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Rationale: This case report describes a rare and severe presentation of post-traumatic eczema that clinically mimics mammary Paget disease. The case is uniquely significant due to its striking, "bark-like" hyperkeratotic appearance. This severity resulted from a combination of diagnostic delay and iatrogenic factors, specifically, prolonged, unsupervised postoperative application of povidone-iodine in a vulnerable elderly patient. This highlights the diagnostic challenge of differentiating between benign and malignant eczematous breast lesions.

Patient concerns: An 81-year-old female with limited mobility developed a severe, crusted, lichenified eczematous lesion in her left breast following a surgical procedure. The condition persisted for 6 months without a correct diagnosis, resulting in a markedly severe presentation.

Diagnoses: The initial clinical differential diagnosis was mammary Paget disease. However, histopathological examination confirmed a definitive diagnosis of post-traumatic eczema with a clear iatrogenic link to prolonged contact with povidone-iodine.

Interventions: The patient was treated with topical corticosteroids and an oral antihistamine.

Outcomes: The patient responded well to treatment, with complete resolution of the skin lesion. At the 1-year follow-up, there were no signs of recurrence.

Lessons: This case underscores the critical importance of histopathology in differentiating inflammatory dermatoses of the breast from malignancies, as the clinical appearance can be highly misleading. Furthermore, it serves as a crucial cautionary example of the need for diligent and appropriate postoperative wound care management, particularly in vulnerable patient populations, to prevent severe iatrogenic complications.

Background: Esophageal cancer exhibits peak incidence in Asia and Africa, representing the sixth most common malignancy and seventh leading cause of global cancer mortality. Esophageal squamous cell carcinoma (ESCC) constitutes 90% of esophageal cancer cases. The European Medicines Agency approved programmed death 1 (PD-1) inhibitors plus chemotherapy as a first-line treatment for high PD-1-expressing ESCC.

Methods: We systematically searched randomized controlled trials of PD-1 or PD-L1 inhibitors as first-line treatment from PubMed, Embase, and Cochrane Library. The following outcomes were combined: overall survival, progression-free survival, objective response rate, and treatment-related adverse events (TRAEs). Bias risk was rigorously evaluated using the Cochrane Risk of Bias Tool. RevMan 5.3 and R Studio (Boston) were utilized for data synthesis in this meta-analysis, with sensitivity analyses comparing fixed- and random-effects models to reinforce findings.

Results: A total of 4702 patients (PD-1 inhibitors plus chemotherapy: 2529; chemotherapy: 2173) were enrolled in 8 randomized controlled trials. Compared with conventional chemotherapy, first-line PD-1 inhibitors plus chemotherapy significantly improved the overall survival (hazard ratio = 0.68, 95% confidence interval (CI): 0.63-0.74; P < .00001) and objective response rate (relative risk [RR] = 2.03, 95% CI: 1.80-2.29; P < .00001) of advanced ESCC patients. Moreover, PD-1 inhibitor-based therapy provided benefits in progression-free survival (hazard ratio = 0.62, 95% CI: 0.58-0.66; P < .00001). But PD-1 inhibitors were not associated with statistically lower incidences of TRAEs and grade 3 to 5 TRAEs. In subgroup analyses, except the limited benefit observed in the programmed death-ligand 1 (PD-L1) combined positive score < 1 subgroup, none of the following factors significantly influenced the efficacy of PD-1 inhibitor therapy: advanced age, metastatic status, number of metastatic organs, or presence of liver metastases.

Conclusion: The combination of PD-1 inhibitors with chemotherapy demonstrates superior efficacy as first-line therapy for advanced esophageal squamous cell carcinoma. Both elderly patients and those with metastatic involvement derive universal benefit without increased adverse risks. However, patients with PD-L1 combined positive score < 1 may experience restricted clinical benefits. Thus, more precise predictive markers are required to stratify potential responders, enabling broader patient populations to derive benefits from PD-1 inhibitor-chemotherapy regimens.

