Advances in Chiral Metabolomic Profiling and Biomarker Discovery.

Chiral metabolomics entails the enantioselective measurement of the metabolome present in a biological system. Over recent years, it has garnered significant interest for its potential in discovering disease biomarkers and aiding clinical diagnostics. D-Amino acids and D-hydroxy acids, traditionally overlooked as unnatural, are now emerging as novel signaling molecules and potential biomarkers for a range of metabolic disorders, brain diseases, kidney disease, diabetes, and cancer. Despite their significance, simultaneous measurements of multiple classes of chiral metabolites in a biological system remain challenging. Hence, limited information is available regarding the metabolic pathways responsible for synthesizing D-amino/hydroxy acid and their associated pathophysiological mechanisms in various diseases. Capitalizing on recent advancements in sensitive analytical techniques, researchers have developed various targeted chiral metabolomic methods for the analysis of chiral biomarkers. Here, we highlight the pivotal role of chiral metabolic profiling studies in disease diagnosis, prognosis, and therapeutic interventions. Furthermore, we describe cutting-edge chromatographic and mass spectrometry methods that enable enantioselective analysis of chiral metabolites. These advanced techniques are instrumental in unraveling the complexities of disease biomarkers, contributing to the ongoing efforts in disease biomarker discovery.

Role of per- and polyfluoroalkyl substances in the cardiorenal system: Unraveling crosstalk from the network of pollutants and phenotypes.

Although per- and polyfluoroalkyl substances (PFAS) have been frequently linked to cardiovascular and renal disease separately, evidence remains scarce regarding their systematic effect. Therefore, we recruited 546 newly diagnosed acute coronary syndrome (ACS) patients and detected seven myocardial enzymes and six kidney function biomarkers. Twelve PFAS were also assessed with ultra-high-performance liquid chromatography-tandem mass spectrometry. Generalized linear model and restricted cubic spline model were applied to single pollutant analysis. Quantile g-computation was used for mixture analysis. Network model was utilized to identify central and bridge nodes of pollutants and phenotypes. In the present study, perfluorohexane sulfonic acid was positively associated with uric acid (UA) (ß= 0.04, 95% confidence interval (CI): 0.01, 0.07), and perfluorobutanoic acid was negatively associated with estimated glomerular filtration rate (ß= -0.04, 95% CI: -0.07, -0.01) but positively associated with UA (ß= 0.03, 95% CI: 0.01, 0.06). In mixture analysis, each quantile increase in the PFAS mixture was significantly associated with UA (ß= 0.08, 95% CI: 0.04, 0.11). Network analysis revealed that perfluorooctanoate, UA, and myoglobin were denoted as bridge nodes, and the first principal component of lactate dehydrogenase and creatine kinase- myocardial band was identified as the node with the highest strength and expected influence. This study investigates the systematic impact of PFAS exposure through cardiorenal interaction network, which highlights that PFAS may serve as an upstream approach in UA-modulated cardiorenal network to affect cardiorenal system comprehensively.

Proteomic insights from extracellular vesicles into the molecular mechanisms of health effects induced by Per- and polyfluoroalkyl substances.

Per- and polyfluoroalkyl substances (PFASs) can induce a range of adverse health effects, with the precise molecular mechanisms remaining elusive. Extracellular vesicles (EVs) have demonstrated their potential to elucidate unknown molecular mechanisms. Building upon the close alignment of their biological functions with the observed health effects of PFASs, this study innovatively focuses on proteomic insights from EVs into the molecular mechanisms underlying the systemic health effects of PFASs. Through rat exposure experiments and proteomics technology, it not only demonstrated the occurrence of PFASs in EVs but also revealed the alterations in the serum EVs and the expression of their protein cargos following mixed exposure to PFASs, leading to changes in related pathways. These changes encompass various biological processes, including proteasome activity, immune response, cytoskeletal organization, oxidative stress, cell signaling, and nervous system function. Particularly noteworthy is the uncovering of the activation of the proteasome pathway, highlighting significant key contributing proteins. These novel findings provide a new perspective for exploring the molecular mechanism underlying the systemic health effects of PFASs and offer reliable screening for potential biomarkers. Additionally, comparisons with serum confirmed the potential of serum EVs as biological responders and measurable endpoints for evaluating PFASs-induced toxicity.

