Potential Biomarkers Research Articles

Uncovering a novel SERPING1 pathogenic variant: insights into the aggregation of C1-INH in hereditary angioedema

BackgroundHereditary angioedema (HAE) is a rare autosomal dominant genetic disease characterized by recurrent edema and a potentially fatal risk. Despite its severity, there is a notable lack of effective methods for predicting and preventing HAE attacks. This study aims to thoroughly investigate the underlying pathological mechanisms of HAE and identify potential biomarkers that could aid in its prediction and prevention.ResultsIn our investigation, we have discovered a novel pathogenic variant of the SERPING1 gene, specifically c.708T > G, in a Han family affected by HAE. Our observations indicate that this variant leads to an increase in the accumulation of C1-INH within the endoplasmic reticulum (ER), resulting in the upregulation of GRP75 protein expression. This cascade of events resulted in Ca2+ overload, disruption of mitochondrial structure and function, and eventually triggered apoptosis. Using siRNA to knock down GRP75 mitigates cellular calcium overload and mitochondrial damage induced by the SERPING1 mutation.ConclusionBased on our findings, we propose that the detection of intracellular Ca2+ concentration could serve as a valuable biomarker for predicting acute attacks of HAE in patients. This discovery holds significant implications for the development of more targeted and effective strategies in the management of HAE.

Endoplasmic reticulum's role in multiple sclerosis, exploring potential biomarkers, and pioneering therapeutic strategies: a comprehensive review of literature.

Multiple Sclerosis (MS) is a complex and chronic autoimmune disease that affects the central nervous system. Inflammation and demyelination characterize it, which results in a range of neurological impairments. The increasing worldwide occurrence of MS, affecting an estimated 2.8 million individuals in 2020, highlights the urgent requirement for further research to tackle the significant impact it has on individuals and healthcare systems globally. In this study, we wanted to explore the complex function of the endoplasmic reticulum (ER) in the origin, development, and resolution of MS, emphasizing its importance in neuroinflammatory illnesses. The ER has become a central focus in comprehending the pathogenesis of MS. Upon reviewing the literature, we observed a lack of thorough analysis that explores the involvement of endoplasmic reticulum stress in multiple sclerosis. Thus, we aimed through this research to examine the correlations between ER stress and its influence on immunological dysregulation, demyelination, and neurodegeneration in MS. Based on the latest clinical trials, we suggested theories that explore possible biomarkers linked to ER stress and the unfolded protein response. Identifying molecules that are suggestive of early stages of illness and can serve as prognostic tools for improving our understanding of the heterogeneity of MS and offering novel approaches for managing the disease. Finally, through our comprehensive search, we wanted to offer a plan for future research, suggesting new and creative methods for managing MS and encouraging the creation of specific treatments that aim to reduce the impact of MS on individuals worldwide.

Diagnose Alzheimer’s disease and mild cognitive impairment using deep CascadeNet and handcrafted features from EEG signals

Alzheimer’s disease (AD) is the most prevalent clinically diagnosed neurodegenerative disorder. Early detection of mild cognitive impairment (MCI) is crucial for implementing effective interventions and potentially preventing further cognitive decline. Due to its efficiency, the electroencephalogram (EEG) is a promising tool for AD diagnosis. This paper proposes a computer-aided diagnostic model for identifying AD using EEG data analysis. The proposed approach comprises two key steps: signal processing and classification. Initially, the EEG signal is decomposed into sub-bands using the discrete wavelet transform (DWT), followed by windowing for data augmentation. Subsequently, an improved CascadeNet model is employed for feature extraction and classification from the windowed EEG data. CascadeNet’s architecture was specifically chosen for its ability to achieve high accuracy in AD and MCI detection, even with relatively small EEG datasets. Moreover, the paper explores the potential of deep Cascade learning for AD prediction. The effectiveness of the proposed strategy is evaluated using metrics such as F-measures, specificity, sensitivity, and overall detection accuracy. The developed model attains impressive accuracies of 98.84 % and 97.78 % on the Figshare and Brazilian datasets, respectively, significantly outperforming existing methods. To validate the model’s efficacy, it was applied to the Figshare and Brazilian datasets encompassing control (CO), MCI, and AD classes. The results confirm the proposed method as a valuable tool for identifying potential biomarkers aiding in the clinical diagnosis of AD. Notably, unlike previous methods limited to two-class identification, this technique effectively distinguishes between MCI and AD.

