Objective: To analyze the epidemiological characteristics of varicella among nursery children in Shenzhen from 2015 to 2024, and to evaluate the impact of optimizing varicella vaccine (VarV) immunization strategies on varicella incidence. Methods: Varicella incidence data for nursery children in Shenzhen from 2015 to 2024 were obtained from the China Disease Prevention and Control Information System. The study period was divided into three phases:one dose self pay VarV (January 2015 to October 2017), two dose self pay VarV (November 2017 to October 2019), and two dose free VarV (November 2019 to December 2024). Interrupted time series (ITS) analysis was conducted to assess changes in the level and trend of varicella incidence associated with each phase of policy implementation. Results: A total of 27 517 varicella cases was reported among nursery children from 2015 to 2024, with an average annual incidence of 514.01/100 000. During the same period, 136 clustered outbreaks were reported in nursery institutions, involving a cumulative total of 1 091 cases. ITS analysis showed that during the self pay 1 dose stage, the varicella incidence among nursery children showed an upward trend, with an average monthly increase of 2.58/100 000 (95% CI =2.21/ 100 000 -2.95/100 000, P <0.01). After the implementation of the self pay 2 dose strategy, the incidence decreased, with a change in incidence of -26.12/100 000 (95% CI =-37.30/100 000 to -14.94/100 000) and a change in slope of -2.65/100 000 (95% CI = -3.38/100 000 to -1.93/100 000)(all P <0.01). After the implementation of the free 2 dose strategy, the incidence decreased further, with a change in incidence of -40.03/100 000 (95% CI =-50.39/100 000 to -29.66/100 000, P <0.01) and a change in slope of -0.56/100 000 (95% CI =-1.20/100 000-0.08/100 000, P =0.09). Conclusion The gradual optimization of the VarV vaccination strategy in Shenzhen from self pay 1 dose to free 2 dose has significantly reduced the varicella incidence among nursery children, demonstrating good short term control and long term intervention effectiveness.

After continuous development and improvement, lung transplantation has become the preferred means to treat a variety of benign end-stage lung diseases. However, the field of lung transplantation still faces many challenges, including shortage of donor resources, preservation and maintenance of donor lungs, and postoperative complications. Lung tissue samples removed after lung transplantation are excellent clinical resources for the study of benign end-stage lung disease and perioperative complications of lung transplantation. However, at present, the collection, storage and utilization of tissue samples after lung transplantation are limited to a single study, and unified technical specifications have not been formed. Based on the construction plan of the biobank for lung transplantation in the First Affiliated Hospital of Xi'an Jiaotong University, this study reviewed the practical experience in the collection, storage and utilization of lung transplant tissue samples in the aspects of ethical review, staffing, collection process, storage method, quality control and efficient utilization, in order to provide references for lung transplant related research.

OBJECTIVE To provide a reference for the quality control of Hypericum perforatum. METHODS High- performance liquid chromatography （HPLC） was used to establish fingerprints for 20 batches of H. perforatum and determine the contents of its main components： chlorogenic acid， rutin， hyperin， isoquercitrin， avicularin， quercitrin and quercetin. Cluster analysis was conducted using SPSS 26.0 software. The chromaticity values （luminance value L*， red-green value a*， and yellow- blue value b*） of H. perforatum powder were measured using electronic eye. A prediction model for the contents of seven components in H. perforatum based on its appearance chromaticity values was established using machine learning algorithms. The predictive performance of the models was evaluated using root-mean-square-error （RMSE）. RESULTS A total of 16 common peaks were calibrated in the fingerprints of 20 batches of H. perforatum， and 9 peaks were identified， which were chlorogenic acid， rutin， hyperin， isoquercitrin， avicularin， quercitrin， quercetin， hypericin and hyperforin； the similarities of the 20 batches of samples and reference fingerprint ranged from 0.889-0.987. The contents of chlorogenic acid， rutin， hyperin， isoquercitrin， avicularin， quercitrin and quercetin were 0.025%-0.166%， 0.048%-0.339%， 0.082%-0.419%， 0.017%-0.209%， 0.011%-0.134%， 0.020%-0.135%， 0.041%-0.235%， respectively. Cluster analysis results showed that 18 batches of qualified H. perforatum were grouped into three categories， when the Euclidean distance was set to 1.4. L* of the 20 batches of H. perforatum ranged from 62.814 to 75.668， a* ranged from 1.409 to 3.490， and b* ranged from 25.249 to 30.759. RMSE of three prediction models， namely XGBoost， LightGBM， and AdaBoost， ranged from 0.008 to 0.070， indicating good fitting performance. XGBoost model predicted the contents of the other six components with high accuracy， except for rutin. CONCLUSIONS The established fingerprints and content determination methods are accurate， reproducible， and reliable. The content prediction model based on appearance chromaticity values， combined with machine learning algorithms， can be used for the quality control of H. perforatum.

