ObjectiveTo evaluate the efficacy and safety of Xiaoji Hufei Formula in protecting children with close contact exposure to influenza， and to provide reference and evidence-based support for better clinical prevention and treatment of influenza in children. MethodsA multicenter， prospective， non-randomized， parallel， controlled trial was conducted from October 2021 to May 2022 in five hospitals， including Dongfang Hospital of Beijing University of Chinese Medicine. Confirmed influenza cases and influenza-like illness （ILI） cases were collected， and eligible children with close contact exposure to these cases were recruited in the outpatient clinics. According to whether the enrolled close contacts were willing to take Xiaoji Hufei formula for influenza prevention， they were assigned to the observation group （108 cases） or the control group （108 cases）. Follow-up visits were conducted on days 7 and 14 after enrollment. The primary outcomes were the incidence of ILI and the rate of laboratory-confirmed influenza. Secondary outcomes included traditional Chinese medicine （TCM） symptom score scale for influenza， influenza-related emergency （outpatient） visit rate， influenza hospitalization rate， and time to onset after exposure to influenza cases. ResultsA total of 216 participants were enrolled， with 108 in the observation group and 108 in the control group. Primary outcomes： （1） Incidence of ILI： The incidence was 12.0% （13/108） in the observation group and 23.1% （25/108） in the control group， with the observation group showing a significantly lower incidence （χ²=4.6， P<0.05）. （2） Influenza confirmation rate： 3.7% （4/108） in the observation group and 4.6% （5/108） in the control group， with no statistically significant difference. Secondary outcomes： （1） TCM symptom score scale： after onset， nasal congestion and runny nose scores differed significantly between the two groups （P<0.05）， while other symptoms such as fever， sore throat， and cough showed no significant differences. （2） Influenza-related emergency （outpatient） visit rate： 84.6% （11 cases） in the observation group and 96.0% （24 cases） in the control group， with no significant difference. （3） Time to onset after exposure： The median onset time after exposure to index patients was 7 days in the observation group and 4 days in the control group， with a statistically significant difference （P<0.05）. ConclusionIn previously healthy children exposed to infectious influenza cases under unprotected conditions， Xiaoji Hufei formula prophylaxis significantly reduced the incidence of ILI. Xiaoji Hufei Formula can be recommended as a specific preventive prescription for influenza in children.

ObjectiveTo evaluate the efficacy and safety of Xiaoji Hufei Formula in protecting children with close contact exposure to influenza， and to provide reference and evidence-based support for better clinical prevention and treatment of influenza in children. MethodsA multicenter， prospective， non-randomized， parallel， controlled trial was conducted from October 2021 to May 2022 in five hospitals， including Dongfang Hospital of Beijing University of Chinese Medicine. Confirmed influenza cases and influenza-like illness （ILI） cases were collected， and eligible children with close contact exposure to these cases were recruited in the outpatient clinics. According to whether the enrolled close contacts were willing to take Xiaoji Hufei formula for influenza prevention， they were assigned to the observation group （108 cases） or the control group （108 cases）. Follow-up visits were conducted on days 7 and 14 after enrollment. The primary outcomes were the incidence of ILI and the rate of laboratory-confirmed influenza. Secondary outcomes included traditional Chinese medicine （TCM） symptom score scale for influenza， influenza-related emergency （outpatient） visit rate， influenza hospitalization rate， and time to onset after exposure to influenza cases. ResultsA total of 216 participants were enrolled， with 108 in the observation group and 108 in the control group. Primary outcomes： （1） Incidence of ILI： The incidence was 12.0% （13/108） in the observation group and 23.1% （25/108） in the control group， with the observation group showing a significantly lower incidence （χ²=4.6， P<0.05）. （2） Influenza confirmation rate： 3.7% （4/108） in the observation group and 4.6% （5/108） in the control group， with no statistically significant difference. Secondary outcomes： （1） TCM symptom score scale： after onset， nasal congestion and runny nose scores differed significantly between the two groups （P<0.05）， while other symptoms such as fever， sore throat， and cough showed no significant differences. （2） Influenza-related emergency （outpatient） visit rate： 84.6% （11 cases） in the observation group and 96.0% （24 cases） in the control group， with no significant difference. （3） Time to onset after exposure： The median onset time after exposure to index patients was 7 days in the observation group and 4 days in the control group， with a statistically significant difference （P<0.05）. ConclusionIn previously healthy children exposed to infectious influenza cases under unprotected conditions， Xiaoji Hufei formula prophylaxis significantly reduced the incidence of ILI. Xiaoji Hufei Formula can be recommended as a specific preventive prescription for influenza in children.

