Community-acquired pneumonia （CAP） refers to an infectious inflammation of the lung parenchyma （including the alveolar wall，that is，the broad pulmonary interstitium） acquired outside the hospital. Its common pathogens include streptococcus pneumoniae，respiratory viruses， mycoplasma pneumoniae， and so on. The related factors for the occurrence and development of CAP include patient characteristics （immune function，mucus production and clearance function，coagulation function，physical condition， and comorbidity） and pathogen characteristics （susceptibility，virulence，and antibiotic resistance）. The pathogenesis of CAP lies in immune deficiency，pathogen invasion，inflammatory response disorder，mucus production and clearance disorder， coagulation disorder， and so on. The pathogenesis of CAP in traditional Chinese medicine can be described as "vital Qi deficiency and toxic stasis". Vital Qi deficiency （lack of immunity） is the potential pathogenesis of the disease and easy to be invaded by external pathogens （respiratory pathogens）. Toxic stasis （inflammatory disorder，mucus production and clearance disorder，and coagulation dysfunction） is the key pathogenic factor. Vital Qi deficiency and toxic stasis are intermingled in a state of deficiency and excess，which suggests that the treatment of CAP lies in strengthening vital Qi and eliminating pathogenic factors. This involves strengthening vital Qi in the whole process to consolidate body resistance and nourish promordial Qi. It also involves clearing heat，eliminating phlegm，removing dampness，and dispelling stasis to dispel pathogenic toxins based on the syndrome differentiation. Its action mechanism is to regulate immune and inflammatory responses，resist pathogens，and improve mucus production and clearance， as well as coagulation disorders. Starting from the key pathogenesis of CAP，"vital Qi deficiency and toxic stasis"， this paper discussed the pathogenesis of CAP and summarized the action mechanism of vital Qi strengthening for detoxification in its treatment. It is intended to complement the theoretical system by identifying "vital Qi deficiency and toxic stasis" as the key pathogenesis underlying CAP and the scientific connotation of treating CAP with vital Qi strengthening for detoxification，thereby providing insights for its clinical application.

Over the past two decades, genome-wide association study(GWAS) has identified numerous genetic variants and loci associated with heritable diseases. With the gradual maturation and saturation of GWAS methodologies, transcriptome-wide association study(TWAS) offers a novel perspective by linkinggenetic phenotypes to gene expression levels. By integrating TWAS with other multi-omics analyses, researchers can gain a deeper understanding of heritable diseases. This article provides an overview of recent groundbreaking and representative TWAS methods and tools, analyzes their strengths and limitations, and discusses future trends in TWAS development.

Objective To explore scientific and accurate prediction methods for the incidence of hand, foot, and mouth disease in the post-pandemic era, and to address modeling challenges caused by abnormal fluctuations in case numbers from 2020 to 2023. Methods The seasonal index was used to pre-process the data. The traditional seasonal autoregressive integrated moving average (SARIMA) model, singular spectrum analysis (SSA)-ARIMA model, ARIMA-Long short-term memory (LSTM) model, and SSA-ARIMA-LSTM model were used to fit the incidence from 2013 to 2023, and the incidence of hand, foot and mouth disease in 2024 was predicted. The real data collected in 2024 were used as the test set to compare the prediction performance of the models. Results The fitting performance of the constructed models was as follows: the ARIMA model had MAE=107.50 and RMSE=144.53, the SSA-ARIMA model showed MAE=2.84 and RMSE=4.33, the ARIMA-LSTM model achieved MAE=99.46 and RMSE=131.59, and the SSA-ARIMA-LSTM model had MAE=96.35 and RMSE=132.13. In terms of prediction performance, the ARIMA model resulted in MAE=151.64 and RMSE=146.70, the SSA-ARIMA model demonstrated MAE=41.22 and RMSE=57.01, the ARIMA-LSTM model yielded MAE=220.75 and RMSE=257.89, and the SSA-ARIMA-LSTM model recorded MAE=58.83 and RMSE=72.06. Conclusion The SSA-ARIMA model has the best fitting degree and the highest prediction accuracy, and is suitable for predicting the incidence trend of hand, foot and mouth disease.