(Pre)Clinical Metabolomics Analysis.

Metabolomics is the scientific field with the eager goal to comprehensively analyze the entirety of all small molecules of a biological system, i.e., the metabolome. Over the last few years, metabolomics has matured to become an analytical cornerstone of life science research across diverse fields, from fundamental biochemical applications to preclinical studies, including biomarker discovery and drug development. In this chapter, we provide an introduction to (pre)clinical metabolomics. We define key metabolomics aspects and provide the basis to thoroughly understand the relevance of this field in a biological and clinical context. We present and explain state-of-the-art analytical technologies devoted to metabolomic analysis as well as emerging technologies, discussing both strengths and weaknesses. Given the ever-increasing demand for handling complex datasets, the role of bioinformatics approaches in the context of metabolomic analysis is also illustrated.

Extracting Knowledge from MS Clinical Metabolomic Data: Processing and Analysis Strategies.

Assessing potential alterations of metabolic pathways using large-scale approaches plays today a central role in clinical research. Because several thousands of mass features can be measured for each sample with separation techniques hyphenated to mass spectrometry (MS) detection, adapted strategies have to be implemented to detect altered pathways and help to elucidate the mechanisms of pathologies. These procedures include peak detection, sample alignment, normalization, statistical analysis, and metabolite annotation. Interestingly, considerable advances have been made over the last years in terms of analytics, bioinformatics, and chemometrics to help massive and complex metabolomic data to be more adequately handled with automated processing and data analysis workflows. Recent developments and remaining challenges related to MS signal processing, metabolite annotation, and biomarker discovery based on statistical models are illustrated in this chapter in light of their application to clinical research.

Integrated multi-omics revealed that dysregulated lipid metabolism played an important role in RA patients with metabolic diseases.

OBJECTIVES: Patients with rheumatoid arthritis (RA) commonly experience a high prevalence of multiple metabolic diseases (MD), leading to higher morbidity and premature mortality. Here, we aimed to investigate the pathogenesis of MD in RA patients (RA_MD) through an integrated multi-omics approach. METHODS: Fecal and blood samples were collected from a total of 181 subjects in this study for multi-omics analyses, including 16S rRNA and internally transcribed spacer (ITS) gene sequencing, metabolomics, transcriptomics, proteomics and phosphoproteomics. Spearman's correlation and protein-protein interaction networks were used to assess the multi-omics data correlations. The Least Absolute Shrinkage and Selection Operator (LASSO) machine learning algorithm were used to identify disease-specific biomarkers for RA_MD diagnosis. RESULTS: Our results found that RA_MD was associated with differential abundance of gut microbiota such as Turicibacter and Neocosmospora, metabolites including decreased unsaturated fatty acid, genes related to linoleic acid metabolism and arachidonic acid metabolism, as well as downregulation of proteins and phosphoproteins involved in cholesterol metabolism. Furthermore, a multi-omics classifier differentiated RA_MD from RA with high accuracy (AUC: 0.958). Compared to gouty arthritis and systemic lupus erythematosus, dysregulation of lipid metabolism showed disease-specificity in RA_MD. CONCLUSIONS: The integration of multi-omics data demonstrates that lipid metabolic pathways play a crucial role in RA_MD, providing the basis and direction for the prevention and early diagnosis of MD, as well as new insights to complement clinical treatment options.

Identification of metabolism related biomarkers in obesity based on adipose bioinformatics and machine learning.