A comprehensive non-targeted approach for the analysis of biomarkers in exhaled breath across different nicotine product categories

In the context of the evolving landscape of nicotine consumption, the assessment of biomarkers plays a crucial role in understanding the health impact of different product categories. Exhaled breath (EB) emerges as a promising, non-invasive matrix for biomarker analysis, complementary to conventional urine and plasma data. This study explores distinctive EB biomarker profiles among users of combustible cigarettes (CC), heated tobacco products (HTP), electronic cigarettes (EC), smokeless/oral tobacco (OT), and oral/dermal nicotine products (NRT). We have successfully developed and validated a non-targeted GC-TOF-MS method for the analysis of EB samples across the aforementioned product categories. A total of 66 compounds were identified, with significantly elevated levels in at least one study group. The study found that CC users had higher levels of established VOCs associated with smoking, which supports the proof-of-concept of the method. Breathomic analysis identified increased levels of p-cymene and α-pinene in EC users, while HTP users showed potential biomarker candidates like γ-butyrolactone. This study underscores the utility of EB biomarkers for a comprehensive evaluation of diverse nicotine products. The unique advantages offered by EB analysis position it as a valuable tool for understanding the relationship between exposure and health outcomes.

Prognostic impact of nectin-like molecule-5 (CD155) expression in non-small cell lung cancer

BackgroundCD155 is a transmembrane protein that inhibits antitumor immune response and represents a predictor of worse prognosis in non-small-cell lung cancer (NSCLC). However, it remains unexplored its association with clinical characteristics and genomic status of Latin American patients. This study characterizes the CD155 expression and its clinical implications in this population.MethodsTissue biopsies from 86 patients with locally-advanced or metastatic NSCLC were assessed for CD155 protein expression, ALK rearrangements and EGFR mutations. Cutoff values for high CD155 expression (CD155high) were determined from receiver operating characteristic (ROC) curves according to 2-year survival. It was evaluated its association with clinicopathological features, median progression-free survival (mPFS) and overall survival (mOS).Resultsthe cutoff score for CD155high was 155 in the entire cohort and in patients without oncogenic alterations, and it was 110 in patients with oncogenic alterations. Eighty-four patients (97.7%) were CD155 positive, of which fifty-six (65.0%) had CD155high. EGFR L858R mutation related to lower CD155 IHC score than exon 19 deletion. Individuals with CD155high showed a shorter mOS (13.0 vs. 30.8 months; HR: 1.96 [95% CI, 1.15–3.35]; p = 0.014). Patients without oncogenic alterations having a CD155high displayed shorter mPFS (1.6 vs. 6.4 months, HR: 2.09 [95% CI, 1.06–4.20]; p = 0.034) and mOS (2.9 vs. 23.1 months; HR: 1.27 [95% CI, 1.07– 4.42]; p = 0.032). Patients with oncogenic alterations having CD155high only showed a trend to shorter mOS (26.3 vs. 52.0 months; HR: 2.39 [95% CI, 0.98–5.83]; p = 0.058).ConclusionCD155high is a predictor of worse outcomes in patients with advanced NSCLC, predominantly among those without oncogenic alterations. CD155 could be a potential biomarker and a molecular target in patients with poor responses to current therapies.

