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.

Purpose To investigate the value of intratumoral and different ranges of peritumoral radiomics features of the primary lesion of breast cancer based on ultrasound images in predicting axillary lymph node metastasis(ALNM),and to explore the best peritumoral range.Materials and Methods A total of 312 cases confirmed by pathology in breast cancer patients with preoperative ultrasound images from June 2022 to February 2024 in Binzhou Medical University Hospital were retrospectively enrolled,and were randomly divided into training set and testing set according to the 7∶3 proportion.The tumor border of the ultrasound images was manually delineated as the intratumoral region of interest,and the peritumoral region of interest was obtained by conformal automatically extended different range(1,2,3,4 and 5 mm).The radiomics features were screened.Based on the selected optimal radiomics features,random forest classifier was used to construct three types of radiomics models(intratumoral model,5 peritumoral models,and 5 intratumoral+peritumoral models).The performance and clinical practicability of the models was assessed the area under the curve(AUC)and decision curve analysis.Results The AUCs of the intratumoral+peritumoral radiomics models for predicting ALNM in the training set and test set were 0.807-0.873,0.728-0.780,respectively,which were superior to those of the single intratumoral radiomics models(0.822,0.758)and peritumoral radiomics models(0.722-0.768,0.650-0.710).The intratumoral+peritumoral 3 mm radiomics model showed the best predictive performance,with AUC of 0.873 in the training set and 0.780 in the test set,respectively,and the decision curve showed that the model had a good clinical net benefit.Conclusion The combined intratumoral and peritumoral radiomics features of the primary lesion of breast cancer based on ultrasound images can effectively predict ALNM,and 3 mm peritumoral may be the best peritumoral range for predicting ALNM.

This study aims to explore protective effects of Lycium barbarum polysaccharides(LBP)on intestinal damage caused by lipopolysaccharide(LPS)-induced inflammatory bowel disease(IBD)in chicks.Network pharmacology was initially employed to determine the target proteins of wolfberry in the prevention and treatment of IBD.Following this,protein-protein interaction analy-sis,GO and KEGG pathway enrichment analysis,and molecular docking studies were conducted.Subsequently,an animal study was conducted:a total of 100 one-day-old male Hy-line brown lay-ing hens were randomly divided into five groups:a blank control group(CON),an LPS treatment group(LPS),a low-dose LBP group(LPS+LBP 0.25 g/L,L-LBP),a medium-dose LBP group(LPS+LBP 0.5 g/L,M-LBP),and a high-dose LBP group(LPS+LBP 1 g/L,H-LBP).Upon reac-hing 21 days old,duodenal,jejunal,ileal,and cecal tissues were collected to determine SOD and GSH-Px levels.Furthermore,the mRNA expression levels of TNF-α,AKT1,IL-6,IL-1β and TP53 in the intestinal tissues were measured using quantitative real-time PCR.The results demonstrated that network pharmacology identified 45 active ingredients in wolfberry that target 116 key protein sites,including TNF,AKT1 and IL6.The primary objectives focus on signaling pathways including AGE-RAGE,IL-17,TNF,HIF-1,and NF-κB.Molecular docking showed excellent ligand-receptor docking scores,with stable binding facilitated by hydrogen bonds and hydrophobic interactions.Compared to the LPS group,the 0.5 g/L LBP exhibited notably higher levels of SOD and T-AOC.In comparison with the LPS group,the medium and high-dose LBP experimental groups showed notably decreased the mRNA expressions of TNF-α,AKT1,IL-6,and IL-1β,while TP53 mRNA expression was significantly upregulated(P＜0.01).In summary,wolfberry exerts preventive and therapeutic effects on IBD through a multi-component,multi-target,and multi-pathway mecha-nism.

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.

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 determine the levels of interleukin-2 (IL-22) and interleukin-26 (IL-26) in pleural effusion and serum in patients with tuberculous pleurisy, and analyze the diagnostic value of their combination for tuberculous pleurisy.Methods:From January 2016 to December 2023, 310 patients with tuberculous pleurisy admitted to Xi′an Hi-tech Hospital were included as the study group, and another 310 patients with non tuberculous pleurisy were regarded as the control group. The pleural effusion and serum samples of hospitalized patients were collected. Serum samples were collected from patients at 2 months after treatment for testing of IL-22 and IL-26 levels. The diagnostic value and correlation of IL-22 and IL-26 levels in pleural effusion and serum were analyzed.Results:Compared with the control group, the levels of pleural effusion and serum IL-26 and IL-22 in the study group were increased: (84.68 ± 12.17) ng/L vs. (69.33 ± 9.82) ng/L, (82.45 ± 11.75) ng/L vs. (39.53 ± 5.66) ng/L and (74.38 ± 10.57) ng/L vs. (53.32 ± 7.54) ng/L, (21.45 ± 3.14) ng/L vs. (14.45 ± 2.05) ng/L, and the differences were statistically significant ( P<0.01). Compared with before treatment, serum IL-26 and IL-22 levels decreased after treatment: (61.66 ± 8.72) ng/L vs. (74.38 ± 10.57) ng/L, (17.38 ± 2.59) ng/L vs. (21.45 ± 3.14) ng/L, and the difference was statistically significant ( P<0.01). The AUC of IL-26 and IL-22 in pleural effusion and serum samples in the diagnosis of tuberculous pleurisy was 0.895, 0.820, 0.929 and 0.893, respectively. The AUC of IL-26 and IL-22 combined detection in pleural effusion and serum samples in the diagnosis of tuberculous pleurisy was 0.964 and 0.970, respectively. Pleural effusion IL-26 was positively correlated with serum IL-26 ( r = 0.733, P<0.01) and positively correlated with pleural effusion IL-22 ( r = 0.544, P<0.01); pleural effusion IL-22 was positively correlated with serum IL-22 ( r = 0.532, P<0.01); serum IL-22 was positively correlated with serum IL-26 ( r = 0.619, P<0.01). Conclusions:The levels of IL-22 and IL-26 in pleural effusion and serum of patients with tuberculous pleurisy are elevated, and the two may jointly participate in the immune regulation of tuberculous pleurisy. It can be used as one of the biochemical diagnostic methods for patients with tuberculous pleurisy.

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.