ObjectiveThis paper aims to analyze the clinical characteristics and medication rationality of liver injury related to Epimedii Folium preparation （EP） and explore the possible risk factors of liver injury， so as to provide a reference for the safe clinical application of Epimedii Folium （EF）. MethodA retrospective analysis was conducted on liver injury cases related to EP from 2012 to 2016. ResultThe number of reported liver injury cases and the proportion of severe cases related to the use of EP show an increasing trend， indicating the objective existence of liver injury caused by EP. There are more cases of liver injury related to EP in women than in men， with an onset age range of 15-91 years old and a median onset age of 60 years old （median onset ages for men and women are 59 and 60 years old， respectively）. The time span from taking EP alone to the occurrence of liver injury is 1-386 days， with a median of 38 days. The time span from taking both EP and Western medicine to the occurrence of liver injury is 1-794 days， with a median of 34 days. EF-related liver injury preparations are mostly composed of traditional Chinese medicines that promote immunity and tonify the liver and kidney， indicating that immune stress in the body may be the mechanism of liver injury caused by the use of EP alone or in combination. There is no increasing trend of toxicity with time or dose in the liver injury caused by EP. By further exploring its risk factors， it is found that patients have unreasonable medication methods such as excessive dosage， repeated use， and multi-drug combination， which may also be one of the important risk factors for EF-related liver injury. ConclusionEP has a certain risk of liver injury and should be emphasized in clinical diagnosis and treatment. Immune stress may be the mechanism of liver injury caused by EP， and in clinical use， it is necessary to be vigilant about the risk of liver injury caused by unreasonable use and combined use with Western medicine.

BACKGROUND:After the internal fixation of cannulated screws in femoral neck fractures,because the affected limb is often unable to bear weight in the short term and the implants with high stiffness have a stress shielding effect on the fracture end,it is easy to cause osteoporosis of the affected limb and changes in the biomechanical distribution of the proximal femur,the incidence of osteonecrosis of the femoral head is high after surgery.At present,few studies have been conducted on the biomechanical effects of osteoporosis at the proximal end of the femur occurring after femoral neck fracture surgery on femoral neck fracture treated with cannulated screws. OBJECTIVE:Using finite element analysis,to investigate the biomechanical effects of osteoporosis occurring after femoral neck fracture surgery on femoral neck fracture treated with cannulated screws and explore the role of biomechanical factors in osteonecrosis of the femoral head. METHODS:Based on the obtained CT scan data of the femur in a patient with a femoral neck fracture,a proximal femoral model for internal fixation for femoral neck fracture was established by Mimics 19.0,3-Matic,UG 11.0,Hypermesh 14.0,and Abaqus software.One finite element model of the proximal femur without osteoporosis and three finite element models of the proximal femur with osteoporosis were analyzed using Abaqus software.The stress,contact pressure,displacement peak and cloud map under different components of the four models were measured and analyzed,and the internal stress changes and distribution of the femoral head were compared and analyzed. RESULTS AND CONCLUSION:The stresses and contact pressures of the femoral head and lower anterior cannulated screws varied more with the degree of osteoporosis.The peak displacement of the four models increased slowly with the degree of osteoporosis.By one-way analysis of variance,there was no significant effect of the degree of osteoporosis on the peak stress,contact pressure,and displacement of the different components.The internal stress distribution of the femoral head changed with the degree of osteoporosis.Changes in the biomechanical environment of the proximal femur have an important impact on osteonecrosis of the femoral head.