RAC1 serves as a prognostic factor and correlated with immune infiltration in liver hepatocellular carcinoma

BackgroundHepatocellular carcinoma (LIHC) has severe consequences due to late diagnosis and the lack of effective therapies. Currently, potential biomarkers for the diagnosis and prognosis of LIHC have not been systematically evaluated. Previous studies have reported that RAC1 is associated with the B cell receptor signaling pathway in various tumor microenvironments, but its relationship with LIHC remains unclear. We investigated the relationship between RAC1 and the prognosis and immune infiltration microenvironment of LIHC, exploring its potential as a prognostic biomarker for this type of cancer.MethodsIn this study, we analyzed data from The Cancer Genome Atlas (TCGA) using the Wilcoxon signed-rank test and logistic regression to assess the association between RAC1 expression and clinical characteristics in LIHC patients. Additionally, Kaplan-Meier and Cox regression methods were employed to confirm the impact of RAC1 expression levels on overall survival. Immunohistochemistry was used to validate RAC1 protein expression in LIHC. We constructed RAC1 knockdown LIHC cells and studied the effects of RAC1 protein on cell proliferation and migration at both cellular and animal levels.ResultsRAC1 expression levels were significantly elevated in LIHC tissues compared to normal tissues. High RAC1 expression was strongly associated with advanced pathological stages and was identified as an independent factor negatively affecting overall survival. At both cellular and animal levels, RAC1 knockdown significantly inhibited the proliferation and migration of LIHC cells. Furthermore, RAC1 expression was positively correlated with the infiltration of Th2 cells and macrophages in the tumor microenvironment, suggesting that RAC1 may contribute to the deterioration of the tumor immunosuppressive microenvironment and potentially lead to reduced patient survival.ConclusionThese findings indicate that RAC1 expression promotes LIHC proliferation and migration and influences the landscape of immune cell infiltration in the tumor microenvironment. Based on these results, RAC1 is proposed as a potential prognostic biomarker for LIHC, associated with both cancer progression and tumor immune cell infiltration.

Unveiling therapeutic biomarkers and druggable targets in ALS: An integrative microarray analysis, molecular docking, and structural dynamic studies

Amyotrophic lateral sclerosis (ALS), commonly known as Lou Gehrig's disease, is a debilitating neurodegenerative disorder characterized by the progressive degeneration of nerve cells in the brain and spinal cord. Despite extensive research, its precise etiology remains elusive, and early diagnosis is challenging due to the absence of specific tests. This study aimed to identify potential blood-based biomarkers for early ALS detection and monitoring using datasets from whole blood samples (GSE112680) and oligodendrocytes, astrocytes, and fibroblasts (GSE87385) obtained from the NCBI-GEO repository. Through bioinformatics analysis, including protein-protein interactions and molecular pathway analyses, we identified differentially expressed genes (DEGs) associated with ALS. Notably, ALS2, ADH7, ALDH8A1, ALDH3B1, ABHD2, ABHD17B, ABHD12, ABHD13, PGAM2, AURKB, ANAPC11, VAPA, UNC45B, and TNNT2 emerged as top-ranked DEGs, implicated in drug metabolism, protein depalmytilation, and the AKT/mTOR signaling pathways. Among these, AurKB established as a potential therapeutic biomarker with relevance to various neurological conditions. Consequently, AurKB was selected for identifying potential therapeutic molecules and utilized for in silico structural characterization studies. Exploration of the IMPATT database led to the discovery of a lead compound similar to Fostamatinib, currently used for AurKB. Initial molecular docking and MMGBSA-based binding energy analysis were followed by molecular dynamics simulation (MDS) and free energy landscape (FEL) analysis to validate the ligand's binding efficacy and understand dynamic processes within the biological system. The identified potential biomarkers and lead molecule provide novel insights into the correlation between blood cell transcripts and ALS pathology, paving the way for blood-based diagnostic tools for early ALS detection and ongoing disease monitoring.

Perioperative platform for muscle-invasive bladder cancer trials: designs and endpoints.

This review explores the design and endpoints of perioperative platforms in clinical trials for muscle-invasive bladder cancer (MIBC). The choice of clinical trial design in perioperative platforms for MIBC must align with specific research objectives to ensure robust and meaningful outcomes. Novel designs in perioperative platforms for MIBC integrate bladder-sparing approaches. Primary endpoints such as pathological complete response and disease-free survival are highlighted for their role in expediting trial results in perioperative setting. Incorporating patient-reported outcomes is important to inform healthcare decision makers about the outcomes most meaningful to patients. Given the growing body of evidence, potential biomarkers, predictive and prognostic tools should be considered and implemented when designing trials in perioperative platforms for MIBC. Effective perioperative platforms for MIBC trials are critical in enhancing patient outcomes. The careful selection and standardization of study designs and endpoints in the perioperative platform are essential for the successful implementation of new therapies and the advancement of personalized treatment approaches in MIBC.