BACKGROUND: Postoperative pulmonary complications (PPCs) are major adverse events in neurosurgical patients. This study aimed to develop and validate machine learning models predicting PPCs after neurosurgery. METHODS: PPCs were defined according to the European Perioperative Clinical Outcome standards as occurring within 7 postoperative days. Data of cases meeting inclusion/exclusion criteria were extracted from the anesthesia information management system to create three datasets: The development (data of Huashan Hospital, Fudan University from 2018 to 2020), temporal validation (data of Huashan Hospital, Fudan University in 2021) and external validation (data of other three hospitals in 2023) datasets. Machine learning models of six algorithms were trained using either 35 retrievable and plausible features or the 11 features selected by Lasso regression. Temporal validation was conducted for all models and the 11-feature models were also externally validated. Independent risk factors were identified and feature importance in top models was analyzed. RESULTS: PPCs occurred in 712 of 7533 (9.5%), 258 of 2824 (9.1%), and 207 of 2300 (9.0%) patients in the development, temporal validation and external validation datasets, respectively. During cross-validation training, all models except Bayes demonstrated good discrimination with an area under the receiver operating characteristic curve (AUC) of 0.840. In temporal validation of full-feature models, deep neural network (DNN) performed the best with an AUC of 0.835 (95% confidence interval [CI]: 0.805-0.858) and a Brier score of 0.069, followed by Logistic regression (LR), random forest and XGBoost. The 11-feature models performed comparable to full-feature models with very close but statistically significantly lower AUCs, with the top models of DNN and LR in temporal and external validations. An 11-feature nomogram was drawn based on the LR algorithm and it outperformed the minimally modified Assess respiratory RIsk in Surgical patients in CATalonia (ARISCAT) and Laparoscopic Surgery Video Educational Guidelines (LAS VEGAS) scores with a higher AUC (LR: 0.824, ARISCAT: 0.672, LAS: 0.663). Independent risk factors based on multivariate LR mostly overlapped with Lasso-selected features, but lacked consistency with the important features using the Shapley additive explanation (SHAP) method of the LR model. CONCLUSIONS: The developed models, especially the DNN model and the nomogram, had good discrimination and calibration, and could be used for predicting PPCs in neurosurgical patients. The establishment of machine learning models and the ascertainment of risk factors might assist clinical decision support for improving surgical outcomes. TRIAL REGISTRATION ChiCTR 2100047474; https://www.chictr.org.cn/showproj.html?proj=128279 .
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Algorithms ; Lung Diseases/etiology* ; Machine Learning ; Neurosurgical Procedures/adverse effects* ; Postoperative Complications/diagnosis* ; Risk Factors ; ROC Curve

OBJECTIVE To investigate the selection strategy for recipient vessels in free flap reconstruction of head and neck defects.METHODS A retrospective analysis was conducted on 96 patients who underwent 99 free flap reconstructions for head and neck defects between January 2020 and December 2024.Recipient vessel selection,flap survival,and postoperative complications were analyzed based on defect location and flap type.RESULTS In 99 cases microvessel anastomosis,the recipient arteries were superior thyroid artery in 49 branches,facial artery in 28 branches,superficial temporal artery in 14 branches,lingual artery in 5 branches.external carotid artery in 1 branch,transverse cervical artery in 1 branch,and superior laryngeal artery in 1 branch.Venous anastomosis was performed in 104 branches,with 94 cases in 1 venous anastomosis and 5 cases in 2 venous anastomoses.The recipient veins selected were facial vein in 62 branches,external jugular vein in 21 branches,superficial temporal vein in 12 branches,retromandibular vein in 3 branches,middle thyroid vein in 2 branches,internal jugular vein in 2 branches,middle temporal vein in 1 branch,and superior thyroid vein in 1 branch.Complete flap necrosis occurred in 5 cases,and partial necrosis occurred in 4 cases.When the recipient vessels were deficient,the lingual artery was chosen in 3 cases,the facial artery in 1 case,the external jugular vein in 3 cases,the internal jugular vein with end-to-side anastomosis in 1 case,and the common facial vein with end-to-side anastomosis in 1 case.CONCLUSION In free flap reconstruction for head and neck defects,the superior thyroid artery,facial artery,and superficial temporal artery are commonly used as recipient arteries,while the facial vein,external jugular vein,and superficial temporal vein are frequently selected as recipient veins.When recipient vessels are scarce,the ipsilateral lingual artery,transverse cervical artery,and main trunk of the internal jugular vein can serve as alternative recipient vessels.