BACKGROUND: Obesity has emerged as a growing global public health concern over recent decades. Obesity prevalence exhibits substantial global variation, ranging from less than 5% in regions like China, Japan, and Africa to rates exceeding 75% in urban areas of Samoa. AIM: To examine the involvement of metabolism-related genes. METHODS: Gene expression datasets GSE110729 and GSE205668 were accessed from the GEO database. DEGs between obese and lean groups were identified through DESeq2. Metabolism-related genes and pathways were detected using enrichment analysis, WGCNA, Random Forest, and XGBoost. The identified signature genes were validated by real-time quantitative PCR (qRT-PCR) in mouse models. RESULTS: A total of 389 genes exhibiting differential expression were discovered, showing significant enrichment in metabolic pathways, particularly in the propanoate metabolism pathway. The orangered4 module, which exhibited the highest correlation with propanoate metabolism, was identified using Weighted Correlation Network Analysis (WGCNA). By integrating the DEGs, WGCNA results, and machine learning methods, the identification of two metabolism-related genes, Storkhead Box 1 (STOX1), NACHT and WD repeat domain-containing protein 2(NWD2) was achieved. These signature genes successfully distinguished between obese and lean individuals. qRT-PCR analysis confirmed the downregulation of STOX1 and NWD2 in mouse models of obesity. CONCLUSION: This study has analyzed the available GEO dataset in order to identify novel factors associated with obesity metabolism and found that STOX1 and NWD2 may serve as diagnostic biomarkers.

Characterization of GABAergic marker expression in prefrontal cortex in dexamethasone induced depression/anxiety model.

Background: The pivotal responsibility of GABAergic interneurons is inhibitory neurotransmission; in this way, their significance lies in regulating the maintenance of excitation/inhibition (E/I) balance in cortical circuits. An abundance of glucocorticoids (GCs) exposure results in a disorder of GABAergic interneurons in the prefrontal cortex (PFC); the relationship between this status and an enhanced vulnerability to neuropsychiatric ailments, like depression and anxiety, has been identified, but this connection is still poorly understood because systematic and comprehensive research is lacking. Here, we aim to investigate the impact of dexamethasone (DEX, a GC receptor agonist) on GABAergic interneurons in the PFC of eight-week-old adult male mice. Methods: A double-blind study was conducted where thirty-two mice were treated subcutaneously either saline or DEX (0.2 mg/10 ml per kg of body weight) dissolved in saline daily for 21 days. Weight measurements were taken at five-day intervals to assess the emotional changes in mice as well as the response to DEX treatment. Following the 21-day regimen of DEX injections, mice underwent examinations for depression/anxiety-like behaviours and GABAergic marker expression in PFC. Results: In a depression/anxiety model generated by chronic DEX treatment, we found that our DEX procedure did trigger depression/anxiety-like behaviors in mice. Furthermore, DEX treatment reduced the expression levels of a GABA-synthesizing enzyme (GAD67), Reelin, calcium-binding proteins (parvalbumin and calretinin) and neuropeptides co-expressed in GABAergic neurons (somatostatin, neuropeptide Y and vasoactive intestinal peptide) in the PFC were reduced after 21 days of DEX treatment; these reductions were accompanied by decreases in brain size and cerebral cortex thickness. Conclusion: Our results indicate that a reduction in the number of GABAergic interneurons may result in deficiencies in cortical inhibitory neurotransmission, potentially causing an E/I imbalance in the PFC; this insight suggests a potential breakthrough strategy for the treatment of depression and anxiety.

The Potential of Gingival Crevicular Fluid as a Tool for Molecular Diagnosis: A Systematic Review.

A biomarker is defined as a characteristic that is measured as an indicator of a normal biological or pathological process, a response to an exposure or intervention. Biomarkers with a diagnostic approach must identify not only the presence but also the absence of the disease with high precision, so having the biological source of the said marker is of vital importance to ensure precision and accuracy; the aim was to carry out a review of its diagnostic potential. The search strategy was carried out in three databases: PubMed, ScienceDirect, and Scopus. The keywords that were used were as follows: "gingival crevicular fluid", "Biomarker", and "Diagnosis", using the Boolean operator "AND". The filter was used at 10 years. Within the type of molecules most studied, the cytokine family was the most abundant with 25.42% of the studies, followed by metalloproteinases and proteins with 16.9% each one. Studies that included RNA-type genetic material were less frequently found. As has been demonstrated, the use of GCF as a source of biomolecules for diagnostic use has been increasing, both for oral diseases, which reflects the local conditions of the disease; it also has the ability to reflect the development of distant diseases; and this is because GCF is a blood ultrafiltrate.