BackgroundAlcohol use disorder （AUD） is a common chronic and relapsing psychiatric disorders. Identifying severe AUD early and intervening promptly is crucial to prevent irreversible harm. Currently， the assessment of AUD severity primarily relies on psychiatric examination by clinicians， and there is limited research on the factors influencing AUD severity and the development of prediction models. ObjectiveTo analyze the factors influencing AUD severity， and construct a risk prediction model to aid in the assessment of disease progression in AUD patients. MethodsA retrospective analysis was conducted on 1 358 first-time hospitalized patients admitted to Nanning Fifth People's Hospital from January 1， 2017 to December 31， 2022. These patients met the Diagnostic and Statistical Manual of Mental Disorders， fifth edition （DSM-5） criteria for AUD. Basic patient data was collected， and the patients were divided into two groups based on disease severity： mild-moderate group （n=330） and severe group （n=1 028）. The patients were randomly divided into training and test sets in a 7∶3 ratio. A Logistic regression model was constructed in the training set， and the predictive ability of the model for disease severity was evaluated using the receiver operating characteristic （ROC） curve in the test set. ResultsCompared with the mild-moderate group， the severe group had a higher proportion of patients living in urban areas （χ²=7.804）， were farmers （χ²=17.991）， had a higher frequency of alcohol consumption （more than 1 to 2 drinks/day） （χ²=35.267）， had a higher age at first drinking （t=-3.858）， had a greater number of comorbid somatic disorders （Z=-22.782）， and had higher proportions of γ-Glutamyl transpeptidase （χ²=259.940） and total bilirubin abnormalities （χ²=148.552） （P<0.01）. Logistic analysis conducted in the training set showed that being a farmer （OR=2.024， 95% CI： 1.352~3.029）， having an older age at first drinking （OR=1.075， 95% CI： 1.025~1.129）， drinking outside of mealtimes （OR=3.988， 95% CI： 2.408~6.606）， having total bilirubin abnormalities （OR=1.034， 95% CI： 1.000~1.069）， and having more comorbid somatic diseases （OR=4.386， 95% CI： 2.636~7.298） were identified as risk factors for disease severity in AUD patients. The area under curve （AUC） for this model in the test set was 0.906. ConclusionIn psychiatric hospitals， being a farmer， having an older age at first drinking， drinking outside of mealtimes， having abnormal total bilirubin levels， and having comorbidities with somatic illnesses may be risk factors for severe AUD.

The uncertainty of prognosis for chronic diseases is a persistent problem in the medical community.Mechanical attribution such as questioning disease （stubbornness）， equipment （medical and nursing level）， and patients （low compliance）， it is believed that there is a bias in analyzing the deep causes of low compliance on prognosis after breakthrough progress in the treatment of this type of diseases.Taking hepatitis B patients from the perspective of hermeneutics as an example， this paper clarified the reasons for the non-identity of doctor-patient cognition on the problem of doctor-patient communication in health education.Meanwhile， it was found that there is a pre-structure of heterogeneous experience in the ontology of understanding between doctors and patients， which presents inherent limitations， openness， generativity， and external micro power penetration in their history of dialogue logic.Finally， epistemologically， this paper clarified the essence such as the history of identity effect was pointed by the reconstruction of heterogeneous “meaning ideals” of doctors and patients， the understanding of certainty and elimination of subjectivity was guided by meaning ideals under the fusion of horizons， as well as good personalized prognostic behavioral literacy was led by cultivations of embodied practice.

Objective To evaluate the performance of ChatGPT-4.0 and ERNIE Bot-4.0 in the field of laboratory medicine,and ex-plore their application potential and challenges in this professional domain.Methods Using the national clinical medical laboratory technology(intermediate)examination questions as a benchmark,we compared the performance of the two models in mastering labora-tory medicine knowledge and answering consistency.We also and assessed the models'ability in interpreting test results and assisting diagnosis through 30 laboratory medicine cases.Results In the clinical medical examination technology test,both models passed the 60％qualification threshold.ChatGPT-4.0 was superior to ERNIE Bot-4.0 in terms of answering speed and consistency,but its answer-ing accuracy was significantly lower than that of ERNIE Bot-4.0(73.25％vs 80.75％).ERNIE Bot-4.0's accuracy rate was higher than the average accuracy rate of clinical aboratory personnel in this examination(78.03％).In the accuracy analysis of different question types,both performed worst in experimental technology questions(ERNIE Bot-4.0:66.32％,ChatGPT-4.0:60.53％)and best in bas-ic medical knowledge questions(both scoring 86.00％).In the case analysis test,ERNIE Bot-4.0 outperformed ChatGPT-4.0 in all cat-egories.Both models performed well in routine case analysis but made errors in complex case analysis.Conclusion In the field of la-boratory medicine,both large language models have shown certain application potential,especially in a Chinese context,where ERNIE Bot-4.0 significantly outperforms ChatGPT-4.0 in terms of answering accuracy and case analysis ability,indicating its relative advantage in domestic applications.However,both models still need improvement in experimental technical knowledge,complex case analysis ca-pabilities,and the accuracy and consistency of result output.At the current stage,there are still certain risks in directly applying such general large language models to clinical test result interpretation and assisted diagnosis,which provides a new research direction for the interpretation of test reports.

