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.

In recent years, the application of artificial intelligence (AI) in the field of biology has witnessed remarkable advancements. Among these, the most notable achievements have emerged in the domain of protein structure prediction and design, with AlphaFold and related innovations earning the 2024 Nobel Prize in Chemistry. These breakthroughs have transformed our ability to understand protein folding and molecular interactions, marking a pivotal milestone in computational biology. Looking ahead, it is foreseeable that the accurate prediction of various physicochemical properties of proteins—beyond static structure—will become the next critical frontier in this rapidly evolving field. One of the most important protein properties is thermodynamic stability, which refers to a protein’s ability to maintain its native conformation under physiological or stress conditions. Accurate prediction of protein stability, especially upon single-point mutations, plays a vital role in numerous scientific and industrial domains. These include understanding the molecular basis of disease, rational drug design, development of therapeutic proteins, design of more robust industrial enzymes, and engineering of biosensors. Consequently, the ability to reliably forecast the stability changes caused by mutations has broad and transformative implications across biomedical and biotechnological applications. Historically, protein stability was assessed via experimental methods such as differential scanning calorimetry (DSC) and circular dichroism (CD), which, while precise, are time-consuming and resource-intensive. This prompted the development of computational approaches, including empirical energy functions and physics-based simulations. However, these traditional models often fall short in capturing the complex, high-dimensional nature of protein conformational landscapes and mutational effects. Recent advances in machine learning (ML) have significantly improved predictive performance in this area. Early ML models used handcrafted features derived from sequence and structure, whereas modern deep learning models leverage massive datasets and learn representations directly from data. Deep neural networks (DNNs), graph neural networks (GNNs), and attention-based architectures such as transformers have shown particular promise. GNNs, in particular, excel at modeling spatial and topological relationships in molecular structures, making them well-suited for protein modeling tasks. Furthermore, attention mechanisms enable models to dynamically weigh the contribution of specific residues or regions, capturing long-range interactions and allosteric effects. Nevertheless, several key challenges remain. These include the imbalance and scarcity of high-quality experimental datasets, particularly for rare or functionally significant mutations, which can lead to biased or overfitted models. Additionally, the inherently dynamic nature of proteins—their conformational flexibility and context-dependent behavior—is difficult to encode in static structural representations. Current models often rely on a single structure or average conformation, which may overlook important aspects of stability modulation. Efforts are ongoing to incorporate multi-conformational ensembles, molecular dynamics simulations, and physics-informed learning frameworks into predictive models. This paper presents a comprehensive review of the evolution of protein thermodynamic stability prediction techniques, with emphasis on the recent progress enabled by machine learning. It highlights representative datasets, modeling strategies, evaluation benchmarks, and the integration of structural and biochemical features. The aim is to provide researchers with a structured and up-to-date reference, guiding the development of more robust, generalizable, and interpretable models for predicting protein stability changes upon mutation. As the field moves forward, the synergy between data-driven AI methods and domain-specific biological knowledge will be key to unlocking deeper understanding and broader applications of protein engineering.

