ObjectiveTo observe the changes in the levels of different subtypes of epidermal growth factor receptor （ErbB）， namely ErbB1， ErbB2， ErbB3， and ErbB4， in the spinal dorsal horn of inflammatory pain model rats， and to explore their mechanism of mediating hyperalgesia as well as the intervention mechanism of electroacupuncture at "Zusanli （ST 36）" and "Kunlun （BL 60）". MethodsThe study was divided into five parts. In experiment 1， 14 Sprague Dawley （SD） rats were randomly divided into control and inflammatory pain group （7 rats each group） to observe the pain behavior and the protein expression of different ErbB receptor subtypes in the spinal dorsal horn. In experiment 2， 30 rats were randomly divided into control group 1， inflammatory pain group 1， and low-， medium-， and high-concentration TX1-85-1 groups， with 6 rats in each group， to observe the effect of inhibiting spinal ErbB3 on inflammatory pain. In experiment 3， 12 rats were randomly divided into control virus group and ErbB3 knockdown virus group， with 6 rats in each group， to observe the effect of knocking down ErbB3 in the spinal dorsal horn on inflammatory pain. In experiment 4， 44 rats were randomly divided into control group 2， inflammatory pain group 2， electroacupuncture group， and sham electroacupuncture group， with 11 rats in each group， to observe the effect of electroacupuncture. In experiment 5， 40 rats were randomly divided into control group 3， inflammatory pain group 3， electroacupuncture group 1， and electroacupuncture + NRG1 group， with 10 rats in each group， to observe the effect of activating ErbB3 on electroacupuncture. A rat model of inflammatory pain was established by subcutaneous injection of 100 μl of complete Freund's adjuvant into the sole of the unilateral hind foot of SD rats. Rats in the low-， medium-， and high-concentration TX1-85-1 groups were intrathecally injected with ErbB3 inhibitor TX1-85-1 on day 5 to day 7 after modeling. Rats in the ErbB3 knockdown virus group were injected with ErbB3 knockdown virus packaged with adenovirus vector-based short hairpin RNA （shRNA） into the spinal dorsal horn in situ 3 weeks before modeling. Rats in each electroacupuncture group received electroacupuncture at bilateral "Zusanli （ST 36）" and "Kunlun （BL 60）" from day 1 to day 7 after modeling， with dense-sparse waves at a frequency of 2 Hz/100 Hz and a current of 0.5-1.5 mA for 30 minutes once a day. Rats in the electroacupuncture + NRG1 group were intrathecally injected with ErbB3 ligand recombinant human neuregulin-1 （NRG1） after electroacupuncture intervention from day 5 to day 7 after modeling. The mechanical withdrawal threshold and thermal withdrawal latency of rats were measured on day 1， 3， 5， and 7 after modeling to evaluate behavior， and Western Blot was used to detect the protein and phosphorylation levels of each ErbB subtype in the spinal dorsal horn. ResultsCompared with the control group， rats in the inflammatory pain group showed decreased mechanical withdrawal threshold and thermal withdrawal latency of rats， and increased expression of phosphorylated ErbB3 （p-ErbB3） protein in the spinal dorsal horn on days 1， 3， 5， and 7 after modeling （P＜0.01）. On day 5 and day 7 after modeling， compared with the inflammatory pain group 1， the mecha-nical withdrawal threshold and thermal withdrawal latency of rats in the medium- and high-concentration TX1-85-1 groups increased， and the expression of p-ErbB3 protein decreased （P＜0.05）. On day 1， 3， 5， and 7 after modeling， compared with the control virus group， the mechanical withdrawal threshold and thermal withdrawal latency of rats in the ErbB3 knockdown virus group increased （P＜0.05）. On day 5 and day 7 after modeling， compared with the inflammatory pain group 2 and the sham electroacupuncture group， the mechanical withdrawal threshold and thermal withdrawal latency of rats in the electroacupuncture group increased， and the expression of p-ErbB3 protein decreased （P＜0.05）. On day 5 and day 7 after modeling， compared with the electroacupuncture + NRG1 group， the mechanical withdrawal threshold and thermal withdrawal latency of rats in the electroacupuncture group 1 increased （P＜0.05）. ConclusionThe p-ErbB3 in the spinal dorsal horn involved in hyperalgesia in rats with inflammatory pain， and electroacupuncture at "Zusanli （ST 36）" and "Kunlun （BL 60）" can alleviate inflammatory pain by inhibiting the expression of p-ErbB3 protein in the spinal dorsal horn of rats.

