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 association of working hours and shift work with occupational stress, fatigue accumulation, and depressive symptoms among primary community medical workers. Methods A total of 516 medical workers from five community medical service centers in Pudong New Area, Shanghai City, were selected as the research subjects using the convenience sampling method. The Core Scale of Occupational Stress Measurement, the Workers' Fatigue Accumulation Self-diagnosis Questionnaire, and the Patient Health Questionnaire were used to assess research subjects' occupational stress, fatigue accumulation, and depressive symptoms, respectively. Results Long working hours (>40 hours/week) were reported by 50.4% of workers among the research subjects, while shift works were reported by 16.9% of the workers. The detection rates of occupational stress, fatigue accumulation, and depressive symptoms were 26.6%, 41.7%, and 30.8%, respectively. Multivariate logistic regression analysis result revealed that, after adjusting for confounders such as age, sex, and education level, longer working hours were associated with higher risks of occupational stress, fatigue accumulation, and depressive symptoms (all P<0.05). Shift workers in community medical centers had higher risks of occupational stress, fatigue accumulation, and depressive symptoms compared with non-shift workers (all P<0.05). Conclusion Long working hours and shift work could increase the risks of occupational stress, fatigue accumulation, and depressive symptoms among community medical workers.

With the rapid advancement of artificial intelligence technology, chatbots have shown great potential in the healthcare sector. From personalized health advice to chronic disease management and psychological support, chatbots have demonstrated significant advantages in improving the efficiency and quality of healthcare services. As the scope of their applications expands, the relationship between technological complexity and practical application scenarios has become increasingly intertwined, necessitating a more comprehensive evaluation of both aspects. This paper, from the perspective of he althcare applications, systematically reviews the technological pathways and development of chatbots in the medical field, providing an in-depth analysis of their performance across various medical scenarios. It thoroughly examines the advantages and limitations of chatbots, aiming to offer theoretical support for future research and propose feasible recommendations for the broader adoption of chatbot technologies in healthcare.

With the widespread promotion of patient-centered care models,narrative medicine,as a medical practice that emphasizes patients'personal stories and experiences,has garnered increasing attention in the medical field.Through a systematic literature review and field observations,this paper analyzed the ethical challenges faced by narrative medicine in reconstructing doctor-patient relationships,which mainly include information asymmetry and uneven distribution of medical resources,insufficient informed consent and privacy protection of patients,and difficulties in ensuring the authenticity and completeness of narrative content.In response,this paper proposed strategies such as enhancing patients'understanding of their rights and narrative medicine,promoting continuous ethical evaluations of narrative medicine practices,and exploring interdisciplinary collaborations to foster harmonious doctor-patient relationships.It also emphasized the importance of establishing multidisciplinary cooperation mechanisms and strengthening ethical training for medical personnel,as well as the necessity of ensuring information security through technological means,aiming to promote harmonious doctor-patient relationships,better realize the ethical practices of narrative medicine,optimize doctor-patient relationships,and improve the quality of medical services.

With the change of infectious disease incidence pattern and the development of related technologies, progresses have been made in the research of infectious disease epidemiology. In recent years, due to the change in the requirements of infectious disease prevention and control, the research focus has expanded from common infectious diseases to diseases which have been eliminated or might be eliminated, as well as emerging and re-emerging infectious diseases. Infectious disease data has been characterized by multiple sources and modalities. Along with the rapid development of pathogen detection methods, infectious disease surveillance has shifted from a single disease-targted one to a comprehensive one. Moreover, novel technologies such as multi-omics and artificial intelligence have been applied in infectious disease epidemiology research. The international cooperation in this field has become increasingly crucial, and the revision of the International Health Regulations and the negotiation of pandemic agreement will have a profound impact. In the future, infectious disease epidemiology research will develop with more powerful tools to improve its capabilities.

