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.

Tropical diseases, the transmission of which is affected by multiple natural and social factors, pose a great challenge to global public health, notably in African countries. During the past several decades, China and African countries have continuously collaborated for the control of neglected tropical diseases and malaria, which has become an important part of global South-to-South cooperation and global health governance. This article reviews the history of China-Africa cooperation for tropical diseases control, summarizes the experiences and achievements over the past decade, analyzes the current challenges in the coopera tion, and proposes future recommendations. The China-Africa cooperation has achieved significant progress in the control of tropical diseases, such as malaria, schistosomiasis, and filariasis, and established a China-Africa cooperation network for tropical diseases control. Through the "Three-Step" strategy of China-Africa cooperation, the effectiveness of China's integrated control strategies has been validated in Africa, and the application of China's tropical disease control technologies has been promoted in African disease-epidemic countries. Currently, China-Africa collaboration, however, still experiences multiple realistic challenges, such as insufficient resources, difficulty in technology transfer, and weak primary healthcare systems. In the future, both sides are recommended to further strengthen policy coordination, deepen technological cooperation, innovate cooperation models, aiming to continuously promote the high-quality development of China-Africa cooperation for tropical diseases control.

ObjectiveTo evaluate the efficacy and possible mechanism of Wei's Huoxue Tongluo Formula （韦氏活血通络方，WHTF） in treating atrophic-stage non-arteritic anterior ischemic optic neuropathy （NAION） with qi deficiency and blood stasis. MethodsA total of 82 atrophic-stage NAION patients with qi deficiency and blood stasis were randomly divided into a treatment group and a control group， with 41 cases in each group. The treatment group was given oral administration of WHTF twice a day plus acupoint injection of distilled water 2 ml at Taiyang （EX-HN5） once daily， while the control group received injection of compound anisodine injection 2 ml at Taiyang （EX-HN5） once daily and oral administration of WHTF placebo twice a day. Both groups received treatment for a course of 14 days. The best-corrected visual acuity （BCVA）， optic disc perfusion density （PD）， flux index （FI）， macular superficial PD， vascular density （VD）， and traditional Chinese medicine （TCM） syndrome scores were compared between groups before treatment and on day 7 and day 14 of treatment. Additionally， mean defect （MD） and mean sensitivity （MS） of visual fields were measured before treatment and on day 14， along with safety evaluation. ResultsAfter treatment， both groups showed significant improvement in BCVA， visual field MD and MS， and TCM syndrome scores （P＜0.05 or P＜0.01）. On day 14 of treatment， the TCM syndrome score in the treatment group was significantly lower than that in the control group （P＜0.05）. There was no significant improvement in optic disc PD and FI， and macular superficial PD and VD after treatment in either group （P＞0.05） except that on day 7 the macular superficial foveal PD in the control group was significantly better than that in the treatment group （P＜0.05）. During the treatment period， no serious adverse events occurred in either group. ConclusionWHTF can improve the visual function indicators including visual acuity and visual field， as well as TCM syndrome scores in atrophic-stage NAION patients with qi deficiency and blood stasis. It shows clinical safety， although it does not appear to have a significant effect on optic disc or macular blood flow.

Objective To compare the performance of seasonal difference autoregressive moving average exogenous variable model (SARIMAX), backpropagation neural network (BPNN), long short-term memory network (LSTM), and various combination models in fitting and predicting the incidence of hand, foot, and mouth disease (HFMD), and to provide a reference for optimizing HFMD prediction and prevention and control decisions. Methods Taking the monthly HFMD incidence data in Changsha from 2010 to 2024 as the training set, and the monthly HFMD incidence data from January to August 2025 as the validation set, the single models, SARIMAX-BPNN, SARIMAX-LSTM and weight combination models were constructed respectively. The R², mean absolute percentage error (MAPE), mean absolute error (MAE), and root mean square error (RMSE) were used to evaluate the model fitting and prediction performance. Results Based solely on MAPE, the LSTM model showed the best fitting and prediction performance. A comprehensive analysis of multiple indicators including R², MAPE, MAE, and RMSE indicated that the combination models had superior prediction performance. Compared with the LSTM, RMSE predicted by equal weight combination, different weight combination and SARIMAX-BPNN decreased by 40.97%, 37.50%, and 25.00%, respectively, while MAPE increased by 124.84%, 96.90%, and 89.69%, respectively. The fitting effect was as follows: LSTM > unequal weight combination > equal weight combination > SARIMAX-LSTM > SARIMAX-BPNN > BPNN > SARIMAX. The prediction performance was as follows: SARIMAX-BPNN > unequal weight combination > equal weight combination > LSTM > SARIMAX-LSTM > BPNN > SARIMAX. Conclusion SARIMAX-BPNN combination or weighted combination of multiple different models are two more robust combination strategies for predicting the incidence trend of hand, foot and mouth disease in Changsha.

