To thoroughly implement the strategic deployment outlined in the Opinions of the Central Committee of the Communist Party of China and the State Council on Promoting the Inheritance and Innovative Development of Traditional Chinese Medicine regarding research on dominant diseases of traditional Chinese medicine and to uphold the development philosophy of equal emphasis on traditional Chinese medicine and western medicine，the China Association of Chinese Medicine has fully played a leading academic role by systematically organizing and conducting a series of academic youth salons on clinical dominant diseases of traditional Chinese medicine. On September 13，2024，the 36^th Youth Salon on Clinical Dominant Diseases was successfully held in Nanjing，focusing on the advantages of traditional Chinese medicine and the integrative traditional Chinese medicine and western medicine in the diagnosis and treatment of erectile dysfunction （ED）. The conference brought together leading experts from traditional Chinese medicine，western medicine，and interdisciplinary fields，facilitating in-depth multidisciplinary discussions that led to key consensus on optimizing traditional Chinese medicine treatment protocols for ED，researching and developing new drugs of traditional Chinese medicine，and advancing interdisciplinary development in traditional Chinese medicine. This salon systematically sorted out the clinical strengths and distinctive features of traditional Chinese medicine in the diagnosis and treatment of ED. Based on current research foundations and clinical needs，it identified key directions for future scientific layout and scientific research tackling： （1） Standardization of syndrome differentiation system of traditional Chinese medicine for ED. （2） Optimization and standardization of intervention methods of integrated traditional Chinese medicine and western medicine. （3） High-quality clinical research guided by evidence-based medicine. （4） In-depth analysis of the pharmacological mechanisms of traditional Chinese medicine in the treatment of ED. （5） Clinical translation and application promotion of new drugs of traditional Chinese medicine. （6） Interdisciplinary integration and innovation in traditional Chinese medicine. For each research direction，key focus areas，expected objectives，and clinical value were further refined，along with the establishment of a scientifically sound priority funding level evaluation system. Therefore，building on the series of salons on the ED-focused dominant diseases of traditional Chinese medicine，this paper provides standardized guidance for clinical practice of traditional Chinese medicine in ED management，effectively contributing to the high-quality development of traditional Chinese medicine. It serves as a valuable reference for national scientific and technological strategic layout， research and development decision-making in new drugs of traditional Chinese medicine，research topic planning，and clinical guideline formulation.

BACKGROUND: While emotional distress, encompassing anxiety and depression, has been associated with negative clinical outcomes, its impact across various clinical departments and general hospitals has been less explored. Previous studies with limited sample sizes have examined the effectiveness of specific treatments (e.g., antidepressants) rather than a systemic management strategy for outcome improvement in non-psychiatric inpatients. To enhance the understanding of the importance of addressing mental health care needs among non-psychiatric patients in general hospitals, this study retrospectively investigated the impacts of emotional distress and the effects of early detection and management of depression and anxiety on hospital length of stay (LOS) and rate of long LOS (LLOS, i.e., LOS >30 days) in a large sample of non-psychiatric inpatients. METHODS: This retrospective cohort study included 487,871 inpatients from 20 non-psychiatric departments of a general hospital. They were divided, according to whether they underwent a novel strategy to manage emotional distress which deployed the Huaxi Emotional Distress Index (HEI) for brief screening with grading psychological services (BS-GPS), into BS-GPS ( n = 178,883) and non-BS-GPS ( n = 308,988) cohorts. The LOS and rate of LLOS between the BS-GPS and non-BS-GPS cohorts and between subcohorts with and without clinically significant anxiety and/or depression (CSAD, i.e., HEI score ≥11 on admission to the hospital) in the BS-GPS cohort were compared using univariable analyses, multilevel analyses, and/or propensity score-matched analyses, respectively. RESULTS: The detection rate of CSAD in the BS-GPS cohort varied from 2.64% (95% confidence interval [CI]: 2.49%-2.81%) to 20.50% (95% CI: 19.43%-21.62%) across the 20 departments, with a average rate of 5.36%. Significant differences were observed in both the LOS and LLOS rates between the subcohorts with CSAD (12.7 days, 535/9590) and without CSAD (9.5 days, 3800/169,293) and between the BS-GPS (9.6 days, 4335/178,883) and non-BS-GPS (10.8 days, 11,483/308,988) cohorts. These differences remained significant after controlling for confounders using propensity score-matched comparisons. A multilevel analysis indicated that BS-GPS was negatively associated with both LOS and LLOS after controlling for sociodemographics and the departments of patient discharge and remained negatively associated with LLOS after controlling additionally for the year of patient discharge. CONCLUSION Emotional distress significantly prolonged the LOS and increased the LLOS of non-psychiatric inpatients across most departments and general hospitals. These impacts were moderated by the implementation of BS-GPS. Thus, BS-GPS has the potential as an effective, resource-saving strategy for enhancing mental health care and optimizing medical resources in general hospitals.
Humans ; Retrospective Studies ; Male ; Length of Stay/statistics & numerical data* ; Female ; Middle Aged ; Adult ; Psychological Distress ; Inpatients/psychology* ; Aged ; Anxiety/diagnosis* ; Depression/diagnosis*

