The purpose of this study was to elucidate the effects and mechanisms of liraglutide in repairing diabetic corneal epithelial injury, explore its therapeutic advantages, and provide experimental evidence for clinical application.

GLP-1R expression in mouse corneal epithelium was detected by Western blotting, qPCR, and immunofluorescence. Streptozotocin-induced diabetic mice and 30 mM high-glucose (HC) cell models were established. Epithelial wound healing, viability, proliferation, migration, and apoptosis were evaluated by fluorescein staining, TUNEL, CCK-8, EdU, and scratch assay. Bioinformatics was used to screen differentially expressed genes. Reactive oxygen species (ROS), redox indicators, and key proteins in Keap1-Nrf2-ARE, PI3K/Akt, and MAPK pathways were measured. PI3K inhibitors were applied to verify the pathway function, and macrophage polarization and recruitment were further assessed.

GLP-1R was mainly located in the deep basal cells of corneal epithelium. Liraglutide improved cell viability, promoted proliferation and migration, and inhibited apoptosis. It restored redox balance and alleviated oxidative stress via activating Nrf2/HO-1/NQO-1 pathway, and enhanced cell functions through PI3K/Akt pathway; these effects were abolished by PI3K inhibition. Moreover, liraglutide reduced aberrant macrophage infiltration, promoted anti-inflammatory macrophage polarization, and decreased pro-inflammatory factors, which was associated with MAPK pathway downregulation.

Liraglutide exerts multi-mechanism protective effects on diabetic corneal epithelium. It regulates Nrf2 and PI3K/Akt pathways to attenuate oxidative stress and improve cell functions, and modulates macrophage polarization and recruitment via MAPK pathway to relieve inflammation, thereby promoting corneal epithelial injury repair.

The transition from pediatric to adult healthcare is a critical and vulnerable period for adolescents and young adults with type 1 diabetes, often associated with increased risks of acute complications and long-term adverse outcomes. Despite its recognized clinical importance, the concept of "transition readiness" remains inconsistently defined and operationalized in research and practice. This study aimed to clarify the concept using Walker and Avant's method of concept analysis.

A comprehensive literature search was conducted across multiple databases, complemented by reference screening and a targeted Google search for dictionary definitions, and documented using the PRISMA 2020 flow diagram.

Forty-two papers met the inclusion criteria and were analyzed to identify defining attributes, antecedents, consequences, and empirical referents of transition readiness. Four defining attributes emerged: multidimensional maturity; diabetes knowledge and health literacy; self-management competencies; and healthcare system navigation skills. Antecedents included developmental maturation, a gradual shift from parent-led to autonomous care, and engagement in structured transition pathways. Consequences were identified at patient, care-process, and health-system levels, highlighting potential improvements in clinical outcomes and continuity of care. Empirical referents mainly consisted of diabetes-specific readiness tools, though psychometric limitations persist.

Transition readiness appears as a multidimensional, measurable, and actionable construct that promotes safer, higher‑quality transitional diabetes care. A clearer conceptualization of transition readiness can support healthcare professionals in identifying adolescents' preparedness for transfer and planning individualized, developmentally appropriate interventions. Standardized assessment of readiness may improve continuity of care, reduce transition-related risks, and strengthen patient engagement in self-management.

Hemoglobin A1c (HbA1c) is a biomarker for diabetes mellitus. Twin studies suggest substantial heritability, but the molecular basis of non-genetic variation remains unclear. We aimed to explore genetic and epigenetic factors associated with HbA1c through multi-omics analysis of monozygotic twins.

A total of 285 monozygotic and 27 dizygotic twin pairs were enrolled in Japan. Genome-wide single-nucleotide variant (SNV) genotyping, DNA methylation profiling, and RNA sequencing were performed. Based on HbA1c levels, twin pairs were classified as high concordant, low concordant, or discordant. Structural equation modeling estimated heritability, and GWAS, within-pair methylation comparisons, and expression-methylation correlation analyses were conducted.