Study based on metabolomics and network pharmacology to explore the mechanism of Ginseng-Douch compound fermentation products in the treatment of hyperlipidemia.

Ginseng-Douchi (GD) is a complex fermented product of ginseng and soybean, similar to natto, and is effective in the treatment of hyperlipidemia, but the mechanism of action involved needs to be further explored. The present study combines a comprehensive strategy of network pharmacology and metabolomics to explore the lipid-lowering mechanism of GD. First, a hyperlipidemia rats model induced by a high-fat diet was established to evaluate the therapeutic effects of GD. Second, potential biomarkers were identified using serum metabolomics and metabolic pathway analysis was performed with MetaboAnalyst. Third, network pharmacology is used to find potential therapeutic targets based on the blood-influencing components of GD. Finally, core targets were obtained through a target-metabolite and the enrichment analysis of biomarkers-genes. Biochemistry analysis showed that GD exerted hypolipidemic effects on hyperlipidemic rats. Nineteen potential biomarkers for the GD treatment of hyperlipidemia were identified by metabolomics, which was mainly involved in linoleic acid metabolism, glycerophospholipid metabolism, ether lipid metabolism, alpha-linolenic acid metabolism and glycosylphosphatidylinositol-anchor biosynthesis. GD had a callback function for ether lipid metabolism and glycerophospholipid metabolism pathways. Eighteen blood components were identified in serum, associated with 85 potential therapeutic targets. The joint analysis showed that three core therapeutic targets were regulated by GD, including PIK3CA, AKT1 and EGFR. This study combines serum medicinal chemistry of traditional Chinese medicine, network pharmacology and metabolomics to reveal the regulatory mechanism of GD on hyperlipidemia. © 2024 Society of Chemical Industry.

Identification of mitochondria-related genes as diagnostic biomarkers for diabetic nephropathy and their correlation with immune infiltration: New insights from bioinformatics analysis

BackgroundDiabetic nephropathy (DN) is a common and severe microvascular complication of diabetes. Mitochondrial dysfunction and immune inflammation are important factors in the pathogenesis of DN. However, the specific mechanisms and their intricate interactions in DN remain unclear. Besides, there are no effective specific predictive or diagnostic biomarkers for DN so far. Therefore, this study aims to elucidate the role of mitochondrial-related genes and their possibility as predictive or diagnostic biomarkers, as well as their crosstalk with immune infiltration in the progression of DN. MethodsBased on the GEO database and limma R package, the differentially expressed genes (DEGs) of DN were identified. Mitochondrial-related DEGs (MitoDEGs) were then obtained by intersecting these DEGs with mitochondria-related genes from the MitoCarta 3.0 database. Subsequently, the candidate hub genes were further screened by gene co-expression network analysis (WGCNA), and verified mRNA levels of these genes by real-time quantitative PCR (qRT-PCR) in high-glucose-treated human proximal tubular (HK-2) cells. The verified hub genes were utilized to construct a combined diagnostic model for DN, with its diagnostic efficacy assessed across the GSE30122 and GSE96804 datasets. Additionally, the immune infiltration pattern in DN was assessed with the CIBERSORT algorithm, and the Nephroseq v5 database was used to analyze the correlation between hub genes and clinical features of DN. ResultsSeven mitochondria-related candidate hub genes were screened from 56 MitoDEGs. Subsequently, the expression levels of six of them, namely EFHD1, CASP3, AASS, MPC1, NT5DC2, and BCL2A1, exhibited significant inter-group differences in the HK-2 cell model. The diagnostic model based on the six genes demonstrated good diagnostic efficacy in both training and validation sets. Furthermore, correlation analysis indicated that EFHD1 and AASS, downregulated in DN, are positively correlated with eGFR and negatively with serum creatinine. Conversely, CASP3, NT5DC2, and BCL2A1, upregulated in DN, show opposite correlations. In addition, spearman analysis revealed that the six hub genes were significantly associated with the infiltration of immune cells, including M1 and M2 macrophages, mast cells, resting NK cells, gamma delta T cells, and follicular helper T cells. ConclusionThis study elucidated the characteristics of mitochondria-related genes and their correlation with immune cell infiltration in DN, providing new insights for exploring the pathogenesis of DN and facilitating the identification of new potential biomarkers and therapeutic targets.