Objective:To explore the latent profile characteristics of fatigue in patients with non-small cell lung cancer (NSCLC) complicated by radiation-induced pulmonary fibrosis (RIPF), and to provide evidence for developing precision nursing strategies.Methods:A convenience sample of 120 patients with RIPF and NSCLC who received treatment at the First Affiliated Hospital of Zhengzhou University between January 2022 and December 2023 was recruited. Baseline demographic and clinical data and the Multidimensional Fatigue Inventory (MFI) were collected. Latent profile analysis (LPA) was performed to classify fatigue levels, and multinomial logistic regression was used to identify influencing factors. A total of 120 questionnaires were distributed, and 116 valid responses were obtained, with a valid response rate of 96.67% (116/120) .Results:LPA identified three latent classes of fatigue among the 116 patients: the physiological-cognitive compound fatigue group ( n=52), the emotional-sleep disturbance group ( n=38), and the mildly adaptive group ( n=26). Multinomial logistic regression revealed that age, Eastern Cooperative Oncology Group performance status (ECOG-PS), Karnofsky Performance Status (KPS), sleep quality, and anxiety were significant factors associated with the physiological-cognitive compound fatigue group ( P<0.05). Sleep quality, anxiety, depression, pain, and KPS were significant factors associated with the emotional-sleep disturbance group ( P<0.05) . Conclusions:Patients with RIPF and NSCLC can be classified into three subtypes of fatigue. Differentiated nursing strategies should be developed accordingly to achieve precise and individualized interventions.

Objective:To investigate the current status of fertility concerns among early-stage endometrial cancer (EC) and atypical endometrial hyperplasia (AEH) patients with fertility preservation, and analyze its influencing factors.Methods:Convenience sampling was used to select early-stage EC and AEH patients with fertility preservation at Peking University People's Hospital from January to December 2021 as study subjects. The study subjects were surveyed using the General Information Questionnaire, Chinese version of the Reproductive Concerns after Cancer Scale (RCAC), and Social Support Rating Scale (SSRS). Pearson correlation was employed to examine the relationship between fertility concerns and social support in early-stage EC and AEH patients with fertility preservation. Single-factor analysis and multiple linear regression analysis were used to examine the factors influencing fertility concerns in early-stage EC and AEH patients with fertility preservation.Results:A total of 170 questionnaires were distributed, and 167 valid questionnaires were collected, with a valid response rate of 98.24% (167/170). The RCAC and SSRS scores of 167 early-stage EC and AEH patients with fertility preservation were (56.58±10.58) and (34.22±8.21), respectively. Educational level, disease type and staging, marital status, and social support were statistically significant factors influencing fertility concerns among early-stage EC and AEH patients undergoing fertility preservation ( P<0.05), explaining 32.80% of the total variance. Conclusions:Early-stage EC and AEH patients with fertility preservation exhibit high levels of fertility concerns. Clinical practice should develop individualized psychological intervention programs for patients with high education level, unmarried status, high pathological grading, and lack of social support to improve their physical and mental health.

Objective To investigate the incidence of placental abruption in the third trimester of pregnancy in Hebei Province,identify its associated risk factors,and develop a corresponding nomogram prediction model.Methods Data from pregnant women at 22 monitored hospitals in Hebei Province,collected between 2013 and 2023,were analyzed to assess the incidence,trends,and associated risk factors of placental abruption.A prediction model was developed and visualized using R programming to generate the receiver operating characteristic(ROC)curve.The model's predictive performance was evaluated using the area under the curve(AUC)and calibration curve parameters.Results A total of 480 690 pregnant women were included in this study over the specified period.The incidence of placental abruption was 0.29%(1 395 out of 480 690),with an average annual percentage change of 2.73%,indicating a stable trend(P=0.34).Multivariate logistic regression analysis revealed that pregnancy-related anemia,preeclampsia,placenta previa,vaginal bleeding before 28 weeks'gestation,and between 28 and 31+6 weeks'gestation were significant risk factors for placental abruption(all P<0.05).In contrast,regular prenatal examinations(7～11 visits)and multiple pregnancies were identified as protective factors against placental abruption(both P<0.05).The area under the curve(AUC)for the nomogram model constructed based on independent risk factors for placental abruption was 0.79,and the calibration curve demonstrated that the predicted values closely aligned with the observed values.Conclusions Prenatal examinations should be prioritized,especially for women with preeclampsia,placenta previa,or a history of vaginal bleeding before 28 weeks of gesta-tion.Management of pregnancies between 28 and 31+6 weeks is also crucial to reduce the incidence of placental abruption and mitigate adverse maternal and neonatal outcomes.The nomogram model constructed based on these factors exhibits excellent predictive performance,providing a solid theoretical foundation for the prevention and clinical management of placental abruption.