Our analysis estimated that genetic factors accounted for 68% of the phenotypic variance in covariate-adjusted HbA1c. Within-pair methylation comparisons revealed several suggestive CpG sites associated with HbA1c variation. GWAS revealed one suggestive SNV associated with higher HbA1c and three SNVs potentially associated with susceptibility to HbA1c variation. Genotype-stratified analyses showed exploratory genetic background-dependent methylation changes at CpG sites, some of which correlated with gene expression.

This study estimated the heritability of adjusted HbA1c and revealed preliminary genetic and epigenetic signals in Japanese twins. Further studies are needed to confirm their biological and clinical significance.

Tirzepatide, a dual glucose-dependent insulinotropic polypeptide and glucagon-like peptide-1 receptor agonist, has demonstrated substantial efficacy for weight loss in individuals with overweight and obesity. However, the extent to which this weight reduction affects lean mass remains unclear. This review evaluates current evidence on the impact of tirzepatide on body composition, with a particular focus on changes in lean mass and its proxy measures. A structured literature search identified ten studies, including randomized controlled trials, observational studies and modelling analyses, encompassing diverse populations with and without type 2 diabetes mellitus. Across studies, tirzepatide consistently produced large, dose-dependent reductions in body weight. These reductions were predominantly attributable to fat mass, although decreases in lean mass, fat-free mass or skeletal muscle mass were observed in all studies. The proportion of total weight loss attributable to lean mass generally ranged from 10% to 30%, broadly aligning with expected physiological responses to weight loss. However, substantial heterogeneity in study design, population characteristics, and body composition assessment methods limits the ability to draw definitive conclusions. Most studies relied on indirect measures such as bioelectrical impedance analysis, with limited use of dual-energy X-ray absorptiometry. Importantly, functional outcomes were not assessed, and the clinical significance of observed lean mass reductions remains uncertain. Overall, tirzepatide appears to promote preferential fat loss, though some loss of lean mass occurs. Strategies such as resistance training and adequate protein intake may help mitigate potential adverse effects on muscle mass during treatment.

Trigger finger is a common hand condition frequently treated with open A1 pulley release. Although surgical outcomes are generally favorable, early wound healing disturbances may impair recovery. Evidence regarding the relationship between preoperative vitamin D status and postoperative wound healing in trigger finger surgery remains limited.

This retrospective single-center study included 150 patients undergoing open trigger finger release. Early wound healing status was evaluated at postoperative day 14 using predefined clinical wound criteria. Preoperative serum 25-hydroxyvitamin D [25(OH)D] levels were analyzed as continuous and categorical variables. Functional outcomes were assessed using the Michigan Hand Outcomes Questionnaire (MHQ). Multivariable logistic regression analysis was performed to identify independent predictors of delayed wound healing after adjustment for clinical confounders.

Delayed wound healing occurred in 30 patients (20%). Mean preoperative serum 25(OH)D levels were significantly lower in patients with delayed healing than in those with normal healing (18.2 ± 6.9 vs 29.8 ± 8.5 ng/mL; P < .001). Lower serum 25(OH)D level independently predicted delayed wound healing (odds ratio [OR], 0.92, 95% CI, 0.88-0.96, P < .001), together with higher body mass index, diabetes mellitus, elevated glycated hemoglobin, and advanced Quinnell grade. Patients with normal wound healing demonstrated significantly superior MHQ outcomes at postoperative week 6 and month 3.

Low preoperative serum 25(OH)D level was independently associated with delayed wound healing and poorer functional recovery after open trigger finger release. Preoperative vitamin D status may represent a clinically relevant risk marker requiring further prospective investigation.

Level III Retrospective Comparative Cohort Study.

