Objective: To analyze the epidemiological characteristics of category C intestinal infectious diseases among children and adolescents in Shenzhen from 2012 to 2024 and the association with meteorological factors, so as to provide a scientific basis for the targeted prevention and control of infectious diseases for children and adolescents. Methods: Using data from the "Infectious Disease Reporting Information Management System" of the "China Disease Prevention and Control Information System" covering the period from January 1, 2012 to December 31, 2024, the study analyzed clinical and confirmed cases of hand, foot, and mouth disease, other infectious diarrhea, and acute hemorrhagic conjunctivitis among individuals aged 6-19 years old to describe demographic and temporal characteristics. It used Joinpoint regression to calculate the average annual percent change (AAPC) and annual percent change (APC) to analyze incidence trends, and Spearman s correlation was combined to generalize linear models so as to assess the association between category C intestinal infectious diseases and meteorological factors. Results: From 2012 to 2024, a cumulative total of 61 019 cases of hand, foot, and mouth disease among children and adolescents, 58 498 cases of other infectious diarrhea, and 6 377 cases of acute hemorrhagic conjunctivitis were reported. The AAPC in the incidence rates of these three diseases was 19.19%, 31.03% and 31.48 %, respectively(all P <0.05). Notably, the incidence of hand, foot, and mouth disease increased significantly after 2022 (APC= 133.66 %, P <0.01). The temporal distribution showed that hand,foot,and mouth disease was most prevalent in May,June and July (seasonal index of 2.39,3.64,1.97), other infectious diarrhea was most prevalent in February,March and December (seasonal index of 1.22,1.25,1.47), and acute hemorrhagic conjunctivitis peaked in September and October (seasonal index of 4.22,2.16). Monthly average temperature could increase the risk of hand,foot,and mouth disease( β = 0.18 ,95% CI =0.11-0.25); as monthly average wind speed increased, the incidence of other infectious diarrhea ( β =-0.86, 95% CI = -1.50 to -0.22) and acute hemorrhagic conjunctivitis ( β =-1.32, 95% CI =-2.60 to -0.05) both decreased (all P < 0.05 ). Conclusions Among children and adolescents in Shenzhen, category C intestinal infectious diseases remain prevalent throughout the year；the number of reported hand, foot, and mouth disease cases has shown an upward trend in recent years.Temperature and wind speed significantly affect the number of reported cases of three types with category C intestinal infectious diseases.

Objective To construct large medical model named by "Huaxi HongYi"and explore its application effectiveness in assisting medical record generation. Methods By the way of a full-chain medical large model construction paradigm of "data annotation - model training - scenario incubation", through strategies such as multimodal data fusion, domain adaptation training, and localization of hardware adaptation, "Huaxi HongYi" with 72 billion parameters was constructed. Combined with technologies such as speech recognition, knowledge graphs, and reinforcement learning, an application system for assisting in the generation of medical records was developed. Results Taking the assisted generation of discharge records as an example, in the pilot department, after using the application system, the average completion times of writing a medical records shortened (21 min vs. 5 min) with efficiency increased by 3.2 time, the accuracy rate of the model output reached 92.4%. Conclusion It is feasible for medical institutions to build independently controllable medical large models and incubate various applications based on these models, providing a reference pathway for artificial intelligence development in similar institutions.

OBJECTIVE To explore comprehensive management and potential issues associated with long-term prescriptions medications of psychiatry， in order to provide a reference for the comprehensive management of long-term prescriptions of psychiatry in psychiatric hospitals and other medical institutions’ pharmacies. METHODS Starting from the applicable principles for long-term prescriptions of psychiatry， this study introduced the standardized assessment and precautions before issuing long-term prescriptions， the formulation and adjustment of the drug list， as well as the rational management of the long-term prescriptions. It also analyzed potential issues that may arise in the comprehensive management of long-term prescription medications and proposed corresponding countermeasures and suggestions. RESULTS & CONCLUSIONS Prior to initiating long-term prescriptions， a standardized assessment should be conducted on patients from the aspects of their psychiatric condition and long-term potential risk factors， pharmacological treatment plans and other non-pharmacological therapies， physical illnesses. Additionally， healthcare providers should fulfill their obligation to inform patients or their family members. The comprehensive management of long-term prescription medications should be jointly established and improved by multiple departments， and the formulation of drug catalogs should avoid including drugs with potential social harm or medication risks while complying with policy requirements. Furthermore， measures such as adding special identifiers to long-term prescriptions， providing patients with reminders about （No.YGLX202537） prescription expiration， or offering online consultations can also effectively enhance the rationality of medication use under long-term prescriptions. Currently， the implementation of long-term prescriptions in psychiatry remains challenged by inconsistencies in prescription duration， incomplete coverage of diagnostic categories， poor patient adherence， and the risk of deviation in clinical assessments. In this regard， measures such as collaborating with multiple departments to strengthen long-term prescription information management， providing matching pharmaceutical services， ensuring the quality and rationality of long-term prescription implementation， and using modern methods to screen high-risk patients can be taken to improve patient medication compliance and safety.

