Value of long non-coding RNA HAS2-AS1 as a diagnostic and prognostic marker of glioma / El valor de ARNncl HAS2-AS1 como marcador diagnóstico y pronóstico del glioma

Background: Glioma presents high incidence and poor prognosis, and therefore more effective treatments are needed. Studies have confirmed that long non-coding RNAs (lncRNAs) basically regulate various human diseases including glioma. It has been theorized that HAS2-AS1 serves as an lncRNA to exert an oncogenic role in varying cancers. This study aimed to assess the value of lncRNA HAS2-AS1 as a diagnostic and prognostic marker for glioma. Methods: The miRNA expression data and clinical data of glioma were downloaded from the TCGA database for differential analysis and survival analysis. In addition, pathological specimens and specimens of adjacent normal tissue from 80 patients with glioma were used to observe the expression of HAS2-AS1. The receiver operating characteristic (ROC) curve was used to analyze the diagnostic ability and prognostic value of HAS2-AS1 in glioma. Meanwhile, a Kaplan–Meier survival curve was plotted to evaluate the survival of glioma patients with different HAS2-AS1 expression levels. Results: HAS2-AS1 was significantly upregulated in glioma tissues compared with normal tissue. The survival curves showed that overexpression of HAS2-AS1 was associated with poor overall survival (OS) and progression-free survival (PFS). Several clinicopathological factors of glioma patients, including tumor size and WHO grade, were significantly correlated with HAS2-AS1 expression in tissues. The ROC curve showed an area under the curve (AUC) value of 0.863, indicating that HAS2-AS1 had good diagnostic value. The ROC curve for the predicted OS showed an AUC of 0.906, while the ROC curve for predicted PFS showed an AUC of 0.88. Both suggested that overexpression of HAS2-AS1 was associated with poor prognosis.Conclusions: Normal tissues could be clearly distinguished from glioma tissues based on HAS2-AS1 expression. Moreover, overexpression of HAS2-AS1 indicated poor prognosis in glioma patients.(AU)

Introducción: Los gliomas presentan una alta incidencia y un mal pronóstico, por lo que es necesario un tratamiento más efectivo. Algunos estudios han confirmado que los ARN no codificantes de cadena larga (ARNncl) regulan diferentes enfermedades, entre las que se incluyen los gliomas. Se ha postulado que HAS2-AS1 actúa como un ARNncl, con un efecto oncogénico en diferentes tipos de cáncer. Este estudio tiene como objetivo analizar el valor del ARNncl HAS2-AS1 como marcador diagnóstico y pronóstico de glioma. Métodos: Descargamos los datos clínicos y de expresión de micro-ARN de la base de datos del Atlas del Genoma del Cáncer (TCGA) para realizar el análisis diferencial y de supervivencia. También analizamos la expresión de HAS2-AS1 en muestras patológicas y muestras de tejido adyacente normal de 80 pacientes con glioma. Para analizar la capacidad diagnóstica y el valor pronóstico de HAS2-AS1 en el glioma, recurrimos a la curva ROC. También utilizamos curvas de Kaplan-Meier para evaluar la supervivencia de los pacientes con glioma con diferentes niveles de expresión de HAS2-AS1. Resultados: La expresión de HAS2-AS1 era significativamente mayor en las muestras patológicas que en el tejido normal. Las curvas de supervivencia demostraron que la sobreexpresión de HAS2-AS1 estaba relacionada con una menor supervivencia general y supervivencia libre de progresión. Algunos factores clínico-patológicos de los pacientes con glioma, como el tamaño del tumor y su grado, según la clasificación de la OMS, mostraron una correlación significativa con la expresión de HAS2-AS1 en los tejidos afectados. La curva ROC mostró un área bajo la curva de 0,863, lo que indica que la expresión de HAS2-AS1 posee un importante valor diagnóstico. El área bajo la curva de la supervivencia general estimada fue de 0,906; para la supervivencia libre de progresión estimada, de 0,88. Ambos valores muestran que la sobreexpresión de HAS2-AS1 se asocia con un mal pronóstico...(AU)

Intestinal microbiota and biliary system diseases.

