Skin cutaneous melanoma is characterized by high malignancy and prognostic heterogeneity. Immune cell networks are critical to the biological progression of melanoma through the tumor microenvironment. Thus, identifying effective biomarkers for skin cutaneous melanoma from the perspective of the tumor microenvironment may offer strategies for precise prognosis prediction and treatment selection. A total of 470 cases from The Cancer Genome Atlas and 214 from the Gene Expression Omnibus were systematically evaluated to construct an optimal independent immune cell risk model with predictive value using weighted gene co-expression network analysis, Cox regression, and least absolute shrinkage and selection operator assay. The predictive power of the developed model was estimated through receiver operating characteristic curves and Kaplan-Meier analysis. The association of the model with tumor microenvironment status, immune checkpoints, and mutation burden was assessed using multiple algorithms. Additionally, the sensitivity of immune and chemotherapeutics was evaluated using the ImmunophenScore and pRRophetic algorithm. Furthermore, the expression profiles of risk genes were validated using gene expression profiling interactive analysis and Human Protein Atlas resources. The risk model integrated seven immune-related genes: ARNTL, N4BP2L1, PARP11, NUB1, GSDMD, HAPLN3, and IRX3. The model demonstrated considerable predictive ability and was positively associated with clinical and molecular characteristics. It can be utilized as a prognostic factor for skin cutaneous melanoma, where a high-risk score was linked to a poor prognosis and indicated an immunosuppressive microenvironment. Furthermore, the model revealed several potential target checkpoints and predicted the therapeutic benefits of multiple clinically used drugs. Our findings provide a comprehensive landscape of the tumor immune microenvironment in skin cutaneous melanoma and identify prognostic markers that may serve as efficient clinical diagnosis and treatment selection tools.

Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) is a leading chronic liver disorder closely linked to diabetes mellitus (DM) and its cardiovascular and renal complications. Early identification of diabetes risk in this population is essential for timely intervention. Objective: To develop machine learning (ML) models to predict diabetes risk in individuals with MASLD and to identify key predictive factors using a nationally representative dataset. Methods: Data from 6310 MASLD participants (2007–2018) were analysed and classified into DM and non-DM groups. Feature selection was performed using Random Forest, Least Absolute Shrinkage and Selection Operator and Support Vector Machine Recursive Feature Elimination. Based on selected features, nine ML models were developed. Model performance was evaluated using accuracy, sensitivity, area under the curve, F1 score, Rank Score and Brier Score. SHapley Additive exPlanations (SHAP) were used for interpretability. Results: Eight key variables (age, urinary albumin (Ualb), total cholesterol (TC), lipid accumulation product (LAP), urinary creatinine, white blood cell count, uric acid and Visceral Adiposity Index) were identified and used for model construction. Among nine algorithms, the Light Gradient Boosting Machine (LightGBM) model showed superior predictive performance. SHAP analysis revealed that Ualb, age, TC and LAP were the most influential predictors. Conclusion: Our ML-based model effectively identifies individuals with MASLD at high risk for developing DM. The LightGBM algorithm outperformed other models in both accuracy and interpretability. Key predictors such as Ualb and LAP highlight the importance of renal and metabolic markers in early diabetes risk prediction, offering a new approach for individualised intervention and clinical decision-making.

Background: Obesity is a prevalent and clinically significant complication among individuals with diabetes mellitus (DM), contributing to increased cardiovascular risk, metabolic burden, and reduced quality of life. Despite its high prevalence, the risk factors for obesity within this population remain incompletely understood. With the growing availability of large-scale health datasets and advancements in machine learning, there is an opportunity to improve risk stratification. This study aimed to identify key predictors of obesity and develop a machine learning-based predictive model for patients with T2DM using data from the National Health and Nutrition Examination Survey (NHANES).Methods: Data from adults with diabetes were extracted from the NHANES 2007–2018 cycles. Participants were categorized into obese and non-obese groups based on BMI. Least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation was used to select relevant features. Subsequently, nine machine learning algorithms—including logistic regression, random forest (RF), radial support vector machine (RSVM), k-nearest neighbors (KNN), XGBoost, LightGBM, decision tree (DT), elastic net regression (ENet), and multilayer perceptron (MLP)—were employed to construct predictive models. Model performance was evaluated based on area under the ROC curve (AUC), calibration curves, Brier score, and decision curve analysis (DCA). The best-performing model was visualized using a nomogram to enhance clinical applicability.Results: A total of 3,794 participants with type 2 diabetes were included in the analysis, of whom 57.0% were classified as obese. LASSO regression identified 19 key variables associated with obesity. Among the nine machine learning models evaluated, the logistic regression model demonstrated the best overall performance, with the lowest Brier score. It also showed good discrimination (AUC = 0.751 in the training set and 0.781 in the test set), favorable calibration, and consistent clinical utility based on decision curve analysis (DCA). A nomogram was constructed based on the logistic regression model to facilitate individualized risk prediction, with total points corresponding to predicted probabilities of obesity.Conclusion: Obesity remains highly prevalent among individuals with type 2 diabetes. Our findings highlight key clinical features associated with obesity risk and provide a practical tool to aid in early identification and individualized management of high-risk patients.