Objective: To investigate the awareness of knowledge about hepatitis C prevention and control among outpatients in Ningbo City, Zhejiang Province, and its influencing factors, so as to provide the evidence for strengthening health education on hepatitis C prevention and control. Methods: Based on sentinel surveillance of hepatitis C, the outpatients aged 15 to 65 years at seven hospitals in Yinzhou District, Cixi City and Xiangshan County of Ningbo City were selected using the convenient sampling method from April to June during 2020 and 2022. Demographic information, knowledge and behaviors related to hepatitis C prevention and control were collected through questionnaire surveys. The influencing factors for knowledge about hepatitis C prevention and control were analyzed using a multivariable logistic regression model. Results: A total of 2 792 participants were surveyed, including 1 157 males (41.44%) and 1 635 females (58.56%). The awareness rate of knowledge about hepatitis C prevention and control was 56.23%, and was lower in knowledge about hepatitis C vaccine and treatment. The awareness rates of knowledge about hepatitis C prevention and control among outpatients from 2020 to 2022 were 47.11%, 53.22% and 70.65%, respectively, showing an upward trend (P<0.05). Multivariable logistic regression analysis showed that participants aged 25 to <50 years (OR=1.358, 95%CI: 1.073-1.719), with an educational level of high school or junior college (OR=1.431, 95%CI: 1.134-1.806) or above junior college (OR=3.728, 95%CI: 2.958-4.699), with household monthly income per capita of 3 000 to <5 000 yuan (OR=1.828, 95%CI: 1.344-2.486) or ≥5 000 yuan (OR=1.858, 95%CI: 1.366-2.526), without a history of invasive treatments such as pedicure in public places (OR=1.287, 95%CI: 1.024-1.618), without a history of contact with family members' blood-contaminated items (OR=2.050, 95%CI: 1.552-2.707), and always using condoms during sexual contacts (OR=1.740, 95%CI: 1.273-2.378) had higher awareness of knowledge about hepatitis C prevention and control. Conclusions The awareness of knowledge about hepatitis C vaccine and treatment among outpatients in Ningbo City needs to be improved. Age, educational level, household monthly income per capita, history of invasive treatments such as pedicure in public places, history of contact with family members' blood-contaminated items and frequency of condom use during sexual contacts are associated with outpatients' awareness of knowledge about hepatitis C prevention and control.

ObjectiveFor prepubertal and urgently treated malignant tumor patients, ovarian tissue cryopreservation and transplantation represent more appropriate fertility preservation methods. Current clinical practices often involve freezing ovarian tissue with high concentrations of cryoprotectants (CPAs) and thawing with water baths. These processes lead to varying degrees of toxicity and devitrification damage to ovarian tissue. Therefore, this paper proposes optimized methods for vitrification of ovarian tissues based on sodium alginate hydrogel encapsulation and magnetic induction nanowarming technology. MethodsFirstly, the study investigated the effects of sodium alginate concentration, the sequence of hydrogel encapsulation and CPAs loading on vitrification efficiency of encapsulated ovarian tissue. Additionally, the capability of sodium alginate hydrogel encapsulation to reduce the required concentration of CPAs was validated. Secondly, a platform combining water bath and magnetic induction nanowarming was established to rewarm ovarian tissue under various concentrations of magnetic nanoparticles and magnetic field strengths. The post-warming follicle survival rate, antioxidant capacity, and ovarian tissue integrity were evaluated to assess the efficacy of the method. ResultsThe study found that ovarian tissue encapsulated with 2% sodium alginate hydrogel exhibited the highest follicle survival rate after vitrification. The method of loading CPAs prior to encapsulation proved more suitable for ovarian tissue cryopreservation, effectively reducing the required concentration of CPAs by 50%. A combination of 8 g/L Fe₃O₄ nanoparticles and an alternating magnetic field of 300 Gs showed optimal warming effectiveness for ovarian tissue. Combining water bath rewarming with magnetic induction nanowarming yielded the highest follicle survival rate, enhanced antioxidant capacity, and preserved tissue morphology. ConclusionSodium alginate hydrogel encapsulation of ovarian tissue reduces the concentration of CPAs required during the freezing process. The combination of magnetic induction nanowarming with water bath provides an efficient method ovarian tissue rewarming. This study offers novel approaches to optimize ovarian tissues vitrification.

