ObjectiveBased on the concept of quality by design（QbD）， the processing process of calcined Pyritum was optimized， and its X-ray diffraction（XRD） fingerprint was established. MethodsThe safety， effectiveness and quality controllability of calcined Pyritum were taken as the quality profile（QTPP）， the color， hardness， metallic luster， phase composition， the contents of heavy metals and hazardous elements were taken as the critical quality attributes（CQAs）， and the calcination temperature， calcination time， paving thickness and particle size were determined as the critical process parameters（CPPs）. Differential thermal analysis， X-ray diffraction（XRD） and inductively coupled plasma mass spectrometry（ICP-MS） were used to analyze the correlation between the calcination temperature and CQAs of calcined Pyritum. Then， based on the criteria importance through intercriteria correlation（CRITIC）-entropy weight method， the optimal processing process of calcined Pyritum was optimized by orthogonal test. Powder XRD was used to analyze the phase of calcined Pyritum samples processed according to the best process， and the mean and median maps of calcined Pyritum were established by the superposition of geometric topological figures， and similarity evaluation and cluster analysis were carried out. ResultsThe results of single factor experiments showed that the physical phase of Pyritum changed from FeS₂ to Fe₇S₈ during the process of temperature increase， the color gradually deepened from dark yellow， and the contents of heavy metals and harmful elements decreased. The optimized processing process of calcined Pyritum was as follows：calcination temperature at 750 ℃， calcination time of 2.5 h， paving thickness of 3 cm， particle size of 0.8-1.2 cm， vinegar quenching 1 time［Pyritum-vinegar（10∶3）］. After calcination， the internal structure of Pyritum was honeycomb-shaped， which was conducive to the dissolution of active ingredients. XRD fingerprints of 13 batches of calcined Pyritum characterized by 10 common peaks were established. The similarities of the relative peak intensities of the XRD fingerprints of the analyzed samples were>0.96， and it could effectively distinguish the raw products and unqualified products. ConclusionTemperature is the main factor affecting the quality of calcined Pyritum. After processing， the dissolution of the effective components in Pyritum increases， and the contents of heavy metals and harmful substances decrease， reflecting the function of processing to increase efficiency and reduce toxicity. The optimized processing process is stable and feasible， and the established XRD fingerprint can be used as one of the quality control standards of calcined Pyritum.

ObjectiveBased on the concept of quality by design（QbD）， the processing process of calcined Pyritum was optimized， and its X-ray diffraction（XRD） fingerprint was established. MethodsThe safety， effectiveness and quality controllability of calcined Pyritum were taken as the quality profile（QTPP）， the color， hardness， metallic luster， phase composition， the contents of heavy metals and hazardous elements were taken as the critical quality attributes（CQAs）， and the calcination temperature， calcination time， paving thickness and particle size were determined as the critical process parameters（CPPs）. Differential thermal analysis， X-ray diffraction（XRD） and inductively coupled plasma mass spectrometry（ICP-MS） were used to analyze the correlation between the calcination temperature and CQAs of calcined Pyritum. Then， based on the criteria importance through intercriteria correlation（CRITIC）-entropy weight method， the optimal processing process of calcined Pyritum was optimized by orthogonal test. Powder XRD was used to analyze the phase of calcined Pyritum samples processed according to the best process， and the mean and median maps of calcined Pyritum were established by the superposition of geometric topological figures， and similarity evaluation and cluster analysis were carried out. ResultsThe results of single factor experiments showed that the physical phase of Pyritum changed from FeS₂ to Fe₇S₈ during the process of temperature increase， the color gradually deepened from dark yellow， and the contents of heavy metals and harmful elements decreased. The optimized processing process of calcined Pyritum was as follows：calcination temperature at 750 ℃， calcination time of 2.5 h， paving thickness of 3 cm， particle size of 0.8-1.2 cm， vinegar quenching 1 time［Pyritum-vinegar（10∶3）］. After calcination， the internal structure of Pyritum was honeycomb-shaped， which was conducive to the dissolution of active ingredients. XRD fingerprints of 13 batches of calcined Pyritum characterized by 10 common peaks were established. The similarities of the relative peak intensities of the XRD fingerprints of the analyzed samples were>0.96， and it could effectively distinguish the raw products and unqualified products. ConclusionTemperature is the main factor affecting the quality of calcined Pyritum. After processing， the dissolution of the effective components in Pyritum increases， and the contents of heavy metals and harmful substances decrease， reflecting the function of processing to increase efficiency and reduce toxicity. The optimized processing process is stable and feasible， and the established XRD fingerprint can be used as one of the quality control standards of calcined Pyritum.