Traditional development of small protein scaffolds has relied on display technologies and mutation-based engineering, which limit sequence and functional diversity, thereby constraining their therapeutic and application potential. Protein design tools have significantly advanced the creation of novel protein sequences, structures, and functions. However, further improvements in design strategies are still needed to more efficiently optimize the functional performance of protein-based drugs and enhance their druggability. Here, we extended an evolution-based design protocol to create a novel minibinder, BindHer, against the human epidermal growth factor receptor 2 (HER2). It not only exhibits super stability and binding selectivity but also demonstrates remarkable properties in tissue specificity. Radiolabeling experiments with ^99mTc, ⁶⁸Ga, and ¹⁸F revealed that BindHer efficiently targets tumors in HER2-positive breast cancer mouse models, with minimal nonspecific liver absorption, outperforming scaffolds designed through traditional engineering. These findings highlight a new rational approach to automated protein design, offering significant potential for large-scale applications in therapeutic mini-protein development.

Objective To develop a machine learning model integrating preoperative chest CT radiomic features with clinical data for predicting 5-year postoperative recurrence risk in stage Ⅰ non-small cell lung cancer(NSCLC)patients undergoing surgical resection.Methods A total of 217 patients with pathologically confirmed stage Ⅰ NSCLC(selected from 778 initially screened cases based on our inclusion and exclusion criteria)treated in Army Medical Center of PLA between January 2014 and December 2019 were retrospectively enrolled,including 53 recurrence cases and 164 non-recurrence cases within 5-year follow-up.They were randomly divided into a training set(n=173)and a validation set(n=44)in a ratio of 8:2.Radiomic models were established based on extracted features from tumor-dominant regions of interest(ROI)on CT images,while clinical models were developed using demographic characteristics and preoperative laboratory examinations.A combined model was further constructed by integrating both feature sets,and model performance was compared to identify the optimal predictive model.Results This study screened the features from non-contrast CT images and ultimately selected 7 radiomic features for constructing radiomic model.Among 6 machine learning algorithms,the adaptive boosting(Adaboost)model demonstrated the best overall predictive performance,with an area under the curve(AUC)of 0.866(95%CI:0.808～0.923;accuracy:0.832,specificity:0.884)in the training set and of 0.806(95%CI:0.630～0.983;accuracy:0.795,specificity:0.971)in the validation set.Univariate and multivariate logistic regression analyses identified 4 clinical features for clinical model construction.The clinical model achieved an AUC value of 0.874(95%CI:0.821～0.928;accuracy:0.827,specificity:0.891)in the training set and 0.813(95%CI:0.677～0.948;accuracy:0.636,specificity:0.600)in the validation set.By integrating the 7 radiomic features and 4 clinical features using a feature-level fusion strategy,the combined model exhibited further improved predictive performance,with an AUC value of 0.953(95%CI:0.924～0.983;accuracy:0.884,specificity:0.860)and 0.852(95%CI:0.729～0.976;accuracy:0.682,specificity:0.629),respectively in the training set and the validation set.Conclusion The combined model integrating preoperative CT radiomic features with clinical risk factors may provide an evidence-based framework for evaluating 5-year postoperative recurrence risk in stage Ⅰ NSCLC patients.