Acute liver failure (ALF) is lack of broadly approved therapeutic strategy except liver transplantation. As a glycogen metabolic intermediate, UDP-glucose (UDP-G) has been considered to accelerate liver repairment. Nevertheless, the role of UDP-G and its receptor P2Y purinoceptor 14 (P2Y₁₄R) in ALF remains unknown. The present study aims to investigate the role and underlying mechanisms of UDP-G/P2Y₁₄R axis in ALF. In this study, hepatic P2Y₁₄R is significantly increased in TAA-induced and partial hepatectomy-induced ALF, while knockout of whole-body P2Y₁₄R aggravates liver failure, manifested by inhibiting β-Catenin-mediated liver regeneration. Consistently, P2Y₁₄R deficiency exhibits impaired liver regeneration in mice suffer partial hepatectomy. Importantly, only hepatocellular specific deletion of P2Y₁₄R (P2Y₁₄R ^flox/flox Alb ^cre/+ ) mice shows a similar phenomenon, rather than stellate cell specific deletion of P2Y₁₄R (P2Y₁₄R ^flox/flox Lrat ^cre/+ ) mice. Mechanistically, P2Y₁₄R induction regulates methylation of Dact-2 through CREB/DNMT3b signals in hepatocytes, subsequently inhibiting the expression of Dact-2 which is a stabilizer of β-Catenin degradation complex, leading to the activation of β-Catenin -mediated liver regeneration. Interestingly, the administration of exogenous UDP-G can accelerate liver regeneration and liver function recovery after partial hepatectomy in hepatocellular carcinoma mice. Together, the findings propose an unrecognized role of P2Y₁₄R in ALF and provide an effective adjuvant strategy for treatment of ALF.

ObjectiveTo explore the construction of a machine learning model for the diagnosis of traditional Chinese medicine （TCM） syndromes in chronic cough and the optimization of this model using the Voting ensemble algorithm. MethodsA retrospective analysis was conducted using clinical data from 921 patients with chronic cough treated at the Respiratory Department of Dongfang Hospital， Beijing University of Chinese Medicine. After standardized processing， 84 clinical features were extracted to determine TCM syndrome types. A specialized dataset for TCM syndrome diagnosis in chronic cough was formed by selecting syndrome types with more than 50 cases. The synthetic minority over-sampling technique （SMOTE） was employed to balance the dataset. Four base models， logistic regression （LR）， decision tree （dt）， multilayer perceptron （MLP）， and Bagging， were constructed and integrated using a hard voting strategy to form a Voting ensemble model. Model performance was evaluated using accuracy， recall， precision， F1-score， receiver operating characteristic （ROC） curve， area under the curve （AUC）， and confusion matrix. ResultsAmong the 921 cases， six syndrome types had over 50 cases each， phlegm-heat obstructing the lung （294 cases）， wind pathogen latent in the lung （103 cases）， cold-phlegm obstructing the lung （102 cases）， damp-heat stagnating in the lung （64 cases）， lung yang deficiency （54 cases）， and phlegm-damp obstructing the lung （53 cases）， yielding a total of 670 cases in the specialized dataset. High-frequency symptoms among these patients included cough， expectoration， odor-induced cough， throat itchiness， itch-induced cough， and cough triggered by cold wind. Among the four base models， the MLP model showed the best diagnostic performance （test accuracy： 0.9104； AUC： 0.9828）. Compared with the base models， the Voting ensemble model achieved superior performance with an accuracy of 0.9289 on the training set and 0.9253 on the test set， showing a minimal overfitting gap of 0.0036. It also achieved the highest AUC （0.9836） in the test set， outperforming all base models. The model exhi-bited especially strong diagnostic performance for damp-heat stagnating in the lung （AUC： 0.9984） and wind pathogen latent in the lung （AUC： 0.9970）. ConclusionThe Voting ensemble algorithm effectively integrates the strengths of multiple machine learning models， resulting in an optimized diagnostic model for TCM syndromes in chronic cough with high accuracy and enhanced generalization ability.

Lung transplantation is the ultimate therapeutic option for end-stage pulmonary diseases such as interstitial pneumonia, chronic obstructive pulmonary disease and pneumoconiosis. Currently, the shortage of allogeneic lung donors significantly limits the opportunity for end-stage lung disease patients to receive lung transplantation. In recent years, with the rapid development of biomedical engineering technologies, especially the major breakthroughs in genetic modification and cloning, xenogeneic lung transplantation has shown important potential for clinical translation. Among them, genetically modified pigs have become the most promising xenogeneic lung source due to the close similarity of organ size and physiological characteristics to humans, and the ability to perform targeted gene knockouts (such as α-Gal antigen knockout) to reduce the occurrence of hyperacute rejection. This article focuses on the research progress of porcine xenogeneic lung transplantation, systematically reviews the latest achievements and challenges in animal experiments and human trials, and introduces the technical experience accumulated by Shenzhen Third People's Hospital in the porcine-to-monkey xenogeneic lung transplantation model, in the hope of providing practical references for future research in this field.