Introduction: The incidence of biliary system diseases has been continuously increasing in the past decade. Biliary system diseases bring a heavy burden to humanity and society. However, the specific etiology and pathogenesis are still unknown. The biliary system, as a bridge between the liver and intestine, plays an indispensable role in maintaining the physiological metabolism of the body. Therefore, prevention and treatment of biliary diseases are crucial. It is worth noting that the microorganisms participate in the lipid metabolism of the bile duct, especially the largest proportion of intestinal bacteria. Methods: We systematically reviewed the intestinal microbiota in patients with gallstones (GS), non-calculous biliary inflammatory, and biliary tract cancer (BTC). And searched Pubmed, Embase and Web of science for research studies published up to November 2023. Results: We found that the abundance of Faecalibacterium genus is decreased in GS, primary sclerosing cholangitis (PSC), primary biliary cholangitis (PBC) and BTC. Veillonella, Lactobacillus, Streptococcus and Enterococcus genus were significantly increased in PSC, PBC and BTC. Interestingly, we found that the relative abundance of Clostridium was generally reduced in GS, PBC and BTC. However, Clostridium was generally increased in PSC. Discussion: The existing research mostly focuses on exploring the mechanisms of bacteria targeting a single disease. Lacking comparison of multiple diseases and changes in bacteria during the disease process. We hope to provide biomarkers forearly diagnosis of biliary system diseases and provide new directions for the mechanism of intestinal microbiota in biliary diseases.

Integrating machine learning and nontargeted plasma lipidomics to explore lipid characteristics of premetabolic syndrome and metabolic syndrome.

Objective: To identify plasma lipid characteristics associated with premetabolic syndrome (pre-MetS) and metabolic syndrome (MetS) and provide biomarkers through machine learning methods. Methods: Plasma lipidomics profiling was conducted using samples from healthy individuals, pre-MetS patients, and MetS patients. Orthogonal partial least squares-discriminant analysis (OPLS-DA) models were employed to identify dysregulated lipids in the comparative groups. Biomarkers were selected using support vector machine recursive feature elimination (SVM-RFE), random forest (rf), and least absolute shrinkage and selection operator (LASSO) regression, and the performance of two biomarker panels was compared across five machine learning models. Results: In the OPLS-DA models, 50 and 89 lipid metabolites were associated with pre-MetS and MetS patients, respectively. Further machine learning identified two sets of plasma metabolites composed of PS(38:3), DG(16:0/18:1), and TG(16:0/14:1/22:6), TG(16:0/18:2/20:4), and TG(14:0/18:2/18:3), which were used as biomarkers for the pre-MetS and MetS discrimination models in this study. Conclusion: In the initial lipidomics analysis of pre-MetS and MetS, we identified relevant lipid features primarily linked to insulin resistance in key biochemical pathways. Biomarker panels composed of lipidomics components can reflect metabolic changes across different stages of MetS, offering valuable insights for the differential diagnosis of pre-MetS and MetS.

Investigation of the feasibility of NRAV as a biomarker for hepatocellular carcinoma.

The long non-coding RNA, Negative Regulator of Antiviral Response (NRAV) has been identified as a participant in both respiratory virus replication and immune checkpoints, however, its involvement in pan-cancer immune regulation and prognosis, particularly those of hepatocellular carcinoma (HCC), remains unclear. To address this knowledge gap, we analyzed expression profiles obtained from The Cancer Genome Atlas (TCGA) database, comparing normal and malignant tumor tissues. We found that NRAV expression is significantly upregulated in tumor tissues compared to adjacent nontumor tissues. Kaplan-Meier (K-M) analysis revealed the prognostic power of NRAV, wherein overexpression was significantly linked to reduced overall survival in a diverse range of tumor patients. Furthermore, noteworthy associations were observed between NRAV, immune checkpoints, immune cell infiltration, genes related to autophagy, epithelial-mesenchymal transition (EMT), pyroptosis, tumor mutational burden (TMB), and microsatellite instability (MSI) across different cancer types, including HCC. Moreover, NRAV upregulation expression was associated with multiple pathological stages by clinical observations. Furthermore, our investigation revealed a substantial elevation in the expression of NRAV in both HCC tumor tissues and cells compared to normal tissues and cells. The inhibition of NRAV resulted in the inhibition of cell proliferation, migration, and invasion in HCC cells, while also influencing the expression of CD274 (PD-L1) and CD44, along with various biomarkers associated with EMT, autophagy, and pyroptosis. The aforementioned results propose NRAV as a promising prognostic biomarker for HCC.

Salivary α-amylase activity is associated with cardiometabolic and inflammatory biomarkers in overweight/obese, non-diabetic Qatari women.