This study aims to investigate the mediating role of the systemic immune-inflammation index (SII) in the relationship between obesity-related glycolipid indices and in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) outcomes in women with polycystic ovary syndrome (PCOS). A total of 598 women diagnosed with PCOS according to the Rotterdam criteria and undergoing their first IVF/ICSI cycle at the Reproduction Medicine Center, Taizhou People's Hospital Affiliated with Nanjing Medical University, Jiangsu, China between January 2021 and December 2023 were included. Key exposures included obesity-related metabolic indices (e.g., triglyceride to high-density lipoprotein ratio [TG/HDL], homeostasis model assessment of insulin resistance [HOMA-IR]) and the SII. The primary outcome was the live birth rate per initiated cycle. Associations were evaluated using multivariate generalized linear models, and causal mediation analysis was performed to quantify the proportion of the effect mediated by the SII. Higher TG/HDL, total cholesterol to HDL ratio (TC/HDL), low-density lipoprotein to HDL ratio (LDL/HDL), and HOMA-IR levels showed dose-dependent negative correlations with oocyte yield, fertilization rate, embryo quality, and live birth rate (all P < .05). An elevated SII was an independent predictor of a reduced live birth rate (β = -0.08, P = .008) and mediated 8.8% to 10.7% of the adverse effects of dyslipidemia (via TC/HDL and LDL/HDL) on live birth. This study shows that the SII is statistically linked to and potentially mediates the connection between metabolic dysfunction and poor IVF/ICSI outcomes in PCOS. Integrated strategies targeting both metabolism and inflammation may optimize fertility success in this population.

This study aimed to investigate the association between statin use and the risk of major adverse cardiovascular events (MACE) among patients with chronic kidney disease (CKD) complicated by cardiomyopathy, and to further evaluate the effects of different statin doses and types on cardiovascular outcomes. This single-center retrospective case-control study included 765 patients with CKD and cardiomyopathy hospitalized between January 2016 and February 2025. Patients were categorized according to statin exposure and atorvastatin-equivalent doses. Multivariable logistic regression and subgroup analyses were performed to evaluate the association between statin use and MACE, adjusting for demographic, clinical, and laboratory confounders. A total of 765 eligible patients with CKD and cardiomyopathy were included, comprising 255 cases and 510 controls. Overall, the prevalence of statin use was 59.6%, which was significantly lower in the case group than in controls (48.2% vs 65.3%, P <.001). Univariate analysis showed that statin use was significantly associated with a reduced risk of MACE (unadjusted odds ratio = 0.52, 95% CI: 0.38-0.70, P <.001). Dose-response analysis revealed that the incidence of MACE decreased progressively across nonuser, low-to-moderate, and high-dose groups (41.1% vs 28.7% vs 20.2%, P for trend = .002). In multivariable logistic regression adjusting for age, sex, body mass index, diabetes and hypertension control, estimated glomerular filtration rate (eGFR), and β-blocker use, regular statin therapy remained independently associated with lower MACE risk, with the strongest benefit observed in the high-dose group (adjusted odds ratio = 0.47, 95% CI: 0.31-0.72, P = .001). Subgroup analyses demonstrated consistent protective effects of statin therapy across CKD stages and cardiomyopathy etiologies (ischemic vs nonnon; P for interaction >.05). In patients with CKD and cardiomyopathy, regular statin therapy significantly reduces the incidence of MACE in a dose-dependent manner. The cardioprotective effect remains consistent across different CKD stages and cardiomyopathy etiologies. Statin treatment is generally well tolerated, with only mild, reversible elevations in liver enzymes or muscle enzymes and no serious adverse reactions.

Artificial intelligence (AI) can transform osteoporosis (OP) screening, but its application in high-risk, complex populations like postmenopausal women with chronic kidney disease remains limited. We sought to develop and interpret an explainable AI model specifically for identifying low bone mass (LBM) or OP in this vulnerable group. Using data from the National Health and Nutrition Examination Survey (2005-2018), a nationally representative cross-sectional study, we developed and compared 8 AI models. For the purpose of algorithm development and comparison, the complex survey weights of National Health and Nutrition Examination Survey were not applied. Model performance was assessed by AUC-ROC, recall, precision, F1 score, and Brier score. Interpretability was achieved using SHapley Additive exPlanations, local interpretable model-agnostic explanations (LIME) and generalized additive models to identify key features and their nonlinear interactions. The multilayer perceptron model emerged as the most effective (AUC: 0.72; Precision: 0.84). Interpretation revealed that weight, age, estimated glomerular filtration rate (eGFR), age when heaviest weight, total cholesterol (TC), and age at last menstrual period were top predictors. Critically, we discovered that the relationship between weight and LBM/OP is nonlinear, and that complex interactions exist between weight, age, and age when heaviest weight. This study presents a well-validated and interpretable AI model for LBM/OP screening in postmenopausal women with chronic kidney disease. By moving beyond a "black-box" approach, we provide a tool with potential clinical utility and novel pathophysiological insights, though its implementation requires further external validation in prospective, independent cohorts. This work underscores the potential of AI to enhance understanding of OP risk and highlights the necessity of personalized risk evaluation in this population.