Identifying drug-drug interactions (DDIs) is essential to prevent adverse effects from polypharmacy. Although deep learning has advanced DDI identification, the gap between powerful models and their lack of clinical application and evaluation has hindered clinical benefits. Here, we developed a Multi-Dimensional Feature Fusion model named MDFF, which integrates one-dimensional simplified molecular input line entry system sequence features, two-dimensional molecular graph features, and three-dimensional geometric features to enhance drug representations for predicting DDIs. MDFF was trained and validated on two DDI datasets, evaluated across three distinct scenarios, and compared with advanced DDI prediction models using accuracy, precision, recall, area under the curve, and F1 score metrics. MDFF achieved state-of-the-art performance across all metrics. Ablation experiments showed that integrating multi-dimensional drug features yielded the best results. More importantly, we obtained adverse drug reaction reports uploaded by Xiangya Hospital of Central South University from 2021 to 2023 and used MDFF to identify potential adverse DDIs. Among 12 real-world adverse drug reaction reports, the predictions of 9 reports were supported by relevant evidence. Additionally, MDFF demonstrated the ability to explain adverse DDI mechanisms, providing insights into the mechanisms behind one specific report and highlighting its potential to assist practitioners in improving medical practice.

The unique characteristics of the deep space environment, microgravity, cosmic radiation, and extreme temperature fluctuations, are emerging as major driving forces for pharmaceutical innovation. These factors provide new avenues for optimizing drug formulations, improving crystal structure quality, and accelerating the discovery of therapeutic targets. Advances in deep space research not only help overcome critical bottlenecks in terrestrial drug development but also promote progress in structure-based drug design and deepen understanding of cellular stress-response mechanisms. Current progress in space-based pharmaceutical research primarily includes the study of disease mechanisms under microgravity, protein crystallization in microgravity, and drug development utilizing deep space radiation and resources. However, the operational complexity, high costs, and limited data reproducibility of space experiments remain key challenges hindering widespread application. Looking ahead, with the integration of automation, artificial intelligence analysis, and on-orbit manufacturing, deep space drug development is expected to achieve greater scalability and precision, opening a new frontier in biopharmaceutical science.
Drug Design ; Drug Development/methods* ; Humans ; Weightlessness ; Space Flight ; Artificial Intelligence ; Extraterrestrial Environment

Objective To investigate the effect of microRNA(miR)-34a-5p on neurological function of rats with intracerebral hemorrhage(ICH)by adjusting PTEN-induced kinase 1(PINK1)/Parkin pathway.Methods SD rats were assigned into sham surgery group(Sham),ICH group,inhibitor NC group,miR-34a-5p inhibitor group,miR-34a-5p inhibitor+DMSO group,and miR-34a-5p inhibitor+Mdivi-1 group,with 8 rats in each group.Modified neurological severity score(mNSS)was used to assess changes in neurological function of rats;HE staining was used to observe the pathological changes in the brain tissue of rats;transmission electron microscopy was used to observe autophagy in brain tissue;TUNEL staining was used to observe cell apoptosis;qRT-PCR experiment was used to detect the mRNA levels of miR-34a-5p,PINK1 and Parkin in the brain tissues;Western blot experiments were used to measure PINK1,Parkin,Beclin1 and P62 proteins in the brain tissues of rats;dual luciferase reporter gene assay was used to determine the targeting relationship between miR-34a-5p and PINK1.Results Compared with the inhibitor NC group,the miR-34a-5p inhibitor group demonstrated lower levels of neuronal necrosis,red blood cell amount,inflammatory cell amount,autophagic vacuole amount,mNSS score,TUNEL positivity rate,miR-34a-5p expression and p62 protein,but higher levels of PINK1,Parkin mRNA and protein expression,and Beclin1 protein expression(P＜0.05).Compared with the miR-34a-5p inhibitor+DMSO group,the changes mentioned above in rat of the miR-34a-5p inhibitor+Mdivi-1 group are all reversed(P＜0.05).In the dual luciferase reporter gene experiment,the relative luciferase activity of cells in the miR-34a-5p mimic and PINK1-WT cotransfected group was greatly reduced(P＜0.05).Conclusion The downregulation of miR-34a-5p may alleviate neurological dysfunction in ICH rats by adjusting PINK1/Parkin pathway.