Introduction: Obesity, prevalent in approximately 80% of Qatar's adult population, increases the risk of complications like type 2 diabetes and cardiovascular diseases. Predictive biomarkers are crucial for preventive strategies. Salivary α-amylase activity (sAAa) inversely correlates with obesity and insulin resistance in adults and children. However, the connection between sAAa and cardiometabolic risk factors or chronic low-grade inflammation markers remains unclear. This study explores the association between serum sAAa and adiposity markers related to cardiovascular diseases, as well as markers indicative of chronic low-grade inflammation. Methods: Serum samples and clinical data of 1500 adult, non-diabetic, Overweight/Obese participants were obtained from Qatar Biobank (QBB). We quantified sAAa and C reactive protein (CRP) levels with an autoanalyzer. Cytokines, adipokines, and adiponectin of a subset of 228 samples were quantified using a bead-based multiplex assay. The associations between the sAAa and the adiposity indices and low-grade inflammatory protein CRP and multiple cytokines were assessed using Pearson's correlation and adjusted linear regression. Results: The mean age of the participants was 36 ± 10 years for both sexes of which 76.6% are women. Our analysis revealed a significant linear association between sAAa and adiposity-associated biomarkers, including body mass index ß -0.032 [95% CI -0.049 to -0.05], waist circumference ß -0.05 [95% CI -0.09 to -0.02], hip circumference ß -0.052 [95% CI -0.087 to -0.017], and HDL ß 0.002 [95% CI 0.001 to 0.004], albeit only in women. Additionally, sAAa demonstrated a significant positive association with adiponectin ß 0.007 [95% CI 0.001 to 0.01]while concurrently displaying significant negative associations with CRP ß -0.02 [95% CI -0.044 to -0.0001], TNF-α ß -0.105 [95% CI -0.207 to -0.004], IL-6 ß [95% CI -0.39 -0.75 to -0.04], and ghrelin ß -5.95 [95% CI -11.71 to -0.20], specifically within the female population. Conclusion: Our findings delineate significant associations between sAAa and markers indicative of cardiovascular disease risk and inflammation among overweight/obese adult Qatari females. Subsequent investigations are warranted to elucidate the nuances of these gender-specific associations comprehensively.

Identifying diagnostic indicators for type 2 diabetes mellitus from physical examination using interpretable machine learning approach.

Background: Identification of patients at risk for type 2 diabetes mellitus (T2DM) can not only prevent complications and reduce suffering but also ease the health care burden. While routine physical examination can provide useful information for diagnosis, manual exploration of routine physical examination records is not feasible due to the high prevalence of T2DM. Objectives: We aim to build interpretable machine learning models for T2DM diagnosis and uncover important diagnostic indicators from physical examination, including age- and sex-related indicators. Methods: In this study, we present three weighted diversity density (WDD)-based algorithms for T2DM screening that use physical examination indicators, the algorithms are highly transparent and interpretable, two of which are missing value tolerant algorithms. Patients: Regarding the dataset, we collected 43 physical examination indicator data from 11,071 cases of T2DM patients and 126,622 healthy controls at the Affiliated Hospital of Southwest Medical University. After data processing, we used a data matrix containing 16004 EHRs and 43 clinical indicators for modelling. Results: The indicators were ranked according to their model weights, and the top 25% of indicators were found to be directly or indirectly related to T2DM. We further investigated the clinical characteristics of different age and sex groups, and found that the algorithms can detect relevant indicators specific to these groups. The algorithms performed well in T2DM screening, with the highest area under the receiver operating characteristic curve (AUC) reaching 0.9185. Conclusion: This work utilized the interpretable WDD-based algorithms to construct T2DM diagnostic models based on physical examination indicators. By modeling data grouped by age and sex, we identified several predictive markers related to age and sex, uncovering characteristic differences among various groups of T2DM patients.

Characteristics and Prognosis of Type 2 Myocardial Infarction Through Worsening Renal Function and NT-proBNP in Older Adults with Pneumonia.

