Biomedical subjects

Yu Zhou

Publications and source records attributed to Yu Zhou.

8 recordsLinked to original sources

PubMed2026

[Study of a patient with azoospermia due to variant of MOV10L1 gene].

OBJECTIVE: To explore the clinical and genotypic characteristics of a patient with Sertoli cell-only syndrome (SCOS) due to variants of MOV10L1 gene. METHODS: A 27-year-old patient with Non-obstructive azoospermia (NOA) underwent routine semen analysis. Serum levels of follicle-stimulating hormone (FSH), luteinizing hormone (LH), progesterone (P), estradiol (E2), prolactin (PRL), and testosterone (T) were determined by chemiluminescence assays. Peripheral blood samples were collected for G-banded karyotyping analysis. Multiplex PCR fluorescence detection was used to screen for AZF gene microdeletions. Whole exome sequencing (WES) and Sanger sequencing were performed simultaneously. Testicular biopsy tissues were subjected to Hematoxylin-Eosin (HE) staining to assess seminiferous tubule cell composition, and MOV10L1 protein expression was detected by immunohistochemical staining. Bioinformatics tools were employed to predict the pathogenicity of variants and their impact on protein structure and function. This study was approved by the Medical Ethics Committee of the Guangdong Institute of Reproductive Sciences [Ethics No.: 2023(01)]. RESULTS: The patient's two semen analyses had failed to detect any sperm. Hormone tests indicated elevated FSH (22.32 mIU/mL) and PRL (397.6 mIU/mL), while T (3.68 nmol/L) and E2 (38.32 pmol/L) were reduced. Chromosomal karyotyping revealed 46,XY, and no AZF gene deletion was detected. WES and Sanger sequencing detected compound heterozygous variants of the MOV10L1 gene, including a c.345C>A (p.C115X) nonsense variant and a c.3323C>T (p.T1108I) missense variant, with the former being unreported previously. HE staining showed only Sertoli cells in the seminiferous tubules, confirming the diagnosis of SCOS. Immunohistochemical staining revealed absent MOV10L1 protein expression in the testicular tissue. Based on the guidelines from American College of Medical Genetics and Genomics (ACMG), the c.345C>A (p.C115X) was classified as a pathogenic variant (PVS1+PM2_Supporting+PP4), while the c.3323C>T (p.T1108I) was deemed variant of uncertain significance (PM2_Supporting+PP3_Supporting+PP4). Bioinformatics analysis demonstrated that c.345C>A (p.C115X) may cause premature termination of protein translation, while c.3323C>T (p.T1108I) may disrupt the hydrophobicity of the RNA helicase domain, reducing the active pocket volume and decreasing its affinity for MILI protein. CONCLUSION: This study has diagnosed a case of SCOS due to compound heterozygous variants of the MOV10L1 gene, which also enriched its mutational spectrum.

Humans↗

PubMed2026

Impact of NR4A3 on wound healing in chronic venous ulcers and its association with the PI3K/Akt signaling pathway.

BACKGROUND: To investigate the role of NR4A3 in chronic venous ulcer (VU) wound healing and to explore its potential regulatory mechanism involving the PI3K/Akt pathway. METHODS: Differential expression and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using the GSE174661 dataset. DEGs were filtered by |log2FC| > 1 and adjusted P < 0.05, with KEGG significance set at P < 0.05. NR4A3 was identified as the core gene. NR4A3 knockdown and overexpression were established in HaCaT cells to evaluate proliferation, migration, and inflammatory cytokines. TNF-α was used to mimic the inflammatory microenvironment. Western blotting assessed phosphorylation of GSK3β, mTOR, PI3K, and Akt. PI3K/Akt agonist 740Y-P and inhibitor LY294002 were used in rescue experiments. RESULTS: Bioinformatic analysis revealed that NR4A3 expression was markedly downregulated in chronic venous ulcer (VU) tissues relative to normal skin and ordinary acute wound tissues. Differentially expressed genes were significantly enriched in the PI3K/Akt signaling pathway. TNF-α stimulation significantly upregulated NR4A3 expression and increased phosphorylation of GSK3β and mTOR in HaCaT cells. In cultured HaCaT keratinocytes, NR4A3 knockdown suppressed cell proliferation and invasion, enhanced cell migration, and elevated the expression and secretion of pro-inflammatory cytokines (IL-6, IL-8, CXCL5), accompanied by reduced phosphorylation of PI3K and Akt. Conversely, NR4A3 overexpression promoted cell proliferation and invasion, restrained migration, and dampened inflammatory responses, while increasing PI3K/Akt phosphorylation. Treatment with the PI3K/Akt agonist 740Y-P partially rescued the impaired proliferation, aberrant migration, and excessive inflammation caused by NR4A3 silencing, whereas PI3K/Akt inhibitor LY294002 aggravated pathway suppression. These findings suggest that NR4A3-associated changes in keratinocyte functions and inflammatory reactions are functionally linked to PI3K/Akt pathway activity, and inflammatory stimulation activates GSK3β/mTOR signaling accompanied by compensatory NR4A3 upregulation. CONCLUSION: These findings suggest that NR4A3 is associated with keratinocyte behavior and inflammatory responses via the PI3K/Akt pathway, potentially affecting chronic VU progression and healing. Reduced NR4A3 may impair wound repair through inflammation and abnormal cell migration, while TNF-α induces compensatory NR4A3 elevation.