The Prominence of the Broad-Spectrum Protease inhibitor gene A2ML1 as a potential biomarker in cervical cancer diagnostics using Immunotherapeutic and Multi-Omics approaches

One of the venereal tumors that threaten human life is cervical cancer. A2ML1 is detected in advanced-stage cancer patients and is found to be strongly associated with cervical cancer. A2ML1 was shown to be substantially expressed in cervical cancer in this study, which used data from the TCGA database. Those with high A2ML1 expression had a lower chance of survival than patients with low A2ML1 expression. Both univariate and multivariate Cox regression analyses were utilized to investigate the relationship between clinical variables and overall survival rates. An investigation into the link between A2ML1 and immune infiltration was subsequently conducted. Utilizing the immune cell database, research was conducted to investigate the dispersion of 24 immune cells and their correlation to A2ML1 expression. In addition to this, the favorable correlation between immune cells and A2ML1 was validated using all three immune cell methodologies. The Genomics of Drug Sensitivity in Cancer database was used to confirm the idea that there is a link between A2ML1 expression and the efficacy of chemotherapy or immunotherapy. The findings demonstrated that A2ML1 is a potential biomarker for cervical cancer diagnostics. This biomarker may be used to chaperone immunotherapy, as well as to explain the elucidates of cervical cancer caused by the immunological microenvironment.

Dysregulation of a novel m6A regulator YWHAG is correlated with metastasis and poor prognosis in oral squamous cell carcinoma – A cross-sectional study

ObjectiveThis study aimed to investigate the role of a novel m6A and cell cycle regulator YWHAG in oral squamous cell carcinoma (OSCC) by analyzing its expression and functional implications. DesignTumor samples (n = 51) and adjacent non-tumor samples (n = 38) were collected from patients with OSCC, and cell lines were processed. YWHAG mRNA expression was assessed using quantitative reverse transcription PCR (RT-qPCR) analysis. Various tools, such as UALCAN, Protein-Atlas analysis, TIMER 2.0, and other in silico tools, were used to explore clinicopathological correlations, protein expression, immune cell infiltration, and functional associations of YWHAG. ResultsYWHAG mRNA and protein expression were significantly upregulated in OSCC tumor tissues and OSCC cell lines compared to non-tumor tissues and normal cells (p < 0.001). High YWHAG expression significantly correlated with advanced tumor stage, higher grade, lymph node metastasis, and poor prognosis (p < 0.05). Functional analysis revealed that YWHAG is associated with pathways involved in aggressive cancer progression. YWHAG expression positively correlated with its target gene CTTN expression, which was also upregulated in OSCC and associated with poor prognosis (p < 0.05). ConclusionsStudy findings indicate that YWHAG may contribute to the progression of OSCC and could be a potential therapeutic target or prognostic biomarker. Further investigation is necessary to understand the underlying mechanisms and assess the clinical implications of YWHAG dysregulation in OSCC.

Somatic mutations in 3929 HPV positive cervical cells associated with infection outcome and HPV type

Invasive cervical cancers (ICC), caused by HPV infections, have a heterogeneous molecular landscape. We investigate the detection, timing, and HPV type specificity of somatic mutations in 3929 HPV-positive exfoliated cervical cell samples from individuals undergoing cervical screening in the U.S. using deep targeted sequencing in ICC cases, precancers, and HPV-positive controls. We discover a subset of hotspot mutations rare in controls (2.6%) but significantly more prevalent in precancers, particularly glandular precancer lesions (10.2%), and cancers (25.7%), supporting their involvement in ICC carcinogenesis. Hotspot mutations differ by HPV type, and HPV18/45-positive ICC are more likely to have multiple hotspot mutations compared to HPV16-positive ICC. The proportion of cells containing hotspot mutations is higher (i.e., higher variant allele fraction) in ICC and mutations are detectable up to 6 years prior to cancer diagnosis. Our findings demonstrate the feasibility of using exfoliated cervical cells for detection of somatic mutations as potential diagnostic biomarkers.