Background: Type 2 myocardial infarction (MI) is becoming more recognized. This study aimed to assess the factors linked to type 2 MI in older adults with pneumonia and further determine the predictive factors of 90-day adverse events (refractory heart failure, cardiogenic shock, and all-cause mortality). Methods: A single-center retrospective analysis was conducted among older adults with pneumonia. The primary outcome was the prevalence of type 2 MI. The secondary objective was to assess the adverse events in these patients with type 2 MI within 90 days. Results: A total of 2618 patients were included. Of these, 361 patients (13.8%) suffered from type 2 MI. Multivariable predictors of type 2 MI were chronic kidney disease (CKD), age-adjusted Charlson comorbidity index (ACCI) score, and NT-proBNP > 4165pg/mL. Moreover, the independent predictive factors of 90-day adverse events included NT-proBNP > 4165pg/mL, age, ACCI score, and CKD. The Kaplan-Meier adverse events curves revealed that the type 2 MI patients with CKD and NT-proBNP > 4165pg/mL had a higher risk than CKD or NT-proBNP > 4165pg/mL alone. Conclusion: Type 2 MI in older pneumonia hospitalization represents a heterogeneous population. Elevated NT-proBNP level and prevalence of CKD are important predictors of type 2 MI and 90-day adverse events in type 2 MI patients.

Transcranial magnetic stimulation enhances the specificity of multiple sclerosis diagnostic criteria: a critical narrative review.

Background: Multiple sclerosis (MS) is an immune-mediated neurodegenerative disease that involves attacks of inflammatory demyelination and axonal damage, with variable but continuous disability accumulation. Transcranial magnetic stimulation (TMS) is a noninvasive method to characterize conduction loss and axonal damage in the corticospinal tract. TMS as a technique provides indices of corticospinal tract function that may serve as putative MS biomarkers. To date, no reviews have directly addressed the diagnostic performance of TMS in MS. The authors aimed to conduct a critical narrative review on the diagnostic performance of TMS in MS. Methods: The authors searched the Embase, PubMed, Scopus, and Web of Science databases for studies that reported the sensitivity and/or specificity of any reported TMS technique compared to established clinical MS diagnostic criteria. Studies were summarized and critically appraised for their quality and validity. Results: Seventeen of 1,073 records were included for data extraction and critical appraisal. Markers of demyelination and axonal damage-most notably, central motor conduction time (CMCT)-were specific, but not sensitive, for MS. Thirteen (76%), two (12%), and two (12%) studies exhibited high, unclear, and low risk of bias, respectively. No study demonstrated validity for TMS techniques as diagnostic biomarkers in MS. Conclusions: CMCT has the potential to: (1) enhance the specificity of clinical MS diagnostic criteria by "ruling in" true-positives, or (2) revise a diagnosis from relapsing to progressive forms of MS. However, there is presently insufficient high-quality evidence to recommend any TMS technique in the diagnostic algorithm for MS.

Metabolic Profile and Long-Term Risk of Depression, Anxiety, and Stress-Related Disorders.

Importance: Biomarkers of lipid, apolipoprotein, and carbohydrate metabolism have been previously suggested to be associated with the risk for depression, anxiety, and stress-related disorders, but results are inconsistent. Objective: To examine whether the biomarkers of carbohydrate, lipid, and apolipoprotein metabolism are associated with the risk of depression, anxiety, and stress-related disorders. Design, Setting, and Participants: This population-based cohort study with longitudinal data collection assessed 211 200 participants from the Apolipoprotein-Related Mortality Risk (AMORIS) cohort who underwent occupational health screening between January 1, 1985, and December 31, 1996, mainly in the Stockholm region in Sweden. Statistical analysis was performed during 2022 to 2023. Exposures: Lipid, apolipoprotein, and carbohydrate biomarkers measured in blood. Main Outcomes and Measures: The associations between biomarker levels and the risk of developing depression, anxiety, and stress-related disorders through the end of 2020 were examined using Cox proportional hazards regression models. In addition, nested case-control analyses were conducted within the cohort, including all incident cases of depression, anxiety, and stress-related disorders, and up to 10 control individuals per case who were individually matched to the case by year of birth, sex, and year of enrollment to the AMORIS cohort, using incidence density sampling. Population trajectories were used to illustrate the temporal trends in biomarker levels for cases and controls. Results: A total of 211 200 individuals (mean [SD] age at first biomarker measurement, 42.1 [12.6] years; 122 535 [58.0%] male; 188 895 [89.4%] born in Sweden) participated in the study. During a mean (SD) follow-up of 21.0 (6.7) years, a total of 16 256 individuals were diagnosed with depression, anxiety, or stress-related disorders. High levels of glucose (hazard ratio [HR], 1.30; 95% CI, 1.20-1.41) and triglycerides (HR, 1.15; 95% CI, 1.10-1.20) were associated with an increased subsequent risk of all tested psychiatric disorders, whereas high levels of high-density lipoprotein (HR, 0.88; 95% CI, 0.80-0.97) were associated with a reduced risk. These results were similar for male and female participants as well as for all tested disorders. The nested case-control analyses demonstrated that patients with depression, anxiety, or stress-related disorders had higher levels of glucose, triglycerides, and total cholesterol during the 20 years preceding diagnosis, as well as higher levels of apolipoprotein A-I and apolipoprotein B during the 10 years preceding diagnosis, compared with control participants. Conclusions and Relevance: In this cohort study of more than 200 000 participants, high levels of glucose and triglycerides and low levels of high-density lipoprotein were associated with future risk of depression, anxiety, and stress-related disorders. These findings may support closer follow-up of individuals with metabolic dysregulations for the prevention and diagnosis of psychiatric disorders.

