The translation of genetic findings from genome-wide association studies into actionable therapeutics persists as a critical challenge in Alzheimer's disease (AD) research. Here, we present PI4AD, a computational medicine framework that integrates multi-omics data, systems biology, and artificial neural networks for therapeutic discovery. This framework leverages multi-omic and network evidence to deliver three core functionalities: clinical target prioritisation; self-organising prioritisation map construction, distinguishing AD-specific targets from those linked to neuropsychiatric disorders; and pathway crosstalk-informed therapeutic discovery. PI4AD successfully recovers clinically validated targets like APP and ESR1, confirming its prioritisation efficacy. Its artificial neural network component identifies disease-specific molecular signatures, while pathway crosstalk analysis reveals critical nodal genes (e.g., HRAS and MAPK1), drug repurposing candidates, and clinically relevant network modules. By validating targets, elucidating disease-specific therapeutic potentials, and exploring crosstalk mechanisms, PI4AD bridges genetic insights with pathway-level biology, establishing a systems genetics foundation for rational therapeutic development. Importantly, its emphasis on Ras-centred pathways-implicated in synaptic dysfunction and neuroinflammation-provides a strategy to disrupt AD progression, complementing conventional amyloid/tau-focused paradigms, with the future potential to redefine treatment strategies in conjunction with mRNA therapeutics and thereby advance translational medicine in neurodegeneration.

Proteolysis-targeting chimeras (PROTACs) represent a promising class of drugs that can target disease-causing proteins more effectively than traditional small molecule inhibitors can, potentially revolutionizing drug discovery and treatment strategies. However, the links between in vitro and in vivo data are poorly understood, hindering a comprehensive understanding of the absorption, distribution, metabolism, and excretion (ADME) of PROTACs. In this work, ¹⁴C-labeled vepdegestrant (ARV-471), which is currently in phase III clinical trials for breast cancer, was synthesized as a model PROTAC to characterize its preclinical ADME properties and simulate its clinical pharmacokinetics (PK) by establishing a physiologically based pharmacokinetics (PBPK) model. For in vitro-in vivo extrapolation (IVIVE), hepatocyte clearance correlated more closely with in vivo rat PK data than liver microsomal clearance did. PBPK models, which were initially developed and validated in rats, accurately simulate ARV-471's PK across fed and fasted states, with parameters within 1.75-fold of the observed values. Human models, informed by in vitro ADME data, closely mirrored postoral dose plasma profiles at 30 mg. Furthermore, no human-specific metabolites were identified in vitro and the metabolic profile of rats could overlap that of humans. This work presents a roadmap for developing future PROTAC medications by elucidating the correlation between in vitro and in vivo characteristics.

Objective To explore the effect of playing moderate-intensity table tennis and badminton on myo-pia prevention among children between 7 and 9 years old,so as to provide related theoretical basis.Methods A total of 536 myopic children were initially screened from two schools in Guangzhou via questionnaires and in-terviews.Among them,36 children(7-9 years old)were selected and randomly divided into a control group,a table tennis group and a badminton group,each of 12.All groups underwent 1.5-hour training three times a week for six months,covering training on dynamic visual acuity,sports vision and attention training and the ex-ercise intensity was adjusted according the heart rate,rating of perceived exertion(RPE)and blood oxygen lev-els.Moreover,the equivalent spherical degree(SED),ocular biology,and accommodative parameters were mea-sured before and after the intervention.Results After intervention,the left eye SEDs of the control and badmin-ton groups were lower than before intervention(P<0.05).Moreover,the axial length(AL)in the left and right eyes and the axial length/corneal radius of curvature(AL/CR)of all 3 groups after intervention were significant-ly higher than before that(P<0.05).The table tennis group showed significant improvement in accommodative fa-cility(AF)in both eyes(P<0.05),while the badminton group demonstrated significant increases in AF of both eyes,amplitude of accommodation(AMP)in the right eye,but a significant decrease in the positive relative adjustment(PRA)(P<0.05).Moreover,after intervention,SE and AF of both eyes in the table tennis group was significantly higher,with lower AL and AL/CR ratios in the left eye compared to the control group(P<0.05).The badminton group exhibited greater AMP in the right eye and AF improvements in both eyes com-pared to the control and table tennis groups(P<0.05),but lower PRA(P<0.05).Moreover,the left-eye SE of the badminton group was higher than the control group(P<0.05),while the right-eye corneal curvature and the left-eye AL/CR were lower than the latter(P<0.05).Meanwhile,after the intervention,the left-eye AL and PRA of the badminton group were significantly lower than the table tennis group(P<0.05).Conclusion Pupils can effectively increase their reserve of eye adjustment,improve the adjustment function of the eye,and delay the development of myopia by playing table tennis and badminton with moderate intensity.Playing table tennis excels in delaying refractive power changes,while doing badminton significantly improves PRA,AMP,and AF.

