Purpose: The aim of this study is to determine the impact of solute carrier family 3 member 2 (SLC3A2) on the malignant phenotype of gliomas and its role in regulating ferroptosis sensitivity. Materials and Methods: The malignant phenotype of glioma was assessed by cell proliferation assay, colony formation assay, EdU assay, wound healing, and Transwell experiments. We further validated the impact of reduced SLC3A2 expression on the sensitivity to ferroptosis in glioma cells through Cell Counting Kit-8 assays, flow cytometry, western blotting, and transmission electron microscopy. Western blot was used to explore how SLC3A2 affects glioma sensitivity to ferroptosis through the AKT/NF-E2-related factor 2 (NRF2)/glutathione peroxidase 4 (GPX4) axis. By establishing a subcutaneous xenograft tumor model in BALB/c-nude mice, we investigated the growth of tumors following the knockout of SLC3A2 in glioma cells. Results: Downregulation of SLC3A2 suppressed the malignant phenotype of glioma by blocking the cell cycle and epithelial-mesenchymal transition processes. On the other hand, loss of SLC3A2 not only downregulated SLC7A11 but also prevented the activation of the AKT/NRF2/GPX4 axis. These lead to increased accumulation of reactive oxygen species and lipid peroxides, ultimately enhancing the susceptibility of glioma to ferroptosis. Conclusion Our findings suggest that SLC3A2 is an oncogene in gliomas, promoting their occurrence and development. It plays a critical role in ferroptosis resistance through the AKT/NRF2/GPX4 axis.

Objective To investigate the effect of intravenous lidocaine on postoperative fatigue syndrome(POFS)in patients undergoing laparoscopic resection for gastric carcinoma.Methods A total of 80 patients who underwent elective laparoscopic resection for gastric carcinoma at Xuzhou Central Hospital between September 2023 and June 2024 were enrolled.Inclusion criteria included age 18～75 years,ASA physical status classificationⅠ～Ⅲ,body mass index(BMI)of 18.5～27.9 kg/m2,preoperative Christensen score≤4,and estimated operation time≤4 hours.Patients were randomly allocated into either the lidocaine group(Group L)or the saline group(Group C)using a random number table,with 40 patients in each group.Group L received an intravenous infusion of lidocaine at a dose of 1.5 mg·kg?1 over 15 minutes,initiated 30 minutes before anesthesia induction.If no adverse reactions occurred,lidocaine was maintained at a rate of 1.5 mg/(kg·h)throughout the surgery until its conclusion.Group C received an equivalent volume of normal saline administered in the same manner.The Christensen score and Visual Analogue Scale(VAS)scores were recorded on postoperative days 1,3,5,and 7,and the time-weighted average(TWA)of the Christensen score was calculated.Postoperative inflammatory markers were measured,and additional outcomes including extubation time,post-anesthesia care unit(PACU)stay duration,postoperative nausea and vomiting(PONV),consumption of rescue analgesics,time to first flatus and defecation,and length of hospital stay were also documented.Results Compared with Group C,the TWA of the Christensen score in Group L decreased by 0.44 points(95％CI:0.11～0.76;P<0.05).The VAS scores were significantly lower in Group L on postoperative days 1 and 3(P<0.05).Levels of IL-6 and TNF-α at the end of surgery and 24 hours after surgery were also lower in Group L(P<0.05).The time to first flatus and defecation was significantly shorter in Group L(P<0.05).There were no significant differences between the two groups regarding extubation time,PACU stay duration,incidence of PONV,postoperative consumption of remedial analgesic drugs,or length of hospital stay(P>0.05).Conclusion Intravenous lidocaine may improve POFS in patients following laparo-scopic resection for gastric carcinoma by attenuating inflammatory responses,alleviating pain,and facilitating gastrointestinal function recovery,while maintaining a favorable safety profile.

With the rapid advancement of artificial intelligence (AI) technology, particularly breakthroughs in large models, AI applications in healthcare are becoming increasingly widespread.Ophthalmology, as a critical branch of medicine, has emerged as a clinical specialty with comprehensive AI research and applications, especially in the AI-driven analysis of ophthalmic imaging.Currently, AI in ophthalmology is undergoing profound transformation driven by large model technology.AI large models, with their robust data comprehension and multimodal interaction capabilities, offer new possibilities for the diagnosis, treatment, and health education of ophthalmic diseases, powerfully propelling the evolution of current AI paradigms in ophthalmology.This article explores the novel opportunities brought by the extensive application of AI large models in ophthalmic research and practice, and provides recommendations for ophthalmologists and insights for industry development.

