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Scars-including keloids, hypertrophic scars, and acne scars-pose substantial functional and psychosocial burdens that current empirical treatments often address by trial-and-error. Quantitative evidence now supports a precision framework. Validated clinical tools (eg, VSS, POSAS) and imaging modalities (3D photogrammetry; high-frequency ultrasound elastography) provide objective baselines, while emerging AI models deliver measurable gains: an automated scar-type classifier achieved precision 80.7%, recall 71.0%, AUC 0.846 for image-based categorization, and a clinical recurrence model for keloids reported AUC 0.889 with sensitivity 78.7% and specificity 86.8%, enabling earlier risk-stratified interventions and fewer ineffective treatment cycles in model-informed pathways. We synthesize cytokine/fibroblast signatures and genetic predisposition with multimodal (clinical-imaging-molecular) learning, detail validation challenges, and propose actionable safeguards (TRIPOD+AI-aligned reporting, internal-external validation, bias audits, SHAP-based interpretability, and federated learning to preserve privacy and improve generalizability). A pragmatic roadmap-including funding mechanisms, stakeholder roles, and a barrier-solution matrix-aims to accelerate translation toward predictive, preventive, and personalized scar care.

Scarring represents a persistent clinical and psychosocial challenge, with considerable variability in treatment response among patients. While both clinical and morphologic factors can influence outcomes, robust, individualized prediction of scar treatment efficacy remains elusive. To develop and validate an integrated predictive model for scar treatment outcomes using a combination of clinical and image-derived features in a Chinese cohort, and to translate this model into a web-based calculator for practical clinical application. This model requires validation in other ethnicities. We retrospectively analyzed 117 Chinese patients with scars treated at a single center, dividing them into a training ( = 83) and validation cohort ( = 34). Clinical data (including age, scar height) and quantitative features extracted from standardized scar photographs (solidity and mean saturation [S_mean]) were used to construct clinical, image-based, and combined predictive models. Feature selection was performed via LASSO regression, and models were developed using multivariate logistic regression. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), calibration metrics (Brier score, log loss, HL test), and decision curve analysis (DCA). Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were calculated. A user-friendly web calculator was subsequently developed. Scar height and age (clinical factors) as well as solidity and S_mean (image-derived metrics) were identified as independent predictors of poor treatment outcome. The combined model demonstrated superior discrimination (AUC 0.970 [training], 0.908 [test]), calibration, and clinical utility compared to clinical or image-based models alone. Calibration curves and metrics indicated excellent agreement between predicted and observed probabilities for the combined model. DCA, NRI, and IDI analyses further highlighted the incremental value and net benefit of the integrated approach. A web-based calculator was developed to enable individualized outcome prediction and support clinical decision-making. Integration of clinical and image-derived features enables robust, individualized prediction of scar treatment outcomes in this Chinese cohort. Our validated combined model, accessible via an easy-to-use web-based calculator, may enhance treatment planning, risk stratification, and patient counseling in scar management. Validation in diverse ethnic populations is essential.

Background Pancreatic cancer is a highly lethal malignancy, usually diagnosed when metastasis is present. Detailed analysis of distant (DM) and lymph node (LN) metastasis patterns is crucial for prognosis. This study evaluated metastasis-specific prognosis in pancreatic cancer patients from 2010 to 2016 and developed a validated nomogram for survival prediction. Methods Data of patients with metastatic pancreatic cancer who received chemotherapy or radiotherapy were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. Pearson’s chi-square test was utilized to analyze clinicopathological variables. Kaplan–Meier survival analysis and log-rank tests were employed to evaluate overall survival and prognostic outcomes. A nomogram for prognosis prediction was constructed using univariate and multivariate analyses, combined with the Cox proportional hazards regression model. Results Distant metastasis (DM) most commonly occurred in the liver (44.8%), followed by the bone (9.5%). Approximately 63.2% of patients had single-site metastasis, whereas 26.0% had metastases in two distant organs. Regional lymph node (LN) involvement was observed in 14.3% of pancreatic cancer patients, among whom only 3.7% concurrently had DM, with the liver being the most frequently involved organ, in which the liver was the most common organ. At the end of the follow-up period 152 patients in 1805 were alive. For pancreatic carcinoma patients with DM the median survival decreased to 4.0 months, while with LN that was 31.05 months. The nomogram was proven to be acceptable consistency in train and validation sets, and the c-index was 0.763. Conclusion We conducted a comparative analysis of clinicopathological characteristics and clinical outcomes among cases of metastatic pancreatic cancer. Furthermore, we developed a prognostic nomogram that demonstrated excellent performance in both the training and validation cohorts.

