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.

Anti-CD20 monoclonal antibodies for the treatment of refractory nephrotic syndrome （RNS） in children. The first- generation rituximab is the most widely used in clinical practice； it shows definite efficacy in children with RNS， is recommended by guidelines， particularly for achieving a high remission rate in minimal change nephrosis， and can significantly reduce the cumulative use of glucocorticoids and immunosuppressants. The second-generation ofatumumab has potential as an alternative treatment for patients who are intolerant or resistant to rituximab， while the third-generation obinutuzumab has shown efficacy in complex cases such as rituximab resistance or post-transplant recurrence. However， there is still controversy regarding the optimization of rituximab treatment dosage and whether ofatumumab and obinutuzumab offer greater advantages than rituximab for the treatment of RNS in children. The most common adverse reaction induced by anti-CD20 monoclonal antibodies is infusion reactions， and long-term adverse events mainly include increased risks of sustained immunosuppression and infections. Rituximab has significant economic advantages for the treatment of RNS， but additional pharmacoeconomic research based on China’s healthcare environment is needed to evaluate the cost-effectiveness of ofatumumab and obinutuzumab in this population. Given that the current use of ofatumumab and obinutuzumab in this field is considered off-label use， clinical application should only proceed after a rigorous evaluation of the patient’s benefits and risks.

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.

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: 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: 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 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.