Multiomics analysis to explore blood metabolite biomarkers in an Alzheimer's Disease Neuroimaging Initiative cohort.

Alzheimer's disease (AD) is a neurodegenerative disease that commonly causes dementia. Identifying biomarkers for the early detection of AD is an emerging need, as brain dysfunction begins two decades before the onset of clinical symptoms. To this end, we reanalyzed untargeted metabolomic mass spectrometry data from 905 patients enrolled in the AD Neuroimaging Initiative (ADNI) cohort using MS-DIAL, with 1,304,633 spectra of 39,108 unique biomolecules. Metabolic profiles of 93 hydrophilic metabolites were determined. Additionally, we integrated targeted lipidomic data (4873 samples from 1524 patients) to explore candidate biomarkers for predicting progressive mild cognitive impairment (pMCI) in patients diagnosed with AD within two years using the baseline metabolome. Patients with lower ergothioneine levels had a 12% higher rate of AD progression with the significance of P = 0.012 (Wald test). Furthermore, an increase in ganglioside (GM3) and decrease in plasmalogen lipids, many of which are associated with apolipoprotein E polymorphism, were confirmed in AD patients, and the higher levels of lysophosphatidylcholine (18:1) and GM3 d18:1/20:0 showed 19% and 17% higher rates of AD progression, respectively (Wald test: P = 3.9 × 10-8 and 4.3 × 10-7). Palmitoleamide, oleamide, diacylglycerols, and ether lipids were also identified as significantly altered metabolites at baseline in patients with pMCI. The integrated analysis of metabolites and genomics data showed that combining information on metabolites and genotypes enhances the predictive performance of AD progression, suggesting that metabolomics is essential to complement genomic data. In conclusion, the reanalysis of multiomics data provides new insights to detect early development of AD pathology and to partially understand metabolic changes in age-related onset of AD.

Conditioned pain modulation (CPM) paradigm type affects its sensitivity as a biomarker of fibromyalgia.

Fibromyalgia (FM) is a widespread chronic pain syndrome, possibly associated with the presence of central dysfunction in descending pain inhibition pathways. Conditioned Pain Modulation (CPM) has been proposed as a biomarker of FM. Nonetheless, the wide variety of methods used to measure CPM has hampered robust conclusions being reached. To clarify the validity of CPM as a biomarker of FM, we tested two CPM paradigms (parallel and sequential) in a sample of 23 female patients and 23 healthy women by applying test (mechanical) stimuli and conditioning (pressure cuff) stimuli. We evaluated whether CPM indices could correctly classify patients and controls, and we also determined the correlations between the indices and clinical variables such as symptomatology, disease impact, depression, quality of life, pain intensity, pain interference, fatigue and numbness. In addition, we compared the clinical status of CPM responders (efficient pain inhibitory mechanism) and non-responders. We observed that only parallel CPM testing correctly classified about 70% of patients with FM. In addition, more than 80% of healthy participants were found to be responders, while the rate was about 50% in the FM patients. The sequential CPM test was not as sensitive, with a decrease of up to 40% in the response rate for both groups. On the other hand, we did not observe any correlation between CPM measures and clinical symptoms. In summary, our findings demonstrate the influence of the CPM paradigm used and confirm that CPM may be a useful marker to complement FM diagnosis. However, the findings also cast doubts on the sensitivity of CPM as a marker of pain severity in FM.

UTRN as a potential biomarker in breast cancer: a comprehensive bioinformatics and in vitro study.