Psoriasis is an incurable chronic inflammatory disease that requires new interventions. Here, we found that fibroblasts exacerbate psoriasis progression by promoting macrophage recruitment via CCL2 secretion by single-cell multi-omics analysis. The natural small molecule celastrol was screened to interfere with the secretion of CCL2 by fibroblasts and improve the psoriasis-like symptoms in both murine and cynomolgus monkey models. Mechanistically, celastrol directly bound to the low-density lipoprotein receptor-related protein 1 (LRP1) β-chain and abolished its binding to the transcription factor c-Jun in the nucleus, which in turn inhibited CCL2 production by skin fibroblasts, blocked fibroblast-macrophage crosstalk, and ameliorated psoriasis progression. Notably, fibroblast-specific LRP1 knockout mice exhibited a significant reduction in psoriasis like inflammation. Taken together, from clinical samples and combined with various mouse models, we revealed the pathogenesis of psoriasis from the perspective of fibroblast-macrophage crosstalk, and provided a foundation for LRP1 as a novel potential target for psoriasis treatment.

Antimicrobial resistance (AMR) is a growing public health crisis that requires innovative solutions. Emerging multidrug resistant (MDR) Salmonella typhimurium has raised concern for its effect on pathogenic infection and mortality in humans caused by enteric diseases. To combat these MDR Salmonella typhimurium pathogens, highly effective and broad-spectrum antibiotics such as flufenicol (FFC) need to be evaluated for their potent antibacterial activity against Salmonella typhimurium. However, the low solubility and low oral bioavailability of flufenicol need to be addressed to better combat AMR. In this work, we develop a novel nano-formulation, flufenicol nano-micelles (FTPPM), which are based on d-α-tocopherol polyethylene glycol 1,000 succinate (TPGS)/poloxamer 188 (P188), for the targeted treatment of biofilms formed by drug-resistant Salmonella typhimurium in the intestine. Herein, FTPPM were prepared via a thin film hydration method. The preparation process for the mixed micelles is simple and convenient compared with other existing nanodrug delivery systems, which can further decrease production costs. The optimized FTPPM demonstrated outstanding stability and sustained release. An evaluation of the in vivo anti-drug-resistant Salmonella typhimurium efficacy demonstrated that FTPPM showed a stronger efficacy (68.17 %) than did florfenicol-loaded TPGS polymer micelles (FTPM), flufenicol active pharmaceutical ingredients (FFC-API), and flufenicol commercially available medicine (FFC-CAM), and also exhibited outstanding biocompatibility. Notably, FTPPM also inhibited drug-resistant Salmonella typhimurium from forming biofilms. More importantly, FTPPM effectively restored intestinal flora disorders induced by drug-resistant Salmonella typhimurium in mice. In summary, FTPPM significantly improved the solubility and oral bioavailability of florfenicol, enhancing its efficacy against drug-resistant Salmonella typhimurium both in vitro and in vivo. FTPPM represent a promising drug-resistant Salmonella typhimurium treatment for curbing bacterial resistance via oral administration.

Antimicrobial resistance(AMR)is a growing public health crisis that requires innovative solutions.Emerging multidrug resistant(MDR)Salmonella typhimurium has raised concern for its effect on path-ogenic infection and mortality in humans caused by enteric diseases.To combat these MDR Salmonella typhimurium pathogens,highly effective and broad-spectrum antibiotics such as flufenicol(FFC)need to be evaluated for their potent antibacterial activity against Salmonella typhimurium.However,the low solubility and low oral bioavailability of flufenicol need to be addressed to better combat AMR.In this work,we develop a novel nano-formulation,flufenicol nano-micelles(FTPPM),which are based on D-α-tocopherol polyethylene glycol 1,000 succinate(TPGS)/poloxamer 188(P188),for the targeted treatment of biofilms formed by drug-resistant Salmonella typhimurium in the intestine.Herein,FTPPM were prepared via a thin film hydration method.The preparation process for the mixed micelles is simple and convenient compared with other existing nanodrug delivery systems,which can further decrease pro-duction costs.The optimized FTPPM demonstrated outstanding stability and sustained release.An evaluation of the in vivo anti-drug-resistant Salmonella typhimurium efficacy demonstrated that FTPPM showed a stronger efficacy(68.17％)than did florfenicol-loaded TPGS polymer micelles(FTPM),flufenicol active pharmaceutical ingredients(FFC-API),and flufenicol commercially available medicine(FFC-CAM),and also exhibited outstanding biocompatibility.Notably,FTPPM also inhibited drug-resistant Salmonella typhimurium from forming biofilms.More importantly,FTPPM effectively restored intestinal flora dis-orders induced by drug-resistant Salmonella typhimurium in mice.In summary,FTPPM significantly improved the solubility and oral bioavailability of florfenicol,enhancing its efficacy against drug-resistant Salmonella typhimurium both in vitro and in vivo.FTPPM represent a promising drug-resistant Salmonella typhimurium treatment for curbing bacterial resistance via oral administration.