Objective:To evaluate the diagnostic efficacy of the multiparametric MRI (mpMRI) deep learning artificial intelligence (AI) analysis system combined with 68Ga-prostate specific membrane antigen (PSMA) PET for prostate cancer. Methods:Data of 103 patients (age: 45-85 years) who underwent 68Ga-PSMA PET/MR at Union Hospital, Tongji Medical College, Huazhong University of Science and Technology from May 2018 to October 2023 for suspected or confirmed prostate cancer were retrospectively collected. ROI was delineated to measure SUV max of primary tumor or prostate, and a deep learning AI system was applied to analyze MR images of the prostate. The diagnostic efficacies of T 2 weighted imaging (WI), diffusion WI (DWI), mpMRI, PET SUV max, and PET/MR for prostate cancer were assessed, with using the pathological results as the gold standard. Results:Among 103 patients, 82 cases (79.61%) were with prostate cancer. PET unimodality demonstrated the best specificity (100%, 21/21), positive predictive value (100%, 58/58), and AUC (0.860, 95% CI: 0.777-0.920). The mpMRI AI analysis provided rapid diagnostic results and the sensitivity and accuracy were improved by combining with PET (sensitivities of PET, mpMRI and the combination of the two were 70.73%(58/82), 86.59%(71/82), and 92.68%(76/82), respectively; the accuracies were 76.70%(79/103), 81.55%(84/103) and 86.41%(89/103), respectively). Among 44 patients with negative PET, 30 patients received an accurate diagnosis when the results of mpMRI AI analysis were added. Conclusions:68Ga-PSMA PET demonstrates good specificity for prostate cancer and mpMRI AI analysis is time-saving. The combined application improves the diagnostic sensitivity and accuracy, which provides a valuable tool for 68Ga-PSMA PET/MR image analysis.

In recent years, there has been increasing evidence that nonalcoholic fatty liver disease (NAFLD) is a manifestation of a systemic metabolic disease in liver. However, limitations in the definition and diagnostic criteria of NAFLD could not fully and accurately describe the metabolic disorder. A consensus statement proposed renaming NAFLD to metabolic associated fatty liver disease (MAFLD). Compared with NAFLD, MAFLD links fatty liver disease with metabolic factors such as overweight or obesity and diabetes, and the diagnosis of the disease no longer excludes the presence of metabolic abnormalities in patients with alcohol consumption or other chronic liver diseases. The authors focus on definition changes of fatty liver disease, discuss the epidemiological changes from NAFLD to MAFLD and the relationship of MAFLD with liver fibrosis and extrahepatic diseases, and also the significance of MAFLD for public health.

Objective:To investigate the effect of visceral obesity on the short-term curative effect of Da Vinci robotic-assisted radical resec-tion for rectal cancers.Methods:Clinical and pathological data of patients with rectal cancer undergoing Da Vinci robotic-assisted surgery,admitted to People's Hospital of Zhengzhou University and Cancer Hospital of Zhengzhou University from November 2019 to June 2022 were retrospectively analyzed.Visceral fat area(VFA)≥100 cm2 was used as the standard to define visceral obesity.Patients were categorized in-to visceral and non-visceral obesity groups.The short-term efficacy of the two groups was evaluated,and the influencing factors of post-operative complications were analyzed using univariate and multivariate Logistic regression.Results:Among a total of 169 patients,93 were included in the visceral obesity group and 76 in the non-visceral obesity group.There was no significant difference in the baseline data between the two groups(P>0.05).There was no conversion to laparotomy in the non-visceral obesity group,and the conversion rate was 1.1%(1/93)in the visceral obesity group.The second operation rate was 2.2%(2/93)in the visceral obesity group and 1.3%(1/76)in the non-visceral obesity group with no statistical difference between the two groups.There were no significant differences in the operation dur-ation,intraoperative blood loss,number of lymph node dissections,and total postoperative complication rate between the two groups(P>0.05).Multivariate Logistic regression analysis revealed that an NRS≥3 independently contributed as a risk factor for postoperative com-plications(OR=3.190,95%CI:1.240-8.210,P=0.016).Conclusions:An NRS≥3 is an independent risk factor for complications post-robotic rad-ical rectal cancer surgery.The robotic surgical platform can overcome obesity-related limitations and is equally safe and effective for pa-tients with visceral obesity presenting with rectal cancer.

