Arsenic exposure and oxidative damage to lipid, DNA, and protein among general Chinese adults: A repeated-measures cross-sectional and longitudinal study.

Arsenic-related oxidative stress and resultant diseases have attracted global concern, while longitudinal studies are scarce. To assess the relationship between arsenic exposure and systemic oxidative damage, we performed two repeated measures among 5236 observations (4067 participants) in the Wuhan-Zhuhai cohort at the baseline and follow-up after 3 years. Urinary total arsenic, biomarkers of DNA oxidative damage (8-hydroxy-2'-deoxyguanosine (8-OHdG)), lipid peroxidation (8-isoprostaglandin F2alpha (8-isoPGF2α)), and protein oxidative damage (protein carbonyls (PCO)) were detected for all observations. Here we used linear mixed models to estimate the cross-sectional and longitudinal associations between arsenic exposure and oxidative damage. Exposure-response curves were constructed by utilizing the generalized additive mixed models with thin plate regressions. After adjusting for potential confounders, arsenic level was significantly and positively related to the levels of global oxidative damage and their annual increased rates in dose-response manners. In cross-sectional analyses, each 1% increase in arsenic level was associated with a 0.406% (95% confidence interval (CI): 0.379% to 0.433%), 0.360% (0.301% to 0.420%), and 0.079% (0.055% to 0.103%) increase in 8-isoPGF2α, 8-OHdG, and PCO, respectively. More importantly, arsenic was further found to be associated with increased annual change rates of 8-isoPGF2α (ß: 0.147; 95% CI: 0.130 to 0.164), 8-OHdG (0.155; 0.118 to 0.192), and PCO (0.050; 0.035 to 0.064) in the longitudinal analyses. Our study suggested that arsenic exposure was not only positively related with global oxidative damage to lipid, DNA, and protein in cross-sectional analyses, but also associated with annual increased rates of these biomarkers in dose-dependent manners.

Dual excitation channel ratiometric fluorescent probes for visual and fluorescent detection of anthrax spore biomarker and tetracycline hydrochloride.

Long-term and excessive use of tetracycline hydrochloride (TC) can lead to its accumulation in the environment, which can cause water contamination, bacterial resistance, and food safety problems. 2,6-Pyridine dicarboxylic acid (DPA) is a major biomarker of Bacillus anthracis spores, and its rapid and sensitive detection is of great significance for disease prevention and counter-terrorism. A bifunctional ratiometric fluorescent nanoprobe has been fabricated to detect DPA and TC. 3,5-dicarboxyphenylboronic acid (BOP) was intercalated into layered europium hydroxide (LEuH) by the ion-exchange method and exfoliated into nanosheets as a fluorescent nanoprobe (PNP). DPA and TC could significantly enhance the red fluorescence of Eu3+ through the antenna effect under different excitation wavelengths, while the fluorescence of BOP can be used as a reference based on the constant emission intensity, realizing ratiometric detection. A low limit of detection (LOD) for the target (DPA: 9.7 nM, TC: 21.9 nM) can be achieved. In addition, visual detection of DPA and TC was realized using color recognition software based on the obvious color changes. This is the first ratiometric fluorescent nanoprobe based on layered rare-earth hydroxide (LRH) for the detection of DPA and TC simultaneously, which opens new ideas in the design of multifunctional probes.

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.

Associations of polycyclic aromatic hydrocarbons exposure with serum uric acid and hyperuricemia in US adults: The role of systemic inflammation.

