confirmed effective compositions that allowed reliable wireless detection of elevated pH (>8) and XOD (>500 U/L) levels in urine, with a response time of less than 3 hours. The sensors maintained stable performance over 14 days under physiologically relevant conditions with high specificity, as verified through tests with both artificial and real urine. As proof of concept, the fully integrated SUB system was tested under simulated patient conditions with varying pH and XOD levels. The platform successfully identified high-risk UTI conditions, enabling timely alerts to healthcare providers for immediate intervention. This platform aims to enhance patient monitoring in ICUs by reducing healthcare provider burden, facilitating early identification of UTI risks, and promoting timely interventions, ultimately reducing antibiotic overuse and addressing the challenge of rising antibiotic-resistant bacterial strains.

Identification of STAT3 and MYC as critical ferroptosis-related biomarkers in septic cardiomyopathy: a bioinformatics and experimental study.

Ferroptosis is the well-known mechanism of septic cardiomyopathy (SCM). Bioinformatics analysis was employed to identify ferroptosis-related SCM differentially expressed genes (DEG). DEGs' functional enrichment was explored. Weighted gene co-expression network analysis (WGCNA) was employed to form gene clusters. The identified hub genes, signal transducer and activator of transcription 3 (STAT3) and myelocytomatosis (MYC) were further evaluated by generating receiver operator characteristic (ROC) curves and a nomogram prediction model. Additionally, survival rate, cardiac damage markers, and cardiac function and ferroptosis markers were evaluated in septic mouse model. STAT3 and MYC levels were measured in SCM heart tissue via immunohistochemical (IHC) staining, real-time polymerase chain reaction (qPCR) and western blot analysis. Analysis identified 225 DEGs and revealed 22 intersected genes. Of the 7 hub genes, STAT3 and MYC showed enrichment in septic heart tissue and a strong predicative ability based on AUC values. Cardiac damage, iron metabolism, and lipid peroxidation occurred in the SCM model. By experiments, STAT3 and MYC expression was increased in the SCM model. Impairment was reversed with a ferroptosis inhibitor, Fer-1. As conclusion, STAT3 and MYC are related with ferroptosis and may serve as potential SCM predictor indicators. KEY MESSAGES: Septic cardiomyopathy (SCM) often leads to high mortality in septic patients, and the diagnostic criteria still remains unclear. Ferroptosis as the pathogenic mechanism of SCM could help predict its progression and clinical outcomes. STAT3 and MYC are related with ferroptosis and may serve as potential SCM predictor biomarkers.

Boosted Meat Flavor by the Metabolomic Effects of Nile Tilapia Dietary Inclusion of Zophobas atratus Larval Meal.

Zophobas atratus larval meal (ZLM) is a high-quality feed supplement with potential activities that can improve fish growth performance and promote meat quality. However, there have been limited recent studies investigating the metabolic effects of ZLM. Therefore, this study aims to uncover the metabolomic mechanism through which ZLM improves tilapia meat flavor using metabolomic strategies. In this study, soybean meal in the basal diets was replaced with 15%, 30%, or 60% ZLM, where anti-nutrient factors were destroyed by high temperature treatment. After being fed these ZLM supplements for 30 days, dorsal muscles were collected from tilapia for meat sensory evaluation tests. Liver samples were also collected for metabolomic analysis using the gas chromatography-mass spectrometry (GC-MS) platform and combined with biochemical assays to verify metabolism-related enzyme activities and reveal crucial metabolic pathways and critical biomarkers associated with ZLM's ability to improve meat flavor. In tilapia livers, ZLM enhanced the activity of enzymes involved in energy metabolism including succinate dehydrogenase (SDH), pyruvate dehydrogenase (PDH), α-ketoglutarate dehydrogenase (α-KGDH), NADP-malate dehydrogenase (NAD-MDH) and mitochondrial isocitrate dehydrogenase (ICDHm). This resulted in increased levels of reduced nicotinamide adenine dinucleotide (NADH), acetyl CoA and ATP which led to accumulation of flavor fatty acids such as arachidonic acid, linoleic acid (9,12-Octadecadienoic acid), linolenic acid (9,12,15-Octadecatrienoic acid) and oleic acid (9-Octadecenoic acid). Additionally, there was an increase in flavor nucleotides like guanosine adenosine-5'-monophosphate and uridine-5'-monophosphate while off-flavor metabolites like inosine and hypoxanthine decreased. Furthermore, beneficial metabolomic responses led to a decrease in off-flavor metabolites such as 2-methylisoborneol trimethylamine and geosmin while increasing umami metabolites like 2-methyl-3-furanthiol and nonanal. This metabolomic study demonstrates that inclusion of ZLM diets enhances the flavor profile of tilapia dorsal muscle. The accumulation of flavor compounds, coupled with a reduction in earthy taste and off-flavor metabolites, contributes to an improved meat flavor and freshness. Additionally, there is an increase in the levels of flavor-related amino acids and nucleotides. These previously unidentified metabolic effects highlight the potential significance of ZLM as a dietary supplement for enhancing the biosynthesis of flavor metabolites in tilapia.