Objective To investigate the status and developmental trends of clinical trials for weight control drugs in China,and to provide data support for sponsors,researchers,and regulatory authorities.Methods The drug clinical trial registration and information platform of the National Medical Products Administration was utilized to search for registered clinical trials of weight control drugs from November 2012 to June 2024.The search employed"overweight","obesity",and"weight loss"as keywords.The information collected included project registration time,drug name,dosage form,drug classification,indications,trial staging,study progress,design type,lead unit,and sponsor.Microsoft Office Excel software was employed for data entry,organization,and extraction.Results A total of 95 registered clinical trials of weight control drugs were identified,comprising 40 domestic multicenter trials,47 domestic single-center trials,and 8 international multicenter trials.Regarding trial phasing,46(48.4％)were phase Ⅰ clinical trials,17(17.9％)were phase Ⅱ clinical trials,19(20.0％)were phase Ⅲ clinical trials,and 13(13.7％)were bioequivalence trials.The drug categorization encompassed 22 chemical drugs,20 biological products,and 1 traditional Chinese medicine/natural drug.Concerning drug dosage forms,there were 32 items of injectable dosage forms,8 items of tablets,2 items of capsules,and 1 item of chewable tablets.Conclusions Registered clinical trials for weight-loss medications in China are predominantly concentrated in regions with developed medical resources.Injectable biologics constitute most test drugs,with most drugs in the early stages of research and development.The examination of the safety and efficacy of these drugs remains to be substantiated,and it is anticipated that a considerable period will elapse before their approval and market introduction.

Objective To investigate the status and developmental trends of clinical trials for weight control drugs in China,and to provide data support for sponsors,researchers,and regulatory authorities.Methods The drug clinical trial registration and information platform of the National Medical Products Administration was utilized to search for registered clinical trials of weight control drugs from November 2012 to June 2024.The search employed"overweight","obesity",and"weight loss"as keywords.The information collected included project registration time,drug name,dosage form,drug classification,indications,trial staging,study progress,design type,lead unit,and sponsor.Microsoft Office Excel software was employed for data entry,organization,and extraction.Results A total of 95 registered clinical trials of weight control drugs were identified,comprising 40 domestic multicenter trials,47 domestic single-center trials,and 8 international multicenter trials.Regarding trial phasing,46(48.4％)were phase Ⅰ clinical trials,17(17.9％)were phase Ⅱ clinical trials,19(20.0％)were phase Ⅲ clinical trials,and 13(13.7％)were bioequivalence trials.The drug categorization encompassed 22 chemical drugs,20 biological products,and 1 traditional Chinese medicine/natural drug.Concerning drug dosage forms,there were 32 items of injectable dosage forms,8 items of tablets,2 items of capsules,and 1 item of chewable tablets.Conclusions Registered clinical trials for weight-loss medications in China are predominantly concentrated in regions with developed medical resources.Injectable biologics constitute most test drugs,with most drugs in the early stages of research and development.The examination of the safety and efficacy of these drugs remains to be substantiated,and it is anticipated that a considerable period will elapse before their approval and market introduction.

Objective To standardize the management of clinical trials in our hospital,reduce the incidence of protocol deviations,and provide a reference for improving the quality of clinical trials.Methods The healthcare failure mode and effect analysis(HFMEA)method was used to determine the potential failure modes of the current protocol deviation.The frequency,severity and detectability of failure modes were quantified and evaluated.The risk priority number(RPN)was calculated and the corresponding improvement measures were proposed.The RPN values before and after the implementation of HFMEA were statistically analyzed to evaluate the improvement effect.Results After the implementation of HFMEA activities,the RPN values of 14 potential failure modes decreased significantly(P<0.05);The risk level of 12 potential failure modes decreased.The HFMEA team members'ability in finding and solving problems,communication and cooperation were significantly improved.Conclusions The implementation of HFMEA activities contributes to the management of protocol deviation in clinical trials,can effectively reduce the occurrence of protocol deviation,and provides experience for improving the quality of drug clinical trials.