The associations of polycyclic aromatic hydrocarbon (PAH) exposure with serum uric acid (SUA) or hyperuricemia have been rarely assessed. We aimed to investigate the relationships between urinary PAH metabolites and SUA or hyperuricemia among US adults and to explore the mediating role of systemic inflammation in the associations. A total of 10,307 US adults were conducted to assess the associations of seven urinary hydroxy­PAH with SUA and hyperuricemia and evaluate the role of C-reactive protein (CRP), a biomarker of systemic inflammation, in such associations. Results showed that each 1-unit increase in ln-transformed 2-hydroxynaphthalene (2-OHNa), 1-hydroxyphenanthrene (1-OHPh), 2&3-hydroxyphenanthrene (2&3-OHPh) and total hydroxyphenanthrene (ΣOHPh) was associated with a 1.68 (95% confidence interval (CI): 0.19 to 3.17), 2.46 (0.78 to 4.13), 3.34 (1.59 to 5.09), and 2.99 (1.23 to 4.75) µmol/L increase in SUA, and a 8% (odds ratio (OR): 1.08, 1.02 to 1.15), 9% (OR: 1.09, 1.02 to 1.18), 13% (OR: 1.13, 1.05 to 1.22), and 12% (OR: 1.12, 95% CI: 1.03, 1.21) increase in hyperuricemia, respectively. Co-exposure of seven PAHs was positively associated with SUA and hyperuricemia, with 2&3-OHPh showing the highest weight (components weights: 0.83 and 0.78, respectively). The CRP mediated 11.47% and 10.44% of the associations of ΣOHPh and 2&3-OHPh with SUA and mediated 8.60% and 8.62% in associations of ΣOHPh and 2&3-OHPh with hyperuricemia, respectively. In conclusion, internal levels of PAH metabolites were associated with elevated SUA levels and the increased risk of hyperuricemia among US adults, and CRP played a mediating role in the associations.

Isolation, Culture, and Quality Assessment of Clinical-Grade Corneal Stromal Stem Cells.

The challenge of treating corneal scarring through keratoplasties lies in the limited availability of donor tissue. Various studies have shown the therapeutic use of cultivated corneal stromal stem cells (CSSCs) to mitigate tissue inflammation and suppress fibrosis and scar tissue formation in preclinical corneal wound models. To develop CSSC therapy for clinical trials on patients with corneal scarring, it is necessary to generate clinical-grade CSSCs in compliant to Good Manufacturing Practice (GMP) regulations. This chapter elucidates human CSSC isolation, culture, and cryopreservation under GMP-compliant conditions. It underscores quality assessment encompassing morphological traits, expression of stemness markers, anti-inflammatory activity, and keratocyte differentiation potency.

Copeptin associates with major adverse cardiovascular events in patients on maintenance hemodialysis.

BACKGROUND: End-stage renal disease (ESRD) necessitating hemodialysis pose substantial cardiovascular risks, with cardiovascular disease (CVD) as a leading cause of mortality. Biomarkers like copeptin have emerged as potential indicators of cardiovascular stress and prognosis in CKD populations. OBJECTIVE: This study aimed to assess the prognostic value of copeptin in predicting major adverse cardiovascular events (MACEs) among hemodialysis patients, alongside traditional cardiac biomarkers. METHODS: ESRD patients undergoing maintenance hemodialysis were enrolled. Copeptin levels were measured, and patients were followed for MACEs, defined as cardiovascular deaths, myocardial infarction, stroke, or heart failure-related hospitalizations. Cox proportional-hazards models were used to evaluate the association between copeptin and outcomes, adjusting for relevant covariates. RESULTS: Among 351 patients followed for a median of 22.7 months, elevated copeptin levels were significantly associated with an increased risk of MACEs (HR 1.519, 95 % CI 1.140 to 2.023; p = 0.00425). Copeptin demonstrated predictive capability across multiple statistical tests (Log-rank p = 0.024; Gehan p < 0.001; Tarone-Ware p < 0.001; Peto-Peto p = 0.027), although significance was attenuated in pairwise comparisons post-adjustment for multiple testing. Combining copeptin with NT-proBNP or hs-cTnT further enhanced risk stratification for MACEs. CONCLUSION: Elevated copeptin levels independently predict adverse cardiovascular outcomes in hemodialysis patients. Integrating copeptin with traditional cardiac biomarkers may refine risk stratification and guide personalized therapeutic strategies in this high-risk population.

Interval-specific likelihood ratios and probability-based models for interpreting combined CSF biomarkers for Alzheimer's disease.

BACKGROUND: In Alzheimer's disease (AD) diagnosis, a cerebrospinal fluid (CSF) biomarker panel is commonly interpreted with binary cutoff values. However, these values are not generic and do not reflect the disease continuum. We explored the use of interval-specific likelihood ratios (LRs) and probability-based models for AD using a CSF biomarker panel. METHODS: CSF biomarker (Aß1-42, tTau and pTau181) data for both a clinical discovery cohort of 241 patients (measured with INNOTEST) and a clinical validation cohort of 129 patients (measured with EUROIMMUN), both including AD and non-AD dementia/cognitive complaints were retrospectively retrieved in a single-center study. Interval-specific LRs for AD were calculated and validated for univariate and combined (Aß1-42/tTau and pTau181) biomarkers, and a continuous bivariate probability-based model for AD, plotting Aß1-42/tTau versus pTau181 was constructed and validated. RESULTS: LR for AD increased as individual CSF biomarker values deviated from normal. Interval-specific LRs of a combined biomarker model showed that once one biomarker became abnormal, LRs increased even further when another biomarker largely deviated from normal, as replicated in the validation cohort. A bivariate probability-based model predicted AD with a validated accuracy of 88% on a continuous scale. CONCLUSIONS: Interval-specific LRs in a combined biomarker model and prediction of AD using a continuous bivariate biomarker probability-based model, offer a more meaningful interpretation of CSF AD biomarkers on a (semi-)continuous scale with respect to the post-test probability of AD across different assays and cohorts.