Melanoma early detection has been widely promoted on the assumption that diagnosing early tumors improves outcomes, but epidemiologic data increasingly challenge this assumption. In fair-skinned populations subjected to intensified surveillance, diagnoses of thin invasive and in situ melanomas have risen sharply without corresponding declines in advanced disease or melanoma-related mortality, a pattern consistent with cancer overdiagnosis. Heightened scrutiny preferentially detects biologically indolent melanocytic proliferations that often share clinical and histologic features with genuinely aggressive tumors, revealing the limitations of the traditional benign-malignant binary when applied to subtle melanocytic lesions. Drawing on historical anatomic pathology, particularly Virchow's concept of tumors as quantitative deviations along a continuum rather than discrete entities, we argue that many early "melanomas" are better understood as risk states than as fully realized malignant disease. The MPATH-Dx framework operationalizes this perspective by mapping melanocytic lesions into probabilistic risk classes linked to recommended management, rather than forcing a categorical melanoma versus nevus distinction. Its updated Version 2.0 simplifies classification and grading of atypia, introduces reproducible cytomorphologic thresholds, and defines a low-risk subset of thin invasive melanomas that may ultimately be reclassified as melanocytic neoplasms with high-grade atypia (low malignant potential). Transitioning from a binary to a risk-based diagnostic paradigm could reduce overdiagnosis and overtreatment, improve communication of uncertainty, and better align clinical, administrative, and legal practices with underlying tumor biology. Such a shift, however, will require reeducation of clinicians, patients, payers, and policymakers, as well as adaptation of coding, registry, and malpractice frameworks to a more nuanced understanding of melanoma risk.

Accurate diagnosis and risk stratification are central for optimal management of patients with melanoma. Current American Joint Committee on Cancer (AJCC) staging systems inadequately stratify patients with clinically meaningful metastatic potential. Manual histopathologic interpretation compounds this limitation, particularly for diagnostically ambiguous lesions at the benign-malignant interface, where concordance is highly variable. This review examines how computational pathology and convolutional neural networks (CNNs) can improve histopathologic diagnosis and risk stratification of melanoma, evaluate current image-based deep learning (DL) approaches, and outline a path toward explainable, multimodal prognostic tools. We review published DL models applied to hematoxylin and eosin whole-slide images (H&E WSIs) for melanoma diagnosis, subtype classification, and survival prediction, and discuss integration with transcriptomic and spatial proteomic data modalities. Computational pathology, informed by deep learning, can reliably identify melanomas at risk of disease recurrence and progression. These inferences can be enhanced by integration of spatial molecular profiling, which can also provide mechanistic explainability to the H&E-based DL models. However, limitations in dataset diversity, external validation, model interpretability, and generalizability across populations and image acquisition protocols currently prevent clinical adoption. Outcome-anchored, multimodal computational pathology pipelines integrating H&E WSIs with spatial multi-omic profiling offer a biologically grounded and scalable framework for personalized risk stratification in stage I-III CM, with potential to standardize diagnosis, discover novel prognostic features, and inform individualized treatment strategies.

Melanoma in skin of color (SOC), defined as Fitzpatrick skin tones IV-VI, presents unique epidemiologic, diagnostic, and therapeutic challenges compared to non-Hispanic White (NHW). Although SOC patients have a lower incidence of melanoma, they experience higher melanoma-specific fatality rates, which may be attributable to later-stage diagnoses, differences in the distribution patterns of melanoma subtypes, and systemic barriers to care. SOC patients, particularly non-Hispanic Black individuals, have a higher proportion of acral lentiginous and mucosal melanomas, which arise in non-sun-exposed areas, carry lower mutational burdens, and respond less favorably to immunotherapy and BRAF/MEK-targeted therapies. These anatomic and biological differences contribute to delayed recognition and reduced treatment efficacy. Concurrently, the rising incidence of melanoma in situ across racial and ethnic groups without corresponding decreases in invasive disease or similarly increased mortality rates raises concern for overdiagnosis. Artificial intelligence-based diagnostic tools show promise in NHW populations but are limited by the underrepresentation of SOC in training datasets and diminished positive predictive value in low-prevalence populations. The estimated number needed to screen to prevent one melanoma death in SOC populations ranges from approximately 46,000 to 201,000, underscoring the challenge of melanoma screening in this group. Suggested solutions and improvements include expanding healthcare access through patient navigators, developing culturally tailored education programs focused on identifying early invasive disease, diversifying AI training datasets to include acral and mucosal melanoma, biobanking rare melanoma subtypes, and decentralizing clinical trials into community settings. A multifaceted approach is essential to addressing overdiagnosis and improving melanoma outcomes in the SOC populations.

