Neonatal diabetes mellitus （NDM） is a rare monogenic disorder primarily caused by insufficient insulin secretion resulting from mutations in the KCNJ11 and ABCC8 genes. Sulfonylureas， represented by glibenclamide， have become the standard therapy for this type of NDM by precisely closing the mutated ATP-sensitive potassium channels in pancreatic β cells， thereby restoring insulin secretion. Clinical studies confirm that sulfonylureas enable over 90% of patients to successfully transition from insulin to oral treatment， achieving long-term stable glycemic control and improving neurological outcomes to a certain extent. In terms of safety， severe hypoglycemia induced by sulfonylureas is relatively rare and gastrointestinal reactions are mild； moreover， sulfonylureas show good long-term tolerability， and have no adverse effects on child growth and development. In the future， by further refining the full-chain management pathway of “rapid genetic diagnosis-early intervention-specialized dosage forms-long-term follow-up”， the clinical application of sulfonylureas is expected to provide NDM patients with an optimized treatment regimen and maximize their health benefits.

Metastatic dissemination is the major cause of death from breast-cancer (BC). Fusobacterium nucleatum (F.n) is widely enriched in BC and has recently been identified as one of the high-risk factors for promoting BC metastasis. Here, with an experimental model, we demonstrated that intratumoral F.n induced BC aggressiveness by transcriptionally activating Epithelial-mesenchymal transition-associated genes. Therefore, the F.n may be a potential target to prevent metastasis. Given the fact that cancer-associated fibroblasts (CAFs) are abundant in BC and located near blood vessels, we report an optogenetic system that drives CAF to in situ produce human antibacterial peptide LL37, with the characteristics of biosafety and freely intercellular trafficking, for depleting intratumoral F.n, leading to a 72.1% reduction in lung metastatic nodules number without affecting the balance of the systemic flora. Notably, mild photothermal treatment was found that could normalize CAF, contributing to synergistically inhibiting BC metastasis. In addition, the system can also simultaneously encode a gene of TNF-related apoptosis-inducing ligand to suppress the primary tumor. Together, our study highlights the potential of local elimination of tumor pathogenic bacteria to prevent BC metastasis.

Alzheimer's disease (AD) is characterized by cognitive and functional deterioration, with pathological features such as amyloid-beta (Aβ) aggregates in the extracellular spaces of parenchymal neurons and intracellular neurofibrillary tangles formed by the hyperphosphorylation of tau protein. Despite a thorough investigation, current treatments targeting the reduction of Aβ production, promotion of its clearance, and inhibition of tau protein phosphorylation and aggregation have not met clinical expectations, posing a substantial obstacle in the development of drugs for AD. Recently, artificial intelligence (AI), computational biology (CB), and systems biology (SB) have emerged as promising methodologies in AD research. Their capacity to analyze extensive and varied datasets facilitates the identification of intricate patterns, thereby enriching our comprehension of AD pathology. This paper provides a comprehensive examination of the utilization of AI, CB, and SB in the diagnosis of AD, including the use of imaging omics for early detection, drug discovery methods such as lecanemab, and complementary therapies like phototherapy. This review offers novel perspectives and potential avenues for further research in the realm of translational AD studies.

Lymphoma is a highly heterogeneous malignancy characterized by complex molecular regulatory mechanisms that result in significant differences in aggressiveness and prognosis across its subtypes.Gene copy number alteration(CNA)analysis,an emerging technology,has become a pivotal tool in the precision re-search and management of lymphoma.By detecting DNA deletions,amplifications,and chromosomal copy number changes,CNA analysis addresses the limitations of traditional cytogenetic techniques,enhances the ac-curacy of subtype classification,and aids in evaluating tumor heterogeneity and disease progression.This re-view provides a comprehensive summary of CNA detection methods and their applications in lymphoma,with a focus on recent advancements in the field.It offers a comparative analysis of CNA detection techniques and discusses their role in precision diagnosis,subtype classification,monitoring disease progression,predicting therapeutic resistance,and assessing prognosis.Additionally,the review explores the potential applications of CNA analysis in uncovering molecular regulatory mechanisms,optimizing therapeutic strategies,and impro-ving patient survival outcomes.