Utrophin (UTRN), known as a tumor suppressor, potentially regulates tumor development and the immune microenvironment. However, its impact on breast cancer's development and treatment remains unstudied. We conducted a thorough examination of UTRN using both bioinformatic and in vitro experiments in this study. We discovered UTRN expression decreased in breast cancer compared to standard samples. High UTRN expression correlated with better prognosis. Drug sensitivity tests and RT-qPCR assays revealed UTRN's pivotal role in tamoxifen resistance. Furthermore, the Kruskal-Wallis rank test indicated UTRN's potential as a valuable diagnostic biomarker for breast cancer and its utility in detecting T stage of breast cancer. Additionally, our results demonstrated UTRN's close association with immune cells, inhibitors, stimulators, receptors, and chemokines in breast cancer (BRCA). This research provides a novel perspective on UTRN's role in breast cancer's prognostic and therapeutic value. Low UTRN expression may contribute to tamoxifen resistance and a poor prognosis. Specifically, UTRN can improve clinical decision-making and raise the diagnosis accuracy of breast cancer.

Effect of CHST11, a novel biomarker, on the biological functionalities of clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is a common malignant tumor, and the role of carbohydrate sulfotransferase 11 (CHST11) in this cancer remains unclear. Here, by using bioinformatics methods, we comprehensively analyzed the relationship between CHST11 and clinical significance, immune infiltration, functional enrichment, m6A methylation, and protein-protein interaction networks. We found that CHST11 expression was significantly higher in ccRCC samples than in normal tissues. Additionally, CHST11 levels correlated with the clinicopathological features of ccRCC patients and functioned as a prognostic factor for patient survival. Functional analysis revealed the involvement of CHST11 in metabolic pathways. Immune infiltration and m6A methylation analysis suggested the association of CHST11 with immune cell abundance in the tumor microenvironment and specific methylation patterns in ccRCC. The in vitro analysis of the clinical samples and ccRCC cell lines demonstrated that the overexpression of CHST11 promotes ccRCC cell proliferation, migration, and invasion, while its suppression has the opposite effect. Thus, CHST11 may play a remarkable role in the occurrence and progression of ccRCC. Functionally, CHST11 promotes the aggressiveness of ccRCC cells. These findings provide insights into the role of CHST11 in ccRCC progression.Registry and the Registration No. of the study/trial: No. 2021K034.

Deciphering feedback regulation of prostaglandin F2α in blood stasis syndrome using nitrogen-doped porous transition metal carbides.

Blood stasis syndrome (BSS) has persistent health risks; however, its pathogenesis remains elusive. This obscurity may result in missed opportunities for early intervention, increased susceptibility to chronic diseases, and reduced accuracy and efficacy of treatments. Metabolomics, employing the matrix-assisted laser desorption/ionization (MALDI) strategy, presents distinct advantages in biomarker discovery and unraveling molecular mechanisms. Nonetheless, the challenge is to develop efficient matrices for high-sensitivity and high-throughput analysis of diverse potential biomarkers in complex biosamples. This work utilized nitrogen-doped porous transition metal carbides and nitrides (NP-MXene) as a MALDI matrix to delve into the molecular mechanisms underlying BSS pathogenesis. Structural optimization yielded heightened peak sensitivity (by 1.49-fold) and increased peak numbers (by 1.16-fold) in clinical biosamples. Validation with animal models and clinical serum biosamples revealed significant differences in metabolic fingerprints between BSS and control groups, achieving an overall diagnostic efficacy of 0.905 (95% CI, 0.76-0.979). Prostaglandin F2α was identified as a potential biomarker (diagnostics efficiency of 0.711, specificity = 0.7, sensitivity = 0.6), and pathway enrichment analysis disclosed disruptions in arachidonic acid metabolism in BSS. This innovative approach not only advances comprehension of BSS pathogenesis, but also provides valuable insights for personalized treatment and diagnostic precision.

The plasma kynurenine-to-tryptophan ratio as a biomarker of tuberculosis disease in people living with HIV on antiretroviral therapy: an exploratory nested case-control study.