Background: Acute appendicitis remains one of the most common surgical emergencies, yet its diagnosis continues to pose challenges due to overlapping clinical presentations and variable imaging findings. Artificial intelligence (AI) has recently emerged as a promising tool to enhance diagnostic accuracy, assist clinical decision-making, and potentially reduce negative appendectomy rates.

Methods: A systematic literature search was conducted in PubMed, Scopus, and Web of Science databases for English-language studies published between January 2015 and September 2025. Search terms included "artificial intelligence," "machine learning," "deep learning," and "acute appendicitis." Eligible studies focused on diagnostic model development or validation using clinical, laboratory, or imaging data. Extracted parameters included study design, AI algorithm, input modality, and diagnostic performance metrics (area under the curve [AUC], accuracy, sensitivity, specificity, and predictive values). Because of study heterogeneity, a qualitative synthesis was performed instead of a meta-analysis.

Results: Clinical- and laboratory-based AI models frequently outperformed traditional scoring systems, demonstrating high negative predictive value and AUCs ranging from 0.85 to 0.93. Deep learning models applied to computed tomography imaging achieved the highest diagnostic accuracy, often exceeding junior radiologists, with reported AUCs up to 0.96. Ultrasound-based AI frameworks improved operator-independent detection, while magnetic resonance imaging-AI studies remain limited but promising. Hybrid multimodal models integrating clinical, laboratory, and imaging data achieved balanced performance (AUC ≈ 0.90-0.93) and hold the greatest translational potential. However, most studies were retrospective, single-center, with insufficient external validation and inconsistent methodological reporting.

Conclusion: AI demonstrates substantial potential to enhance diagnostic workflows for suspected appendicitis, particularly for identifying complicated cases and minimizing unnecessary imaging or surgery. Future research should emphasize multicenter prospective validation, standardized data annotation, explainable AI methods, and seamless integration into electronic health records and picture archiving systems.

This study aimed to explore the factors influencing corneal edema after phacoemulsification combined with intraocular lens implantation in elderly diabetic cataract (DC) patients and to establish a predictive model. A retrospective analysis was conducted on 300 DC patients who underwent phacoemulsification combined with intraocular lens implantation in our hospital between June 2022 and October 2023. Patients were randomly divided into a modeling group (n = 210) and a validation group (n = 90) in a 7:3 ratio. The modeling group was further divided into corneal-edema group (n = 57) and non-corneal-dema group (n = 153) based on the occurrence of postoperative corneal edema. Univariate and binary logistics regression analyses were employed to identify the influencing factors of postoperative corneal edema in DC patients. A predictive model was constructed using SPSS, and the model's application value was evaluated using receiver operating characteristic curve, calibration curve, and decision curve analysis in R language. There were no statistically significant differences in terms of general information between the modeling group and validation group (P > .05). Significant differences were observed in the comparison of diabetes duration, preoperative endothelial cell density, postoperative corneal endothelial cell loss, hypertension, surgery duration, and age between the corneal-edema group and non-corneal-edema group (P < .05). The results of binary logistics regression analysis showed that diabetes duration, preoperative endothelial cell density, postoperative corneal endothelial cell loss, hypertension, surgery duration, and age were influencing factors for postoperative corneal edema in DC patients (P < .05). The calibration curves of the model in the modeling group and validation group exhibited a slope close to 1, showing good consistency between predicted and actual risks. Receiver operating characteristic analysis results indicated an area under the curve of 0.96 and a standard error of 0.021 (95% confidence interval: 0.872-0.994) in the modeling group and an area under the curve of 0.94 and a standard error of 0.024 (95% confidence interval: 0.867-0.966) in the validation group. Decision curve analysis curve analysis demonstrated that the model had good application efficacy and clinical benefits in both groups. The predictive model based on influencing factors demonstrated good predictive performance and warrants further validation.

