The Expert Consensus on Clinical Application of Qinbaohong Zhike Oral Liquid in Treatment of Acute Bronchitis and Acute Attack of Chronic Bronchitis （GS/CACM 337-2023） was released by the China Association of Chinese Medicine on December 13^th， 2023. This expert consensus was developed by experts in methodology， pharmacy， and Chinese medicine in strict accordance with the development requirements of the China Association of Chinese Medicine （CACM） and based on the latest medical evidence and the clinical medication experience of well-known experts in the fields of respiratory medicine （pulmonary diseases） and pediatrics. This expert consensus defines the application of Qinbaohong Zhike oral liquid in the treatment of cough and excessive sputum caused by phlegm-heat obstructing lung， acute bronchitis， and acute attack of chronic bronchitis from the aspects of applicable populations， efficacy evaluation， usage， dosage， drug combination， and safety. It is expected to guide the rational drug use in medical and health institutions， give full play to the unique value of Qinbaohong Zhike oral liquid， and vigorously promote the inheritance and innovation of Chinese patent medicines.

Transformer models have emerged as pivotal tools within the realm of drug discovery, distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes. Leveraging the innate capabilities of transformer architectures to comprehend intricate hierarchical dependencies inherent in sequential data, these models showcase remarkable efficacy across various tasks, including new drug design and drug target identification. The adaptability of pre-trained transformer-based models renders them indispensable assets for driving data-centric advancements in drug discovery, chemistry, and biology, furnishing a robust framework that expedites innovation and discovery within these domains. Beyond their technical prowess, the success of transformer-based models in drug discovery, chemistry, and biology extends to their interdisciplinary potential, seamlessly combining biological, physical, chemical, and pharmacological insights to bridge gaps across diverse disciplines. This integrative approach not only enhances the depth and breadth of research endeavors but also fosters synergistic collaborations and exchange of ideas among disparate fields. In our review, we elucidate the myriad applications of transformers in drug discovery, as well as chemistry and biology, spanning from protein design and protein engineering, to molecular dynamics (MD), drug target identification, transformer-enabled drug virtual screening (VS), drug lead optimization, drug addiction, small data set challenges, chemical and biological image analysis, chemical language understanding, and single cell data. Finally, we conclude the survey by deliberating on promising trends in transformer models within the context of drug discovery and other sciences.

Transformer models have emerged as pivotal tools within the realm of drug discovery,distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes.Leveraging the innate capabilities of transformer architectures to comprehend intricate hierarchical dependencies inherent in sequential data,these models showcase remarkable efficacy across various tasks,including new drug design and drug target identification.The adaptability of pre-trained trans-former-based models renders them indispensable assets for driving data-centric advancements in drug discovery,chemistry,and biology,furnishing a robust framework that expedites innovation and dis-covery within these domains.Beyond their technical prowess,the success of transformer-based models in drug discovery,chemistry,and biology extends to their interdisciplinary potential,seamlessly combining biological,physical,chemical,and pharmacological insights to bridge gaps across diverse disciplines.This integrative approach not only enhances the depth and breadth of research endeavors but also fosters synergistic collaborations and exchange of ideas among disparate fields.In our review,we elucidate the myriad applications of transformers in drug discovery,as well as chemistry and biology,spanning from protein design and protein engineering,to molecular dynamics(MD),drug target iden-tification,transformer-enabled drug virtual screening(VS),drug lead optimization,drug addiction,small data set challenges,chemical and biological image analysis,chemical language understanding,and single cell data.Finally,we conclude the survey by deliberating on promising trends in transformer models within the context of drug discovery and other sciences.