Objective To observe the effect of preoperative exercise on consciousness,lung function and recovery efficiency of patients undergoing ventriculoperitoneal shunt.Methods A total of 54 patients undergoing elective ventriculoperitoneal shunt in Beijing Bo'ai Hospital from October,2024 to March,2025 were randomly divided into control group(n=27)and prerehabilitation group(n=27).The control group received routine preoperative treatment and nursing,while the prerehabilitation group additionally received exercise for two weeks.They were assessed with Coma Recovery Scale-Revised(CRS-R),and observed diaphragm mobility with sonography,before and three days after operation;and the time to first ambulation after surgery and length of stay in hospital were compared between two groups.Results CRS-R scores improved in both groups after operation(|t|>5.451,P<0.001),and it was greater in the prehabilitation group than in the control group(t=2.812,P<0.01).CRS-R subscale scores improved in auditory and motor functions in the control group(|Z|>2.000,P<0.05),and they were improved in auditory,visual,motor,verbal and arousal functions in the prehabilitation group(|Z|>2.282,P<0.01).CRS-R subscale scores were greater in motor and arousal in the prehabilitation group than in the control group(|Z|>2.320,P<0.05).Diaphragmatic mobility improved in the prehabilitation group(t=-7.782,P<0.001),and it was better than in the control group(t=2.044,P<0.05).The time to first ambulation after surgery and length of stay in hospital were shorter in the prehabilitation group than in the control group(|t|>3.654,P<0.01).Conclusion Preoperative exercise for patients undergoing elective ventriculoperitoneal shunt can improve the consciousness after operation,especially the level of motor and arousal,as well as the lung function,and accelerate the recovery process.

Objective To explore the differences in terahertz(THz)signal characteristics between normal and ischemic liver tissues of New Zealand rabbits using THz time-domain spectroscopy(THz-TDS),thereby providing a novel detection technique for the pathological detection of liver tissues.Methods Liver ischemia models were established in New Zealand rabbits.The THz scanning signal maps of normal and ischemic liver tissues were obtained using a reflective THz-TDS system,and the acquired signals were subjected to principal component analysis.Results Both normal and ischemic liver tissues displayed two distinct peaks in their THz signals.However,the amplitude of the THz signal in ischemic liver tissue was higher than that in normal liver tissue,with significant differences also observed in their signal morphologies.Principal component analysis results revealed a clear clustered distribution between the signals of normal and ischemic liver tissues,indicating that THz-TDS could effectively distinguish between the two tissue types.Conclusion THz-TDS can be applied to the detection of ischemic liver tissue,providing experimental evidence to support further research on the early diagnosis of liver ischemia and exhibiting broad prospects for clinical application.

Objective To explore the the detection of MMP-2,-7,-9,and-12 enzymatic activity using the CEACAM1-derived fluorescent peptide substrate Site 84,investigating the application of substrate Site 84 to distinguishing between MMP-2 and MMP-9 in the gelatinase spectrum of MMPs.Methods The fluorescent enzymatic method was employed to observe the detection of MMP-2,-7,-9,and-12 enzymatic activity using substrate Site 84;further observations were made on the sensitivity and specificity of substrate Site 84 to enzymatic activity of MMP-2 and MMP-9 within the gelatinase spectrum;the kinetic parameters(Km and Kcat)of the enzymatic reaction between substrate Site 84 and MMP-2 were obtained.Results Using Site 84 as a substrate,enzymic kinetics curves for MMP-12,-7,-2 were obtained,but no enzymatic activity curve for MMP-9 was observed.Furthermore,Site 84 specifically detected the enzymatic activity of MMP-2 within the gelatinase spectrum,capable of detecting low concentration(0.6 μM)of MMP-2 enzymatic activity,but no obvious enzymatic reaction was observed for high concentration(6 μM)of MMP-9;the kinetics parameters for the enzymatic reaction between Site 84 and MMP-2 were Km=315 μM,Kcat/Km=2 565/MS.Conclusion The CEACAM1-derived substrate Site 84 serves as a novel fluorescent peptide substrate,enabling the acquisition of enzymatic activity curves for MMP-12,-7 and-2,and specifically detecting the enzymatic activity of MMP-2 within the MMP gelatinase spectrum.