OBJECTIVE To develope a predictive model for medication deviation risks during the hospital-to-home transition period in coronary heart disease （CHD） patients with initial treatment， aiming to assist medical staff in rapidly identifying high-risk groups for medication deviation. METHODS A total of 462 CHD patients with initial treatment from the Affiliated Hospital of North China University of Science and Technology （hereinafter referred to as “our hospital”） between January and July 2024 were enrolled. The patients were randomly divided into a modeling group and an internal validation group. The modeling group was further categorized into a medication deviation group and a non-medication deviation group based on whether medication deviations occurred. Similarly， 57 CHD patients with initial treatment from the cardiology department of our hospital between June and September 2025 were collected as an external validation group. Univariate analysis was used to screen predictive factors， followed by multivariate Logistic regression to construct the predictive model. Internal validation methods were employed to evaluate model performance， while external validation methods were used to test the model’s generalizability. RESULTS The 462 patients were divided into a modeling group （319 cases） and an internal validation group （143 cases）. In the modeling group， the medication deviation group （192 cases， 60.19%） and the non-medication deviation group （127 cases， 39.81%） were identified. Multivariate Logistic regression analysis revealed that age， medication type， medication adherence， and self-efficacy in rational medication use were predictive factors for medication deviations in CHD patients with initial treatment （ P ＜0.05）. The predictive model equation was logit P ＝ln[ P /（1－ P ） ] ＝1.321+1.732×age+4.091×medication type －4.360×medication adherence －3.081×self-efficacy in rational medication use. The model demonstrated good discrimination， with a Hosmer-Lemeshow goodness-of-fit test P -value of 0.439， an area under the receiver operating characteristic curve （AUC） of 0.870， sensitivity of 0.970， and specificity of 0.607. A risk nomogram with a total score of 350 points and a cutoff value of 110 points was plotted. The internal validation group showed an AUC o f 0.787 and a prediction accuracy of 77.6%， while the external validation group exhibited an AUC of 0.802 and a prediction accuracy of 73.7%. CONCLUSIONS This study successfully developed a predictive model for medication deviation risks during the hospital-to-home transition period in CHD patients with initial treatment. The model demonstrates excellent discrimination and predictive accuracy， effectively identifying high-risk populations for medication deviations. Age （＞70 years）， number of drug types≥5， poor medication adherence， and poor self-efficacy in rational medication use are independent risk factors for medication deviations.

BackgroundThe violent aggressive behaviors of patients with schizophrenia usually have the characteristics of suddenness， unpredictability， high severity， and great difficulty in prevention. Early identification and accurate assessment of their risk of violent aggression have significant clinical significance. ObjectiveTo construct a predictive model for the violence risk in hospitalized patients with schizophrenia， to identify the key factors influencing the occurrence of violent behavior in these patients， so as to provide references for clinical precise quantitative assessment and early intervention. MethodsA total of 200 patients with schizophrenia who were hospitalized at Taiyuan Psychiatric Hospital from March 2022 to September 2024 and met the diagnostic criteria of the International Classification of Diseases， eleventh edition （ICD-11） were collected to form the modeling cohort. They were randomly divided into a training set （n=140） and a test set （n=60） at a ratio of 7∶3. Based on the least absolute shrinkage and selection operator （LASSO） regression algorithm， the feature variables were screened and dimension-reduced. The support vector machine （SVM） from machine learning was selected for model training and prediction. The discrimination efficacy of the model was evaluated by the area under the receiver operating characteristic （ROC） curve （AUC）， accuracy， precision， sensitivity， specificity， F1 value， and Brier value. ResultsLASSO regression screening identified 16 feature variables. Pearson correlation analysis revealed a positive correlation between prior violent behavior frequency and clinical psychiatric symptom scores （r=0.580， P<0.01）， a positive correlation between hospitalization compliance and current disease status （r=0.550， P=0.003）， and a positive correlation between educational level and family per capita monthly income （r=0.367， P<0.01）. The SVM model achieved an AUC of 0.853， accuracy of 0.800， precision of 0.810， sensitivity of 0.895， specificity of 0.636， F1 value of 0.850， and Brier value of 0.168. ConclusionThe SVM model has a relatively high level of applicability and overall predictive performance in the assessment of violent risk in schizophrenia patients， which is helpful for the early identification of violent risks in such patients. ［Funded by Specialized Research Project for Enhancing the Competence of Health Professionals in Taiyuan City （number， Y2023006）］

Objective To develop a predictive model for postoperative pulmonary complications (PPC) following video-assisted thoracic surgery (VATS) in lung cancer patients by integrating cardiopulmonary exercise testing (CPET) parameters and machine learning techniques. Methods A retrospective analysis was conducted on patients with early-stage non-small cell lung cancer who underwent CPET and VATS at Guangdong Provincial People’s Hospital between October 2021 and July 2023. Patients were divided into a PPC group and a non-PPC group. The least absolute shrinkage and selection operator (LASSO) regression was used to select important features associated with PPC. Six machine learning algorithms were utilized to construct prediction models, including logistic regression, support vector machine, k-nearest neighbors, random forest, gradient boosting machine, and extreme gradient boosting. The optimal model was interpreted using SHapley Additive exPlanations (SHAP). Results A total of 325 patients were included, with an average age of 60.36 years, and 55.1% were male. Significant differences were observed between the PPC and non-PPC groups in age, diabetes, coronary heart disease, surgical approach, forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), FVC% predicted, peak oxygen uptake (peak VO2), anaerobic threshold (AT), and ventilatory equivalent for carbon dioxide slope (VE/VCO2 slope) (P<0.05). In the predictive model constructed by selecting 7 key features using LASSO regression, the random forest model demonstrated the best overall performance across various metrics, with an area under the receiver operating curve of 0.930, an F1 score of 0.836, and a Brier score of 0.133 in the training set. It also exhibited good predictive ability and calibration in the test set. SHAP analysis ranked feature importance as follows: peak VO2, VE/VCO2 slope, age, FEV1, smoking history, diabetes, and surgical approach. Conclusion Integrating CPET parameters, the random forest model can effectively identify high-risk patients for PPC and has the potential for clinical application.