Untargeted metabolomics analysis of the urinary metabolic signature of acute and chronic gout

BackgroundGout is a common kind of inflammatory arthritis with metabolic disorders. However, the detailed pathogenesis of gout is complex and not fully clear. We investigated the urine metabolic profiling of gout patients by ultra-performance liquid chromatograph quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS). MethodUrine metabolites were extracted from 26 acute gout patients, 31 chronic gout patients, and 32 healthy controls. Metabolite extracts were analyzed by UPLC-Q-TOF-MS for untargeted metabolomics. The peak area of creatinine was used to correct the content variations of urine samples for the semi-quantitative analysis. The value of variable importance in the projection (VIP) was obtained through the orthogonal partial least squares-discrimination analysis (OPLS-DA), and several differential metabolites were screened out. ResultsThe potential metabolic markers of gout in different stages were found based on the t-test. Finally, 18 different metabolites were identified through Human Metabolome Database (HMDB) and Targeted-MS/MS. The receiver operating characteristic (ROC) curve results revealed that all the screened biomarkers exerted high accuracy and diagnostic value. Pathway analysis indicated that the significantly different metabolites were mainly involved in purine metabolism and amino acid metabolism. ConclusionThe identified potential biomarkers are mainly involved in purine metabolism and amino acid metabolism, which leads us to further explore the pathogenesis of gout. This will lead us to further explore the pathogenesis of gout and provide the basis and ideas for the prevention and treatment of gout.

Ceramides as the molecular link between impaired lipid metabolism, saturated fatty acid intake and insulin resistance: are all saturated fatty acids to be blamed for ceramide-mediated lipotoxicity?

Type 2 diabetes mellitus (T2DM) is a metabolic disorder that has reached epidemic proportions worldwide, posing a huge treat on people's health and quality of life. From a pathogenetic prospective, T2DM is driven by insulin resistance defined as a blunted response of tissues to insulin which leads to chronic hyperglycaemia. Mechanistically, lipotoxicity and particularly the intracellular accumulation of ceramides in the skeletal muscle and the liver, is a primary metabolic aberration underpinning insulin resistance. Indeed, intracellular ceramide accumulation can hamper insulin signal transduction pathway thereby promoting insulin resistance. This review will provide an updated overview of the metabolic defects underlaying ceramide buildup and the molecular mechanism by which ceramides imping upon insulin signalling. Additionally, the role of specific ceramide subspecies as potential biomarkers for T2DM and the role of both long- and medium-chain saturated fatty acids as a modulator of ceramide metabolism will be discussed.

Identification of biomarkers for assessing treatment efficacy of chemotherapy plus nivolumab as the first line in patients with unresectable advanced or recurrent gastric cancer: a multicenter study.

In this study, we aimed to identify biomarkers for predicting treatment outcomes and efficacy of chemotherapy plus nivolumab, as well as predict immune-related adverse events (irAEs) characteristics of immune checkpoint inhibitors. This multicenter study included 104 patients who received chemotherapy plus nivolumab as the primary treatment for unresectable advanced recurrent gastric cancer. Blood test results were collected before the start and after two courses of treatment. The neutrophil-lymphocyte ratio, prognostic nutritional index (PNI), and lactate dehydrogenase/albumin ratio (LAR) were examined after treatment in each case to determine changes compared to values before the start of treatment. A total of 57 (54.8%) patients experienced a complete or partial response. The LAR of the stable disease (SD)/progressive disease (PD) group significantly increased (p=0.018). An examination of the presence of grade ≥3 irAEs and changes in related factors showed that the LAR of all patients increased. The LAR was correlated with the best therapeutic response; therefore, it may be a potential biomarker of treatment outcomes and efficacy.