Saliva as a diagnostic tool in soccer: a scoping review.

Background: A high-performance sport like soccer requires training strategies that aim to reach peak performance at the right time for the desired competitions. Thus, the investigation of biochemical markers in saliva is a tool that is beginning to be used in athletes within the physical training process. There is still no evidence on universal saliva collection and analysis protocols in soccer. This review aims to map the use of saliva as a tool for analyzing athletic performance in soccer, from the biomarkers used to the validated protocols for these analyses. Methods: A broad systematic literature search was carried out in the electronic databases Web of Science, Livivo, Scopus, PubMed, LILACS and gray literature (Google Scholar and ProQuest). Two reviewers selected the studies and extracted data on the type of salivary collection used, the salivary biomarker evaluated and monitored. Results: Ninety-three articles were included. The most frequently analyzed salivary biomarkers were cortisol (n = 53), testosterone (n = 35), secretory immunoglobulin A (SIgA) (n = 33), salivary alpha amylase (n = 7), genetic polymorphisms (n = 4) and miRNAs (n = 2). The results of the studies indicated beneficial effects in monitoring salivary biomarkers in the assessment of sports performance, although most studies did not include a control group capable of comparison. Salivary collection and analysis protocols were varied and commonly not reported. Conclusions: This scoping review provides a comprehensive overview of the current landscape of salivary biomarker research in soccer. The findings underscore the importance of these biomarkers in assessing athletes' physiological responses and overall well-being. Future research should focus on refining methodologies, exploring additional biomarkers, and investigating the practical implications of salivary biomarker monitoring in soccer and other sports.

Untargeted Metabolomic Study of Patients with Wet Age-Related Macular Degeneration in Aqueous Humor.

Purpose: The objective of this study was to ascertain metabolic biomarkers and investigate the metabolic alterations associated with aqueous humor (AH) in wet age-related macular degeneration (AMD). Methods: AH samples were collected from a total of 20 participants, including 10 individuals diagnosed with wet AMD and 10 individuals undergoing cataract surgery, serving as the control group. Metabolomics analysis was performed using liquid chromatography-tandem mass spectrometry (LC-MS/MS) to identify and quantify metabolites. Results: A total of 155 metabolites were identified in the AH samples. Among them, 10 metabolites emerged as potential biomarkers capable of differentiating patients with wet AMD from the control group. In the AH of wet AMD patients, there was increased expression of Cardiolipin (CL) (72:5), Diglyceride (DG) (18:3_18:2), DG (36:5e) and Triglyceride (TG) (24:7), while the expression of Ceramides (Cer) (d32:0), Cer (d34:0), Cer (d36:0), Monogalactosyldiacylglycerol (MGDG) (16:1_18:3), Sphingosine (SPH) (d18:0) and TG (16:0_10:4_16:0) was down regulated. Conclusion: Through metabolomics analysis of AH, this study successfully uncovered valuable metabolic biomarkers linked to wet AMD. These findings contribute to a more comprehensive understanding of the pathogenesis of wet AMD and offer potential avenues for the development of innovative treatment strategies for this condition.

Competition and stereotypic behavior in Thoroughbred horses: The value of saliva as a diagnostic marker of stress.