ObjectiveTo construct a multifunctional liposomal delivery system by replacing cholesterol（Chol） in conventional liposomes with saikosaponin D（SSD） and modifying with poloxamer 407（P407） for co-delivery of curcumin（Cur）. The system was evaluated for in vivo tumor targeting and inhibitory effects on mouse subcutaneous solid tumors. MethodsSingle-factor and orthogonal tests combined with information entropy weighting were used to optimize the formulation process of the liposome with encapsulation efficiency and absolute Zeta potential as indexes， and validation studies and liposomal characterization were performed. A subcutaneous solid tumor model was established by injecting H22 hepatocellular carcinoma cells subcutaneously into the dorsal surface of the right forelimb of mice. DiR-loaded traditional Chol liposomes（P407-DiR-Chol-LPs， PDCL） and novel SSD-based liposomes（P407-DiR-SSD-LPs， PDSL） were prepared by the optimized formulation process， and tail vein injection was performed to investigate the impact of SSD on liposome tumor targeting with small animal in vivo imaging. Mice were randomly divided into eight groups， including blank group， model group， free doxorubicin（DOX） group（2 mg·kg^-1）， free Cur group（8 mg·kg^-1）， free SSD group（10 mg·kg^-1）， P407-Cur-Chol-LPs（PCCL） group， P407-SSD-LPs（PSL） group， and P407-Cur-SSD-Lps（PCSL） group. Treatments were administered intraperitoneally every other day for seven doses. Antitumor efficacy and biocompatibility were evaluated by monitoring body weight change， organ indices， tumor volume and mass， relative tumor proliferation rate（T/C）， and tumor growth inhibition rate（TGI）. Histopathological analysis of liver， kidney， and tumor tissues was performed using hematoxylin-eosin（HE） staining. Serum levels of aspartate aminotransferase（AST）， alanine aminotransferase （ALT）， blood urea nitrogen（BUN）， and creatinine（Crea）in mice were quantified by fully automated biochemical analyzer. ResultsOrthogonal test yielded optimal ratios of Cur， SSD， and P407 to soybean phosphatidylcholine（SPC） as 1∶25， 1∶20， and 1∶4. The optimized PCSL exhibited spherical morphology with a particle size of 179.15 nm， a Zeta potential of -47.25 mV， and an encapsulation efficiency of 96.40%. Its in vitro release profile conformed to first-order kinetics， demonstrating excellent storage stability and hemocompatibility. In vivo imaging revealed that the fluorescence signal in tumor tissues and the fluorescence intensity ratio between tumors and organs were significantly higher in the PDSL group than in the PDCL group（P<0.05， P<0.01）. Among the treatment groups， PCSL group showed superior efficacy over free Cur group， free SSD group， PCCL group， and PSL group， with TGI>40% and T/C<60%， indicating pronounced anti-hepatocellular carcinoma effects（P<0.05， P<0.01）. Histopathology and serum biochemistry indicated minimal hepatorenal toxicity and improved hepatic and renal function in PCSL-treated mice. ConclusionReplacing Chol with SSD in preparing multifunctional drug delivery systems not only stabilizes liposomes but also yields superior anti-hepatocellular carcinoma efficacy， achieving the effect of drug-excipient integration. Co-delivery of Cur via this system can be used for treating subcutaneous solid tumors in hepatocellular carcinoma， providing new insights and technical approaches for anti-hepatocellular carcinoma research and the meridian-guiding and messenger-directing theory in traditional Chinese medicine.

