Objective To propose a multiple regression-variational auto-encoders(MR-VAE)model to realize precise and non-invasive prediction of intra-abdominal pressure(IAP)in critically ill patients.Methods At first,a dataset was constructed by retrospectively analysing baseline characteristics and clinical indicators of 100 critically ill patients admitted to the Intensive Care Unit of Daping Hospital of Army Medical University between 30 August 2019 and 30 March 2021.Then,a MR-VAE prediction model was developed by integrating a feedforward neural network for supervised regression onto a variational autoencoder(VAE)framework and incorporating multiple regression strategies to mitigate feature interference.Finally,the MR-VAE model had its performance evaluated by its comparison with five classical models including support vector machines(SVM),convolutional neural networks(CNN),Scikit-learn integrated model(SIM),multi-layer perceptron(MLP)and K-nearest neighbors(KNN),and its prediction accuracy verified by testing the data of 10 randomly selected patients.Results The MR-VAE model behaved the best when compared with the five classical models,with a mean squared error(MSE)of 0.207,a root mean square error(RMSE)of 0.454,a mean absolute error(MAE)of 0.361,a median absolute deviation(MAD)of 0.243,an explained variance score(EVS)of 0.814 and a R2of 0.823,which also outperformed the five models in fitting performance,convergence and final loss.In random sample testing,the MR-VAE model exhibited high consistency between predicted and actual values.Conclusion The MR-VAE model proposed can accurately predict IAP,which has great potential in reducing the repeated measurements of IAP in critically ill patients and providing new ideas for the early diagnosis and treatment of IAH.

Objective To assess the effectiveness of the evaluation of military physical function(EMPF)system in predicting the occurrence of military training injuries among new recruits to provide scientific guidance and methodological choice for military training.Methods A total of 527 new recruits from 5 grassroots units from July 2016 to February 2018 were selected for the study.The recruits underwent EMPF testing,and their military training injuries were monitored over a 2-year follow-up period.Those who sustained injuries during training were divided into injury group(n=163),while the remaining recruits were placed in healthy group(n=364).The predictive ability of the total EMPF score for training injuries was assessed using the receiver operating characteristic curve(ROC),and the correlation between the total EMPF score,individual test scores,and military training injuries were analyzed using binary logistic regression.Results The total EMPF score of new recruits in injury group(19.52±1.97)was significantly lower than that of healthy group(24.31±1.54)(P<0.001),which also demonstrated a high diagnostic value in predicting the risk of military training injuries,with an area under the curve(AUC)of ROC of 0.971(P<0.001).A cut-off value of 22 scores was found to have the highest accuracy in predicting future training injuries,with an odds ratio(OR)of 25.63,sensitivity of 0.939,specificity of 0.879,positive likelihood ratio of 7.76,and a post-test probability of 0.67.Binary logistic regression analysis revealed that 6 EMPF tests,including holding the ball over and leaning back,bending forward and touching the ground with the ball,lunge squat and twist,swallow balance with holding the ball afterward,vertical jump,and respiratory pattern assessment,were negatively associated with the risk of military training injuries(P<0.0001).Conclusion The EMPF system can effectively predict the risk of military training injuries,with military personnel whose total EMPF score is less than 22 being at higher risk of sustaining such injuries.