OBJECTIVE To formulate Guidelines for the standardized implementation of pharmacist-managed clinics （ 2026 edition ） in response to the challenges faced by such clinics in China， including uneven development， large discrepancies in service specifications， insufficient patient awareness， and limited medical insurance coverage. METHODS Led by the Pharmaceutical Affairs Professional Committee of the Chinese Hospital Association， the Evidence-based Pharmacy Professional Committee of the Chinese Pharmaceutical Association， and the Hospital Pharmacy Professional Committee of the Cross-strait Medical and Health Exchange Association， a total of 19 domestic hospital pharmacy experts were organized. Through a systematic review of national policies and literature research， current practical experience was summarized. Consensus on the contents of the guidelines was reached after in-depth discussions. RESULTS &CONCLUSIONS The guidelines covered five sections： definition and connotation of pharmacist-managed clinics， establishment requirements， implementation and management， post competency， and practical research. Firstly， the definition and connotation included three operational forms of pharmacist-managed clinics （independent mode， physician-pharmacist joint mode， and online pharmacist-managed clinic mode） and classified service modes （specialty-specific， drug-specific， and disease-specific pharmacist-managed clinics）. The establishment requirements were further refined， covering system construction （pharmaceutical service management system， quality control and assessment mechanism）， personnel qualifications （professional credentials， continuing education and professional training， etc）， service recipients， as well as service venues and facilities. Subsequently， the implementation and management of pharmacist-managed clinics were proposed， involving service procedures， intervention measures， documentation and records， patient education and follow-up， humanistic care， as well as risk management and quality control. Finally， post competency encompassed the competency requirements for pharmacists providing services in pharmacist-managed clinics， as well as the suggestions on teaching methods； practical research encouraged the conduct of high-quality pharmaceutical practice in the setting of pharmacist-managed clinics. The guidelines provide valuable guidance for the standardized implementation of pharmacist-managed clinics in China in terms of establishment， management， teaching， and research， fill the guideline gap in this field， and can promote the high-quality development of pharmacist-managed clinics.

Objective To gain insights into the biological characteristics of different populations of Culex tritaeniorhynchus within and around the Yellow Sea wetland from the perspective of the occurrence of resistance, we investigated the levels of resistance to deltamethrin and kdr gene mutation in the wetland and its peripheral areas. Methods Specimens were collected from Cx. tritaeniorhynchus populations at two monitoring sites in the Rare Bird National Nature Reserve and Tiaozi Ni Wetland Scenic Area, and also from two populations in Yancheng City and the Liuhe District of Nanjing, and the resistance of these mosquitoes to deltamethrin was determined using the CDC biotest bottle method. For each concentration of deltamethrin assessed, a random subset of exposed specimens was selected for amplification of the kdr gene fragment, followed by Sanger sequencing to identify and analyze resistance-associated mutations. Results The LC50 levels of deltamethrin among mosquitoes from the four populations in Luhe, Yancheng, the Rare Bird National Nature Reserve and the Tiaozi Ni Wetland Scenic Area were 2.048 5, 7.798 2, 3.473 3, and 17.695 5 mg/mL, respectively, with corresponding concentrations of deltamethrin ranging from 0.005 to 5.000,0.050 to 50.000,0.050 to 25.000 and 0.050 to 50.000 mg/mL, respectively. Furthermore, the ranges of the KT50 values were 11.76-107.43, 67.05-216.30,29.77-107.43 and 28.40-329.51 min; the 1-h knockdown rates were 34.58%-99.15%, 9.52%-43.80%, 55.09%-73.01%, and 10.09%-68.07%; and the 24-h mortality rates were 12.15%-67.52%,9.52%-79.56%,13.17%-82.21%, and 11.01%-78.99%, respectively. With respect to kdr gene mutation, we assayed a total of 63,70,59, and 57 mosquitoes for the four populations, for which we detected L1014F mutation frequencies of 14.29%, 35.00%, 20.34%, and 31.58%, respectively, with a majority of these mutations being heterozygous for resistance. In addition, five adult mosquitoes were identified has having synonymous mutations at site 1011[i. e. , AAT(asparagine)mutation to AAC(asparagine)]. Conclusions Our findings revealed the clear resistance of Cx. tritaeniorhynchus to deltamethrin in the Yancheng region of the Yellow Sea wetland, and the resistance phenotype and kdr frequency of Cx. tritaeniorhynchus in the wetland environment were comparable to those of Cx. tritaeniorhynchus in the wetland environment, thereby indicating that the resistance of different populations of Cx. tritaeniorhynchus was homogeneous under the pressure of different insecticide selection within and around the wetland. However, the underlying mechanisms need to be further studied.