Thymoma is a malignant tumor originating from thymus epithelial cells, with an incidence of 1.3-2.6 per million. Due to its low incidence, lack of cells and animal models, there are relatively few studies on thymoma, and its diagnosis and treatment progress is relatively slow. The update of 5th edition of WHO Classification of Thoracic Tumors in 2021 and the NCCN( National Comprehensive Cancer Network) Clinical Practice Guidelines in Oncology: Thymoma and Thymoma Cancer in 2024 put forward many new views and suggestions on the diagnosis and treatment strategy of thymoma. This article reviews the research and treatment of thymoma based on the latest research progress in recent years, aiming to improve the clinician's understanding of thymoma, provide reference for treatment, and promote the research of thymoma.

Proteolysis-targeting chimeras(PROTACs)represent a promising class of drugs that can target disease-causing proteins more effectively than traditional small molecule inhibitors can,potentially revolu-tionizing drug discovery and treatment strategies.However,the links between in vitro and in vivo data are poorly understood,hindering a comprehensive understanding of the absorption,distribution,metabolism,and excretion(ADME)of PROTACs.In this work,14C-labeled vepdegestrant(ARV-471),which is currently in phase Ⅲ clinical trials for breast cancer,was synthesized as a model PROTAC to characterize its preclinical ADME properties and simulate its clinical pharmacokinetics(PK)by estab-lishing a physiologically based pharmacokinetics(PBPK)model.For in vitro-in vivo extrapolation(IVIVE),hepatocyte clearance correlated more closely with in vivo rat PK data than liver microsomal clearance did.PBPK models,which were initially developed and validated in rats,accurately simulate ARV-471's PK across fed and fasted states,with parameters within 1.75-fold of the observed values.Human models,informed by in vitro ADME data,closely mirrored postoral dose plasma profiles at 30 mg.Furthermore,no human-specific metabolites were identified in vitro and the metabolic profile of rats could overlap that of humans.This work presents a roadmap for developing future PROTAC medications by elucidating the correlation between in vitro and in vivo characteristics.

Objective:To develop and validate machine learning-based radiomics models using preoperative CT images for individualized prediction of mitotic index (MI) in patients with gastrointestinal stromal tumors (GIST).Methods:The study was a case-control study. The data of 348 GIST patients confirmed by pathology were retrospectively collected from two independent medical centers: the Affiliated Hospital of Qingdao University (center 1) and Shandong Provincial Hospital Affiliated to Shandong First Medical University (center 2), covering the period from January 2013 to June 2018. Patients from center 1 were divided into a training cohort (176 cases) and an internal validation cohort (75 cases) at a ratio of 7∶3 using random sampling. Patients from center 2 served as an independent external validation cohort (97 cases). The primary endpoint was MI, categorized into high MI (145 cases) and low MI (203 cases) groups. Radiomic features were extracted from the portal venous phase images of preoperative contrast-enhanced CT scans. Five machine learning algorithms, including logistic regression, support vector machine, random forest, decision tree, and extreme gradient boosting (XGBoost),were employed to construct MI prediction models. The optimal model was identified using receiver operating characteristic curves. An individualized prediction model was developed by integrating the the optimal machine learning model combined with selected independent clinical factors, and the importance of features was visualized using Shapley Additive Explanation (SHAP) analysis. Patients were followed up, and Kaplan-Meier curves along with log-rank tests were used to evaluate recurrence-free survival (RFS) differences between the predicted high MI and low MI groups.Results:Among the five constructed machine learning models, the XGBoost model demonstrated the best predictive performance, with area under the curve (AUC) of 0.809 (95% CI 0.738-0.872), 0.693 (95% CI 0.571-0.809), and 0.718 (95% CI 0.605-0.822) in the training cohort, internal validation cohort, and external validation cohort, respectively. An individualized prediction model combining the XGBoost model with independent clinical factors (tumor location and tumor size) was developed. The model achieved AUC of 0.843 (95% CI 0.785-0.899), 0.791 (95% CI 0.680-0.894), and 0.777 (95% CI 0.678-0.861) in the training cohort, internal validation cohort, and external validation cohort, respectively. SHAP analysis indicated that radiomic features had the highest predictive impact. In both the training cohort and internal validation cohort, the RFS of patients predicted to be in the high MI group was lower than that of the low MI group, with statistically significant differences ( χ2=14.58, 9.52, both P<0.001). However, there was no statistically significant difference in RFS in the external validation set ( χ2=6.18, P=0.080). Conclusions:The optimal XGBoost model based on radiomic features extracted from preoperative portal venous phase CT images, when combined with clinical factors, can effectively predict the MI of GIST patients.