NR4A3↗

PubMed2026

Effect of different liposomal bupivacaine concentrations in ultrasound-guided superior trunk block on postoperative analgesia and mobility: a randomized double-blind controlled trial protocol for shoulder arthroscopy.

BACKGROUND: Shoulder arthroscopy frequently causes severe postoperative pain that may impede recovery. Liposomal bupivacaine provides prolonged analgesia, and ultrasound-guided superior trunk block (STB) offers comparable analgesia to interscalene block with a lower risk of hemidiaphragmatic paralysis. However, the optimal concentration of liposomal bupivacaine for STB remains unknown. METHODS: This randomized, double-blind, controlled trial will enrol 282 adult patients scheduled for elective arthroscopic rotator cuff repair. Patients will be randomly allocated (1:1:1) to receive ultrasound-guided STB with liposomal bupivacaine 66 mg (Group A), 44 mg (Group B) or 33 mg (Group C), each diluted to 10 mL. The co-primary outcomes are (1) rest pain Numeric Rating Scale (NRS) score at 48 h post-surgery and (2) cumulative oral morphine milligram equivalents (MME) consumption within 0-48 h after surgery. Secondary outcomes include rest pain NRS scores at 6, 24 and 72 h; motor function assessed by Muscle Balance Scale, Bromage score and American Shoulder and Elbow Surgeons (ASES) score at 6, 24, 48 and 72 h; and Quality of Recovery-15 (QoR-15) score at 24 and 48 h. DISCUSSION: This study will provide evidence on the optimal concentration of liposomal bupivacaine for STB in arthroscopic shoulder surgery, aiming to achieve effective and prolonged analgesia without compromising shoulder mobility.

Humans↗

PubMed2026

Systematic common and rare variant association testing in 392,030 whole genomes in All of Us.

Large-scale genome-wide association studies (GWAS) and rare variant association studies (RVAS) from population biobanks provide valuable resources for gene discovery in complex human traits. We present an analysis of the All of Us Research Program v8 release, which includes whole genome sequencing data and harmonized phenotypic information of 392,030 participants after quality control, enabling a unified investigation of rare and common variants across a spectrum of human traits and diseases. We build an extensive phenome- and genome-wide ("All by All") computational framework to perform GWAS and RVAS on 3,602 phenotypes and identify 49,863 approximately independent, high-quality single-variant and gene-level associations. Meta-analyses of All of Us and UK Biobank, with sample sizes as large as 786,871 participants, further enhance statistical power and find 193 pLoF gene-phenotype associations that are not significant in either cohort alone, including 22 associations not highlighted by previous studies. We also present a public interactive browser that integrates association results for common and rare variants to facilitate interpretation and rapid querying of summary statistics, along with supporting documentation, and a Featured Workspace in the All of Us Researcher Workbench. Our framework will apply to iterative data releases as All of Us grows, empowering researchers worldwide to uncover insights into the functional effects of genetic components on complex traits and diseases.

Journal Article↗

PubMed2026

The utilization of Salmonella phage in milk and chicken: depolymerase identification and potential for anti-biofilm activity.

Bacteriophage (phage)-based biocontrol presents a promising strategy against foodborne pathogens. In this study, a novel phage, PH215, exhibited lytic activity against seven prevalent Salmonella serotypes, was isolated and characterized. PH215 demonstrated remarkable environmental stability, sustaining infectivity across a wide pH range (2-11) and at temperatures from 4 to 50 °C. The multiplicative potential of PH215 was evidenced by a low multiplicity of infection (MOI) of 10-6, a short latent period of 10 min, and a substantial burst size of approximately 50 PFU per infected cell. Genomic analysis revealed a 43,505 kb double-stranded DNA genome encoding 67 putative proteins. Notably, the product of the Peg38 gene, identified as a tail spike protein (termed PH215Depo), possessed depolymerase activity. We have shown that the cloned expression of PH215Depo exhibited enzymatic activity against various Salmonella serotypes and significantly impeded biofilm formation. Furthermore, in practical application models, PH215 application reduced Salmonella counts in milk and chicken by 2.04 to 5.37 log10 CFU/mL. Our findings highlight the potential of depolymerase-encoding phages like PH215 as effective and broad-spectrum biocontrol agents against Salmonella in the food industry.