Biological variation of methylmalonic acid in urine in spanish population.

BACKGROUND-AIM: Methylmalonic acid (MMA) is currently the best biomarker of functional vitamin B12 deficiency. However, for a correct interpretation of the patient's results it is necessary to know its biological variation (BV). No BV data are available for urine MMA values, as measured by mass spectrometry. Hence, the aim of this study was to estimate the within- and between-person coefficients of variation (CVw, CVg) for MMA in a healthy population, and the associated index of individuality (II), as well as to define quality specifications based on BV and the reference change value (RCV). METHODS: Random urine samples from 34 healthy volunteers were collected over four consecutive weeks. Samples were stored at -80 °C until analysis in a single analytical run. MMA excretion was quantified by tandem liquid chromatography coupled to mass spectrometry (HPLC-MS/MS). Results were normalized to urine creatinine. The coefficients of variation were estimated by CV-ANOVA. Confidence intervals (95 %) were calculated. Quality specifications were defined according to international recommendations. RESULTS: A total of 128 samples were included. The coefficients of variation were CVw = 35.7 % (26.1-45.3) and CVg = 67.7 % (58.3-77.0). The associated II was 0.5 and the RCV was 88.1 %. CONCLUSION: Considering the II obtained, MMA in urine has high individuality, therefore, RCV is better to evaluate serial clinical results. Our results will contribute to a better clinical interpretation of this biomarker and will represent a great aid when defining analytical performance specifications for this magnitude.

Persistent acute kidney injury biomarkers: A systematic review and meta-analysis.

BACKGROUND: Various biomarkers reportedly predict persistent acute kidney injury (AKI) despite their varying predictive performance across clinical trials. This study aims to compare the accuracy of various biomarkers in predicting persistent AKI in different populations and regions. METHODS: In this meta-analysis, we searched for urinary C-C motif chemokine ligand 14 (CCL14), Tissue inhibitor of metalloproteinase-2&insulin-like growth factor-binding protein-7 (TIMP-2&IGFBP7), Neutrophil Gelatinase-Associated Lipocalin (NGAL), plasma Cystatin C (pCysC), Soluble urokinase plasminogen activator receptor (suPAR), Proenkephalin (PenK) and urinary dickkopf-3:urinary creatinine (uDKK3:uCr) from various databases including Medline, PubMed, Embase, and Cochrane. This was geared towards predicting persistent AKI in adults (>18 years). Hierarchically summarized subject work characteristic curves (HSROC) and diagnostic odds ratio (DOR) values were used to summarize the diagnostic accuracy of the biomarkers. Further, meta-regression and subgroup analyses were carried out to identify sources of heterogeneity as well as evaluate the best predictive biomarkers in different populations and regions. RESULTS: We screened 31 studies from 2,356 studies and assessed the diagnostic value of 7 biomarkers for persistent AKI. Overall, CCL14 had the best diagnostic efficacy with an AUC of 0.79 (95 % CI 0.75-0.82), whereas TIMP-2 & IGFBP7, NGAL, and pCysC had diagnostic efficacy of 0.75 (95 % CI 0.71-0.79),0.71 (95 % CI 0.67-0.75), and 0.7007, respectively. Due to a limited number of studies, PenK, uDKK3:uCr, and suPAR were not subjected to meta-analysis; however, relevant literature reported diagnostic efficacy above 0.70. Subgroup analyses based on population, region, biomarker detection time, AKI onset time, and AKI duration revealed that in the intensive care unit (ICU) population, the AUC of CCL14 was 0.8070, the AUC of TIMP-2 & IGFBP7 was 0.726, the AUC of pCysC was 0.72, and the AUC of NGAL was 0.7344; in the sepsis population, the AUC of CCL14 was 0.85, the AUC of TIMP-2&IGFBP7 was 0.7438, and the AUC of NGAL was 0.544; in the post-operative population, the AUC of CCL14 was 0.83-0.93, the AUC of TIMP-2&IGFBP7 was 0.71, and the AUC of pCysC was 0.683. Regional differences were observed in biomarker prediction of persistent kidney injury, with AUCs of 0.8558 for CCL14, 0.7563 for TIMP-2 & IGFBP7, and 0.7116 for NGAL in the Eurasian American population. In the sub-African population, TIMP-2 & IGFBP7 had AUCs of 0.7945, 0.7418 for CCL14, 0.7097 for NGAL, and 0.7007 for pCysC. for TIMP-2 & IGFBP7 was 0.7945, AUC for CCL14 was 0.7418, AUC for NGAL was 0.7097, and AUC for pCysC was 0.7007 in the sub-African population. Duration of biomarker detection, AKI onset, and AKI did not influence the optimal predictive performance of CCL14. Subgroup analysis and meta-regression of CCL14-related studies revealed that CCL14 is the most appropriate biomarker for predicting persistent stage 2-3 AKI, with heterogeneity stemming from sample size and AKI staging. CONCLUSION: This meta-analysis discovered CCL14 as the best biomarker to predict persistent AKI, specifically persistent stage 2-3 AKI.