ObjectiveTo observe the clinical effectiveness and safety of Xiaozhong Zhitong Mixture （消肿止痛合剂） combined with antibiotic bone cement in the treatment of diabetic foot ulcer （DFU） with damp-heat obstructing syndrome. MethodsA total of 72 DFU patients with damp-heat obstructing syndrome were randomly assigned to treatment group （36 cases） and the control group （36 cases）. Both groups received standard treatment and topical antibiotic bone cement for ulcer wounds， while the treatment group received oral Xiaozhong Zhitong Mixture （50 ml per time， three times daily） in additionally. Both groups underwent daily wound dressing changes for 21 consecutive days. Ulcer healing rate， serum levels of tumor necrosis factor-alpha （TNF-α）， interleukin-1 beta （IL-1β）， malondialdehyde （MDA）， superoxide dismutase （SOD）， C-reactive protein （CRP）， and white blood cell （WBC） count were observed before and after treatment， and visual analog scale （VAS） scores for wound pain， traditional Chinese medicine （TCM） syndrome scores， and the DFU Healing Scale （DMIST scale） were also compared. Liver and kidney function were evaluated before and after treatment， and adverse events such as allergic reactions， worsening ulcer pain were recorded. ResultsTotally 35 patients in the treatment group and 33 in the control group were included in the final analysis. The ulcer healing rate in the treatment group was （87.93±9.34）%， significantly higher than （81.82±12.02）% in the control group （P = 0.035）. Compared to pre-treatment levels， both groups showed significant reductions in serum CRP， WBC， MDA， IL-1β， and TNF-α levels， with an increase in SOD level （P＜0.05）. TCM syndrome scores， VAS， and DMIST scores also significantly decreased in both groups （P＜0.05）， with greater improvements in the treatment group （P＜0.05）. No significant adverse reactions were observed in either group during treatment. ConclusionXiaozhong Zhitong Mixture combined with antibiotic bone cement has significant advantages in promoting DFU healing， reducing inflammatory response， and alleviating oxidative stress in DFU patients with damp-heat obstructing syndrome， with good safety for DFU patients with damp-heat obstructing syndrome.

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.