Objective : To analyze the epidemiological characteristics and pathogen spectrum of hand，foot mouth disease (HFMD) in Anhui province from 2015 to 2022，and to provide scientific evidence for prevention and control measures of HFMD． Methods : The surveillance data of hand，foot and mouth disease in Anhui province from 2015 to 2022 were analyzed by descriptive epidemiology. Real-time PCR was used to detect and classify HFMD samples. Results : A total of 650 590 HFMD cases were reported in Anhui province from 2015 to 2022，including 1 406 se- vere cases and 17 deaths．The annual reported incidence was 131. 45 /100 000．The epidemic features of“low incidence in odd years and high incidence in even years”were presented from 2015 to 2019．The incidence showed a continuous decline from 2020 to 2022．The monthly distribution showed the characteristics of bimodal epidemic，and the main peak was not obvious in 2020．Hefei，Fuyang，Bozhou，Chuzhou and Suzhou ranked the top five cities in terms of cumulative incidence．The age of onset was mainly distributed in children aged 5 years and below，accounting for 89. 26% of the total cases．The male to female ratio was 1. 48 ∶ 1．A total of 28 657 laboratory-confirmed cases had been reported from 2015 to 2022．EV71 cases accounted for 10. 57% ，Cox A16 cases accounted for 24. 90% ，and other enterovirus cases accounted for 64. 53%．The dominant pathogens showed dynamic changes in different years．Since 2018，the proportion of EV71 decreased significantly，and the proportion of other enteroviruses gradually increased to become the dominant pathogens．Among other enteroviruses，Cox A6 strain was dominant (80. 48% ) ． Conclusion This study suggests that the prevention and control of HFMD in Anhui province should be paid more attention from April to July and from October to December．The focus areas are the cities in northern Anhui and Hefei where the floating population is large．The focus of prevention and control is on children aged 5 years and below．Other enteroviruses have become the dominant pathogens of hand-foot-mouth disease in Anhui province，Cox A6 strain is dominant.

Alternative polyadenylation(APA)is a crucial step in post-transcriptional regulation.Previous bioinformatic studies have mainly focused on the recognition of polyadenylation sites(PASs)in a given genomic sequence,which is a binary classification problem.Recently,computa-tional methods for predicting the usage level of alternative PASs in the same gene have been pro-posed.However,all of them cast the problem as a non-quantitative pairwise comparison task and do not take the competition among multiple PASs into account.To address this,here we propose a deep learning architecture,Deep Regulatory Code and Tools for Alternative Polyadenylation(DeeReCT-APA),to quantitatively predict the usage of all alternative PASs of a given gene.To accommodate different genes with potentially different numbers of PASs,DeeReCT-APA treats the problem as a regression task with a variable-length target.Based on a convolutional neural network-long short-term memory(CNN-LSTM)architecture,DeeReCT-APA extracts sequence features with CNN layers,uses bidirectional LSTM to explicitly model the interactions among com-peting PASs,and outputs percentage scores representing the usage levels of all PASs of a gene.In addition to the fact that only our method can quantitatively predict the usage of all the PASs within a gene,we show that our method consistently outperforms other existing methods on three different tasks for which they are trained:pairwise comparison task,highest usage prediction task,and rank-ing task.Finally,we demonstrate that our method can be used to predict the effect of genetic variations on APA patterns and sheds light on future mechanistic understanding in APA regulation.