With the advent of the big data era, efficiently processing diverse data types, particularly tabular data, poses significant challenges. Traditional methods and Convolutional Neural Networks (CNNs) often exhibit limitations when handling tabular data due to its lack of spatial features. To address this issue, this paper proposes a novel hybrid model named SE-NET-SVM, which integrates the Squeeze-and-Excitation Network (SE-NET) channel attention mechanism with a Support Vector Machine (SVM). The core idea involves reshaping tabular data feature vectors into a pseudo-spatial tensor format and leveraging the SE module to adaptively recalibrate feature importance weights, thereby generating optimized feature vectors for SVM classification. This approach mitigates CNN’s shortcomings in processing independent, discrete tabular data while enhancing feature discriminability. Experimental results demonstrate that the performance of the proposed SE-NET-SVM model varies with different kernel functions in the SVM component, with the Matern kernel (v=2.5) achieving the highest accuracy of 97.15%. The model exhibits significant technical advancement and practical utility, offering an effective solution for machine learning-based tabular data processing.

Colorectal cancer (CRC) is one of the leading causes of cancer-associated mortality. Asiaticoside (AC) exhibits antitumor effects; however, to the best of our knowledge, the biological function of AC in CRC cells remains unclear. Therefore, the aim of the present study was to investigate the effect of AC on CRC cells. In the present study, CCK-8 and colony formation assays were performed to assess the effects of AV on human CRC cell lines (HCT116, SW480 and LoVo). Mitochondrial membrane potential was examined by JC-1 staining. Cell apoptosis and cell cycle were monitored by flow cytometry, and the expression of genes was evaluated using RT-qPCR and western blot analysis. Furthermore, the biological effect of AC in vivo was detected using a xenograft mouse model. The findings revealed that 2 µM AC suppressed the proliferation of CRC cells in a time- and dose-dependent manner, but had no adverse effects on normal human intestinal FHC cells at a range of concentrations. AC decreased the mitochondrial membrane potential and increased the apoptosis of CRC cells in a dose-dependent manner. Furthermore, AC induced cell cycle arrest at the G0/G1 phase. AC attenuated IκBα phosphorylation in a dose-dependent manner, thereby preventing P65 from entering the nucleus, and resulting in inhibition of the NF-κB signaling pathway. In addition, AC significantly reduced the expression of CDK4 and Cyclin D1 in a dose-dependent manner, significantly upregulated the activation of caspase-9 and caspase-3, and decreased the Bcl-2/Bax mRNA ratio. Furthermore, treatment with the NF-κB signaling pathway inhibitor JSH-23 significantly increased the cytotoxicity of AC in CRC cells. Findings of the xenograft mice model experiments revealed that AC significantly inhibited colorectal tumor growth in a dose-dependent manner. Overall, AC suppressed activation of the NF-κB signaling pathway by downregulating IκBα phosphorylation. This resulted in inhibition of CRC cell viability and an increase of cell apoptosis, which may form the basis of AC use in the treatment of patients with CRC.

Purpose AFP appears to be negative in about 30% of overall hepatocellular carcinoma (HCC). Our study aimed to develop a nomogram model to diagnose AFP-negative HCC (AFPN-HCC). Patients and methods The training set included 294 AFPN-HCC patients, 159 healthy objects, 63 patients with chronic hepatitis B(CHB), and 64 patients with liver cirrhosis (LC). And the validation set enrolled 137 healthy controls objects, 47 CHB patients and 45 patients with LC. LASSO, univariate, and multivariable logistic regression analysis were performed to construct the model and then transformed into a visualized nomogram. The receiver operating characteristic (ROC) curves, the calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC) were further used for validation. Results Four variables including age, PIVKA-II, platelet (PLT) counts, and prothrombin time (PT) were selected to establish the nomogram. The area under the curve (AUC) of the ROC to distinguish AFPN-HCC patients was 0.937(95% CI 0.892–0.938) in training set and 0.942(95% CI 0.921–0.963) in validation set. We also found that the model had high diagnostic value for small-size HCC (tumor size < 5 cm) (AUC = 0.886) and HBV surface antigen-positive AFPN-HCC (AUC = 0.883). Conclusions Our model was effective for discrimination of AFPN-HCC from patients with benign liver diseases and healthy controls, and might be helpful for the diagnosis for AFPN-HCC.

Background Hepatectomy is the primary treatment for regional hepatolithiasis, but recurrence rates range from 10 to 20%, often necessitating repeat surgery. Although laparoscopic hepatectomy has been widely adopted for recurrent hepatocellular carcinoma, its use in recurrent hepatolithiasis remains limited due to technical challenges, including severe adhesions, anatomical distortions, and increased risks of complications. No large-scale study has compared laparoscopic and open repeat hepatectomy for recurrent regional hepatolithiasis. Methods This multicenter retrospective study included 913 patients from nine high-volume centers in southeastern China between May 2014 and 2023. Patients were divided into laparoscopic ( n  = 338) and open surgery ( n  = 575) groups. Propensity score matching was used to balance baseline characteristics. Primary outcomes included stone clearance rates and textbook outcomes (TO), a composite measure assessing final stone clearance, hospital stay, bile leakage, major complications, and 30-day readmissions. Secondary outcomes included perioperative metrics, complication rates, and recurrence-free survival. Results After PSM, laparoscopic surgery demonstrated comparable immediate stone clearance rates (81.07% vs. 78.40%, p  = 0.386) but significantly better TO rates (61.54% vs. 46.09%, p  < 0.001) to the open surgery group. The laparoscopic group had reduced blood loss ( p  = 0.016), shorter hospital stays ( p  < 0.001), faster recovery of bowel function ( p  < 0.001), and fewer major complications (13.91% vs. 23.08%, p  = 0.003). Recurrence rates were similar between groups during a median follow-up of 36 months. Hepatic lobe atrophy and biliary strictures were identified as independent risk factors for reduced stone clearance. Conclusion Laparoscopic repeat hepatectomy offers comparable stone clearance rates to open surgery while providing significant advantages in perioperative outcomes, including reduced complications and faster recovery. These findings suggest laparoscopic surgery is a feasible and effective option for recurrent regional hepatolithiasis.