Background The current Bosniak classification system (Bosniak Classification version 2019 [Bosniak v2019]) helps predict cancer, but it is unknown whether it also helps predict unfavorable histologic features. Purpose To determine whether Bosniak v2019 and nodule size help predict cancer and unfavorable histologic features. Materials and Methods This retrospective study included Bosniak III or IV cystic renal masses across 10 institutions and three countries (January 2007 to February 2024). Pretreatment renal CT or MRI scans obtained within 1 year of resection were independently reviewed by two radiologists per site. The reference standard was surgical histopathologic findings. Generalized linear mixed models and multivariable analyses were used to estimate the probability of cancer and cancer with unfavorable histologic features by Bosniak class, subclass, and nodule size, adjusted for mass size. Reader agreement was assessed with the Gwet agreement coefficient (ie, AC1) and intraclass correlation coefficient. Results A total of 419 patients with 421 masses (264 male; median age, 59 years [IQR, 50-67 years]) were included. The probabilities of cancer were 74.8% (95% CI: 69.7, 79.4) and 91.0% (95% CI: 88.2, 93.1) for Bosniak classes III and IV, respectively. The probabilities of cancer with unfavorable histologic features were 10.3% (95% CI: 7.4, 14.2) and 32.3% (95% CI: 28.5, 36.4) for Bosniak classes III and IV, respectively. Bosniak IV subclasses were associated with a greater probability of cancer and unfavorable histologic features than were Bosniak III subclasses (P < .001 to P = .006). There was no evidence of a difference in the odds of cancer or unfavorable histologic features within the Bosniak III (P = .56-.995) or IV (P = .10-.50) subclasses. Nodule size was the strongest predictor of cancer (odds ratio [OR], 4.1 per 10 mm; P = .001) and unfavorable histologic features (OR, 1.9 per 10 mm; P < .001). Interrater agreement was substantial for Bosniak class (Gwet AC1, 0.70 [95% CI: 0.63, 0.77]) and good for nodule size (intraclass correlation coefficient, 0.89 [95% CI: 0.85, 0.91]). Conclusion Bosniak v2019 helps predict cancer and cancer with unfavorable histologic features. Nodule size was strongly associated with unfavorable histologic features. © RSNA, 2026 Supplemental material is available for this article.

Hepatocellular carcinoma (HCC) recurrence after liver transplantation (LT) is uncommon but associated with poor outcomes, requiring effective surveillance. We assessed adherence to and effectiveness of a risk-stratified surveillance protocol and compared it with the RETREAT-guided surveillance protocol.

We performed a single-center retrospective review of patients with HCC on explant after LT (2013-2023). Patients were assigned low- or high-risk surveillance based on explant pathology (AJCC tumor stage, grade, and vascular invasion). Protocol performance, modified surveillance strategies, and comparison with RETREAT across multiple cutoffs were assessed.

Among 480 patients, 55 (11.5%) developed recurrence; 23 (41.8%) of these died, with median post-recurrence survival of 14 months. Only 15.8% were fully adherent, with higher adherence in low-risk patients (p<0.001). The protocol appropriately stratified risk (sensitivity 80.0%, specificity 54.1%) and retained substantially higher specificity than RETREAT even at RETREAT's conventional low-risk cutoff (32.0%) despite matching sensitivity; the two systems captured different patients (McNemar's test, p<0.001). Extrahepatic disease at recurrence was more common in high-risk patients (56.8% vs. 18.2%, p = 0.040). Fully adherent patients had earlier recurrence detection (206.4 vs. 666.9 days, p = 0.015). Modified protocols improved adherence but delayed some diagnoses; most low-risk recurrences occurred within 1 year and high-risk recurrences within 2 years.

This protocol effectively stratifies recurrence risk and is more sensitive than the RETREAT-guided surveillance protocol. Modified strategies may improve adherence and reduce cost while maintaining effectiveness, particularly in low-risk patients.