BACKGROUND: Non-sputum-based tests are needed to predict or diagnose tuberculosis (TB) disease in people living with HIV (PWH). The enzyme indoleamine 2, 3-dioxygenase-1 (IDO1) is expressed in tuberculoid granuloma and catabolizes tryptophan (Trp) to kynurenine (Kyn). IDO1 activity compromises innate and adaptive immune responses, promoting mycobacterial survival. The plasma Kyn-to-Trp (K/T) ratio is a potential TB diagnostic and/or predictive biomarker in PWH on long-term antiretroviral therapy (ART). METHODS: We compared plasma K/T ratios in samples from PWH, who were followed up prospectively and developed TB disease after ART initiation. Controls were matched for age and duration of ART. Kyn and Trp were measured at 3 timepoints; at TB diagnosis, 6 months before TB diagnosis and 6 months after TB diagnosis, using ultra performance liquid chromatography combined with mass spectrometry. RESULTS: The K/T ratios were higher for patients with TB disease at time of diagnosis (median, 0.086; IQR, 0.069-0.123) compared to controls (0.055; IQR 0.045-0.064; p = 0.006), but not before or after TB diagnosis. K/T ratios significantly declined after successful TB treatment, but increased upon treatment failure. The K/T ratios showed a parabolic correlation with CD4 cell counts in participants with TB (p = 0.005), but there was no correlation in controls. CONCLUSIONS: The plasma K/T ratio helped identify TB disease and may serve as an adjunctive biomarker for for monitoring TB treatment in PWH. Validation studies to ascertain these findings and evaluate the optimum cut-off for diagnosis of TB disease in PWH should be undertaken in well-designed prospective cohorts. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT00411983.

Associations between sleep duration, sleep disturbance and cardiovascular disease biomarkers among adults in the United States.

BACKGROUND: Sleep problems are associated with abnormal cardiovascular biomarkers and an increased risk of cardiovascular diseases (CVDs). However, studies investigating associations between sleep problems and CVD biomarkers have reported conflicting findings. This study examined the associations between sleep problems and CVD biomarkers in the United States. METHODS: Data were from the National Health and Nutrition Examination Survey (NHANES) (2007-2018) and analyses were restricted to adults ≥ 20 years (n = 23,749). CVD biomarkers [C-reactive Protein (CRP), low-density lipoproteins, high-density lipoproteins (HDL), triglycerides, insulin, glycosylated hemoglobin (HbA1c), and fasting blood glucose] were categorized as abnormal or normal using standardized cut-off points. Sleep problems were assessed by sleep duration (short [≤ 6 h], long [≥ 9 h], and recommended [> 6 to < 9 h) and self-reported sleep disturbance (yes, no). Multivariable logistic regression models explored the associations between sleep duration, sleep disturbance, and CVD biomarkers adjusting for sociodemographic characteristics and lifestyle behaviors. RESULTS: The mean sleep duration was 7.1 ± 1.5 h and 25.1% of participants reported sleep disturbances. Compared to participants with the recommended sleep duration, those with short sleep duration had higher odds of abnormal levels of HDL (adjusted odds ratio [aOR] = 1.20, 95% confidence interval [CI] = 1.05-1.39), CRP (aOR = 3.08, 95% CI = 1.18-8.05), HbA1c (aOR = 1.25, 95% CI = 1.05-1.49), and insulin (aOR = 1.24, 95% CI = 1.03-1.51). Long sleep duration was associated with increased odds of abnormal CRP (aOR = 6.12, 95% CI = 2.19-17.15), HbA1c (aOR = 1.54, 95% CI = 1.09-2.17), and blood glucose levels (aOR = 1.45, 95% CI = 1.07-1.95). Sleep disturbance predicted abnormal triglyceride (aOR = 1.18, 95% CI = 1.01-1.37) and blood glucose levels (aOR = 1.24, 95% CI = 1.04-1.49). CONCLUSION: Short and long sleep durations were positively associated with abnormal CRP, HDL, HbA1c, blood glucose, and insulin levels, while sleep disturbance was associated with abnormal triglyceride and blood glucose levels. Since sleep is a modifiable factor, adopting healthy sleeping habits may create a balanced metabolism and reduce the risk of developing a CVD. Our study may provide insights into the relationship between sleep duration, sleep disturbance, and CVD risk.

A paradigm shift in cancer research based on integrative multi-omics approaches: glutaminase serves as a pioneering cuproptosis-related gene in pan-cancer.