Many horses exhibit stereotypies, especially when living in human controlled environments that may prevent horses from satisfying natural needs in terms of feeding, drinking, moving, and socializing. In human medicine, obsessive compulsive disorder and other severe psychiatric disturbances are associated with stereotypic behaviors; salivary biomarkers evaluation is considered a reliable tool for diagnosis of common mental health disorders because saliva collection easy to obtain and noninvasive. In this study, we hypothesized that salivary cortisol concentrations, in addition to alpha-amylase (sAA) and butyrylcholinesterase (BChE) activities, are considered stress biomarkers that may be influenced in horses trained for racing competition with stereotypic behaviors. Saliva at rest condition was obtained from ten non-stereotypic Thoroughbreds horses involved in high-level competition; eleven Thoroughbreds high-level competition horses showing stereotypic behaviors, and five Thoroughbreds leisure non-competition horses. Cortisol was found to be higher in high-level competition non-stereotypic horses and sAA was significantly higher in non-stereotypic leisure horses when compared to horses involved in competition, while BChE did not change between groups. These results may represent the basis for further behavioural evaluation to elucidate how stereotypic horses and horses involved in competition overcome stressful situations.

Potential Association of Maker Expression of Low-Density Neutrophils and Their Phenotypes in Patients with Periodontitis: Control Study.

Background: Neutrophils play an important role in maintaining periodontal status in conditions of healthy homeostasis. They achieve their surveillance function by continuously migrating to the gingival sulcus and eradicating periodontal pathogens. In addition, neutrophils are considered an integral element in the pathogenesis of periodontal diseases. Among several neutrophil subsets, low-density neutrophils (LDN) have recently received attention and are linked with cancer, immunological, inflammatory, and infectious diseases. However, the presence, phenotypes, and potential role of LDN in the pathogenesis of periodontitis have not yet been investigated. Objectives: To investigate the presence, subsets (normal, band, suppressive, and active), and phenotypes via marker expression surface protein known as the cluster of differentiation (CD) (CD16b, CD14, CD15, and CD62L) of LDN in patients with periodontitis. Materials and Methods: The observational case-control study was conducted to estimate the potential role of LDNs in periodontitis. Venous blood and periodontal indices were obtained from 40 healthy control individuals and 60 periodontitis patients. Subsequently, CD16b, CD62L, CD14, and CD15 expression on the surface of LDN was examined by multicolor flow cytometry, and their subsets were classified as "normal" (CD16brightCD62Lbright), "bands" (CD16dimCD62Lbright), "suppressive" (CD16brightCD62Ldim), and "active" (CD16brightCD62Lnegative). Results: There was a significant difference in the expression of LDN markers for active and suppressive phenotypes, respectively, favoring periodontitis over the control group. In contrast, there were significantly higher levels of CD16b, CD62L, and CD15 ("normal") in the control group when compared with the periodontitis group. Conclusion: LDN was associated with periodontitis as it was significantly increased in the periodontitis group in comparison with the control group and was positively correlated with all periodontal parameters. Cells from both groups of patients (periodontitis and control) expressed a normal mature phenotype (CD16b + High, CD62L + High, CD15+, and CD14-). Regarding subsets, the normal LDN (CD16brightCD62Lbright) was the most predominant phenotype in both periodontitis and control groups. However, the active subset increased in periodontitis compared to normal, indicating their destructive role in periodontitis.

Unveiling shared diagnostic biomarkers and molecular mechanisms between T2DM and sepsis: Insights from bioinformatics to experimental assays.

Septic patients with T2DM were prone to prolonged recovery and unfavorable prognoses. Thus, this study aimed to pinpoint potential genes related to sepsis with T2DM and develop a predictive model for the disease. The candidate genes were screened using protein-protein interaction networks (PPI) and machine learning algorithms. The nomogram and receiver operating characteristic curve were developed to assess the diagnostic efficiency of the biomarkers. The relationship between sepsis and immune cells was analyzed using the CIBERSORT algorithm. The biomarkers were validated by qPCR and western blotting in basic experiments, and differences in organ damage in mice were studied. Three genes (MMP8, CD177, and S100A12) were identified using PPI and machine learning algorithms, demonstrating strong predictive capabilities. These biomarkers presented significant differences in gene expression patterns between diseased and healthy conditions. Additionally, the expression levels of biomarkers in mouse models and blood samples were consistent with the findings of the bioinformatics analysis. The study elucidated the common molecular mechanisms associated with the pathogenesis of T2DM and sepsis and developed a gene signature-based prediction model for sepsis. These findings provide new targets for the diagnosis and intervention of sepsis complicated with T2DM.