Objective To comprehensively assess the predictive value of C-reactive protein(CRP)/D-dimer(D-D)and albumin/fibrinogen(FAR)in predicting major adverse cardiovascular events(MACE)after percutaneous coronary intervention(PCI)in patients with coronary heart disease(CHD)and to construct a nomogram model for predicting post-procedural MACE in CHD patients.Methods A retrospective study was conducted on 201 CHD patients who underwent PCI at the First Affiliated Hospital of Kunming Medical University between June 2022 and March 2025.These patients were divided into MACE group(n=77)and non-MACE group(n=124)based on whether MACE occurred or not.84 CHD patients from another medical center were also collected as the validation set.The expression levels of CRP/D-D and FAR were compared between the two groups;independent predictors of postoperative MACE in CHD patients were screened by univariate and multivariate logistic regression analyses;the predictive value of CRP/D-D and FAR for the occurrence of postoperative MACE in CHD patients was assessed using ROC curves;A nomogram model was established integrating indicators such as CRP/D-D and FAR,and internal and external validations of the nomogram model were conducted using ROC curves,calibration curves,and decision curve analysis(DCA)curves.Results Compared with CHD patients in the non-MACE group,CRP/D-D and FAR levels were significantly higher in the MACE group(P<0.05).Multivariate analysis showed that age,NTproBNP,WBC,CRP/D-D,and FAR were independent risk factors for postoperative MACE in CHD patients(P<0.05).ROC curve analysis indicated that the AUC predicted by CRP/D-D combined with FAR was higher than that of CRP/D-D alone(Z=3.473,P<0.001),and FAR alone(Z=2.812,P<0.05).The Nomogram model constructed based on the aforementioned factors was validated internally and externally,and the results showed that the Nomogram model had good calibration,excellent discriminative ability,and reliable clinical utility,accurately predicting the risk of postoperative MACE.Conclusion The CRP/D-D ratio and FAR,as emerging composite biomarkers,showed significant predictive ability in predicting the risk of MACE after PCI in patients with CHD,providing a new reliable tool for clinical risk stratification.

Objective:To investigate the knowledge, attitudes, practices (KAP), and demands of medical staff in family physician teams regarding the application of artificial intelligence (AI) in contracted services, and to analyze the influencing factors.Methods:A cross-sectional study was conducted from June to July 2023. A total of 602 medical staff members from family physician teams in Shanghai Minhang District were selected as subjects. Data on demographics (age, gender, institution, position, education, work experience, household registration, professional title, marital status, fertility status) and KAP/demand regarding AI application in contracted services were collected using a self-designed questionnaire. Intergroup differences were analyzed. Multiple stepwise linear regression was employed to identify the main factors influencing AI application demand.Results:Among the 602 participants, 484 (80.4%) were aged 30-49 years, 466 (77.40%) were females, 559 (92.9%) held a bachelor′s degree or higher, and 505 (83.9%) had intermediate or senior professional titles. The awareness rate for knowledge, positive attitude rate, and practice implementation rate regarding AI application were 47.2% (284/602), 73.1% (440/602), and 32.1% (193/602), respectively. The mean scores for knowledge, attitude, and practice were 15.72±3.40, 18.34±3.41, and 14.60±3.89, respectively. Significant differences were found among the items within each KAP dimension (knowledge: F=7.688, P<0.001; attitude: F=5.106, P<0.001; practice: F=6.763, P<0.001). Within knowledge, item K3 (awareness of intelligent elderly monitoring devices) scored lowest (3.00±0.79), differing significantly from K1, K2, K4, and K5 (all P<0.05). Within attitude, item A5 (willingness to fully trust AI′s accuracy and convenience in contracted services) scored lowest (3.57±0.75), differing significantly from A3 and A4 (all P<0.05). Within practice, item P3 (increasing reliance on AI in daily contracted services) scored lowest (2.79±0.93), differing significantly from P1 and P2 (all P<0.05). KAP scores differed significantly across demographic subgroups. Knowledge scores differed significantly by age, gender, and marital status (all P<0.05). Attitude scores differed significantly by gender, household registration, and fertility status (all P<0.05). Practice scores differed significantly by gender, position, and marital status (all P<0.05). Regarding demand, the most frequently selected areas were follow-up services (28.74%, 173/602), data management (26.25%, 158/602), and data collection (25.42%, 153/602). Univariate analysis identified age, gender, education, professional title, fertility status, and KAP scores as significant factors influencing AI application demand (all P<0.05). Multiple stepwise linear regression revealed that older age ( t=3.905, P<0.001), female gender ( t=3.548, P<0.001), and higher practice scores ( t=-3.044, P=0.002) were significant predictors of greater AI application demand. Conclusions:Significant variations exist in the KAP levels regarding AI application among family physician team members. Gender, age, and practice behavior significantly influence demand. Tailored strategies for different subgroups, coupled with timely targeted training and practical exercises, are recommended to enhance the effective and widespread adoption of AI technology in family physician contracted services.