Guangxi Zhuang Autonomous Region Museum

IntroductionRubinstein-Taybi syndrome type 2 (RSTS2; OMIM #613684) is a rare autosomal dominant disorder caused by loss-of-function variants in the EP300 gene (OMIM #602700), characterized by intellectual disability, distinctive craniofacial features, and skeletal anomalies.MethodsWhole-exome sequencing (WES) was performed on five pediatric patients presenting with neurodevelopmental delay. Candidate variants were filtered using the TGex platform and validated by Sanger sequencing for familial segregation analysis. The functional impact of variants was assessed using diverse bioinformatic tools, and pathogenicity classifications were assigned according to ACMG/AMP guidelines.ResultsFive novel EP300 variants were identified in this study: c.4774A>G (p.Lys1592Glu), c.4452 + 5G>C, c.3764A>G (p.His1255Arg), c.3591–2A>G, and c.6439C>T (p.Gln2147*). These alterations impair gene function through mechanisms including amino acid substitution, disruption of mRNA splicing, or premature protein truncation. All variants were classified as pathogenic or likely pathogenic per ACMG/AMP criteria. Literature analysis reveals that the predominant clinical manifestations in the Chinese patients encompassed neurodevelopmental impairment, accompanied by motor delay, growth retardation, and microcephaly. Strikingly, archetypal craniofacial dysmorphisms, such as arched eyebrows, long eyelashes, downslanting palpebral fissures, beaked nose, as well as significant skeletal abnormalities were absent, suggesting EP300 variants may present with a broader and more variable phenotypic spectrum than previously recognized.ConclusionThis study reports five novel pathogenic EP300 variants, expanding the variant repertoire of RSTS2 and providing an important basis for clinical diagnosis and genetic counseling.

BackgroundThe impact of different serum uric acid(SUA) concentrations on bone mineral density(BMD) varied across different BMI categories, which might provide useful insights for clinical uric acid-lowering treatments.PurposeTo explore whether SUA was associated BMD across BMI categories in men aged ≥ 50 years with type 2 diabetes (T2D).MethodsThe participants were divided into six groups according to SUA levels. Analyses were adjusted for the covariates including weight(Wt), total lean mass(TLM), total fat mass(TFM), total fat ratio(TFr), A/G and a range of other baseline laboratory findings.ResultsSUA was associated with total lumbar BMD(TLBMD) (p = 0.013). In the BMI(24–27.999 kg/m2) group, SUA was significantly associated with TLBMD (p = 0.02). There was no correlation between SUA and whole-body BMD, as well as femoral neck BMD and total hip BMD. In the BMI < 24 kg/m2 group, the highest TLBMD value was observed when SUA levels ranged from 420 µmol/L to 479 µmol/L. While BMI:24–27.999 kg/m2, the highest TLBMD value was showed in the SUA(360-419 μmol/L) group based on adjusted models. The SUA(300-360 μmol/L) group exhibited the highest TLBMD value among the patients with BMI ≥ 28 kg/m2.ConclusionSUA was correlated with TLBMD in T2D men aged ≥ 50 years. Among patients with BMI 24–27.999 kg/m2, the SUA concentrations between 360–419 μmol/L might have a protective effect on TLBMD.

ABSTRACT Terrain correction is crucial in enhancing the quality of remote sensing imagery in mountainous regions by mitigating terrain-induced distortions. Despite advancements, current methods fall short in effectively addressing terrain shading issues. The Semi-empirical Method Considering Shadow (SMCS) was developed in this study, and the performance of SMCS, SCS, and SCS+C models on images of varying spatial resolution in karst landscapes was also evaluated. The results demonstrate that the SMCS method effectively enhances remote sensing images of complex mountainous areas. Compared with the traditional terrain correction method, the SMCS method significantly improves the correction effect. The R2, which reflects the relationship between solar incidence angle and image reflectance, decreased from 0.489, 0.037, and 0.067 to 0.046, 0.018, and 0.022, respectively. This indicates that the SMCS method effectively weakens the linear correlation between image reflectance and solar incidence angle. Additionally, the IQRR across bands confirms the SMCS method’s ability to correct spectral discrepancies among similar features. The SMCS model demonstrates robust performance across all tested resolutions, confirming its broad applicability and effectiveness under varying spatial scales. Furthermore, the study highlights that terrain-induced effects become increasingly significant at higher resolutions, posing substantial challenges to conventional correction approaches.

