Delirium is a common cause and complication of hospitalization in the elderly and is associated with higher risk of future dementia and progression of existing dementia, of which 70% is Alzheimer's disease (AD). AD and delirium, which are known to be aggravated by one another, represent significant societal challenges, especially in light of the absence of effective treatments. The intricate biological mechanisms have led to numerous clinical trial setbacks and likely contribute to the limited efficacy of existing therapeutics. Artificial intelligence (AI) presents a promising avenue for overcoming these hurdles by deploying algorithms to uncover hidden patterns across diverse data types. This review explores the pivotal role of AI in revolutionizing drug discovery for AD and delirium from target identification to the development of small molecule and protein-based therapies. Recent advances in deep learning, particularly in accurate protein structure prediction, are facilitating novel approaches to drug design and expediting the discovery pipeline for biological and small molecule therapeutics. This review concludes with an appraisal of current achievements and limitations, and touches on prospects for the use of AI in advancing drug discovery in AD and delirium, emphasizing its transformative potential in addressing these two and possibly other neurodegenerative conditions.

Objective To explore the machine learning model and risk factor analysis for hospital infection caused by carbapenem-resistant enterobacteriaceae(CRE).Methods The clinical data of totally 451 patients infected with extended-spectrum β-lactamases(ESBL)producing Enterobacteriaceae treated in the hospital from 2018 to 2022 were retrospectively collected.The patients were divided into CRE group(115 cases)and sensitive group(336 cases)according to the susceptibility of carbapenem.Four machine learning methods in-cluding Logistic regression analysis,random forest,support vector machine,and neural network were used to build prediction models and receiver operating characteristic curve was used to evaluate.Based on the predic-tion model with the best performance,risk factors for CRE infection were analyzed.Results Random forest model had the best performance,with the area under the curve of 0.952 3.The risk factors for predicting CRE infection by the random forest model included 15 clinical data items,namely fever for more than 3 days,cere-bral injury,drainage fluid sample,trunk surgery,first-level or special-level nursing,ICU treatment,procalcito-nin,anti-anaerobic bacteria,the use of third-generation cephalosporins,age,pre-albumin,creatinine,white blood cell count,and albumin.Conclusion The CRE prediction model developed in this study has good predic-tive value and the risk factors have guiding significance for the early prevention and treatment of CRE infec-tion in clinical practice.

Objective To explore the mechanism of neuropmtective effects of puerarin for the treatment of acute spinal ischenia-reperfusion injury in a rat model.Methods Acute spinal ischemia-reperfusion injury was induced via aortic occlusion in 28 male Sprague-Dawley rats.The animals were randomly divided into four groups,as follows:group negative contrast (NC sham operation),group positive control group (IR+ S ischemia/reperfusion + saline),group puerarin (IR+P ischemia/reperfusion + puerarin),group mscovitine (IR+R ischemia/reperfusion + roscovitine).The motor function,spinal infarction volume,apoptosis indices,and CDK5 and P25 activities were examined.Results Spinal ischemia-reperfusion caused the injury of the spines and was associated with motor deficit,elevation of CDK5 and P25 activities,and increase in the spinal apoptosis and spinal infarction volume.Puerarin improved motor function and decreased apoptosis,spinal infarction volume,and CDK5 and P25 activities.Conclusion The findings of the present study indicated that puerarin treatment-mediated reduction of spinal injury was associated with the inhibition of CDK5 and P25,and that the inhibition was one among the neuroprotective mechanisms of puerarin against acute ischemia/reperfusioninduced spinal injury in rats.

Diseases of glucose and lipid metabolism disorder,presented rather complicated pathological mechanism,often with clinical pattern of multiple concurrent diseases.Therefore,the traditional single-disease based on treatment methods need improving.In view of plenty of clinical practice,theatrical and fundamental researches,the pathological mechanisms of some chronic disorders,such as hyperlipidemia,nonalcoholic fatty liver disease,type 2 diabetes,hypertension,atherosclerosis and severe cardiovascular complications,resulted from the impairment in the metabolism of glucose and lipid were investigated using the method of integrated Chinese and western medicine.Overall,the features of these diseases and their common characteristics were discovered,and accordingly we defined the new concept of glucolipid metabolic disease (GLMD) and put forward the concept of pivot liver of metabolic regulation system.In addition,we developed the therapeutic strategy of modulating liver,starting pivot and cleaning turbidity,for the comprehensive and integrated treatment and prevention of these diseases.The theory of GLMD shared the critical characteristics with precision medicine,taking its own specialty.Finally,the content and approaches for the research of GLMD were proposed,and some essential and core fields in the precision medical research for GLMD were profoundly analyzed and prospected.

Background:The current World Health Organization (WHO) classiifcation of nasopharyngeal carcinoma (NPC) con?veys little prognostic information. This study aimed to propose an NPC histopathologic classiifcation that can poten?tially be used to predict prognosis and treatment response. Methods:We initially developed a histopathologic classiifcation based on the morphologic traits and cell differentia?tion of tumors of 2716 NPC patients who were identiifed at Sun Yat?sen University Cancer Center (SYSUCC) (training cohort). Then, the proposed classiifcation was applied to 1702 patients (retrospective validation cohort) from hospitals outside SYSUCC and 1613 patients (prospective validation cohort) from SYSUCC. The effcacy of radiochemotherapy and radiotherapy modalities was compared between the proposed subtypes. We used Cox proportional hazards models to estimate hazard ratios (HRs) with 95% conifdence intervals (CI) for overall survival (OS). Results:The 5?year OS rates for all NPC patients who were diagnosed with epithelial carcinoma (EC; 3708 patients), mixed sarcomatoid?epithelial carcinoma (MSEC; 1247 patients), sarcomatoid carcinoma (SC; 823 patients), and squamous cell carcinoma (SCC; 253 patients) were 79.4%, 70.5%, 59.6%, and 42.6%, respectively (P<0.001). In mul?tivariate models, patients with MSEC had a shorter OS than patients with EC (HR=1.44, 95% CI=1.27–1.62), SC (HR=2.00, 95% CI=1.76–2.28), or SCC (HR=4.23, 95% CI=3.34–5.38). Radiochemotherapy signiifcantly improved survival compared with radiotherapy alone for patients with EC (HR=0.67, 95% CI=0.56–0.80), MSEC (HR=0.58, 95% CI=0.49–0.75), and possibly for those with SCC (HR=0.63; 95% CI=0.40–0.98), but not for patients with SC (HR=0.97, 95% CI=0.74–1.28). Conclusions:The proposed classiifcation offers more information for the prediction of NPC prognosis compared with the WHO classiifcation and might be a valuable tool to guide treatment decisions for subtypes that are associ?ated with a poor prognosis.