Angiopoietin-like 4 is a potential biomarker for diabetic kidney disease in type 2 diabetes patients.

The association between serum angiopoietin-like 4 (ANGPTL4) levels and the severity of diabetic kidney disease (DKD) in patients with type 2 diabetes mellitus remains unclear. A total of 1,115 type 2 diabetes mellitus patients were analyzed in this cross-sectional study. DKD index included DKD stages defined by estimated glomerular filtration rate, the albuminuria grades and DKD risk management grades. Serum levels of ANGPTL4 and other biomarkers were detected. Multivariable-adjusted linear and logistic analyses were used to study the association between ANGPTL4 and DKD. The protein levels of ANGPTL4 were assessed in the kidney. Renal tubular cells were stimulated with glucose to study ANGPTL4 expression. Compared with the participants in the third or fourth quantile of ANGPTL4, those in the first or second quantile of ANGPTL4 were younger, with lower glycated hemoglobin, triglycerides and urinary albumin creatinine ratio (all P < 0.05). There was a negative nonlinear relationship between ANGPTL4 and estimated glomerular filtration rate in type 2 diabetes mellitus patients. One standard deviation increased serum ANGPTL4 levels, the odds ratio of having DKD was 1.40 (95% confidence interval 1.08-1.80). The mediation analysis showed that triglycerides did not mediate the association between ANGPTL4 and DKD. Furthermore, ANGPTL4 could be the strongest among multiple panels of biomarkers in its association of DKD. Compared with mice at 8 weeks-of-age, db/db mice at 18 weeks-of-age had increased ANGPTL4 expression in glomeruli and tubular segments. In vitro, glucose could stimulate ANGPTL4 expression in tubular cells in a dose-dependent manner. ANGPTL4 could be a potential marker and therapeutic target for DKD treatment.

Personalized fMRI tasks for grief severity in bereaved individuals: Emotional counting Stroop and grief elicitation protocols

Approximately 7–10% of people experiencing bereavement following a death develop prolonged grief disorder, a psychiatric disorder included in the DSM-5-TR. Prolonged grief disorder encompasses core symptoms of intense yearning/longing for and preoccupation with thoughts or memories of the deceased person experienced to a clinically significant degree for at least the last month, other key associated symptoms (e.g., avoidance, emotional pain), and the death must have occurred at least one year prior to diagnosis. Extant research has shown a relationship between activation in the reward pathway (e.g., nucleus accumbens) and grief severity. To date, functional MRI studies have primarily utilized the Emotional Counting Stroop task (ecStroop) and the Grief Elicitation task to explore these relationships. However, these prior studies are not without limitations, including small sample sizes and absence of a unified task protocol, hindering meaningful comparisons between studies. This protocol paper describes the ecStroop task and the Grief Elicitation task, which will be vital for facilitating multisite studies and enabling comparisons across studies. This will aid to advance the field by identifying neurophysiological measures that may, in the future, serve as potential biomarkers of prolonged grief disorder.

IL-2Rα is a potential biomarker for heart failure diagnosis of patients with end-stage renal disease and haemodialysis.