BackgroundIn recent years， the prevalence of obstructive sleep apnea （OSA） is escalating in China， leading to a serious disease burden. However， previous studies on the influencing factors of OSA， such as obesity and sleep position， were mostly cross-sectional studies. This approach inherently hinders the identification of dynamic interaction mechanism among multiple variables， consequently obstructing the formulation of individualized intervention strategies. ObjectiveTo investigate the differences in body mass index （BMI）-oxygen saturation-sleep position-heart rate variability （HRV） network models between OSA and non-OSA populations， thereby offering a reference for the early detection and management of OSA. MethodsA total of 384 adult participants undergoing sleep monitoring at the Affiliated Hospital of Southwest Medical University from July 12， 2022 to October 11， 2023 were included. Subjects were categorized into OSA group （n=203） and control group （n=181） based on an apnea-hypopnea index （AHI） threshold of 5 events per hour. Subsequently， BMI-oxygen saturation-sleep position-HRV networks were constructed and compared between two groups. ResultsThere was no significant difference in the overall edge weight （P=0.55） and overall strength （P=0.28） of the network model between control group and OSA group. Notable differences emerged in both the node connection strength （e.g.， minimum oxygen saturation with BMI， sleep in prone position， and mean RR interval） and node centrality indices （mean oxygen saturation， minimum oxygen saturation， AHI in upright position， AHI in right lateral position and mean heart rate） within the two network models （P<0.05）. ConclusionSignificant differences are observed between the non-OSA and OSA populations in specific factors， including sleep position， heart rate and oxygen saturation.

Objective: Previous studies have discovered a correlation between cigarette smoking and cortical thickness and surface area, but the causal relationship remains unclear. The objective of this investigation is to scrutinize the causal association between them. Methods: To derive summary statistics from a genome-wide association study (GWAS) on cortical thickness, surface area, and four smoking behaviors: 1) age of initiation of regular smoking (AgeSmk); 2) smoking initiation (SmkInit); 3) smoking cessation (SmkCes); 4) cigarettes per day (CigDay). Linkage disequilibrium score regression (LDSC) was employed to examine genetic association analysis. Furthermore, for traits with significant genetic associations, Mendelian randomization (MR) analyses were conducted. Results: The LDSC analysis revealed nominal genetic correlations between AgeSmk and right precentral surface area, left caudal anterior cingulate surface area, left cuneus surface area, left inferior parietal surface area, and right caudal anterior cingulate thickness, as well as between CigDay and left caudal middle frontal surface area, between SmkCes and left entorhinal thickness, and between SmkInit and left rostral anterior cingulate surface area, right rostral anterior cingulate thickness, and right superior frontal thickness (rg=-0.36–0.29, p<0.05). MR analysis showed a unidirectional causal association between left caudal middle frontal surface area and CigDay (βIVW=0.056, pBonferroni=2×10-4). Conclusion Left caudal middle frontal surface area has the potential to serve as a significant predictor of smoking behavior.

Objective To analyze the relationship of red blood cell distribution width(RDW)and blood lipid metabolism indicators with carotid atherosclerotic plaque stability in elderly patients with acute cerebral infarction(ACI).Methods A total of 110 elderly ACI patients admitted in our hospital from March 2021 to November 2024 were retrospectively recruited,and according to their carotid plaque characteristics,they were divided into stable plaque group(48 cases)and unstable plaque group(62 cases).The RDW,and levels of TC,TG,HDLC,LDL-C and homocysteine(Hcy)were detected.Results The unstable plaque group had significantly higher levels of RDW,TC,TG,LDL-C and Hcy,but lower HDL-C level than the stable plaque group(P＜0.01).RDW,TC,TG,HDL-C,LDL-C and Hcy were the influencing factors of carotid atherosclerotic plaque stability in elderly ACI patients(P＜0.05,P＜0.01).ROC curve analysis suggested that the AUC value of combined detection of RDW,TC,TG,HDL-C,LDL-C and Hcy in evaluating the stability of carotid atherosclerotic plaque was 0.940(95％CI:0.898～0.983),and the combination had bet-ter efficiency than single indicator detection(P＜0.05).Conclusion RDW and blood lipid metabo-lism indicators are associated with the stability of carotid atherosclerotic plaque in elderly ACI patients,and they can be used as biochemical evaluation indicators for the stability.

Objective:To investigate the clinicopathological characteristics, diagnosis, origin, and prognosis of primary ovarian mesonephric-like adenocarcinoma.Methods:A total of 17 cases of primary ovarian mesonephric-like adenocarcinoma diagnosed at the Obstetrics and Gynecology Hospital of Fudan University and Jiaxing Maternal and Child Health Care Hospital between January 2018 and September 2024 were included in this study. Histopathological sections were retrospectively reviewed, and clinicopathological data were systematically analyzed. Immunohistochemical analysis, molecular profiling, and clinical follow-up were performed to further characterize the cases.Results:The patients′ age was (57.1±9.3) years. Tumor involvement included 1 bilateral case, 9 left-sided cases, and 7 right-sided cases. Nine cases originated from endometrioid cysts, and 8 cases exhibited coexisting tumor components of other types. Gross examination revealed gray-yellow solid masses or solid components within cysts. Microscopically, the tumors displayed diverse architectural patterns, including papillary, glandular, cystic, tubular, and solid structures, with eosinophilic secretions within glandular lumens and mild to moderate nuclear atypia. Immunohistochemically, the tumors showed variable expression of TTF1, GATA3, CD10, and Calretinin. ER and PR were focally positive in only 2 cases, while others were negative. All cases demonstrated intact DNA mismatch repair proteins expression and wild-type p53 staining patterns. Molecular analysis performed in 10 cases identified pathogenic KRAS mutations in all tested samples. During a follow-up period of 1 to 75 months, 5 cases had recurrence, 1 patient remained alive with disease, and no disease-related death was reported.Conclusions:Ovarian mesonephric-like adenocarcinoma is an aggressive malignancy with a high potential for early recurrence and metastasis. Its frequent association with endometriosis and coexistence with other Müllerian tumors suggest a potential Müllerian origin. The tumor′s diverse morphological spectrum and common admixture with other tumor types often pose diagnostic challenges, making it difficult to distinguish from other gynecological malignancies. Therefore, accurate diagnosis of ovarian mesonephric-like adenocarcinoma is crucial for appropriate clinical management and prognostication.