Objective To propose a multiple regression-variational auto-encoders(MR-VAE)model to realize precise and non-invasive prediction of intra-abdominal pressure(IAP)in critically ill patients.Methods At first,a dataset was constructed by retrospectively analysing baseline characteristics and clinical indicators of 100 critically ill patients admitted to the Intensive Care Unit of Daping Hospital of Army Medical University between 30 August 2019 and 30 March 2021.Then,a MR-VAE prediction model was developed by integrating a feedforward neural network for supervised regression onto a variational autoencoder(VAE)framework and incorporating multiple regression strategies to mitigate feature interference.Finally,the MR-VAE model had its performance evaluated by its comparison with five classical models including support vector machines(SVM),convolutional neural networks(CNN),Scikit-learn integrated model(SIM),multi-layer perceptron(MLP)and K-nearest neighbors(KNN),and its prediction accuracy verified by testing the data of 10 randomly selected patients.Results The MR-VAE model behaved the best when compared with the five classical models,with a mean squared error(MSE)of 0.207,a root mean square error(RMSE)of 0.454,a mean absolute error(MAE)of 0.361,a median absolute deviation(MAD)of 0.243,an explained variance score(EVS)of 0.814 and a R2of 0.823,which also outperformed the five models in fitting performance,convergence and final loss.In random sample testing,the MR-VAE model exhibited high consistency between predicted and actual values.Conclusion The MR-VAE model proposed can accurately predict IAP,which has great potential in reducing the repeated measurements of IAP in critically ill patients and providing new ideas for the early diagnosis and treatment of IAH.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective:To investigate the effects of short-chain fatty acids(SCFAs)on the proliferation,migration and adipogenic ability of dental pulp stem cells(DPSCs).Methods:Dental pulp stem cells were extracted from young permanent teeth and treated with sodium acetate and sodium propionate.CCK-8 assay was used to detect the activity of DPSCs.Immunofluorescence staining was used to detect the proliferation of DPSCs.The scratch test was employed to assess the migratory activity of DPSCs,alizarin red staining was utilized to evaluate the capacity for calcification nodule formation,and red oil O staining was applied to determine the adipogenic potential of DPSCs.Results:The acetate and propionate significantly enhanced the viability,promoted the proliferation,migration and adipogenic differentiation of DPSCs.The osteogenic differentiation ability of DPSCs,which were with sodium acetate and sodium propionate,was observed.There was no significant difference in the osteogenic differentiation ability of DPSCs compared to the control group.Conclusion:The findings suggest that SCFAs have the potential to enhance the viability,proliferation,migra-tion,and adipogenic differentiation of dental pulp stem cells,thereby promoting their application in pulp repair and tissue engineering.

Houpo Sanwutang， included in the Catalogue of Ancient Classical Prescriptions （Second Batch）， was first recorded in the Synopsis of Golden Chamber written by ZHANG Zhongjing from the Eastern Han dynasty and was modified by successive generations of medical experts. A total of 37 pieces of effective data involving 37 ancient Chinese medical books were retrieved from different databases. Through literature mining， statistical analysis， and data processing， combined with modern articles， this study employed bibliometrics to investigate the historical origin， composition， decoction methods， clinical application， and other key information. The results showed that the medicinal origin of Houpo Sanwutang was clearly documented in classic books. Based on the conversion of the measurements from the Han Dynasty， it is recommended that 110.4 g Magnolia Officinalis Cortex， 55.2 g Rhei Radix et Rhizoma， and 72 g Aurantii Fructus Immaturus should be taken. Magnolia Officinalis Cortex and Aurantii Fructus Immaturus should be decocted with 2 400 mL water first， and 1 000 mL should be taken from the decocted liquid. Following this， Rhei Radix et Rhizoma should be added for further decoction， and then 600 mL should be taken from the decocted liquid. A single dose of administration is 200 mL， and the medication can be stopped when patients restore smooth bowel movement. Houpo Sanwutang has the effect of moving Qi， relieving stuffiness and fullness， removing food stagnation， and regulating bowels. It can be used in treating abdominal distending pain， guarding， constipation， and other diseases with the pathogenesis of stagnated heat and stagnated Qi in the stomach. The above results provide reference for the future development and research of Houpo Sanwutang.