Objective To investigate the incidence of placental abruption in the third trimester of pregnancy in Hebei Province,identify its associated risk factors,and develop a corresponding nomogram prediction model.Methods Data from pregnant women at 22 monitored hospitals in Hebei Province,collected between 2013 and 2023,were analyzed to assess the incidence,trends,and associated risk factors of placental abruption.A prediction model was developed and visualized using R programming to generate the receiver operating characteristic(ROC)curve.The model's predictive performance was evaluated using the area under the curve(AUC)and calibration curve parameters.Results A total of 480 690 pregnant women were included in this study over the specified period.The incidence of placental abruption was 0.29%(1 395 out of 480 690),with an average annual percentage change of 2.73%,indicating a stable trend(P=0.34).Multivariate logistic regression analysis revealed that pregnancy-related anemia,preeclampsia,placenta previa,vaginal bleeding before 28 weeks'gestation,and between 28 and 31+6 weeks'gestation were significant risk factors for placental abruption(all P<0.05).In contrast,regular prenatal examinations(7～11 visits)and multiple pregnancies were identified as protective factors against placental abruption(both P<0.05).The area under the curve(AUC)for the nomogram model constructed based on independent risk factors for placental abruption was 0.79,and the calibration curve demonstrated that the predicted values closely aligned with the observed values.Conclusions Prenatal examinations should be prioritized,especially for women with preeclampsia,placenta previa,or a history of vaginal bleeding before 28 weeks of gesta-tion.Management of pregnancies between 28 and 31+6 weeks is also crucial to reduce the incidence of placental abruption and mitigate adverse maternal and neonatal outcomes.The nomogram model constructed based on these factors exhibits excellent predictive performance,providing a solid theoretical foundation for the prevention and clinical management of placental abruption.

Purpose To explore the expression level of let-7b-5p in glioma and its effects and potential mecha-nisms on U251 cell growth.Methods The expression of let-7b-5p in glioma was detected using qRT-PCR.Data from the CGGA database were analyzed to examine the relationship between the let-7b-5p expression levels,WHO grade and overall survival rates of glioma patients.Transient transfection was used to downregulate the expression of let-7b-5p and IGF1R in U251 cells.The role and potential mechanism of let-7b-5p in the U251 cell were evaluated using qRT-PCR,CCK8 assays,clone formation assays,Western blotting,and double luciferase reporter assays.Results The expres-sion of let-7b-5p in glioma cells(A172:3.64±0.64,V251:4.56±0.52,U87-MG:3.31±0.50)and tissues(2.18±0.22)was significantly higher than that in astrocytes(HMC3:1.00±0.21,P＜0.05 or P＜0.01)and nor-mal brain tissues(1.01±0.19,P＜0.05).Let-7b-5p expression was negatively correlated with WHO grades but pos-itively correlated with survival rates in primary and recurrent glioma patients(P＜0.000 1 and P=0.028,respective-ly).Knockdown of let-7b-5p in U251 cells significantly promoted the growth of glioma cells(CCK8:knockdown group 126.00±12.09 vs miR-NC group 90.93±5.13,P＜0.05)and activated PI3K/AKT signal pathway.Suppressing IGF1R expression in U251 cells reversed the effects of let-7b-5p knockdown on glioma cell growth[CCK8:let-7b-5p knockdown+IGF1R knockdown group(92.08±6.14)vs let-7b-5p knockdown+sh-NC group(116.67.08±8.50)]and PI3K/AKT signal pathway activation.Conclusion Let-7b-5p functions as a tumor suppressor gene in glioma.It may regulate glioma cell growth by targeting IGF1R and modulating PI3K/AKT signal pathway.