Objective To investigate the predictive value of preoperative combined detection of neutrophil-to-lymphocyte ratio (NLR) and prothrombin time-international normalized ratio to albumin ratio (PTAR) for early abdominal infection after liver transplantation. Methods Clinical data of 287 recipients who underwent liver transplantation at the Liver Transplant Center of Beijing Friendship Hospital, Affiliated to Capital Medical University, from January 2020 to April 2024 were retrospectively analyzed. The patients were divided into infection group (n=60) and non-infection group (n=227) based on whether abdominal infection occurred within 30 days after surgery. The distribution characteristics of pathogens and infection time in infected patients were analyzed. Spearman correlation analysis was used to assess the correlation between NLR, PTAR, Child-Pugh score and preoperative model for end-stage liver disease (MELD) score. Univariate and multivariate logistic regression analyses were performed to identify risk factors for abdominal infection. Receiver operating characteristic (ROC) curves were plotted for NLR, PTAR, and the combined prediction model to evaluate their predictive efficacy for abdominal infection after liver transplantation. Based on the cutoff value of the combined model, recipients were divided into low-risk and high-risk groups, and Kaplan-Meier analysis was used to compare the cumulative incidence of abdominal infection within 30 days after surgery between the two groups. Results Among the 287 recipients who underwent liver transplantation, 60 developed bacterial or fungal abdominal infections postoperatively. A total of 86 strains were isolated from infected patients, with Gram-negative bacteria accounting for 58%, Gram-positive bacteria for 36%, and fungi for 5%. Preoperative NLR and PTAR were positively correlated with Child-Pugh and MELD scores (all 1 > r > 0, P < 0.05). Logistic regression analysis showed that preoperative NLR, preoperative PTAR, postoperative ICU stay duration and postoperative biliary leakage were risk factors for abdominal infection within 30 days after surgery. The area under the curve (AUC) for NLR, PTAR, Child-Pugh score and MELD score were 0.771, 0.735, 0.650 and 0.741, respectively. The AUC for the combined NLR and PTAR prediction model was 0.824 (95% confidence interval: 0.763-0.885, P < 0.001), with a cutoff value of 0.168. Kaplan-Meier analysis showed that the cumulative incidence of abdominal infection within 30 days after surgery was lower in the low-risk group than in the high-risk group, with statistically significant difference (P < 0.001). Conclusions Preoperative NLR and PTAR are independent risk factors for abdominal infection within 30 days after liver transplantation. The combined prediction model of NLR and PTAR may effectively identify high-risk recipients for early abdominal infection after liver transplantation, providing basis for early intervention.

Background::Breast cancer (BC) risk-stratification tools for Asian women that are highly accurate and can provide improved interpretation ability are lacking. We aimed to develop risk-stratification models to predict long- and short-term BC risk among Chinese women and to simultaneously rank potential non-experimental risk factors.Methods::The Breast Cancer Cohort Study in Chinese Women, a large ongoing prospective dynamic cohort study, includes 122,058 women aged 25-70 years old from the eastern part of China. We developed multiple machine-learning risk prediction models using parametric models (penalized logistic regression, bootstrap, and ensemble learning), which were the short-term ensemble penalized logistic regression (EPLR) risk prediction model and the ensemble penalized long-term (EPLT) risk prediction model to estimate BC risk. The models were assessed based on calibration and discrimination, and following this assessment, they were externally validated in new study participants from 2017 to 2020.Results::The AUC values of the short-term EPLR risk prediction model were 0.800 for the internal validation and 0.751 for the external validation set. For the long-term EPLT risk prediction model, the area under the receiver operating characteristic curve was 0.692 and 0.760 in internal and external validations, respectively. The net reclassification improvement index of the EPLT relative to the Gail and the Han Chinese Breast Cancer Prediction Model (HCBCP) models for external validation was 0.193 and 0.233, respectively, indicating that the EPLT model has higher classification accuracy.Conclusions::We developed the EPLR and EPLT models to screen populations with a high risk of developing BC. These can serve as useful tools to aid in risk-stratified screening and BC prevention.