Objective To explore the correlation between triglyceride-glucose(TyG)index and hyperuricemia in men with normal fasting blood glucose(FPG)levels.Methods A total of 309 men with normal FPG who participated in a health examination at the Ninth Medical Center of the People's Liberation Army General Hospital in April 2024 were enrolled in this study.All the subjects were divided into the normal uric acid(NUA,n=218)group and the hyperuricemia(HUA,n=91)group according to serum uric acid(SUA)levels.Results Scr,TG,weight,SBP,DBP,BMI,ALT,AST,γ-GGT,and TyG index were higher in the HUA group than in the NUA group(P<0.05).Pearson and Spearman correlation analysis showed that SUA were positively correlated with Scr,eGFR,TG,weight,SBP,DBP,BMI,ALT,AST,γ-GGT and TyG(P<0.05),and negatively correlated with HDL-C(P<0.05).Logistic regression analysis showed that after adjusting for confounding factors,TyG index remained an important influencing factor for HUA.ROC curve analysis showed that the area under the ROC curve of TyG index predicting hyperuricemia in men with normal FPG was 0.665,with an cutoff value of 8.45.Conclusions TyG index in men with normal FPG are influencing factors for hyperuricemia,indicating that hyperuricemia has a close association with insulin resistance,and is an important component of metabolic syndrome.

Objective To explore the correlation between triglyceride-glucose(TyG)index and hyperuricemia in men with normal fasting blood glucose(FPG)levels.Methods A total of 309 men with normal FPG who participated in a health examination at the Ninth Medical Center of the People's Liberation Army General Hospital in April 2024 were enrolled in this study.All the subjects were divided into the normal uric acid(NUA,n=218)group and the hyperuricemia(HUA,n=91)group according to serum uric acid(SUA)levels.Results Scr,TG,weight,SBP,DBP,BMI,ALT,AST,γ-GGT,and TyG index were higher in the HUA group than in the NUA group(P<0.05).Pearson and Spearman correlation analysis showed that SUA were positively correlated with Scr,eGFR,TG,weight,SBP,DBP,BMI,ALT,AST,γ-GGT and TyG(P<0.05),and negatively correlated with HDL-C(P<0.05).Logistic regression analysis showed that after adjusting for confounding factors,TyG index remained an important influencing factor for HUA.ROC curve analysis showed that the area under the ROC curve of TyG index predicting hyperuricemia in men with normal FPG was 0.665,with an cutoff value of 8.45.Conclusions TyG index in men with normal FPG are influencing factors for hyperuricemia,indicating that hyperuricemia has a close association with insulin resistance,and is an important component of metabolic syndrome.

Objective To observe the value of artificial intelligence ultrasound combined with serum hyaluronic acid synthase 2(HAS2)and trefoil factor-1(TFF1)for early diagnosis of breast cancer.Methods Totally 176 patients with suspected breast cancer were retrospective enrolled and divided into malignant group(n=50)and benign group(n=126)according to pathological results.Artificial intelligence ultrasound and convolutional neural network algorithms were used to automatically label suspicious breast lesions.The lesions were manually graded based on breast imaging reports and data systems,classifying 0-3 grades as benign lesions,4-5 grades as malignant lesions.Clinical data and artificial intelligence ultrasound manifestations were compared between groups.Receiver operating characteristic curve was drawn,the area under the curve(AUC)was calculated to evaluate the efficacy of HAS2,TFF1,artificial intelligence ultrasound and their combination for diagnosing breast cancer.Results HAS2,TFF1,as well as the proportions of abnormal glandular thickness,low-echo lesions and abnormal blood flow morphology in malignant group were all higher than those in benign group(all P＜0.001).AUC of serum HAS2,TFF1 and artificial intelligence ultrasound for diagnosing breast cancer was 0.772,0.754 and 0.859,respectively.The combined diagnostic efficacy of the above three(AUC=0.925)was higher than single diagnostic efficacy(all P＜0.05).Conclusion Artificial intelligence ultrasound combined with serum HAS2 and TFF1 had good efficacy for early diagnosis of breast cancer.