Pediatric reference values of NT-proBNP and Galectin-3 based on a French cohort.

BACKGROUND: In pediatric cardiology, the fact that some new biomarkers have assay-specific normal values has to be considered for correct clinical decisions. The current study aimed to provide age-adjusted normative values for NT-proBNP and Galectin-3 using the Abbott immunoassay system from a prospective French pediatric cohort sera collection and to validate our data for NT-proBNP on a second retrospective cohort. METHODS: We analyzed 283 consecutive samples for NT-proBNP and 140 samples for Galectin-3 collected from apparently healthy children (0-18 years) with outpatient treatment at our institution (Hôpital Necker-Enfants malades, Paris, France) during 24 months. RESULTS: For NT-proBNP and Galectin-3, we establish four age partitions, respectively two (<2 years / >2 years) and establish upper reference values and their 90 % CI for each biomarker (Galectin-3 (ng/mL): 56 [44-70] / 26 [23-29]). We evaluated the diagnostic performance of our upper reference values of NT-proBNP on a retrospective cohort (n = 428) with positive predictive value of 0.92. CONCLUSIONS: Using Abbott immunoassay system, we report age-specific reference values for NT-proBNP and for the first time for Galectin-3 in a healthy French pediatric cohort. These data call for larger cohort studies to define more robustly percentiles and diagnostic performance for NT-proBNP.

Accuracy study of Angiotensin 1-7 composite index test to predict pulmonary fibrosis and guide treatment.

BACKGROUND: Pulmonary fibrosis can develop after acute respiratory distress syndrome (ARDS). The hypothesis is we are able to measure phenotypes that lie at the origin of ARDS severity and fibrosis development. The aim is an accuracy study of prognostic circulating biomarkers. METHODS: A longitudinal study followed COVID-related ARDS patients with medical imaging, pulmonary function tests and biomarker analysis, generating 444 laboratory data. Comparison to controls used non-parametrical statistics; p < 0·05 was considered significant. Cut-offs were obtained through receiver operating curve. Contingency tables revealed predictive values. Odds ratio was calculated through logistic regression. RESULTS: Angiotensin 1-7 beneath 138 pg/mL defined Angiotensin imbalance phenotype. Hyper-inflammatory phenotype showed a composite index test above 34, based on high Angiotensin 1-7, C-Reactive Protein, Ferritin and Transforming Growth Factor-ß. Analytical study showed conformity to predefined goals. Clinical performance gave a positive predictive value of 95 % (95 % confidence interval, 82 %-99 %), and a negative predictive value of 100 % (95 % confidence interval, 65 %-100 %). Those severe ARDS phenotypes represented 34 (Odds 95 % confidence interval, 3-355) times higher risk for pulmonary fibrosis development (p < 0·001). CONCLUSIONS: Angiotensin 1-7 composite index is an early and objective predictor of ARDS evolving to pulmonary fibrosis. It may guide therapeutic decisions in targeted phenotypes.

Identifying Putative Biomarkers of Foodborne Pathogens Using a Metabolomic Approach.