Objective To evaluate the predictive value of a nomogram model developed by integrating clinical and ultrasound multiparameters for HER-2-positive breast cancer.Methods This study retrospectively enrolled 343 patients with pathologically confirmed breast cancer from three medical centers and randomly divided them into training and validation cohorts.Univariate analysis,LASSO regression,and multivariate logistic regres-sion were conducted on the training set to identify independent prognostic factors and construct a nomogram model.Bootstrap resampling with 1000 iterations was performed to evaluate the model's robustness.Model calibration was assessed using calibration curves and the Hosmer-Lemeshow goodness-of-fit test.Receiver operating characteristic(ROC)curves were generated to evaluate model discrimination,and the area under the curve(AUC)along with other performance metrics were calculated.Decision curve analysis was employed to assess the clinical utility of the model,and the validation cohort was used for external validation.Results Univariate,LASSO,and multivariate regression analyses demonstrated that age,TTP(time to peak),and the presence of a filling defect sign were independent predictors of HER-2-positive breast cancer(all P<0.05).Based on these independent predictors,a nomogram model was constructed.Bootstrap validation with 1,000 resamples indicated that the model's predictive performance was stable.The Hosmer-Lemeshow test confirmed satisfactory model calibration,while the calibration curve illustrated accurate prediction probabilities.The area under the curve(AUC)for the training set was 0.863(95%CI:0.806～0.920),and for the validation set,it was 0.846(95%CI:0.764～0.929),indicating strong discriminative and generalization capabilities.Additionally,the clinical decision curve analysis demonstrated favor-able clinical utility.Conclusion A nomogram model integrating clinical and multimodal ultrasound parameters demonstrates potential utility in predicting HER-2-positive breast cancer.

Objective:To understand the current situation of the specialized nursing alliance team after training, and to provide countermeasures for the further construction of the specialized nurse linkage team in medical institutions.Methods:From April to July in 2024, the descriptive qualitative research method was used, 25 community nursing professionals were interviewed by the objective sampling, and the contents were analyzed and refined by the Colaizzi 7-step analysis method.Results:A total of 25 community specialist nurses were all females, aged 31 - 47 years old. Four themes and 15 sub-themes were extracted: internal benefits after the linkage of the specialized nursing alliance team, external benefits after the linkage of the specialized nursing alliance team, existing difficulties in the construction of the specialized nursing alliance team, and future needs of the specialized nursing alliance team construction. Based on this analysis, the present situation of the team construction of specialized nursing alliance is made.Conclusions:Under the background of medical union, the team construction of specialized nursing alliance meets the needs of patients and policy trends, and has achieved phased results. In the future, it is still necessary to further improve the professional ability of community specialized nurses and strengthen multi-channel sustainable cooperation, including reshaping the structure of medical resources, strengthening capital investment, improving the utilization rate of information technology and strengthening the assessment mechanism to promote the improvement of the specialized nursing alliance team.

Objective To evaluate the predictive value of a nomogram model developed by integrating clinical and ultrasound multiparameters for HER-2-positive breast cancer.Methods This study retrospectively enrolled 343 patients with pathologically confirmed breast cancer from three medical centers and randomly divided them into training and validation cohorts.Univariate analysis,LASSO regression,and multivariate logistic regres-sion were conducted on the training set to identify independent prognostic factors and construct a nomogram model.Bootstrap resampling with 1000 iterations was performed to evaluate the model's robustness.Model calibration was assessed using calibration curves and the Hosmer-Lemeshow goodness-of-fit test.Receiver operating characteristic(ROC)curves were generated to evaluate model discrimination,and the area under the curve(AUC)along with other performance metrics were calculated.Decision curve analysis was employed to assess the clinical utility of the model,and the validation cohort was used for external validation.Results Univariate,LASSO,and multivariate regression analyses demonstrated that age,TTP(time to peak),and the presence of a filling defect sign were independent predictors of HER-2-positive breast cancer(all P<0.05).Based on these independent predictors,a nomogram model was constructed.Bootstrap validation with 1,000 resamples indicated that the model's predictive performance was stable.The Hosmer-Lemeshow test confirmed satisfactory model calibration,while the calibration curve illustrated accurate prediction probabilities.The area under the curve(AUC)for the training set was 0.863(95%CI:0.806～0.920),and for the validation set,it was 0.846(95%CI:0.764～0.929),indicating strong discriminative and generalization capabilities.Additionally,the clinical decision curve analysis demonstrated favor-able clinical utility.Conclusion A nomogram model integrating clinical and multimodal ultrasound parameters demonstrates potential utility in predicting HER-2-positive breast cancer.