Objective:To investigate the correlation between serum neuron-specific enolase (NSE) levels and poor neurological outcomes in cardiac arrest (CA) patients supported by veno-arterial extracorporeal membrane oxygenation (VA-ECMO).Methods:This retrospective analysis was conducted on adult CA patients treated with VA-ECMO at Hangzhou First People's Hospital Affiliated to Westlake University School of Medicine, and Second Affiliated Hospital Zhejiang University School of Medicine, from December 2018 to February 2024. General clinical data and serial serum NSE levels at 24, 48, and 72 h after ECMO initiation were collected. Based on the Glasgow-Pittsburgh Cerebral Performance Category (CPC) at discharge, patients were divided into poor neurological outcome group (CPC 3-5) and good neurological outcome group (CPC 1-2). Differences in serum NSE levels between the two groups were compared. The accuracy of serum NSE levels at three time points in predicting poor neurological outcomes in CA patients was assessed via receiver operating characteristic curves, and the optimal cut-off values were determined by the Youden index. Multivariate logistic regression analysis was performed to determine the relationship between serum NSE levels and poor neurological outcomes. Subgroup analysis was based on age, sex, location of CA, and extracorporeal cardiopulmonary resuscitation (ECPR).Results:A total of 120 eligible CA patients were included, with 88 patients (73.3%) having poor neurological outcomes at discharge. Serum NSE levels at 24, 48, and 72 h after ECMO initiation were higher in the poor outcome group compared to the good outcome group (all P<0.05). The serum NSE level at 72 h had the highest accuracy in predicting poor outcomes, with an area under the curve (AUC) of 0.91 (95% CI: 0.85-0.96), and a cut-off value of 42.0 μg/L. The AUCs for 24 and 48 h were 0.78 (95% CI: 0.69-0.86) and 0.87 (95% CI: 0.80-0.94), with cut-off values of 70.6 μg/L and 64.5 μg/L, respectively. Multivariate logistic regression analysis suggested that the serum NSE level at 72 h was associated with poor outcomes ( P<0.05), and an NSE level >42.0 μg/L was an independent risk factor for poor outcomes ( OR=20.29, 95% CI: 2.90-92.15). Subgroup analysis showed that serum NSE level at 72 h was an independent risk factor for poor neurological outcomes in CA patients aged<60 years old, male or female, out-of-hospital or in-hospital CA, and whether to perform ECPR (all P<0.05). Conclusion:Elevated serum NSE levels at 72 h after VA-ECMO initiation are associated with poor neurological outcomes in CA patients, with the cut-off value of 42.0 μg/L.

The chemical composition of Paeoniae Radix Alba(PRA) is complex, with primary secondary metabolites including monoterpenoids, tannins, triterpenoids, and flavonoids. In previous studies on the material basis of PRA, it was found that, in addition to the widely studied characteristic monoterpene glycosides, tannic acid components also play an important role in the efficacy of PRA. However, their pharmacological effects have not been thoroughly investigated. This paper reviews the tannic acid components in PRA, including pentagaloyl glucose(PGG), tetragaloyl glucose(TGG), trigaloyl glucose(TriGG), and gallic acid, along with their structures, properties, and characteristics to provide a detailed discussion of their pharmacological activities and related mechanisms, aiming to offer a theoretical basis for the material basis research and clinical application of PRA.
Paeonia/chemistry* ; Tannins/chemistry* ; Humans ; Drugs, Chinese Herbal/chemistry* ; Animals ; Plant Extracts

Objective:To investigate the effect of polystyrene microplastics(PS-MPs)combined with high-fat treatment on vascular endothelial cells.Methods:Human umbilical vein endothelial cells(HUVECs)were cultured in the DMEM medium containing 5%fe-tal bovine serum.HUVECs were treated with conventional culture,high-fat treatment,and PS-MPs combined with high-fat treatment.The experiment was conducted in the three groups of control group,high-fat treatment group and PS-MPs+high-fat treatment group.CCK-8 assay was used to measure cell viability,F-actin staining was used to observe cell morphological changes,and flow cytometry,scratch assay,and tube formation assay were used to measure the apoptosis,migration,and tube-forming ability of cells.Results:After HUVECs were exposed to the high-fat environment,there was a significant reduction in cell viability,shrinkage of cells,a signifi-cant increase in cell apoptosis,and significant reductions in cell migration and tube-forming ability.Compared with the high-fat treat-ment group,there were no significant changes in cell viability,cell morphology,cell apoptosis,and cell migration ability after PS-MPs combined with high-fat treatment,but the tube-forming ability of cells was further impaired.Conclusion:High-fat treatment will affect cell viability,change cell morphology,and damage vascular endothelial cell function,and PS-MPs combined with high-fat treat-ment can aggravate the damage of vascular endothelial cell function.