Patients with hepatocellular carcinoma (HCC) exhibit a high rate of recurrence and poor prognosis after surgery, and effective prognostic indicators and stratification strategies are currently lacking. Hence, this study proposes new prognostic markers to provide a theoretical basis for patients with HCC. We aim to build and evaluate a model estimating the effect of alpha-fetoprotein-tumor size ratio (ATR) on the prognosis of patients undergoing hepatectomy for HCC. We retrospectively reviewed hospital records to identify patients who underwent hepatectomy for HCC at the First Affiliated Hospital of Guangxi Medical University from January 2013 to December 2018. Outcomes (recurrence events and mortality) not available in the outpatient medical records were determined through telephone interviews until February 2022. The optimal cutoff value was determined using X-tile (Yale School of Medicine). Independent risk factors for prognosis were investigated by Cox regression modeling, and between-group differences were reduced through propensity score matching. A predictive model for HCC prognosis was constructed using a nomogram, and the predictive performance of the model was evaluated using the C-index. Of the 1628 eligible patients, 1204 patients were included in the analysis. Patients were stratified into low, medium, and high ATR groups with X-tile. Before propensity score matching, ATR was identified as an independent risk factor for overall survival (low vs medium: HR 1.41, 95% CI 1.03-1.94; P=.03; medium versus high: HR 1.59, 95% CI 1.02-2.47; P=.04) and relapse-free survival (low vs medium: HR 1.33, 95% CI 1.03-1.70; P=.03; medium versus high: HR 2.10, 95% CI 1.40-3.15; P<.001) of patients with HCC following hepatectomy. A nomogram incorporating ATR, China Clinic Liver Cancer staging, bleeding, and postoperative transcatheter arterial chemoembolization was developed to predict moderate predictive efficacy for overall survival (C-index: 0.73) and relapse-free survival (C-index: 0.73). ATR was found to be associated with microvascular, macroinvasion, and poor tumor differentiation. ATR is an independent prognostic risk factor in patients with HCC after hepatectomy and is associated with microvascular, macroinvasion, and poor tumor differentiation.

Intratumoral microbiota are implicated in colorectal cancer liver metastasis (CRLM), but their relationships with clinical characteristics and prognosis remain to be explored. Using 5R 16S rRNA gene sequencing method, we analyzed the intratumoral microbiota of patients with colorectal cancer with and without liver metastasis. CRLM patients were classified into three intratumoral microbial community subtypes (IMCSs). These included IMCS1 (sugar metabolism-related liver metastasis with T cell activation, moderate proliferation and invasion, and median disease-free survival (mDFS) of 22 months), IMCS2 (protein metabolism-related liver metastasis with natural killer cell activation, high proliferation and invasion, and mDFS of 12 months), and IMCS3 (lipid metabolism-related liver metastasis with a pauci-immune phenotype, the highest level of proliferation and invasion, and mDFS of 10 months). Our study establishes intratumoral microbiome subtypes as a novel risk stratification strategy for CRLM, facilitating microbiota-directed therapeutic strategies with potential survival benefits.

