Degenerative retinoschisis(RS)is a common peripheral retinal degeneration that typically remains asymptomatic and follows a benign clinical course. However, its morphological resemblance to rhegmatogenous retinal detachment(RRD)presents a diagnostic challenge, as the two conditions demand fundamentally different management and carry distinct prognoses. Precise differentiation is therefore clinically imperative. Recent advances in multimodal imaging have substantially refined the diagnostic accuracy and differential assessment of degenerative RS, establishing spectral-domain optical coherence tomography(SD-OCT)as the definitive reference standard. When the disease progresses to progressive symptomatic schisis-related retinal detachment(PSSRD), prompt surgical intervention, including scleral buckling, pars plana vitrectomy, or a combined approach, is mandated to preserve visual function. Clinical decision-making must be individualized, with the surgical strategy carefully tailored to the specific characteristics of eachcase. This review provides a systematic synthesis of current evidence on the natural history, imaging hallmarks, and surgical management of degenerative RS and its complications, aiming to inform and optimize clinical practice.

AIM:To investigate the effects of modified Danggui Yinzi decoction combined with diclofenac sodium eye drops on tear film stability and tear protein levels in patients with allergic conjunctivitis.METHODS:Prospective study. Patients with allergic conjunctivitis who attended Suzhou Hospital of Integrated Traditional Chinese and Western Medicine from March 2021 to August 2024 were enrolled as study subjects. They were divided into two groups based on their treatment regimen: a control group(treated with diclofenac sodium eye drops alone)and an observation group(treated with modified Danggui Yinzi decoction combined with diclofenac sodium eye drops). Propensity score matching was used to balance baseline characteristics between the two groups. Log-binomial regression was performed to estimate the risk of adverse reactions. Repeated-measures analysis of variance(ANOVA)was used to assess tear film stability indicators. A difference-in-differences(DID)model was employed to assess the net effect of the combined therapy on changes in tear protein levels.RESULTS:After propensity score matching, a total of 172 patients(344 eyes)were finally enrolled, with 86 patients(172 eyes)in each of the control and observation groups. In the control group, there were 51 males and 35 females, with a mean age of 35.91±10.10 y; in the observation group, there were 49 males and 37 females, with a mean age of 37.87±8.09 y. After treatment, symptom scores decreased in both groups compared with baseline(all P<0.05). At 2 and 4 wk after treatment, symptom scores in the observation group were significantly lower than those in the control group(all P<0.05). The overall response rate was significantly higher in the observation group(89.5%)than in the control group(77.9%)(P<0.05), while the incidence of total adverse reactions was significantly lower in the observation group(7.0%)than in the control group(36.0%)(RR=0.133, P<0.001). Compared with baseline, both groups showed increases in BUT and SⅠt, and a decrease in FL score at 2 and 4 wk after treatment(P<0.017). At 2 and 4 wk after treatment, the BUT and SⅠt levels in the observation group were higher than those in the control group, and the FL score was significantly lower(all P<0.05). The DID model showed that the observation group had greater increases than the control group in lactoferrin(0.411 g/L), total protein(2.792 g/L), secretory immunoglobulin A(14.610 mg/L), and lysozyme(0.419 g/L), respectively(all P<0.05), and these differences remained significant after adjusting for covariates.CONCLUSION:Modified Danggui Yinzi decoction combined with diclofenac sodium eye drops significantly improves tear film stability indicators and tear protein levels in patients with allergic conjunctivitis, and its therapeutic efficacy is superior to that of diclofenac sodium eye drops alone.

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.

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.

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 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.

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.