Background: Composite tissue defects (CTD) of the forearm and hand commonly result from industrial, agricultural, and traffic-related injuries and often involve multiple anatomical structures, making reconstruction challenging. Simple skin-flap coverage may leave deep dead space and fail to restore functional integrity. This study reports our experience using the free anterolateral thigh chimeric muscle flap (ALT-CMF) for CTD reconstruction and evaluates its effectiveness in achieving reliable defect coverage and functional recovery. Methods: We retrospectively reviewed patients who underwent ALT-CMF reconstruction between February 2018 and January 2023. All patients had CTD of the forearm or hand and were followed for at least six months. Flap viability, postoperative complications, and donor-site outcomes were recorded. Digital functional recovery was assessed using the Total Active Motion (TAM) score. Two-point discrimination (2PD) were used to evaluate sensory recovery in the transplanted skin flaps. Results: Twenty patients (15 males, 5 females; mean age 39.40 ± 9.90 years) were included. During surgery, the median area of the harvested anterolateral thigh perforator skin flap was 80.0 (66.0–99.0) cm ² , while the median area of the muscle flap component was 21.25 (20.00–30.00) cm ² . All donor sites were primarily closed. Over a mean follow-up period of 15.30 ± 5.78 months, 15 flaps (75.0%) survived uneventfully, while 5 patients (25.0%) developed vascular crisis; four flaps were salvaged after urgent re-exploration, and one experienced complete necrosis. At the six-month follow-up, the mean TAM score was 235.79° ± 8.35°, indicating satisfactory recovery of digital mobility. 2PD testing demonstrated no significant difference in sensory recovery between the transplanted flaps and the contralateral side (19.42 ± 2.59 mm vs. 18.16 ± 3.20 mm, P = 0.063). Conclusions: The ALT-CMF is an effective reconstructive option for composite tissue defects of the forearm and hand. By providing reliable soft-tissue coverage, eliminating dead space through its chimeric muscle component, and supporting functional restoration in terms of joint mobility and sensation. This technique enables one-stage reconstruction with low donor-site morbidity and promotes favorable limb recovery.

Distal finger defects (DFDs) are common hand injuries that impair both appearance and fine motor function. Because these defects often involve the nail bed

Introduction: Free anterolateral thigh perforator flap (ALTPF) reconstruction is a conventional approach for the treatment of diabetic foot ulcers (DFU), whereas tibial cortex transverse transport (TTT) represents an emerging alternative. However, direct comparative studies evaluating their therapeutic efficacy remain limited. Methods: A retrospective analysis was performed on patients with DFU treated at the First Affiliated Hospital of Guangxi Medical University between January 2016 and December 2022. All patients underwent either TTT or ALTPF reconstruction. Treatment and follow-up data were obtained from the hospital's Computer Information Center and the outpatient follow-up system. Patient demographics and wound-related information were collected. Ulcer healing status was assessed using wound photographs and follow-up records, with healing time, recurrence, and amputation events documented. Foot sensory function was evaluated using the Semmes–Weinstein monofilament test (SWMT) and nerve conduction velocity measurements, while postoperative foot function was assessed according to the Maryland Foot Score. Results: A total of 174 patients with DFU were included in this study, of whom 88 underwent TTT and 86 received ALTPF reconstruction. The TTT group had significantly shorter operative time, less intraoperative blood loss, and a lower transfusion rate than the ALTPF group ( P < 0.05). The ulcer healing rate was higher in the TTT group (98% vs. 88%, P = 0.015), whereas the recurrence and major amputation rates were significantly lower (both P < 0.05). The TTT group also showed a higher rate of positive SWMT and faster nerve conduction velocity compared with the ALTPF group ( P < 0.05), along with better Maryland Foot Scores. Two cases of pin-tract infection occurred in the TTT group, while flap necrosis developed in nine cases in the ALTPF group. Conclusions: TTT demonstrated superior therapeutic efficacy to ALTPF in the management of severe DFU. TTT offered advantages including shorter operative time, reduced blood loss, lower transfusion and complication rates, higher healing rate, and better functional recovery. However, further randomized controlled trials are warranted to validate these findings.