Serum metabonomics reveal the effectiveness of human placental mesenchymal stem cell therapy for primary sclerosing cholangitis.

BACKGROUND: The metabolic patterns of human placental-derived mesenchymal stem cell (hP-MSC) treatment for primary sclerosing cholangitis (PSC) remain unclear, and therapeutic effects significantly vary due to individual differences. Therefore, it is crucial to investigate the serological response to hP-MSC transplantation through small molecular metabolites and identify easily detectable markers for efficacy evaluation. METHODS: Using Mdr2-/- mice as a PSC model and Mdr2+/+ mice as controls, the efficacy of hP-MSC treatment was assessed based on liver pathology, liver enzymes, and inflammatory factors. Serum samples were collected for 12C-/13C-dansylation and DmPA labeling LC-MS analysis to investigate changes in metabolic pathways after hP-MSC treatment. Key metabolites and regulatory enzymes were validated by qRT-PCR and Western blotting. Potential biomarkers of hP-MSC efficacy were identified through correlation analysis and machine learning. RESULTS: Collectively, the results of the liver histology, serum liver enzyme levels, and inflammatory factors supported the therapeutic efficacy of hP-MSC treatment. Based on significant differences, 41 differentially expressed metabolites were initially identified; these were enriched in bile acid, lipid, and hydroxyproline metabolism. After treatment, bile acid transport was accelerated, whereas bile acid production was reduced; unsaturated fatty acid synthesis was upregulated overall, with increased FADS2 and elongase expression and enhanced fatty acid ß-oxidation; hepatic proline 4-hydroxylase expression was decreased, leading to reduced hydroxyproline production. Correlation analysis of liver enzymes and metabolites, combined with time trends, identified eight potential biomarkers: 2-aminomuconate semialdehyde, L-1-pyrroline-3-hydroxy-5-carboxylic acid, L-isoglutamine, and maleamic acid were more abundant in model mice but decreased after hP-MSC treatment. Conversely, 15-methylpalmitic, eicosenoic, nonadecanoic, and octadecanoic acids were less abundant in model mice but increased after hP-MSC treatment. CONCLUSIONS: This study revealed metabolic regulatory changes in PSC model mice after hP-MSC treatment and identified eight promising biomarkers, providing preclinical evidence to support therapeutic applications of hP-MSC.

Utilize proteomic analysis to identify potential therapeutic targets for combating sepsis and sepsis-related death.

Background: Sepsis is an inflammatory disease that leads to severe mortality, highlighting the urgent need to identify new therapeutic strategies for sepsis. Proteomic research serves as a primary source for drug target identification. We employed proteome-wide Mendelian randomization (MR), genetic correlation analysis, and colocalization analysis to identify potential targets for sepsis and sepsis-related death. Methods: Genetic data for plasma proteomics were obtained from 35,559 Icelandic individuals and an initial MR analysis was conducted using 13,531 sepsis cases from the FinnGen R10 cohort to identify associations between plasma proteins and sepsis. Subsequently, significant proteins underwent genetic correlation analysis, followed by replication in 54,306 participants from the UK Biobank Pharma Proteomics Project and validation in 11,643 sepsis cases from the UK Biobank. The identified proteins were then subjected to colocalization analysis, enrichment analysis, and protein-protein interaction network analysis. Additionally, we also investigated a MR analysis using plasma proteins on 1,896 sepsis cases with 28-day mortality from the UK Biobank. Results: After FDR correction, MR analysis results showed a significant causal relationship between 113 plasma proteins and sepsis. Genetic correlation analysis revealed that only 8 proteins had genetic correlations with sepsis. In the UKB-PPP replication analysis, only 4 proteins were found to be closely associated with sepsis, while validation in the UK Biobank sepsis cases found overlaps for 21 proteins. In total, 30 proteins were identified in the aforementioned analyses, and colocalization analysis revealed that only 2 of these proteins were closely associated with sepsis. Additionally, in the 28-day mortality MR analysis of sepsis, we also found that only 2 proteins were significant. Conclusions: The identified plasma proteins and their associated metabolic pathways have enhanced our understanding of the complex relationship between proteins and sepsis. This provides new avenues for the development of drug targets and paves the way for further research in this field.