Biofilms↗

PubMed2026

Machine learning-based clinical prediction model and multi-omics integration for assessing pancreatic cancer risk in new-onset diabetes.

BACKGROUND: Given that pancreatic cancer (PC) is typically diagnosed at an advanced stage but is often preceded by new-onset diabetes mellitus (NODM), providing a window for early detection, we sought to develop and validate an interpretable machine-learning model integrated with multi-omics profiling to identify early biomarkers of NODM-associated PC. METHODS: In a population-based cohort, individuals with NODM-associated PC and NODM without PC were identified and randomly divided (70:30) into training and validation sets after feature selection. Eight machine learning (ML) classifiers were compared using fivefold cross-validation, and model performance was evaluated in terms of discrimination, calibration, and decision curve–based clinical utility. We evaluated interpretability using the Shapley additive explanations (SHAP) analyses. Mechanistically, Olink proteomic profiling and metabolomics were analyzed through clinical classifications and model-defined risk strata. RESULTS: Categorical boosting achieved the best performance in the independent validation set (AUROC = 0.844). The NODM cohort was stratified into high- (n = 2,362) and low-risk (n = 5,030) groups, and internal validation together with SHAP analyses demonstrated consistent model performance and identified clinically interpretable predictors. Proteomic and metabolomic analyses under clinical and risk-based grouping identified 39 overlapping differentially expressed proteins and 145 overlapping metabolites with enriched across 11 shared KEGG pathways. Cross-platform validation highlighted PLTP, CRTAC1, and ITGAV as serum biomarkers with a strong potential for early NODM-PC detection. CONCLUSIONS: We developed an interpretable ML framework centered on NODM enables practical risk stratification for early PC detection by multi-omics and provides a pathway of ML-based triage followed by biomarker confirmation for earlier detection and diagnosis.

Humans↗

PubMed2024

Evaluating the impact of modeling choices on the performance of integrated genetic and clinical models.

PURPOSE: The value of genetic information for improving the performance of clinical risk prediction models has yielded variable conclusions. Many methodological decisions have the potential to contribute to differential results. We performed multiple modeling experiments integrating clinical and demographic data from electronic health records with genetic data to understand which decisions may affect performance. METHODS: Clinical data in the form of structured diagnostic codes, medications, procedural codes, and demographics were extracted from 2 large independent health systems, and polygenic risk scores (PRS) were generated across all patients of European ancestry with genetic data in the corresponding biobanks. Crohn's disease was studied based on its substantial genetic component, established electronic health records-based definition, and sufficient prevalence for training and testing. We investigated the impact of choices regarding the PRS integration method, training sample, model complexity, and performance metrics. RESULTS: Overall, our results showed that including PRS resulted in higher performance, but this gain was only robust in situations with limited clinical information. We found consistent performance increases from more compute-intensive models, such as random forest, but the impact of other decisions varied by site. CONCLUSION: This work highlights the importance of considering methodological decision points in interpreting the impact of PRS on prediction performance in clinical models.

Humans↗

PubMed2023

Evaluating the impact of modeling choices on the performance of integrated genetic and clinical models.

The value of genetic information for improving the performance of clinical risk prediction models has yielded variable conclusions. Many methodological decisions have the potential to contribute to differential results across studies. Here, we performed multiple modeling experiments integrating clinical and demographic data from electronic health records (EHR) and genetic data to understand which decision points may affect performance. Clinical data in the form of structured diagnostic codes, medications, procedural codes, and demographics were extracted from two large independent health systems and polygenic risk scores (PRS) were generated across all patients with genetic data in the corresponding biobanks. Crohn's disease was used as the model phenotype based on its substantial genetic component, established EHR-based definition, and sufficient prevalence for model training and testing. We investigated the impact of PRS integration method, as well as choices regarding training sample, model complexity, and performance metrics. Overall, our results show that including PRS resulted in higher performance by some metrics but the gain in performance was only robust when combined with demographic data alone. Improvements were inconsistent or negligible after including additional clinical information. The impact of genetic information on performance also varied by PRS integration method, with a small improvement in some cases from combining PRS with the output of a clinical model (late-fusion) compared to its inclusion an additional feature (early-fusion). The effects of other modeling decisions varied between institutions though performance increased with more compute-intensive models such as random forest. This work highlights the importance of considering methodological decision points in interpreting the impact on prediction performance when including PRS information in clinical models.

Preprint↗