Objectives:To develop a machine learning prediction model for poor outcome of acute minor ischemic stroke (AMIS) at 90 days after onset and to explain the importance of various risk factors.Methods:Patients with AMIS admitted to the Second People's Hospital of Hefei from June 2022 to December 2023 were included retrospectively. AMIS was defined as the National Institutes of Health Stroke Scale (NIHSS) score ≤5 at admission. According to the modified Rankin Scale score at 90 days after onset, the patients were divided into a good outcome group (<2) and a poor outcome group (≥2). Recursive feature elimination (RFE) method was used to screen characteristic variables of poor outcome. Based on logistic regression (LR), supported vector machine (SVM), and extreme Gradient Boosting (XGBoost) machine learning algorithms, prediction models for poor outcome of AMIS were developed, and the predictive performance of the models was compared by the area under the curve (AUC) of receiver operating characteristic (ROC) curve and the calibration curve. Shapley Additive exPlanations (SHAP) algorithm was used to explain the role of characteristic variables in the optimal prediction model. Results:A total of 225 patients with AMIS were included, of which 152 (67.56%) had good outcome and 73 (32.44%) had poor outcome. Multivariate analysis showed that baseline NIHSS score, baseline systolic blood pressure, hypertension, diabetes, low-density lipoprotein cholesterol, homocysteine, body mass index, D-dimer, and age were the characteristic variables associated with poor outcome in patients with AMIS. The ROC curve analysis shows that the LR model had the best predictive performance (AUC=0.888, 95% confidence interval [ CI] 0.807-0.970), the next was the XGBoost model (AUC=0.888, 95% CI 0.796-0.980), while the SVM model had the lowest performance (AUC=0.849, 95% CI 0.754-0.944). The calibration curve showed that the LR model performed the best in terms of calibration accuracy. SHAP showed that baseline systolic blood pressure, baseline NIHSS score, diabetes, hypertension and body mass index were the top five risk factors for poor outcome of patients with AMIS. Conclusions:The LR algorithm has stable and superior performance in predicting poor outcome of patients with AMIS. Baseline systolic blood pressure, baseline NIHSS score, diabetes, hypertension and body mass index are the important risk factors for poor outcome of patients with AMIS.

Aim To investigate the intracellular up-take and target protein binding characteristics of two Bruton's tyrosine kinase inhibitors(BTKi)with differ-ent selectivities to provide further insights into the mechanisms of drug off-target-related bleeding risk.Methods Ibrutinib(non-selective BTKi)and za-nubrutinib(selective BTKi)were used as study drugs.After incubation of MEC-1 cells and human platelets with drugs,the cellular thermal shift assay(CETSA)was combined with Western blot to obtain the melting curve and isothermal curve to analyze the binding char-acteristics of the two drugs with the target protein BTK.After incubation of MEC-1 cells and human platelets with drugs,the concentrations of the two drugs were detected by liquid chromatography-tandem mass spectrometry(LC-MS/MS)to analyze the intracellular uptake of the two drugs.Results CETSA analysis confirmed that zanubrutinib was more selective for the target protein BTK compared to ibrutinib.LC-MS/MS analysis showed that both drugs were uptaken intracel-lularly by MEC-1 cells and platelets in a concentration-dependent manner.Conclusions While BTKi targe-ting BTK to B lymphocytes exerts therapeutic effects,off-target effects on platelets due to differences in their intracellular uptake,and target-binding characteristics may be one of the reasons for the differences in bleed-ing risk across selective BTKi.

Objective To observe the value of residual neural network(ResNet)-101-feature pyramid network(FPN)model based on CT for differentiating benign and malignant lung nodules.Methods Totally 2 040 lung nodules in 2 000 patients were retrospectively enrolled,including 1 150 benign and 890 malignant nodules.The nodules were divided into training set(n=1 632)and test set(n=408)at the ratio of 8∶2,the former including 881 benign and 751 malignant ones,while the latter including 269 benign and 139 malignant ones,respectively.Taken ResNet-101 as the backbone network,combined with FPN,a classification model was established based on chest CT,and the efficiency of this model alone and combined with evaluation of physicians for differentiating benign and malignant lung nodules were evaluated.Results Among 269 benign lung nodules in test set,ResNet-101-FPN model alone correctly diagnosed 214 nodules(214/269,79.55%),while combined with evaluation of physicians correctly diagnosed 230 ones(230/269,85.50%).For 139 malignant nodules in test set,ResNet-101-FPN model alone correctly diagnosed 124 nodules(124/139,89.21%),while combined with evaluation of physicians correctly diagnosed 131 ones(131/139,94.24%).The sensitivity,accuracy and precision of ResNet-101-FPN model combined with evaluation of physicians for distinguishing benign and malignant lung nodules were all higher,while the specificity of the combination was lower than those of ResNet-101-FPN model alone,but the differences were not significant(all P＞0.05).Conclusion ResNet-101-FPN model could be used to distinguish benign and malignant lung nodules based on CT.Combining with evaluation of physicians could improve diagnostic efficiency of this model.