Anti-silencing function protein 1 homolog B (ASF1B) has been implicated in the occurrence and development of cancers. The present work explored the functional role and the expression regulation of ASF1B in pancreatic ductal adenocarcinoma (PDAC). Based on the real-time quantitative PCR (qRT-PCR) and immunohistochemistry (IHC), ASF1B was significantly upregulated in PDAC tissues. High expression of ASF1B was associated with a poor overall survival (OS) and recurrence-free survival (DFS) in the PDAC patients. ASF1B also showed a relatively higher expression in PDAC cells (AsPC-1, PANC-1) when compared with human pancreatic ductal epithelial cells (HPDFe-6). CCK8 and clone formation assay demonstrated that silencing ASF1B impaired the proliferation in PANC-1 and AsPC-1 cells, and Annexin V-PI staining showed an increased level of apoptosis upon ASF1B silencing. ASF1B silencing also suppressed the migration and invasion in PDAC cells, as revealed by Transwell assays. We further showed that miR-24-3p was downregulated in PDAC tissues and cells, which functionally interacted with ASF1B by dual-luciferase reporter assay. miR-24-3p negatively regulated ASF1B expression to modulate the malignant phenotype of PDAC cells. ASF1B shows high expression in PDAC, which promotes the malignancy and EMT process of PDAC cells. miR-24-3p is a negative regulator of ASF1B and is downregulated in PDAC cells. Our data suggest that targeting ASF1B/miR-24-3p axis may serve as an intervention strategy for the management of PDAC.

Colorectal cancer (CRC) is one of the leading causes of cancer-associated mortality. Asiaticoside (AC) exhibits antitumor effects; however, to the best of our knowledge, the biological function of AC in CRC cells remains unclear. Therefore, the aim of the present study was to investigate the effect of AC on CRC cells. In the present study, CCK-8 and colony formation assays were performed to assess the effects of AV on human cRc cell lines (HCT116, SW480 and LoVo). Mitochondrial membrane potential was examined by JC-1 staining. cell apoptosis and cell cycle were monitored by flow cytometry, and the expression of genes was evaluated using RT-qPCR and western blot analysis. Furthermore, the biological effect of AC in vivo was detected using a xenograft mouse model. The findings revealed that 2 [micro]M AC suppressed the proliferation of CRC cells in a time- and dose-dependent manner, but had no adverse effects on normal human intestinal FHC cells at a range of concentrations. AC decreased the mitochondrial membrane potential and increased the apoptosis of CRC cells in a dose-dependent manner. Furthermore, AC induced cell cycle arrest at the G0/G1 phase. AC attenuated I[kappa]B[alpha] phosphorylation in a dose-dependent manner, thereby preventing P65 from entering the nucleus, and resulting in inhibition of the NF-[kappa]B signaling pathway. In addition, AC significantly reduced the expression of CDK4 and Cyclin D1 in a dose-dependent manner, significantly upregulated the activation of caspase-9 and caspase-3, and decreased the Bcl-2/Bax mRNA ratio. Furthermore, treatment with the NF-[kappa]B signaling pathway inhibitor JSH-23 significantly increased the cytotoxicity of AC in cRc cells. Findings of the xenograft mice model experiments revealed that AC significantly inhibited colorectal tumor growth in a dose-dependent manner. Overall, AC suppressed activation of the NF-[kappa]B signaling pathway by downregulating I[kappa]B[alpha] phosphorylation. This resulted in inhibition of CRC cell viability and an increase of cell apoptosis, which may form the basis of AC use in the treatment of patients with CRC

Pure electric construction machinery (PECM) is gradually becoming the mainstream choice for industrial construction. This paper presents a new configuration of an integrated charger for PECM. The proposed configuration employs a nine-switch-converter (NSC) that can achieve charging and traction functions for the target application. In charging mode, the motor is reused as a filter inductor and the NSC is reused as a conventional three-phase PWM rectifier. Data-driven adaptive predictive control (DAPC) based on recursive least squares (RLS) is proposed to cope with the motor’s saturation problem in charging mode. This control has the advantages of excellent robustness and fast dynamic response. Although the initial parameters are derived from the system model in the first sampling cycle, the controller subsequently relies entirely on online identification, which significantly reduces the sensitivity to parameter accuracy and eliminates the need for manual tuning of controller gains. In propulsion mode, the NSC enables independent operation of the two motors. The proposed configuration improves the utilization of devices and motors, which greatly reduces the weight, volume, and cost of the charger. Finally, an experimental platform was built to verify the feasibility and validity of the proposed topology and control algorithm.