Metabolomics is the study of low molecular weight biochemical molecules (typically <1500 Da) in a defined biological organism or system. In case of food systems, the term "food metabolomics" is often used. Food metabolomics has been widely explored and applied in various fields including food analysis, food intake, food traceability, and food safety. Food safety applications focusing on the identification of pathogen-specific biomarkers have been promising. This chapter describes a nontargeted metabolite profiling workflow using gas chromatography coupled with mass spectrometry (GC-MS) for characterizing three globally important foodborne pathogens, Escherichia coli O157:H7, Listeria monocytogenes, and Salmonella enterica, from selective enrichment liquid culture media. The workflow involves a detailed description of food spiking experiments followed by procedures for the extraction of polar metabolites from media, the analysis of the extracts using GC-MS, and finally chemometric data analysis using univariate and multivariate statistical tools to identify potential pathogen-specific biomarkers.

An evaluation of serum blood parameters and amyloid-A levels in women with hyperemesis gravidarum; A prospective observational study.

This study aimed to investigate whether serum amyloid A (AA) level can be used as a biomarker in women with hyperemesis gravidarum (HEG). This prospective observational study was conducted at the Antalya Training and Research Hospital Gynecology and Obstetrics Clinic, Türkiye, between July and December 2023. Forty women diagnosed with HEG and 40 healthy women were included. No statistically significant differences were observed between the groups in terms of sociodemographic data such as age, body mass index, family history, educational status, economic level, place of residence, occupation, smoking and alcohol use, or drug habits. However, obstetric characteristics such as number of miscarriages, number of dilatation curettages, and gestational age and laboratory values including complete blood count, hematocrit, leukocyte, neutrophil, lymphocyte, platelet, free T4, albumin, alanine aminotransferase, aspartate aminotransferase, urea, creatinine, hs-C-reactive protein, and sodium (P > .05) all differed significantly. In addition, significant differences were observed between the HEG and healthy groups in terms of numbers of gravidities (2 [1-3] vs 1 [0-1], respectively, P < .001), numbers of parities (1 [0-1] vs 1 [0-1], P < .001), numbers of living children (1 [0-2] vs 1 [0-1], P < .001), presenting complaints (nausea 0 [0%], nausea + vomiting 0 [0%], none 40 [100.0%] vs nausea 27 [67.5%], nausea + vomiting 13 [32.5%], none 0 [0%], P < .001), serum thyroid-stimulating hormone (1.16 ±â€…0.56 vs 1.81 ±â€…0.624, P = .004), potassium (4.1 ±â€…0.7 vs 3.8 ±â€…0.2, P = .001), and AA values (7.29 ±â€…2.61 vs 10.74 ±â€…3.04, P < .001). At receiver operating characteristic analysis, the area under the curve (AUC: 0.881) was statistically significant for serum AA (P: <.001), with a cutoff value of ≥ 8.79 ([95% confidence interval] 0.743-0.919, sensitivity 87.4%, specificity 80.2%). The positive predictive value of serum AA was 81.1% and the negative predictive value was 80.4%. The study results showed that serum AA can be used as a diagnostic biomarker in HEG. Prospective studies involving more participants are now required to confirm our results.

[Chinese expert consensus on cardiac biomarkers for monitoring and management of cardiovascular toxicity in cancer therapy (2024 edition)].

In recent years, the treatment of malignant tumors has continually advanced, significantly extending the survival period of cancer patients. Tumors are increasingly present as chronic diseases over the long term, and the cardiovascular toxicity associated with cancer treatment has become increasingly prominent, severely affecting the effective treatment and survival of cancer patients. As a result, cardio-oncology has gained considerable attention as an emerging discipline. However, cardiovascular diseases in cancer patients often have an insidious onset, and early identification and treatment are frequently overlooked. Throughout the course of cancer treatment, it is crucial to reasonably apply cardiac biomarkers for risk stratification, early identification, and screening of cardiovascular diseases in patients, as well as to improve the early diagnosis and treatment levels of cardiovascular diseases in cancer patients. To this end, Cardio-Oncology Group, Chinese Society of Cardiology in conjunction with Cardiovascular Expert Committee, China Medical Doctor Association of Laboratory Medicine, organized experts in relevant fields to compile this consensus. It aims to provide a basis for clinicians to standardize the screening and management of cardiovascular toxicity related to cancer treatment through the reasonable application of cardiac biomarkers.