Heart failure (HF) is a leading cause of mortality in patients with end-stage renal disease (ESRD) undergoing haemodialysis. Identifying novel predictors of HF is essential for improving diagnostic precision and enhancing patient outcomes. This study included 68 participants from the Haemodialysis Centre at the Second Hospital of Tianjin Medical University. Clinical characteristics and echocardiographic data were collected and analysed. We measured the plasma of 44 cytokines to investigate their correlation with cardiac function and their potential as HF biomarkers. In the HF with reduced ejection fraction (HFrEF) group, the levels of several cytokines, including stem cell growth factor-β (SCGF-β), C-X-C motif chemokine 10 (CXCL10), interleukin-1α (IL-1α), interleukin-6 (IL-6), interleukin-8 (IL-8), interleukin-16 (IL-16), interleukin-1 receptor antagonist protein (IL-1Ra), interferon-γ (IFN-γ), tumour necrosis factor-α (TNF-α), leukaemia inhibitory factor (LIF), C-C motif chemokine 3 (CCL3), interleukin-10 (IL-10), interleukin-2 receptor subunit alpha (IL-2Rα), tumour necrosis factor ligand superfamily member 10 (TNFSF10), macrophage colony-stimulating factor (M-CSF), granulocyte colony-stimulating factor (G-CSF) and stem cell factor (SCF), were significantly increased, while C-C motif chemokine 11 (CCL11)/eotaxin levels were decreased compared with those in the control group (P<0.05). Receiver operating characteristic (ROC) curve analysis highlighted TNF-α [area under the ROC curve (AUC)=0.85, odds ratio (OR)=1.080, 95% confidence interval (CI): 1.033-1.128, P=0.001], IFN-γ (AUC=0.84, OR=1.836, 95% CI: 1.289-2.615, P=0.003) and IL-2Rα (AUC=0.82, OR=1.022, 95% CI: 1.009-1.035, P=0.001) as excellent predictors for HFrEF in haemodialysis patients with ESRD, and they outperformed soluble suppression of tumourigenicity-2 (sST2) but slightly underperformed N-terminal pro-brain natriuretic peptide (NT-proBNP). IL-2Rα (AUC=0.77, OR=1.018, 95% CI: 1.007-1.030, P=0.001) demonstrated superior diagnostic capabilities when distinguishing patients with HF with left ventricular ejection fraction (LVEF) <50% from controls. IL-2Rα emerged as a robust biomarker for left ventricular HF, while TNF-α (AUC=0.89, OR=1.140, 95% CI: 1.039-1.250, P=0.005) showed promise in assessing HF severity in patients with ESRD. IL-2Rα (AUC=0.80, OR=1.017, 95% CI: 1.007-1.027, P=0.001) also significantly predicted right ventricular systolic dysfunction. During a median follow-up of 14months, 10 patients (14.7%) experienced all-cause mortality. Multivariate Cox regression analysis confirmed that plasma IL-2Rα was an independent predictor of all-cause death [hazard ratio (HR): 1.010, 95% CI: 1.001-1.020, P=0.039] after adjusting for other variables. This study underscores the potential of IL-2Rα as a valuable biomarker for HF diagnosis and management in haemodialysis patients with ESRD and contributes to our understanding of this high-risk population.

Transfer RNAs are linked to ischemic stroke and major bleeding in patients with end-stage kidney disease.

Patients with end-stage kidney disease (ESKD) are at very high risk for thromboembolism and bleeding. This study aimed to identify small non-coding RNAs (sncRNAs), specifically microRNAs and transfer-RNA (tRNA)-derived fragments (tRFs), as potential novel biomarkers for predicting thromboembolism and bleeding in this high-risk population. In this sncRNA discovery research, we leveraged the VIVALDI cohort, consisting of 625 ESKD patients on hemodialysis, to conduct two nested case-control studies, each comprising 18 participants. The primary outcomes were ischemic stroke in the first study and major bleeding in the second. Plasma samples were processed using the miND pipeline for RNA-seq analysis to investigate differential expression of microRNAs and tRNA/tRFs between cases and their respective matched controls, with results stringently adjusted for false discovery rate (FDR). No significant differential expression of microRNAs for either ischemic stroke or major bleeding outcomes was observed in the two nested case-control studies. However, we identified four tRNAs significantly differentially expressed in ischemic stroke cases and seven in major bleeding cases, compared to controls (FDR<0.1). Coverage plots indicated that specific tRNA fragments (tRFs), rather than full-length tRNAs, were detected, however. Alternative mapping approaches revealed challenges and technical limitations that precluded in-depth differential expression analyses on these specific tRFs. Yet, they also underscored the potential of tRNAs and tRFs as markers for thromboembolism and bleeding. While microRNAs did not show significant differential expression, our study identifies specific tRNAs/tRFs as potential novel biomarkers for ischemic stroke and major bleeding in ESKD patients. .