In recent years, generative artificial intelligence (AI) technologies have achieved remarkable progress in the early screening, risk prediction, disease progression assessment, and clinical trial design of glaucoma.Using advanced algorithms, such as generative adversarial networks, variational autoencoders, and diffusion models, researchers have synthesized high-quality structural images of the optic disc, macular region, and retinal nerve fiber layer, which effectively alleviates the limitations of scarce clinical imaging data and label imbalance.These methods have substantially improved the accuracy and generalization of deep learning models in visual field defect prediction, structure-function mapping, and longitudinal disease progression simulation.Meanwhile, multimodal generative approaches that integrate imaging data, visual field tests, and clinical features have facilitated individualized prediction of glaucoma progression.In addition, large language models have shown preliminary potential in ophthalmic image interpretation, clinical text information extraction, and decision support, providing new insights into intelligent ophthalmic diagnosis and treatment.However, the clinical implementation of generative AI in glaucoma faces challenges.The pathological authenticity and cross-device consistency of generated images require further validation, which may affect the reliability of early glaucoma detection.The heterogeneous characteristics of different glaucoma subtypes, such as open-angle and angle-closure glaucoma, also limit the generalization of synthetic data.Moreover, issues related to model interpretability (" black-box" nature), artifact generation, data privacy, and ethical governance remain key barriers to clinical translation.In the future, it is expected that establishing large-scale training frameworks that incorporate multicenter, multimodal, and multiethnic datasets will enhance model robustness and clinical applicability.Furthermore, generative AI may contribute to remote ophthalmic care and personalized precision therapy by enhancing low-quality image, reconstructing missing data, and simulating dynamic disease courses.This article reviews the current applications, core technologies, and challenges of generative AI in glaucoma diagnosis and management, and discusses its future directions and translational potential in clinical ophthalmology.

In recent years，with the increase in environmental pollution，organisms are exposed to more internal and external oxidative stress factors than ever before.Nuclear factor erythroid 2-related factor 2（nuclear factor erythroid 2-related factor 2，NRF2），as a core transcription factor in response to oxidative stress，maintains cellular redox homeostasis by inducing the expression of various antioxidant factors.The nasal cavity，as the "gateway" of the respiratory tract，is often accompanied by oxidative stress（oxidative stress，OS）damage，leading to the occurrence of allergic rhinitis（allergic rhinitis，AR）.Recent studies have revealed some associations between the NRF2 signaling pathway and the mechanism of AR development.Activation of NRF2 provides a potential protective effect against AR，and some natural NRF2 activators have shown therapeutic potential in clinical experiments.Therefore，this article briefly reviews the relationship between NRF2 and AR，aiming to provide a new therapeutic target and perspective for the treatment of AR.
NF-E2-Related Factor 2/metabolism* ; Humans ; Rhinitis, Allergic/metabolism* ; Signal Transduction ; Oxidative Stress

Autism Spectrum Disorders (ASDs) are reported as a group of neurodevelopmental disorders. The structural changes of brain regions including the hippocampus were widely reported in autistic patients and mouse models with dysfunction of ASD risk genes, but the underlying mechanisms are not fully understood. Here, we report that deletion of Trio, a high-susceptibility gene of ASDs, causes a postnatal dentate gyrus (DG) hypoplasia with a zigzagged suprapyramidal blade, and the Trio-deficient mice display autism-like behaviors. The impaired morphogenesis of DG is mainly caused by disturbing the postnatal distribution of postmitotic granule cells (GCs), which further results in a migration deficit of neural progenitors. Furthermore, we reveal that Trio plays different roles in various excitatory neural cells by spatial transcriptomic sequencing, especially the role of regulating the migration of postmitotic GCs. In summary, our findings provide evidence of cellular mechanisms that Trio is involved in postnatal DG morphogenesis.
Animals ; Dentate Gyrus/metabolism* ; Mice ; Morphogenesis/physiology* ; Neurons/pathology* ; Cell Movement ; Mice, Inbred C57BL ; Autism Spectrum Disorder/pathology* ; Mice, Knockout ; Neural Stem Cells ; Male ; Neurogenesis ; Autistic Disorder/genetics*

Early correction of childhood malocclusion is timely managing morphological, structural, and functional abnormalities at different dentomaxillofacial developmental stages. The selection of appropriate imaging examination and comprehensive radiological diagnosis and analysis play an important role in early correction of childhood malocclusion. This expert consensus is a collaborative effort by multidisciplinary experts in dentistry across the nation based on the current clinical evidence, aiming to provide general guidance on appropriate imaging examination selection, comprehensive and accurate imaging assessment for early orthodontic treatment patients.
Humans ; Malocclusion/diagnostic imaging* ; Child ; Consensus

With the growing emphasis on maternal and child oral health, the significance of managing oral health across preconception, pregnancy, and infancy stages has become increasingly apparent. Oral health challenges extend beyond affecting maternal well-being, exerting profound influences on fetal and neonatal oral development as well as immune system maturation. This expert consensus paper, developed using a modified Delphi method, reviews current research and provides recommendations on maternal and child oral health management. It underscores the critical role of comprehensive oral assessments prior to conception, diligent oral health management throughout pregnancy, and meticulous oral hygiene practices during infancy. Effective strategies should be seamlessly integrated across the life course, encompassing preconception oral assessments, systematic dental care during pregnancy, and routine infant oral hygiene. Collaborative efforts among pediatric dentists, maternal and child health workers, and obstetricians are crucial to improving outcomes and fostering clinical research, contributing to evidence-based health management strategies.
Humans ; Pregnancy ; Female ; Infant ; Consensus ; Mouth Diseases/therapy* ; Pregnancy Complications/therapy* ; Oral Health ; Infant, Newborn ; Delphi Technique ; Oral Hygiene