Plasma cell-free RNA signatures of inflammatory syndromes in children.

Inflammatory syndromes, including those caused by infection, are a major cause of hospital admissions among children and are often misdiagnosed because of a lack of advanced molecular diagnostic tools. In this study, we explored the utility of circulating cell-free RNA (cfRNA) in plasma as an analyte for the differential diagnosis and characterization of pediatric inflammatory syndromes. We profiled cfRNA in 370 plasma samples from pediatric patients with a range of inflammatory conditions, including Kawasaki disease (KD), multisystem inflammatory syndrome in children (MIS-C), viral infections, and bacterial infections. We developed machine learning models based on these cfRNA profiles, which effectively differentiated KD from MIS-C-two conditions presenting with overlapping symptoms-with high performance [test area under the curve = 0.98]. We further extended this methodology into a multiclass machine learning framework that achieved 80% accuracy in distinguishing among KD, MIS-C, viral, and bacterial infections. We further demonstrated that cfRNA profiles can be used to quantify injury to specific tissues and organs, including the liver, heart, endothelium, nervous system, and the upper respiratory tract. Overall, this study identified cfRNA as a versatile analyte for the differential diagnosis and characterization of a wide range of pediatric inflammatory syndromes.

Unveiling MRI markers for Parkinson's Disease: GABAergic dysfunction and cortical changes.

OBJECTIVE: The study aimed to investigate changes in basal levels of the inhibitory γ-aminobutyric acid (GABA) neurotransmitter in the sensorimotor cortex (SMC) and cortical gyrification in patients with Parkinson's disease (PD), which could further identify potential imaging biomarkers for PD, particularly in patients with early-onset Parkinson's disease (EOPD). METHOD: Fifty patients with PD (EOPD: 10, late-onset Parkinson's disease [LOPD]: 40) and fifty-two age- and gender-matched healthy controls (HC) underwent GABA-edited 1H MRS of the SMC and high-resolution 3D T1-weighted brain imaging. GABA levels and local gyrification index (LGI) were calculated to assess GABAergic and cortical gyrification deficits in PD. RESULT: The Pearson correlation coefficients revealed significant negative associations between eight indicators, including GABA/Cr level and local gyrification index (LGI) of specific cortical regions (precentral, postcentral, entorhinal, superiortemporal, posteriorcingulate, cuneus, and transversetemporal cortex), and the likelihood of Parkinson's disease (r < -0.4, p < 0.001). Additionally, GABA levels were significantly lower in the SMC region of both EOPD and LOPD patients compared to healthy controls (mean ± SD [u.i.]: EOPD=0.081 ± 0.022 vs. Young-HC=0.112 ± 0.021, p = 0.003; LOPD=0.054 ± 0.024 vs. Old-HC=0.099 ± 0.021, p < 0.001). The logistic regression model was established by using multivariate analysis, identifying two statistically significant indicators: GABA/Cr and LGI of the transversetemporal. The combined model exhibited the highest AUC values in both younger and older populations. CONCLUSION: GABAergic dysfunction may play an important role in the pathogenesis of PD patients. Changes in neurotransmitter and morphological may serve as potential markers for the preclinical diagnosis and progression of PD, including EOPD.

A harmonized occupational biomonitoring approach.

Biomonitoring has been widely used in assessing exposures in both occupational and public health complementing chemical risk assessments because it measures the concentrations of chemical substances in human body fluids (e.g., urine and blood). Biomonitoring considers all routes and sources of exposure. An occupational biomonitoring guidance document has been elaborated (OECD Occupational Biomonitoring Guidance) within the OECD framework and specifically, the Working Parties on Exposure and Hazard Assessment by scientific experts from 40 institutes and organizations representing 15 countries. The guidance provides practical information for assessing chemical exposures in occupational settings including the three common routes of exposure: inhalation, skin absorption and ingestion due to hand to mouth contact. The elaborated stepwise approach for conducting biomonitoring is tailored for occupational health professionals, scientists, risk assessors, and regulators. It includes methods for selecting appropriate biomarkers, devising sampling strategies, and assessing laboratories for validated analytical methods for the biomarker of interest, and ensuring timely feedback of results. Furthermore, it describes procedures for setting up efficient biomonitoring programs based on the Similar Exposure Group (SEG) approaches. Derived health-based human exposure biomarker assessment values called Occupational Biomonitoring Levels (OBLs) are proposed for use in occupational exposure and risk assessment. It also helps with the interpretation of biomonitoring results routinely collected and procedures for communicating biomonitoring results at individual, collective, and workplace levels. Ethical considerations associated with biomonitoring are also discussed. The ultimate goal of this biomonitoring approach is to promote harmonized application and interpretation of biomarkers as well as evidence-based occupational risk management measures.