Purpose To explore the value of deep learning feature prediction based on the ResNet50 deep residual network model for predicting early hematoma expansion in spontaneous intracerebral hemorrhage without traditional imaging manifestations. Materials and Methods A retrospective study was performed on 235 patients with spontaneous intracerebral hemorrhage in Jiangdu People's Hospital Affiliated to Yangzhou University from January 2019 and December 2022. These patients had undergone their initial plain cranial CT scan within 6 hours of symptom onset and a subsequent follow-up scan within 24 hours of admission. They were randomly assigned to a training set consisting of 188 cases and a test set of 47 cases,using an 8︰2 ratio. The region of interest (ROI) of hematoma was traced layer by layer on the first plain head CT,and image genomics features were extracted. The maximum two-dimensional cross-sectional ROI of the hematoma 3D-ROI,as well as ROI images at 1 mm and 2 mm above and below the maximum two-dimensional cross-sectional ROI,were then cut and input into the pre-trained ResNet50 model for feature extraction. The image genomics features were then fused with the extracted deep learning features using a least absolute shrinkage and selection operator regression model. A support vector machine classifier was used to construct a prediction model,which was evaluated using receiver operating characteristic curves and decision curve analysis. Results In the training set,the area under curve (AUC) of the deep learning feature model was 0.972,which was higher than that of the image genomics feature model (0.951) and the fused feature model (0.968),but this difference was not statistically significant (P>0.05). In the testing set,the AUCs of the deep learning feature model and the fused feature model were 0.867 and 0.895,respectively,which were significantly higher than that of the image genomics feature model (0.833),with statistically significant differences (Z=-1.794,-2.191,both P<0.05). The AUC of the fused feature model showed an improvement compared to the deep learning feature model,but the difference was not statistically significant (P>0.05). In the test set,decision curve analysis revealed that the fused feature model yielded greater benefits compared to both the deep learning feature model and the radiomic feature model. Conclusion The deep learning feature model based on ResNet50 deep residual network shows better performance in predicting early hematoma expansion than the image genomics feature model,and the fused feature model has a beneficial effect on predicting hematoma expansion. This deep learning approach provides a prediction tool with supervisory capability for clinical decision-making.

Purpose To explore the value of deep learning feature prediction based on the ResNet50 deep residual network model for predicting early hematoma expansion in spontaneous intracerebral hemorrhage without traditional imaging manifestations. Materials and Methods A retrospective study was performed on 235 patients with spontaneous intracerebral hemorrhage in Jiangdu People's Hospital Affiliated to Yangzhou University from January 2019 and December 2022. These patients had undergone their initial plain cranial CT scan within 6 hours of symptom onset and a subsequent follow-up scan within 24 hours of admission. They were randomly assigned to a training set consisting of 188 cases and a test set of 47 cases,using an 8︰2 ratio. The region of interest (ROI) of hematoma was traced layer by layer on the first plain head CT,and image genomics features were extracted. The maximum two-dimensional cross-sectional ROI of the hematoma 3D-ROI,as well as ROI images at 1 mm and 2 mm above and below the maximum two-dimensional cross-sectional ROI,were then cut and input into the pre-trained ResNet50 model for feature extraction. The image genomics features were then fused with the extracted deep learning features using a least absolute shrinkage and selection operator regression model. A support vector machine classifier was used to construct a prediction model,which was evaluated using receiver operating characteristic curves and decision curve analysis. Results In the training set,the area under curve (AUC) of the deep learning feature model was 0.972,which was higher than that of the image genomics feature model (0.951) and the fused feature model (0.968),but this difference was not statistically significant (P>0.05). In the testing set,the AUCs of the deep learning feature model and the fused feature model were 0.867 and 0.895,respectively,which were significantly higher than that of the image genomics feature model (0.833),with statistically significant differences (Z=-1.794,-2.191,both P<0.05). The AUC of the fused feature model showed an improvement compared to the deep learning feature model,but the difference was not statistically significant (P>0.05). In the test set,decision curve analysis revealed that the fused feature model yielded greater benefits compared to both the deep learning feature model and the radiomic feature model. Conclusion The deep learning feature model based on ResNet50 deep residual network shows better performance in predicting early hematoma expansion than the image genomics feature model,and the fused feature model has a beneficial effect on predicting hematoma expansion. This deep learning approach provides a prediction tool with supervisory capability for clinical decision-making.