Revisiting Unaddressed Safety Concerns Regarding Intense Pulsed Light Treatment: Past and Present Perspectives.

BACKGROUND: The light spectrum of intense pulsed light (IPL) comprises visible to near-infrared light. It has been widely employed in the field of aesthetics for approximately 30 years. However, several studies have demonstrated the appearance of various undesirable biomarkers on the skin after IPL irradiation, which remain elucidated. METHODS: We reviewed the evolving concepts and explored the potentially harmful effects of IPL that may have been neglected in the past. RESULTS: Increased levels of reactive oxidative stress, p53, p16, proliferating cell nuclear antigen, interleukin-6, C-reactive protein, and cleaved caspase 3 and decreased albumin levels in human or mouse skin have been observed after IPL treatment. Visible and infrared light can exert harmful and beneficial effects on human skin. CONCLUSION: If perform improperly, IPL treatment may lead to cellular senescence, photoaging, photocarcinogenesis, thermal aging, and inflammaging. Further studies are required to verify the significance of the changes in the relevant biomarkers. The selection of treatment candidates, optimal parameters, and standardized protocols for IPL therapy are necessary.

Diagnostic value and clinical significance of lncRNA NEAT1 combined with miR-425-3p in children with viral myocarditis.

BACKGROUND: Viral myocarditis (VMC) is common in children. Previous studies have reported the clinical value of nuclear paraspeckle assembly transcript 1 (NEAT1) and microRNA-425-3p (miR-425-3p) in certain diseases, but not in VMC. This article was designed to investigate the expression of long noncoding RNA (lncRNA) NEAT1 and miR-425-3p in the serum of patients with VMC and their clinical significance. METHODS: We assessed VMC and healthy patients and analyzed differences in the expression levels of NEAT1 and miR-425-3p. The correlation and targeting relationship between the two were reported by Spearman correlation analysis and luciferase reporter assay. ROC curves were plotted to reflect the diagnostic effect of both. In addition, according to the 12-month prognostic effect grouping, patients with VMC were separated into a group with good vs. poor prognosis, and the difference in the expression levels of NEAT1 and miR-425-3p between the two groups were analyzed. The ability of the two markers in the prognosis of VMC was further analyzed by multiple logistic regression. RESULTS: NEAT1 expression was up-regulated in VMC and miR-425-3p expression was down-regulated, and there was a negative correlation and targeting link between the two. The diagnostic efficacy of both NEAT1 and miR-425-3p was higher than that of a single indicator. High expression of NEAT1 and low expression of miR-425-3p were found in VMC patients with poor prognosis. Both were independent influencers of VMC prognosis. CONCLUSION: NEAT1 and miR-425-3p expressions were affected by VMC and had important clinical implications for VMC, indicating for the first time the clinical function of NEAT1 and miR-425-3p in VMC.

Immunometabolic changes and potential biomarkers in CFS peripheral immune cells revealed by single-cell RNA sequencing.