Identifying drug-drug interactions(DDIs)is essential to prevent adverse effects from polypharmacy.Although deep learning has advanced DDI identification,the gap between powerful models and their lack of clinical application and evaluation has hindered clinical benefits.Here,we developed a Multi-Dimensional Feature Fusion model named MDFF,which integrates one-dimensional simplified molec-ular input line entry system sequence features,two-dimensional molecular graph features,and three-dimensional geometric features to enhance drug representations for predicting DDIs.MDFF was trained and validated on two DDI datasets,evaluated across three distinct scenarios,and compared with advanced DDI prediction models using accuracy,precision,recall,area under the curve,and F1 score metrics.MDFF achieved state-of-the-art performance across all metrics.Ablation experiments showed that integrating multi-dimensional drug features yielded the best results.More importantly,we obtained adverse drug reaction reports uploaded by Xiangya Hospital of Central South University from 2021 to 2023 and used MDFF to identify potential adverse DDIs.Among 12 real-world adverse drug reaction reports,the predictions of 9 reports were supported by relevant evidence.Additionally,MDFF demon-strated the ability to explain adverse DDI mechanisms,providing insights into the mechanisms behind one specific report and highlighting its potential to assist practitioners in improving medical practice.

Objective To investigate the clinical effects of different right lung volume reduction techniques when the donor lung is oversized and mismatched with the recipient. Methods Clinical data of 10 recipients who underwent right lung volume reduction lung transplantation at the First Affiliated Hospital of Xi'an Jiaotong University from October 2022 to June 2024 were collected, including gender, age, primary disease type, and type of transplantation. A retrospective analysis was performed on postoperative complications within 90 days, duration of mechanical ventilation, hospital stay, and survival status to explore the impact of different volume reduction techniques on the survival rate of lung transplant recipients. Results A total of 10 right lung volume reduction recipients were included in this study, with 2 cases of upper lobe reduction, 7 cases of middle lobe reduction, and 1 case of lower lobe reduction. Three recipients developed airway complications (one each with upper, middle, and lower lobe reduction). The 30-day survival rate was 90% and the 1-year survival rate was 70%. One recipient with upper lobe reduction died of septic shock during the perioperative period, one with lower lobe reduction died of airway anastomotic fistula 2 months after surgery, and one with middle lobe reduction died of renal insufficiency 1 year after surgery. All 7 recipients with middle lobe reduction successfully passed the perioperative period, with one case of airway anastomotic stenosis (1/7). The average duration of mechanical ventilation was 71 hours, and the average hospital stay was 26 days. The 30-day survival rate was 7/7, and the 1-year survival rate was 6/7. Conclusions Middle lobe reduction in right lung transplantation surgery has the advantages of low incidence of airway complications, good safety, and minimal loss of lung function, and may be a better right lung volume reduction option with potential for application.