Objective:To understand the current situation of the specialized nursing alliance team after training, and to provide countermeasures for the further construction of the specialized nurse linkage team in medical institutions.Methods:From April to July in 2024, the descriptive qualitative research method was used, 25 community nursing professionals were interviewed by the objective sampling, and the contents were analyzed and refined by the Colaizzi 7-step analysis method.Results:A total of 25 community specialist nurses were all females, aged 31 - 47 years old. Four themes and 15 sub-themes were extracted: internal benefits after the linkage of the specialized nursing alliance team, external benefits after the linkage of the specialized nursing alliance team, existing difficulties in the construction of the specialized nursing alliance team, and future needs of the specialized nursing alliance team construction. Based on this analysis, the present situation of the team construction of specialized nursing alliance is made.Conclusions:Under the background of medical union, the team construction of specialized nursing alliance meets the needs of patients and policy trends, and has achieved phased results. In the future, it is still necessary to further improve the professional ability of community specialized nurses and strengthen multi-channel sustainable cooperation, including reshaping the structure of medical resources, strengthening capital investment, improving the utilization rate of information technology and strengthening the assessment mechanism to promote the improvement of the specialized nursing alliance team.

Aldehyde oxidase (AOX) is a molybdoenzyme that is primarily expressed in the liver and is involved in the metabolism of drugs and other xenobiotics. AOX-mediated metabolism can result in unexpected outcomes, such as the production of toxic metabolites and high metabolic clearance, which can lead to the clinical failure of novel therapeutic agents. Computational models can assist medicinal chemists in rapidly evaluating the AOX metabolic risk of compounds during the early phases of drug discovery and provide valuable clues for manipulating AOX-mediated metabolism liability. In this study, we developed a novel graph neural network called AOMP for predicting AOX-mediated metabolism. AOMP integrated the tasks of metabolic substrate/non-substrate classification and metabolic site prediction, while utilizing transfer learning from ¹³C nuclear magnetic resonance data to enhance its performance on both tasks. AOMP significantly outperformed the benchmark methods in both cross-validation and external testing. Using AOMP, we systematically assessed the AOX-mediated metabolism of common fragments in kinase inhibitors and successfully identified four new scaffolds with AOX metabolism liability, which were validated through in vitro experiments. Furthermore, for the convenience of the community, we established the first online service for AOX metabolism prediction based on AOMP, which is freely available at https://aomp.alphama.com.cn.

BackgroundThere exist differences in the subjective and objective cognitive functions of patients with depressive disorder， ane there are limited research on influencing factors of such phenomenon currently. ObjectiveTo explore the differences in subjective and objective cognitive function in patients with depressive disorder as well as influencing factors， and to provide references for further understanding of cognitive impairment in patients with depressive disorder. MethodsA total of 77 patients with depressive disorder who received outpatient or inpatient treatment in the Fourth People's Hospital of Chengdu from January 13， 2022 to December 11， 2023 were selected for the study. These patients also met the diagnostic criteria of Diagnostic and Statistical Manual of Mental Disorders， fifth edition（DSM-5）. Various tools were employed to assess patients in this study： Montgomery-Asberg Depression Rating Scale （MADRS） for the depressive symptoms， Perceived Deficits Questionnaire for Depression （PDQ-D） and Chinese Version of Brief Neurocognitive Test Battery （C-BCT） for the subjective and objective cognitive function， Sheehan Disability Scale （SDS） for the social function， and Clinical Global Impression-Severity of Illness（CGI-SI） for the severity of patient's condition. Pearson correlation analysis was used to examine the correlation of subjective and objective cognitive function and their differences with age， years of education， MADRS total score， SDS total score， and CGI-SI score. Multiple linear regression was used to explore the influencing factors of the differences between subjective and objective cognitive function. ResultsThere was a statistically significant difference in the total PDQ-D scores and the difference of subjective and objective cognitive function （D value） between depressive patients with and without medication （t=-4.228， -2.392， P<0.05 or 0.01）. There was no statistically significant correlation in subjective and objective cognitive function in patients with depressive disorder （r=-0.148， P>0.05）. Negative correlations can be observed between the PDQ-D total score and age or years of education （r=-0.333， -0.369， P<0.01）. The PDQ-D total score was positively correlated with MADRS total score， SDS total score and CGI-SI score （r=0.487， 0.637， 0.434， P<0.01）. D value was negatively correlated with age and years of education （r=-0.411， -0.362， P<0.01）， while positively correlated with MADRS total score， SDS total score and CGI-SI score （r=0.259， 0.468， 0.299， P<0.05 or 0.01）. Age （β=-0.328， P<0.01） and SDS total score （β=0.409， P<0.01） were two predictive factors for D value. ConclusionThe difference between subjective and objective cognitive function among patients with depressive disorder is related to several factors including age， years of education， severity of symptoms and impairment of social function. ［Funded by Surface Project of National Natural Science Foundation of China （number， 62173069）； Technological Innovation 2030-Major Project of "Brain Science and Brain-Like Research" （number， 2022ZD0211700）； Key R&D Support Program and Major Application Demonstration Project of Chengdu Science and Technology Bureau （number， 2022-YF09-00023-SN）］