Objective To observe the value of artificial intelligence ultrasound combined with serum hyaluronic acid synthase 2(HAS2)and trefoil factor-1(TFF1)for early diagnosis of breast cancer.Methods Totally 176 patients with suspected breast cancer were retrospective enrolled and divided into malignant group(n=50)and benign group(n=126)according to pathological results.Artificial intelligence ultrasound and convolutional neural network algorithms were used to automatically label suspicious breast lesions.The lesions were manually graded based on breast imaging reports and data systems,classifying 0-3 grades as benign lesions,4-5 grades as malignant lesions.Clinical data and artificial intelligence ultrasound manifestations were compared between groups.Receiver operating characteristic curve was drawn,the area under the curve(AUC)was calculated to evaluate the efficacy of HAS2,TFF1,artificial intelligence ultrasound and their combination for diagnosing breast cancer.Results HAS2,TFF1,as well as the proportions of abnormal glandular thickness,low-echo lesions and abnormal blood flow morphology in malignant group were all higher than those in benign group(all P＜0.001).AUC of serum HAS2,TFF1 and artificial intelligence ultrasound for diagnosing breast cancer was 0.772,0.754 and 0.859,respectively.The combined diagnostic efficacy of the above three(AUC=0.925)was higher than single diagnostic efficacy(all P＜0.05).Conclusion Artificial intelligence ultrasound combined with serum HAS2 and TFF1 had good efficacy for early diagnosis of breast cancer.

【Objective】 To systematically evaluate the effects of various rehabilitation therapies, including hydrotherapy, rehabilitation robot, core stability training (CST), whole-body vibration training (WBV), repetitive transcranial magnetic stimulation (rTMS), sling exercise training (SET), task-oriented training (TOT) and virtual reality (VR), on motor dysfunction in children with spastic cerebral palsy, so as to provide a reference for the scientific selection of rehabilitation programs for children. 【Methods】 Randomized controlled trials (RCTs) of the 8 rehabilitation therapies in the treatment of motor dysfunction in children with spastic cerebral palsy were searched from various databases, including PubMed, The Cochrane Library, Web of Science, EmBase, CNKI, CBM, VIP, and WanFang Database, from database inception to December 2022. Two researchers independently conducted literature screening, data extraction, and literature quality evaluation. Network Meta-analysis was performed using ADDIS 1.16.6 software, and Stata 16.0 software was used for graphic representation. 【Results】 A total of 43 RCTs involving 2 722 children with spastic cerebral palsy were included in the analysis. The results of the network Meta-analysis indicated that WBV had the most significant effect in improving the GMFM-88 score (MD=18.56, 95%CI: 34.91 - 2.45, P<0.05). Rehabilitation robot had the most significant effect in improving dimensions D (MD=6.30, 95%CI: 8.44 - 4.41, P<0.05) and E (MD=10.03, 95%CI: 15.03 - 4.84, P<0.05) of the GMFM-88 score. Additionally, hydrotherapy showed the most significant effect in improving the BBS score (MD=11.24, 95%CI: 22.26 - 0.20, P<0.05). 【Conclusions】 For children with spastic cerebral palsy, WBV is the most effective rehabilitation therapy to improve gross motor function, rehabilitation robot is the most effective therapy for improving standing and walking function, and hydrotherapy is the most effective therapy for improving balance.