The classic formula Fangji Fulingtang is from ZHANG Zhongjing's Synopsis of the Golden Chamber in the Eastern Han dynasty. It is composed of Stephaniae Tetrandrae Radix， Astragali Radix， Cinnamomi Ramulus， Poria， and Glycyrrhizae Radix et Rhizoma， with the effects of reinforcing Qi and invigorating spleen， warming Yang and promoting urination. By a review of ancient medical books， this paper summarizes the composition， original plants， processing， dosage， decocting methods， indications and other key information of Fangji Fulingtang， aiming to provide a literature basis for the research， development， and clinical application of preparations based on this formula. Synonyms of Fangji Fulingtang exist in ancient medical books， while the formula composition in the Synopsis of the Golden Chamber is more widespread and far-reaching. In this formula， Stephaniae Tetrandrae Radix， Astragali Radix， Cinnamomi Ramulus， Poria， and Glycyrrhizae Radix et Rhizoma are the dried root of Stephania tetrandra， the dried root of Astragalus embranaceus var. mongholicus， the dried shoot of Cinnamomum cassia， the dried sclerotium of Poria cocos， and the dried root and rhizome of Glycyrrhiza uralensis， respectively. Fangji Fulingtang is mainly produced into powder， with the dosage and decocting method used in the past dynasties basically following the original formula. Each bag is composed of Stephaniae Tetrandrae Radix 13.80 g， Astragali Radix 13.80 g， Cinnamomi Ramulus 13.80 g， Poria 27.60 g， and Glycyrrhizae Radix et Rhizoma 9.20 g. The raw materials are purified， decocted in water from 1 200 mL to 400 mL， and the decoction should be taken warm， 3 times a day. Fangji Fulingtang was originally designed for treating skin edema， and then it was used to treat impediment in the Qing dynasty. In modern times， it is mostly used to treat musculoskeletal and connective tissue diseases and circulatory system diseases， demonstrating definite effects on various types of edema and heart failure. This paper clarifies the inheritance of Fangji Fulingtang and reveals its key information （attached to the end of this paper）， aiming to provide a theoretical basis for the development of preparations based on this formula.

The classic formula Fangji Fulingtang is from ZHANG Zhongjing's Synopsis of the Golden Chamber in the Eastern Han dynasty. It is composed of Stephaniae Tetrandrae Radix， Astragali Radix， Cinnamomi Ramulus， Poria， and Glycyrrhizae Radix et Rhizoma， with the effects of reinforcing Qi and invigorating spleen， warming Yang and promoting urination. By a review of ancient medical books， this paper summarizes the composition， original plants， processing， dosage， decocting methods， indications and other key information of Fangji Fulingtang， aiming to provide a literature basis for the research， development， and clinical application of preparations based on this formula. Synonyms of Fangji Fulingtang exist in ancient medical books， while the formula composition in the Synopsis of the Golden Chamber is more widespread and far-reaching. In this formula， Stephaniae Tetrandrae Radix， Astragali Radix， Cinnamomi Ramulus， Poria， and Glycyrrhizae Radix et Rhizoma are the dried root of Stephania tetrandra， the dried root of Astragalus embranaceus var. mongholicus， the dried shoot of Cinnamomum cassia， the dried sclerotium of Poria cocos， and the dried root and rhizome of Glycyrrhiza uralensis， respectively. Fangji Fulingtang is mainly produced into powder， with the dosage and decocting method used in the past dynasties basically following the original formula. Each bag is composed of Stephaniae Tetrandrae Radix 13.80 g， Astragali Radix 13.80 g， Cinnamomi Ramulus 13.80 g， Poria 27.60 g， and Glycyrrhizae Radix et Rhizoma 9.20 g. The raw materials are purified， decocted in water from 1 200 mL to 400 mL， and the decoction should be taken warm， 3 times a day. Fangji Fulingtang was originally designed for treating skin edema， and then it was used to treat impediment in the Qing dynasty. In modern times， it is mostly used to treat musculoskeletal and connective tissue diseases and circulatory system diseases， demonstrating definite effects on various types of edema and heart failure. This paper clarifies the inheritance of Fangji Fulingtang and reveals its key information （attached to the end of this paper）， aiming to provide a theoretical basis for the development of preparations based on this formula.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.