Objective To construct and verify an early prediction model for visual benefit of diabetic macular edema(DME)after anti-vascular endothelial growth factor(VEGF)treatment based on clinical data,optical coherence tomo-graphy angiography(OCTA),serum brain tissue aquaporin-4(AQP4)mRNA and total bilirubin(TBIL)levels.Methods A total of 480 patients(480 eyes)with DME treated in the First People's Hospital of Zigong City from October 2021 to March 2024 were selected and divided into a modeling set(320 cases)and a validation set(160 cases)at a ratio of 2∶1.According to the visual benefit after anti-VEGF treatment,patients in the modeling set were further divided into a benefit group(80 cases)and a non-benefit group(240 cases).The baseline data of the two groups of patients were collected,and the factors influencing visual benefits in DME patients after anti-VEGF treatment were analyzed.An early prediction model was constructed and validated both internally and externally.Results The inter-group comparison results showed that the diabetes duration in the non-benefit group was longer than that in the benefit group(P＜0.05).The proportion of smokers,the best corrected visual acuity(BCVA),the minimum resolution angle(logMAR)vision,hemoglobin A1c(HbAlc)and AQP4 mRNA levels were higher in the non-benefit group than those in the benefit group(all P＜0.05).The foveal retinal deep capillary plexus blood flow density(DCP-VD),central macular thickness(CMT),and TBIL levels were lower in the non-benefit group than those in the benefit group(all P＜0.05).The least absolute shrinkage and selection operator(LAS-SO)-Logistic regression analysis showed that the factors influencing visual benefit in DME patients after anti-VEGF treat-ment were CMT,BCVA(logMAR),HbAlc,AQP4 mRNA levels,foveal DCP-VD,and TBIL levels.The predictive risk con-sistency index of the nomogram model constructed based on the above-mentioned influencing factors for visual benefit pre-diction after anti-VEGF treatment was 0.844.The receiver operating characteristic(ROC)curve showed that the area un-der the ROC curve(AUC)of the model was 0.844(95％ CI:0.797-0.891)in the modeling set and 0.898(95％ CI:0.847-0.949)in the validation set.The decision analysis curve showed that when the high-risk threshold of the modeling set ranged between 0 and 82％ and that of the validation set ranged between 0 and 100％,the model could bring net clinical benefits.Conclusion CMT,BCVA(logMAR),HbAlc,AQP4 mRNA levels,foveal DCP-VD,and TBIL levels are the fac-tors influencing visual benefit in DME patients after anti-VEGF treatment.The visual benefit prediction model constructed based on these factors has high accuracy and stability,and can be used as an effective tool for clinical prediction of visual benefit after treatment.

Objective To investigate the effect of microRNA(miR)-34a-5p on neurological function of rats with intracerebral hemorrhage(ICH)by adjusting PTEN-induced kinase 1(PINK1)/Parkin pathway.Methods SD rats were assigned into sham surgery group(Sham),ICH group,inhibitor NC group,miR-34a-5p inhibitor group,miR-34a-5p inhibitor+DMSO group,and miR-34a-5p inhibitor+Mdivi-1 group,with 8 rats in each group.Modified neurological severity score(mNSS)was used to assess changes in neurological function of rats;HE staining was used to observe the pathological changes in the brain tissue of rats;transmission electron microscopy was used to observe autophagy in brain tissue;TUNEL staining was used to observe cell apoptosis;qRT-PCR experiment was used to detect the mRNA levels of miR-34a-5p,PINK1 and Parkin in the brain tissues;Western blot experiments were used to measure PINK1,Parkin,Beclin1 and P62 proteins in the brain tissues of rats;dual luciferase reporter gene assay was used to determine the targeting relationship between miR-34a-5p and PINK1.Results Compared with the inhibitor NC group,the miR-34a-5p inhibitor group demonstrated lower levels of neuronal necrosis,red blood cell amount,inflammatory cell amount,autophagic vacuole amount,mNSS score,TUNEL positivity rate,miR-34a-5p expression and p62 protein,but higher levels of PINK1,Parkin mRNA and protein expression,and Beclin1 protein expression(P＜0.05).Compared with the miR-34a-5p inhibitor+DMSO group,the changes mentioned above in rat of the miR-34a-5p inhibitor+Mdivi-1 group are all reversed(P＜0.05).In the dual luciferase reporter gene experiment,the relative luciferase activity of cells in the miR-34a-5p mimic and PINK1-WT cotransfected group was greatly reduced(P＜0.05).Conclusion The downregulation of miR-34a-5p may alleviate neurological dysfunction in ICH rats by adjusting PINK1/Parkin pathway.