Traditional Chinese medicine （TCM） diagnostics is a discipline that studies the basic theories and fundamental skills of diagnostic methods， disease diagnosis， and differentiation in accordance with the theories of TCM. The artificial intelligence （AI） technology has gained remarkable achievements in the intelligentization of the four diagnostic methods in TCM and the standardization of differentiation and diagnosis. However， it still faces many challenges. The standardization of clinical data collection is difficult， and the data quality is uneven， which affects the usability of the data. The integration of the four diagnostic information is insufficient. Most instruments can only collect data from a single diagnostic method， lacking overall integrity. The scientific nature of the diagnostic model needs to be improved. The existing models lack dynamics and the reasoning logic of TCM differentiation. The accuracy of intelligent methods needs to be improved， and the existing evaluation indicators cannot fully reflect the practical application effect of the model. Furthermore， the relevant laws and regulations are still not perfect， and data security and patient privacy lack guarantees. The cultivation of compound talents is insufficient， and there is a lack of interdisciplinary talents who are proficient in both TCM and AI. On this basis， this paper expounded on the current development status， difficulties， and bottlenecks of AI in TCM diagnosis and then explored the development trend of AI in the field of TCM diagnosis. It proposed solutions such as optimizing the data collection process， constructing multimodal diagnostic models， facilitating multi-disciplinary exchanges and cooperation， improving laws and regulations， and cultivating compound talents. It is hoped that modern， standardized， normalized， and intelligent TCM diagnosis can be further promoted， thereby providing new impetus and methods for the inheritance and innovation of TCM.

Objective To analyze the newly reported HIV-1 infection in several prefectures of Hubei Province，and analyze its influencing factors. Methods The limiting antigen avidity enzyme immunoassay(LAg-avidity EIA,LAg) was conducted on HIV-1 positive samples confirmed by Western blot of Hubei in 2017-2022. The demographic characteristics of the newly infected samples were analyzed by χ2 test.Logistic regression model was used to analyze influencing factors of new infection rate and predict the factors associated with the HIV-1 recent infection. Results There were 403 new cases of HIV-1 from 2017 to 2022 in several prefectures of Hubei Province, of which 77 were newly infected sorted by LAg,with a new infection rate of 19.11%. The newly confirmed HIV-1 persons of whom aged ≤24 years (40.00% new infection ratio), unmarried (29.41%), college or above (31.37%), and from Voluntary counseling and testing testing(VCT) clinics (40.00%) had a higher proportion of new infections, and the difference was statistically significant. Multivariate Logistic regression analysis showed that age ≤24 years old (aOR=4.346,95%CI: 1.342-14.075) and screening from the VCT clinic (aOR=6.761,95%CI: 1.460-31.319) were more likely to be newly infected. Conclusion The proportion of new HIV infection in several prefectures of Hubei province is relatively low in recent years.Further effective publicity and intervention measures for young students and the construction of VCT clinic should be continuously promoted to achieve early diagnosis and treatment.

OBJECTIVE To study the improvement effect and mechanism of Dendrobium officinale on skin damage in mice with xeroderma. METHODS The mice were randomly divided into control group， model group， and D. officinale group， with 5 mice in each group. Except for the control group （which only underwent shaving treatment）， the mice in all other groups were induced to develop a xeroderma model using an acetone-ether mixture for five consecutive days. The mice in D. officinale group were treated with 200 μL of D. officinale suspension （0.2 mg/mL） two hours after the first modeling each day. Mice in the control group and the model group were applied with an equal volume of pure water； once a day， until the end of the modeling process. After last medication， skin lesions and pathological morphology of the mice were observed. Immunofluorescence was used to detect the expressions of Filaggrin， Loricrin and Ki67 proteins in skin tissue of the mice. The core pathways through which D. officinale improves skin damage in xeroderma were screened using 16S rRNA sequencing combined with gene ontology and Kyoto Encyclopedia of Genes and Genomes （KEGG） enrichment analyses， and subsequent validation was conducted. RESULTS Compared with the control group， the mice in the model group exhibited obvious scratching behavior， with a large amount of scale on the skin， excessive epidermal keratinization， and thickened stratum spinosum. The skin scale score， epidermal thickness， and the expression levels of Ki67， Filaggrin， and Loricrin proteins in the skin tissue were significantly increased/elevated （ P ＜0.05）. Compared with model group， the mice in the D. officinale group exhibited reduced scratching behavior and scaling， along with a mitigated degree of skin keratinization. The aforementioned quantitative indicators were significantly decreased/reduced （ P ＜0.05）. The results of core pathway screening revealed that the KEGG pathways involving differentially expressed genes included signaling pathways such as interleukin-17 （IL-17） and tumor necrosis factor （TNF）. Further validation experim ents found that after intervention with D. officinale ， mRNA expression of downstream effector molecules CCN1， Hbegf， Tnfrsf12a， and Thbs1 genes in skin tissues were all significantly reduced （ P ＜0.05）. CONCLUSIONS D. officinale can repair skin damage in mice with xeroderma， and its mechanism of action is related to restoring the balance of proliferation and differentiation in keratinocytes and down-regulating the mRNA expressions of CCN1， Hbegf， Tnfrsf12a， and Thbs1.