BACKGROUND: Cuproptosis is a newly identified form of unprogrammed cell death. As a pivotal metabolic regulator, glutaminase (GLS) has recently been discovered to be linked to cuproptosis. Despite this discovery, the oncogenic functions and mechanisms of GLS in various cancers are still not fully understood. METHODS: In this study, a comprehensive omics analysis was performed to investigate the differential expression levels, diagnostic and prognostic potential, correlation with tumor immune infiltration, genetic alterations, and drug sensitivity of GLS across multiple malignancies. RESULTS: Our findings revealed unique expression patterns of GLS across various cancer types and molecular subtypes of carcinomas, underscoring its pivotal role primarily in energy and nutrition metabolism. Additionally, GLS showed remarkable diagnostic and prognostic performance in specific cancers, suggesting its potential as a promising biomarker for cancer detection and prognosis. Furthermore, we focused on uterine corpus endometrial carcinoma (UCEC) and developed a novel prognostic model associated with GLS, indicating a close correlation between GLS and UCEC. Moreover, our exploration into immune infiltration, genetic heterogeneity, tumor stemness, and drug sensitivity provided novel insights and directions for future research and laid the foundation for high-quality verification. CONCLUSION: Collectively, our study is the first comprehensive investigation of the biological and clinical significance of GLS in pan-cancer. In our study, GLS was identified as a promising biomarker for UCEC, providing valuable evidence and a potential target for anti-tumor therapy. Overall, our findings shed light on the multifaceted functions of GLS in cancer and offer new avenues for further research.

Triglyceride-glucose-waist circumference index predicts the incidence of cardiovascular disease in Korean populations: competing risk analysis of an 18-year prospective study.

BACKGROUND: The triglyceride and glucose-waist circumference (TyG-WC) index demonstrated a strong association with insulin resistance, especially in Asian population. However, evidence on the association between TyG-WC index and the occurrence of cardiovascular disease (CVD) is limited. This study aimed to verify association between the TyG-WC index and the occurrence of CVD by considering all-cause mortality as a competing risk. METHODS: The study included 7482 participants divided into four groups based on the TyG-WC index quartiles. Kaplan-Meier curves illustrated cumulative incidence rates of CVD and all-cause mortality during the follow-up period. Log-rank tests determined group differences. The Cox proportional hazard spline curve demonstrates the dose-dependent relationship between the TyG-WC index and incident CVD. Modified Cox regression (Fine and Gray) estimated hazard ratios (HRs) with 95% CIs for incident CVD, treating death as a competing risk. Death event after incident CVD was excluded from the death count. RESULTS: During the median 15.94 year of follow-up period, a total of 691 (9.24%) new-onset CVD cases and 562 (7.51%) all-cause mortality cases were confirmed. Cox proportional hazard spline curves suggested that TyG-WC index exhibited a dose-dependent positive correlation with incident CVD. The cumulative incidence rate of CVD was significantly higher in the groups with higher TyG-WC index quartiles in Kaplan-Meier curves. The adjusted HR (95% CI) for incident CVD in Q2-Q4, compared with Q1, was 1.47 (1.12-1.93), 1.91 (1.44-2.54) and 2.24 (1.63-3.07), respectively. There was no significant association between TyG-WC index and all-cause mortality. Specifically, angina and stroke were significantly associated with the TyG-WC index, in contrast to myocardial infarction and peripheral artery disease. CONCLUSIONS: The TyG-WC index was positively associated with incident CVD even considering all-cause mortality as a competing risk. Therefore, TyG-WC index may be a valuable marker for predicting the occurrence of CVD.

Biomarkers Profile in Provoked Versus Unprovoked Deep Venous Thrombosis.

Venous thromboembolism (VTE), including deep venous thrombosis (DVT) and pulmonary embolism (PE), represents a substantial healthcare challenge. Provoked and unprovoked DVT cases carry distinct risks and treatment considerations. Recognizing the limitations of this classification, molecular markers may enhance diagnostic precision and guide anticoagulation therapy duration relying on patient history and risk factors. This preliminary, open-label, prospective cohort study was conducted including 15 patients (10 provoked DVT and 5 unprovoked DVT) and a control group of healthy plasmatic subjects. Plasma levels of 9 biomarkers were measured at diagnosis (baseline, day 0, and D0) and after 30 days (day 30-D30). Patient demographics, clinical data, and biomarker concentrations were analyzed. Serum concentrations of D-dimer, von Willebrand factor, C-reactive protein, and Anti-Xa were elevated in DVT groups at D0 compared to controls. No significant differences were observed between the provoked and unprovoked groups on the day of diagnosis and 30 days later. Over 30 days, the provoked group exhibited significant biomarker changes related to temporal assessment. No significant differences were noted in the biomarker profile between provoked and unprovoked DVT groups. This study is indicative of the concept of individualized thrombosis assessment and subsequent treatment for VTE. Larger cohorts are warranted to validate these findings and further define the most appropriate use of the molecular markers.