The pathogenesis of Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) remains unclear, though increasing evidence suggests inflammatory processes play key roles. In this study, single-cell RNA sequencing (scRNA-seq) of peripheral blood mononuclear cells (PBMCs) was used to decipher the immunometabolic profile in 4 ME/CFS patients and 4 heathy controls. We analyzed changes in the composition of major PBMC subpopulations and observed an increased frequency of total T cells and a significant reduction in NKs, monocytes, cDCs and pDCs. Further investigation revealed even more complex changes in the proportions of cell subpopulations within each subpopulation. Gene expression patterns revealed upregulated transcription factors related to immune regulation, as well as genes associated with viral infections and neurodegenerative diseases.CD4+ and CD8+ T cells in ME/CFS patients show different differentiation states and altered trajectories, indicating a possible suppression of differentiation. Memory B cells in ME/CFS patients are found early in the pseudotime, indicating a unique subtype specific to ME/CFS, with increased differentiation to plasma cells suggesting B cell overactivity. NK cells in ME/CFS patients exhibit reduced cytotoxicity and impaired responses, with reduced expression of perforin and CD107a upon stimulation. Pseudotime analysis showed abnormal development of adaptive immune cells and an enhanced cell-cell communication network converging on monocytes in particular. Our analysis also identified the estrogen-related receptor alpha (ESRRA)-APP-CD74 signaling pathway as a potential biomarker for ME/CFS in peripheral blood. In addition, data from the GSE214284 database confirmed higher ESRRA expression in the monocyte cell types of male ME/CFS patients. These results suggest a link between immune and neurological symptoms. The results support a disease model of immune dysfunction ranging from autoimmunity to immunodeficiency and point to amyloidotic neurodegenerative signaling pathways in the pathogenesis of ME/CFS. While the study provides important insights, limitations include the modest sample size and the evaluation of peripheral blood only. These findings highlight potential targets for diagnostic biomarkers and therapeutic interventions. Further research is needed to validate these biomarkers and explore their clinical applications in managing ME/CFS.

A geo-gender-based analysis of human health: The presence of cut flower farms can attenuate pesticide exposure in African communities, with women being the most vulnerable.

Background: The rapid expansion of the cut flower industry in Africa has led to pervasive use and potential exposure of pesticides, raising concerns for local communities. Whether the risks associated with pesticide applications are localised or have broader implications remains unclear. Methods: We measured biomarkers of real and perceived pesticide exposure in two Kenyan communities: Naivasha, where the cut flower industry is present, and Mogotio, where the cut flower industry is absent. We measured real exposure by the percentage of acetylcholinesterase (AChE) inhibition and perceived exposure by assessing hair cortisol levels, a biomarker of stress. Additionally, we conducted a demographic survey to evaluate the health and socioeconomic status of participants, as well as their perceptions of pesticide risks associated with the cut flower industry. Results: Perceived pesticide exposure was more common in Naivasha (n = 36, 56%) compared to Mogotio (n = 0, 0%), according to community surveys. However, Mogotio residents had significantly higher mean hair cortisol levels (mean (x̄) = 790 ng/g, standard deviation (SD) = 233) and percentage of AChE inhibition (x̄ = 28.5%, SD = 7.3) compared to Naivasha residents, who had lower mean hair cortisol levels (x̄ = 548 ng/g, SD = 187) and percentage of AChE inhibition (x̄ = 14.5%, SD = 10.1). Location (proximity to cut flower farms) and gender were significant factors influencing pesticide exposure, with individuals living outside the cut flower industrial complexes being at higher risk. Women in both communities were the most vulnerable demographic, showing significantly higher mean hair cortisol levels (x̄ = 646 ng/g, SD = 267.4) and percentage of AChE inhibition (x̄ = 22.5%, SD = 12.4) compared to men hair cortisol levels (x̄ = 558.2 ng/g, SD = 208.2) and percentage of AChE inhibition (x̄ = 10.4%, SD = 13.1). Conclusions: A heightened awareness of the potential risks of pesticide exposure was widespread within cut flower industrial complexes. This may have led to a reduction in exposure of both workers and non-workers living within or close to these complexes. In contrast, communities living outside these complexes showed higher levels of exposure, possibly due to limited chemical awareness and a lack of precautionary measures. Despite this contrast between communities, women remained the most vulnerable members, likely due to their socioeconomic roles in African society. Monitoring women's pesticide exposure is crucial for providing an early warning system for community exposure.