Objective To explore the correlation between the expression of long non-coding RNA(lncRNA)brain ischemia-related factor(BIRF)and focally amplified lncRNA on chromosome 1(lncRNA FAL1)in serum and the degree of white matter lesions(WML)in patients with cerebral small vessel disease(CSVD).Methods From June 2021 to June 2023,102 CSVD patients admitted to Affiliated Hospital of Chengde Medical University were collected,and these patients were grouped into WML group(n=72)and non WML group(n=30)based on WML diagnostic criteria.According to the Fazekas score,the WML group was further grouped into mild WML group(n=24),moderate WML group(n=36)and severe WML group(n=12).Real-time fluorescence quantitative polymerase chain reaction(qRT-PCR)was applied to detect the levels of lncRNA BIRF and lncRNA FAL1 in serum.Pearson correlation was applied to analyze the correlation between serum lncRNA BIRF and lncRNA FAL1 levels.Receiver operating characteristic(ROC)curve was applied to analyze the diagnostic value of serum lncRNA BIRF and lncRNA FAL1 levels for severe WML in CSVD patients.Results The age(70.50±5.86 years),history of hypertension(Yes/No,43/29),history of diabetes(Yes/No,45/27),IL-33(68.35±6.80 pg/ml),IL-18(97.78±9.65 ng/L),ubiquitin carboxyl terminal hydrolase L1(UCH-L1)(0.29±0.10 μg/L)and lncRNA BIRF level(2.45±0.30)of patients in the WML group were higher than those in the non WML group(67.10±5.76 years,11/19,9/21,62.48±6.13 pg/ml,92.56±9.37 ng/L,0.24±0.06 μg/L,1.02±0.11),while the expression of serum lncRNA FAL1(0.52±0.10)was lower than that in the non WML group(1.04±0.15),with significant differences(t=2.683,4.518,8.978,4.085,2.510,2.550,25.346,20.500,all P<0.05).The serum lncRNA BIRF levels of CSVD patients in the mild,moderate and severe WML groups(2.23±0.23,2.47±0.31,2.82±0.42)were increased sequentially,while the expression of serum lncRNA FAL1(0.60±0.15,0.51±0.09,0.40±0.04)was decreased sequentially,with significant differences(F=14.913,13.899,all P<0.05).Pearson correlation analysis,the serum levels of lncRNA BIRF and lncRNA FAL1 in patients with WML were negatively correlated(r=-0.603,P<0.001),serum lncRNA BIRF was positively correlated with Fazekas score in WML patients(r=0.483,P<0.001),but serum lncRNA FAL1 was negatively correlated with Fazekas score(r=-0.507,P<0.001).The AUCs of serum lncRNA BIRF and lncRNA FAL1 levels alone and both combination for predicting severe WML in CSVD patients were 0.756(0.641～0.850),0.839(0.733～0.915)and 0.892(0.796～0.953),respectively,and the combination of the two was superior to the detection of serum lncRNA BIRF alone(Z=2.111,P=0.035).Conclusion The serum lncRNA BIRF level is increased and lncRNA FAL1 is reduced in patients with CSVD and WML,and both are related to the degree of WML in CSVD patients.

Objective To investigate the current status of taste disorders in type 2 diabetes mellitus(T2DM)and to explore whether the taste disorders persists after 3 months of novel corona virus(COVID-19)infection.Methods 95 T2DM out patients(23 without COVID-19 infection history,72 infected with COVID-19 3～4 months ago)visiting the Endocrine Department of the Strategic Support Force Medical Center from February 20 to March 10,2023 were collected.Taste test box was used to test the taste function.General information,biochemical indicators,taste disorders,etc.were compared between the two groups.Results The average age of T2DM patients in this group was(58.3±9.6)years old,61 patients were male(64.2%),the median duration of DM was 11 years,and the median HbA1c was 7.3%.In taste testing,the proportion of sour,sweet,bitter,salty taste perception disorders was 60.0%,45.3%,57.9%,41.1%,84.2%.The average number of days from infection to enrollment into COVID-19 group was 102.4 days.The proportion of acid,sweet,bitter and salty sensory disorders was 61.1%,44.4%,55.6%and 41.7%in COVID-19 group and 56.5%,47.8%,65.2%and 39.1%in non-COVID-19 group.The prevalence of taste disorders in COVID-19 group was higher than that in non-COVID-19 group(86.1%vs 78.3%).Conclusions Taste disorders are common in T2DM patients.Compared with uninfected T2DM patients,there is no significant difference in the prevalence of taste disorders 3 months after COVID-19 infection.