Markers of Maternal Bone and Renal Toxicity Through 50 Weeks Postpartum: IMPAACT 2010 (VESTED) Trial.

BACKGROUND: Safety data from randomized trials of antiretrovirals in pregnancy are scarce. We evaluated maternal bone and renal data from the International Maternal Pediatric Adolescent AIDS Clinical Trials Network 2010 trial, which compared the safety and efficacy of 3 antiretroviral therapy regimens started in pregnancy: dolutegravir + emtricitabine/tenofovir alafenamide (DTG + FTC/TAF), dolutegravir + emtricitabine/tenofovir disoproxil fumarate (DTG + FTC/TDF), and efavirenz/emtricitabine/tenofovir disoproxil fumarate (EFV/FTC/TDF). METHODS: A subset of participants underwent dual-energy X-ray absorptiometry scans at postpartum week 50 only. Maternal bone mineral density (BMD) Z-scores were compared between arms. Maternal creatinine was measured at enrolment and periodically through week 50 postpartum, and by-arm differences in average weekly change in estimated creatinine clearance were compared. RESULTS: Six hundred forty-three participants were randomized to DTG + FTC/TAF (N = 217) or DTG + FTC/TDF (N = 215) or EFV/FTC/TDF (N = 211). Median age = 27 years (IQR 23, 32), median CD4 count = 466 cells/mm3 (IQR 308, 624); 564 (88%) women enrolled in Africa and 479 (74%) breastfed. Week 50 postpartum dual-energy X-ray absorptiometry results from 154 women were included in the analysis. Hip and spine BMD was on average higher in women in the DTG + FTC/TAF and lower in the DTG + FTC/TDF and EFV/FTC/TDF arms, but no significant differences in BMD Z-scores were observed between treatment groups. The weekly rate of change in estimated creatinine clearance differed among treatment groups during the antepartum period, but not over the full study follow-up. CONCLUSIONS: Markers of bone and renal toxicity did not differ significantly through week 50 postpartum among women randomized to start DTG + FTC/TAF or DTG + FTC/TDF or EFV/FTC/TDF in pregnancy.

Local-structure-preservation and redundancy-removal-based feature selection method and its application to the identification of biomarkers for schizophrenia.

Accurate diagnosis of mental disorders is expected to be achieved through the identification of reliable neuroimaging biomarkers with the help of cutting-edge feature selection techniques. However, existing feature selection methods often fall short in capturing the local structural characteristics among samples and effectively eliminating redundant features, resulting in inadequate performance in disorder prediction. To address this gap, we propose a novel supervised method named local-structure-preservation and redundancy-removal-based feature selection (LRFS), and then apply it to the identification of meaningful biomarkers for schizophrenia (SZ). LRFS method leverages graph-based regularization to preserve original sample similarity relationships during data transformation, thus retaining crucial local structure information. Additionally, it introduces redundancy-removal regularization based on interrelationships among features to exclude similar and redundant features from high-dimensional data. Moreover, LRFS method incorporates l2,1 sparse regularization that enables selecting a sparse and noise-robust feature subset. Experimental evaluations on eight public datasets with diverse properties demonstrate the superior performance of our method over nine popular feature selection methods in identifying discriminative features, with average classification accuracy gains ranging from 1.30 % to 9.11 %. Furthermore, the LRFS method demonstrates superior discriminability in four functional magnetic resonance imaging (fMRI) datasets from 708 healthy controls (HCs) and 537 SZ patients, with an average increase in classification accuracy ranging from 1.89 % to 9.24 % compared to other nine methods. Notably, our method reveals reproducible and significant changes in SZ patients relative to HCs across the four datasets, predominantly in the thalamus-related functional network connectivity, which exhibit a significant correlation with clinical symptoms. Convergence analysis, parameter sensitivity analysis, and ablation studies further demonstrate the effectiveness and robustness of our method. In short, our proposed feature selection method effectively identifies discriminative and reliable features that hold the potential to be biomarkers, paving the way for the elucidation of brain abnormalities and the advancement of precise diagnosis of mental disorders.