ObjectiveTo construct and evaluate various machine learning models for predicting the risk of chronic obstructive pulmonary disease （COPD） in individuals， thereby providing data support for early screening and intervention. MethodsA total of 823 subjects were selected for this study， comprising 142 individuals in the high-risk group for COPD and 681 individuals in the low-risk group. Data collected included demographic characteristics， smoking history， symptoms （such as cough and shortness of breath）， and scores from the Chronic obstructive pulmonary disease screening questionnaire. Four machine learning algorithms—Logistic regression， random forest， support vector machine， and XGBoost—were utilized to construct risk prediction models. The performance of these models was assessed using 5-fold cross-validation， with evaluation metrics including accuracy， precision， recall， F1-score， area under the receiver operating characteristic curve （AUC）， and average precision （AP）. Furthermore， a feature importance analysis was performed. ResultsThe Logistic Regression model exhibited superior performance， achieving an AUC of 0.982 and an AP of 0.939. This was closely followed by the Random Forest model， which recorded an AUC of 0.975 and an AP of 0.890. Feature importance analysis revealed that smoking history， symptoms of shortness of breath， and body weight were significant predictors. All models demonstrated robust performance in identifying low-risk populations； however， variations were observed in their efficacy in identifying high-risk populations. ConclusionMachine learning models have proven effective in identifying individuals at high risk for COPD. Among these， the Logistic regression model exhibits the best overall performance， efficiently identifying high-risk populations and serving as a valuable clinical auxiliary screening tool. Various models， each with distinct performance characteristics， are suited to different clinical screening scenarios， thereby offering targeted decision-making support for the establishment of a hierarchical and intelligent COPD screening pathway.

ObjectiveTo evaluate the clinical efficacy of Huayu Tongluo Formula （化瘀通络方， HTF） in patients with mid-stage diabetic kidney disease of blood stasis syndrome and explore its potential mechanisms. MethodsA multi-center， randomized， double-blind， placebo-controlled clinical trial was conducted. Ninety patients of mid-stage diabetic kidney disease of blood stasis syndrome were divided into a control group of 46 cases and a treatment group of 44 cases. Both groups received conventional western medicine treatment， the treatment group additionally taking HTF， while the control group taking a placebo of the formula. The treatment was administered once daily for 24 weeks. The primary outcomes included 24-hour urine total protein （24 h-UTP）， serum albumin （Alb）， glycated hemoglobin （HbA1c）， and serum creatinine （Scr）.The secondary outcomes included changes in levels of endothelin-1 （ET-1）， nitric oxide （NO）， vascular endothelial growth factor （VEGF）， and traditional Chinese medicine （TCM） syndrome scores before and after treatment. Clinical efficacy was evaluated based on TCM syndrome scores and overall disease outcomes. Adverse reactions and endpoint events were recorded. ResultsIn the treatment group after treatment， 24 h-UTP， ET-1， and VEGF levels significantly decreased （P＜0.05）， Alb and NO levels significantly increased （P＜0.05）； while the TCM syndrome scores for edema， lumbar pain， numbness of limbs， dark purple lips， dark purple tongue or purpura， and thin， rough pulse all significantly decreased （P＜0.05）. In the control group， no significant changes were observed in any of the indicators after treatment （P＞0.05）.Compared with the control group， the treatment group showed significant reductions in 24 h-UTP， ET-1， and VEGF levels， and increases in Alb and NO levels （P＜0.05）. The TCM syndrome scores for edema， lumbar pain， dark purple tongue or purpura， and thin， rough pulse were all lower in the treatment group than in the control group （P＜0.05）. The total effective rate of TCM syndrome in the treatment group was 59.09% （26/44）， and the overall clinical effective rate was 45.45% （20/44）. In the control group， these rates were 15.22% （7/46） and 8.7% （4/46）， respectively， with the treatment group showing significantly better outcomes （P＜0.05）. A total of 7 adverse events occurred across both groups， with no significant difference （P＞0.05）. No endpoint events occurred during the study. ConclusionOn the basis of conventional treatment of Western medicine， HTF can further reduce urinary protein levels and improve clinical symptoms in patients with mid-stage diabetic kidney disease of blood stasis syndrome. The mechanism may be related to its effects on endothelial function.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Purpose Based on a variety of combinations of cranial MR Dixon images,the deep learning method is used to generate CT images,and the reconstruction efficiency is evaluated by comparing with the corresponding CT images.Materials and Methods A total of 77 cranial CT and MR images were collected retrospectively in Ruijin Hospital,Shanghai Jiaotong University School of Medicine from June to December 2021.The U-Net neural network was used for network training,with 62 cases in the training set and 15 cases in the test set.CT image reconstruction was performed using four kinds of Dixon images and a total of seven models among the various combinations.Mean absolute error,mean squared error,Pearson correlation coefficient and skull area Dice similarity coefficient were used to evaluate the image reconstruction efficiency.Results The generated CT images of the various Dixon image combination models showed strong correlation with the corresponding CT images(R>0.75,P<0.05),and the CT images reconstructed by the four-channel model had the closest value to the actual CT images[mean absolute error=147.516±30.802,mean squared error=(8.648±3.403)×104],the highest correlation coefficient(R=0.796±0.055),and the highest similarity coefficient in the cranial region(Dice similarity coefficient=0.800±0.036).Conclusion Deep learning training through Dixon images can be used to generate CT images,and the combination of four kinds of Dixon contrast images can improve the CT image reconstruction efficiency.