Activity cliffs (ACs) are generally defined as pairs of similar compounds that only differ by a minor structural modification but exhibit a large difference in their binding affinity for a given target. ACs offer crucial insights that aid medicinal chemists in optimizing molecular structures. Nonetheless, they also form a major source of prediction error in structure-activity relationship (SAR) models. To date, several studies have demonstrated that deep neural networks based on molecular images or graphs might need to be improved further in predicting the potency of ACs. In this paper, we integrated the triplet loss in face recognition with pre-training strategy to develop a prediction model ACtriplet, tailored for ACs. Through extensive comparison with multiple baseline models on 30 benchmark datasets, the results showed that ACtriplet was significantly better than those deep learning (DL) models without pre-training. In addition, we explored the effect of pre-training on data representation. Finally, the case study demonstrated that our model's interpretability module could explain the prediction results reasonably. In the dilemma that the amount of data could not be increased rapidly, this innovative framework would better make use of the existing data, which would propel the potential of DL in the early stage of drug discovery and optimization.

OBJECTIVE: To explore the relationship between the geriatric nutritional risk index (GNRI) and cognitive function. METHODS: A cross-sectional study method was conducted. People aged ≥ 60 years from the National Health and Nutrition Examination Survey (NHANES) databases from 1999 to 2002 and 2011 to 2014 were included as study subjects. The participants were divided into three groups based on their GNRI scores: a medium-high risk group (82 ≤ GNRI < 92), a low risk group (92 ≤ GNRI < 98), and a no-risk group (GNRI ≥ 98). Demographic characteristics (gender, age, race, education), chronic diseases [chronic bronchitis, emphysema, thyroid problems, coronary heart disease, angina pectoris, stroke, hypertension, diabetes mellitus, and depression score on the patient health questionnaire (PHQ-9)], lifestyle habits (history of smoking, hours of sleep), etc., were collected. Cognitive function was assessed using the consortium to establish a registry for Alzheimer's disease word learning subtest (CERAD-WL), animal fluency test (AFT), and digit symbol substitution test (DSST) for the 2011-2014 data, while only the DSST was used for the 1999-2002 data. Differences in the above information among the GNRI cohorts were compared. Factors affecting cognitive function in the population were analyzed using multifactorial Logistic regression. RESULTS: 2 653 participants from 2011 to 2014 and 2 380 participants from 1999 to 2002 were enrolled, with a total of 5 033 participants in the study. There were statistically significant differences in age, stroke, diabetes mellitus, DSST score, AFT score, CERAD score test 1 recall (Cst1), and CERAD score test 2 recall (Cst2) among the GNRI groups. Multifactorial Logistic regression analysis of data from 2011 to 2014 showed that in model 3 (DSST score, age, gender, race, marriage, education, hours of sleep, history of smoking, emphysema, thyroid problems, chronic bronchitis, coronary heart disease, angina pectoris, hypertension, diabetes mellitus, depression score on the PHQ-9, and stroke) adjusted for all covariates, GNRI was a protective factor for DSST [odds ratio (OR) = 1.03, 95% confidence interval (95%CI) was 1.00 to 1.05, P = 0.03]; Logistic regression analyse for 1999 to 2002 and 2011 to 2014 showed a significant association even after adjustment for covariates (OR = 1.02, 95%CI was 1.00 to 1.03, P = 0.02). Subgroup Logistic regression analyses of the total population from 2011 to 2014 showed a significant association between GNRI and DSST scores (OR = 1.02, 95%CI was 1.01 to 1.03, P < 0.001), with significant associations in the age subgroups of 60 to 64 years old, across gender, non-Hispanic Whites and Blacks, by education, and by marital status associations were significant (all P < 0.05). Subgroup Logistic regression analyse of the total populations from 1999 to 2002 and 2011 to 2014 showed a significant association between the GNRI and DSST score (OR = 1.01, 95%CI was 1.01 to 1.02, P < 0.001), but did not show a significant year difference (interaction P = 0.503), and the newly found in the smoking population the association was also more significant (P < 0.01). CONCLUSION The GNRI correlates with the presence of cognitive functions related to processing speed, sustained attention, and executive function, and may be able to serve as an indicator for the assessment or prediction of related cognitive functions.
Humans ; Cross-Sectional Studies ; Aged ; Middle Aged ; Nutrition Surveys ; Cognition ; Female ; Male ; Nutritional Status ; Risk Factors ; Geriatric Assessment