Objective:To analyze the changes in the incidence trend of liver cancer in Taizhou of Jiangsu Province, from 2012 to 2020 and provide reference for tumor prevention and control and management.Methods:Liver cancer incidence data from 2012 to 2020 were extracted from the Taizhou Center for Disease Control and Prevention's tumor registry system. Demographic data were used to calculate the crude incidence rate, age-standardized incidence rate (ASIR), Chinese age-standardized incidence rate (CASIR; based on China's 2010 standard population), and world age-standardized incidence rate (WASIR; based on Segi's world standard population). The Joinpoint regression model was applied to identify inflection points in liver cancer incidence trends during 2012-2020, and annual percentage change (APC) with average annual percentage change (AAPC) were calculated.Results:In 2020, the crude incidence ratio (CIR) of liver cancer in Taizhou was 34.6 per 100 000, with CASIR and WASIR at 19.6 per 100 000 and 14.9 per 100 000, respectively. From 2012 to 2020, the male-to-female ratio of new liver cancer cases was 2.94∶1 (10 455 males vs. 3 559 females), with male incidence consistently higher than female. Overall liver cancer incidence in Taizhou initially increased and then decreased after 2017 (2012-2017: APC=6.4%, P=0.014; 2017-2020: APC=-9.5%, P=0.035), peaking at a CASIR of 26.2 per 100 000 in 2017. The trend in male incidence mirrored the overall pattern, rising before 2017 and declining thereafter (2012-2017: APC=6.2%, P=0.005; 2017-2020: APC=-9.0%, P=0.016). Female incidence remained relatively stable (2012-2016: APC=11.0%, P=0.054; 2016-2020: APC=-6.5%, P=0.130). Conclusions:Liver cancer incidence in Taizhou increased before 2017 and declined thereafter, with 2017 as the turning point. Amid population aging, liver cancer remains a persistent public health challenge requiring sustained attention.

Objective:To develop PixelSIFT-UNet, a novel semantic segmentation model that integrates deep learning with scale-invariant feature transform (SIFT) algorithm to improve the segmentation accuracy of oral mucosal lesions.Methods:This investigation utilized 838 standard clinical white light images of oral mucosal diseases acquired from January 2020 to December 2022 at the Stomatology Hospital Zhejiang University School of Medicine. Randomization was achieved through Python′s random.seed function implementation. The random sample function was subsequently applied for sampling distribution. The dataset was stratified into three subsets with a 6∶2∶2 ratio: training ( n=506), validation ( n=166), and testing ( n=166). Lesion boundaries were annotated using Labelme software, and a PixelSIFT-UNet-based deep learning model was developed with VGG-16 and ResNet-50 backbone networks. Model parameters were optimized using the validation set, and performance metrics [including Dice coefficient, mean intersection over union (mIoU), mean pixel accuracy (mPA), and Precision] were assessed on the test set. The model′s performance was benchmarked against conventional semantic segmentation frameworks (U-Net and PSPNet). Results:The developed PixelSIFT-UNet model could achieve precise segmentation of three common oral mucosal lesions: oral lichen planus, oral leukoplakia, and oral submucous fibrosis. Utilizing VGG-16 as the backbone network, the model achieved Dice coefficient, mIoU, mPA, and Precision values of 0.642, 0.699, 0.836, and 0.792, respectively. Implementation with ResNet-50 backbone network yielded metrics of 0.668, 0.733, 0.872 and 0.817, demonstrating significant improvements across all performance indicators compared to conventional U-Net model (relevant metrics: 0.662, 0.717, 0.861 and 0.809) and PSPNet model (relevant metrics: 0.671, 0.721, 0.858 and 0.813).Conclusions:The proposed PixelSIFT-UNet architecture demonstrates superior performance in oral mucosal lesion segmentation tasks, surpassing conventional semantic segmentation models and providing robust quantitative improvements in segmentation accuracy.