Distortions in gut microbiota homeostasis may contribute to abnormal bone metabolism and osteoporosis. This study aimed to examine the association between the dietary index for gut microbiota (DI-GM) and osteoporosis among U.S. adults, identify key dietary components, and develop a clinically applicable risk prediction model incorporating dietary and clinical factors. We conducted a cross-sectional analysis of 9065 adults. DI-GM was calculated from the dietary intake data, and osteoporosis was the primary outcome. Weighted multivariable logistic regression models were used to evaluate the association between DI-GM and osteoporosis, and odds ratios (ORs) with 95% confidence intervals (CIs) were calculated. Additionally, least absolute shrinkage and selection operator regression was applied to identify critical dietary components. Finally, a prediction model integrating clinical and nutritional factors was developed and evaluated using receiver operating characteristic curve analysis. Each unit increase in DI-GM reduced the risk of osteoporosis by 10% (adjusted OR = 0.90, 95% CI: 0.83-0.99). A higher intake of DI-GM-beneficial components was also associated with reduced osteoporosis risk (OR: 0.88; 95% CI: 0.79-0.98). Least absolute shrinkage and selection operator highlighted soy, fermented dairy, and processed meat as the most influential dietary factors; higher intake of soybeans and fermented dairy was associated with a decreased risk of osteoporosis, whereas higher processed meat intake was associated with an increased risk. The prediction model demonstrated good discrimination (area under the curve = 0.757). A higher DI-GM was associated with a reduced risk of osteoporosis, and specific dietary components may drive this association. The proposed model may support individualized dietary risk stratification and prevention strategies for populations with an elevated osteoporosis risk.

Rationale: Although vitamin B12 deficiency is a well-known complication following total gastrectomy, subacute combined degeneration (SCD) rarely develops beyond 5 years postoperatively. Eight-year latency cases are rarely reported, especially when combined with chronic alcohol use, which accelerates neurologic decline and worsens prognosis. This report aims to present a rare case of delayed SCD after total gastrectomy in a patient with chronic alcoholism, emphasizing the importance of lifelong vitamin B12 monitoring in high-risk individuals.

Patient concerns: A 49-year-old man presented with progressive quadriparesis and sensory disturbances eight years after total gastrectomy for gastric cancer. He also had a significant history of chronic alcohol consumption, raising suspicion for multifactorial vitamin B12 deficiency.

Diagnoses: Laboratory tests revealed severe vitamin B12 deficiency, pancytopenia, and elevated homocysteine and methylmalonic acid levels. Cervical spine magnetic resonance imaging revealed T2-weighted hyperintensity in the dorsal columns, consistent with SCD.

Interventions: The patient received vitamin B12 supplementation and underwent a comprehensive rehabilitation program aimed at improving motor function.

Outcomes: After rehabilitation, he regained independent ambulation using a cane.

Lessons: This case shows an unusually prolonged latency of SCD after total gastrectomy and is notable for the coexistence of 2 established risk factors: gastrectomy and chronic alcoholism. It emphasizes the importance of lifelong surveillance of vitamin B12 status to prevent avoidable and potentially irreversible neurologic complications in high-risk populations.

This study investigates the psychological status of patients before undergoing dynamic hip screw fixation and analyzes the association between the preoperative psychological status and the occurrence of postoperative stress responses. A retrospective study was conducted on all patients who underwent dynamic hip screw fixation for intertrochanteric femoral fractures between January 2022 and March 2024. Baseline data, preoperative psychological status, and perioperative information were collected. Univariate and multivariate logistic regression analyses were performed to identify risk factors for postoperative stress responses following dynamic hip screw fixation. Postoperative stress occurred in 16.07% (9/56) of patients who underwent dynamic hip screw fixation. In the postoperative stress group, diabetes (P = .006) and preoperative psychological status (P = .001) showed statistically significant differences compared to the non-stress group. Univariate logistic regression analysis identified diabetes (P = .011) and preoperative psychological status (P = .001) as risk factors for postoperative stress. Multivariate regression analysis revealed that preoperative psychological status (P = .001) was an independent risk factor for the development of postoperative stress responses. Preoperative psychological status is an independent risk factor for postoperative stress following dynamic hip screw fixation responses.