BackgroundThe global prevalence of non-alcoholic fatty liver disease (NAFLD) has reached an alarming 25%, based on recent population-based studies. NAFLD diagnosis primarily relies on imaging methods, which may be invasive, costly, or limited. Existing prediction models, such as the Fatty Liver Index (FLI) and NAFLD Liver Fat Score (NLFS), mainly incorporate metabolic parameters from Western populations and neglect non-metabolic factors, limiting their generalizability. To address these limitations, this study developed a screening model integrating both metabolic and non-metabolic indicators, tailored specifically to diverse populations undergoing routine physical examinations.MethodsA retrospective cohort (N = 6,461, 2024) served to construct and validate the NAFLD prediction model, while an external cohort (N = 2,687, 2023) provided additional validation. NAFLD was confirmed by ultrasound. The primary cohort was randomly divided into training (70%) and internal validation (30%) sets. Initially, 20 candidate variables underwent univariate analysis (p < 0.1), and statistically significant variables (p < 0.05) were selected for the final multivariate logistic regression model. Model performance was assessed using area under the curve (AUC), calibration plots, and decision curve analysis (DCA). Robustness was verified through 10 × 10-fold cross-validation and external validation.ResultsThe final predictive model incorporated 12 predictors (age, sex, body mass index (BMI), uric acid (UA), high-density lipoprotein cholesterol (HDL-C), red blood cell count (RBC), creatinine (Cr), free thyroxine (FT4), aspartate aminotransferase (AST), alanine aminotransferase (ALT), alpha-fetoprotein (AFP), triglyceride-glucose index (TyG)). The model demonstrated excellent discrimination, with a training AUC of 0.909 (95% CI: 0.900–0.919) and internal validation AUC of 0.905 (95% CI: 0.891–0.919). Optimal thresholds were 0.307 in training (sensitivity 78.8%, specificity 86.3%) and 0.205 in validation (sensitivity 88.3%, specificity 77.8%). Calibration was excellent (p = 0.989 training, p = 0.263 validation). DCA indicated substantial net benefits within thresholds from 1 to 98%. External validation confirmed strong model performance regarding discrimination, net benefit, and calibration.ConclusionThis study identified crucial risk factors and constructed a robust NAFLD prediction model suitable for routine physical examination populations, offering clinicians a valuable tool for early disease detection and intervention.

BACKGROUNDThe prevalence of colorectal cancer (CRC) in younger people is increasing. Despite advances in precision medicine, the challenges of drug resistance and high costs persist. Nitidine chloride (NC) has pharmacological potential, and kinesin family member 20A (KIF20A) is overexpressed in various tumors; however, their interaction in CRC remains unexplored.AIMTo investigate the KIF20A expression characteristics in CRC cells and determine whether it is a potential target gene for NC in inhibiting CRC treatment.METHODSSingle-cell RNA sequencing (scRNA-seq), spatial transcriptomics, and mRNA expression profiling were used to analyze KIF20A expression in CRC cells. Immunohistochemical staining was used to verify KIF20A expression in 416 clinical samples (208 CRC tissue samples and 208 noncancerous control tissue samples). Clustered regularly interspaced short palindromic repeats (CRISPR) technology was used to evaluate the impact of knocking out KIF20A on CRC cell growth. Molecular docking was applied to analyze NC–KIF20A binding. Finally, RNA sequencing and functional enrichment analysis were performed to explore the mechanism of action of NC in CRC cells.RESULTSTreating HCT116 cells with NC was found to significantly downregulate KIF20A (P < 0.05), and the molecular docking analysis revealed high-affinity binding between NC and KIF20A (binding energy = -9.6 kcal/mol). The scRNA-seq, spatial transcriptomics, and mRNA expression profiling results confirmed the significantly high expression of KIF20A in CRC tissues (standardized mean difference = 1.33, 95% confidence interval: 0.885-1.77, summary receiver operating characteristic curve area = 0.94). The immunohistochemical analysis of the clinical samples showed high KIF20A expression in the CRC tissues (P < 0.05), with significant correlation between the level of expression and gender, tumor size, and tumor grade (P < 0.05). Knocking out KIF20A significantly inhibited the growth of various CRC cell lines (CRISPR score < -0.3). The functional enrichment analysis indicated that NC may inhibit CRC by disrupting several biological processes, such as mitotic nuclear division, chromosome segregation, and microtubule binding.CONCLUSIONOur results indicate that NC binds to KIF20A with high affinity and downregulates its expression in CRC cells, leading to reduced proliferation. Hence, NC has promise as a therapeutic agent in the treatment of CRC, and targeting KIF20A also has potential as a therapeutic strategy. Further KIF20A knockout studies are needed to confirm the binding specificity and mechanistic roles of NC in CRC.