BACKGROUND:Dexmedetomidine has the effect of anti-ischemia-reperfusion injury,but the comprehensive and systematic review of its signaling pathway is less. OBJECTIVE:To focus on the review of dexmedetomidine's signaling pathway in the mechanisms of antioxidant stress,inhibition of inflammation,anti-apoptosis,autophagy,and so on. METHODS:The relevant articles on PubMed,CNKI,WanFang,and VIP databases were searched by computer with the key words"ischemia-reperfusion inquiry;dexmedetomidine;signal path;oxidative stress;inflammation;apoptosis"in Chinese and English.After excluding repetitive research and some basic articles with low correlation,57 articles were finally included for review. RESULTS AND CONCLUSION:(1)Dexmedetomidine plays an important role in organ protection through many mechanisms,such as anti-oxidative stress injury,anti-inflammation,anti-apoptosis and autophagy.This involves many pathways,including Nrf2 and its downstream protein antioxidant stress pathway,Toll-like receptor 4 family and nuclear factor-κB-related anti-inflammatory pathway,JAK2/STAT3-related anti-inflammatory pathway,and cholinergic anti-inflammatory pathway,and the cholinergic pathway is the upstream mechanism of many nuclear factor-κB signaling pathways.(2)PI3K/Akt pathway plays different roles according to its activated downstream signals,inhibiting the activation of NLRP3 inflammatory body,activating signal molecules endothelial nitric oxide synthase,mammalian target of rapamycin,and hypoxia-inducible factor 1α to play an anti-inflammatory role,and activate Bad or Bax residues to play an anti-apoptotic role,and PI3K/Akt activates glycogen synthetase kinase-3β.It can also play an anti-inflammatory and anti-apoptotic role.(3)Dexmedetomidine activates SIRT3 to mediate anti-apoptosis and inhibit endoplasmic reticulum stress to produce anti-apoptosis.(4)The detailed review of the anti-ischemia-reperfusion injury signaling pathway of dexmedetomidine can provide a basis for future mechanism research and diagnosis and treatment decisions.

177Lu-prostate specific membrane antigen(PSMA)radio-ligand therapy has been approved abroad for advanced prostate cancer and has been in several clinical trials in China.Based on domestic clinical practice and experimental data and referred to international experience and viewpoints,the expert group forms a consensus on the clinical application of 177Lu-PSMA radio-ligand therapy in prostate cancer to guide clinical practice.

Objective:To report the results of national surveillance on the distribution and antimicrobial resistance profile of clinical Gram-negative bacteria isolates from bloodstream infections in China in 2022.Methods:The clinical isolates of Gram-negative bacteria from blood cultures in member hospitals of national bloodstream infection Bacterial Resistant Investigation Collaborative System（BRICS）were collected during January 2022 to December 2022. Antibiotic susceptibility tests were conducted by agar dilution or broth dilution methods recommended by Clinical and Laboratory Standards Institute（CLSI）. WHONET 5.6 and SPSS 25.0 software were used to analyze the data.Results:During the study period，9 035 strains of Gram-negative bacteria were collected from 51 hospitals，of which 7 895（87.4%）were Enterobacteriaceae and 1 140（12.6%）were non-fermenting bacteria. The top 5 bacterial species were Escherichia coli（ n=4 510，49.9%）， Klebsiella pneumoniae（ n=2 340，25.9%）， Pseudomonas aeruginosa（ n=534，5.9%）， Acinetobacter baumannii complex（ n=405，4.5%）and Enterobacter cloacae（ n=327，3.6%）. The ESBLs-producing rates in Escherichia coli， Klebsiella pneumoniae and Proteus spp. were 47.1%（2 095/4 452），21.0%（427/2 033）and 41.1%（58/141），respectively. The prevalence of carbapenem-resistant Escherichia coli（CREC）and carbapenem-resistant Klebsiella pneumoniae（CRKP）were 1.3%（58/4 510）and 13.1%（307/2 340）；62.1%（36/58）and 9.8%（30/307）of CREC and CRKP were resistant to ceftazidime/avibactam combination，respectively. The prevalence of carbapenem-resistant Acinetobacter baumannii（CRAB）complex was 59.5%（241/405），while less than 5% of Acinetobacter baumannii complex was resistant to tigecycline and polymyxin B. The prevalence of carbapenem-resistant Pseudomonas aeruginosa（CRPA）was 18.4%（98/534）. There were differences in the composition ratio of Gram-negative bacteria in bloodstream infections and the prevalence of main Gram-negative bacteria resistance among different regions，with statistically significant differences in the prevalence of CRKP and CRPA（ χ2=20.489 and 20.252， P<0.001）. The prevalence of CREC，CRKP，CRPA，CRAB，ESBLs-producing Escherichia coli and Klebsiella pneumoniae were higher in provinicial hospitals than those in municipal hospitals（ χ2=11.953，81.183，10.404，5.915，12.415 and 6.459， P<0.01 or <0.05），while the prevalence of CRPA was higher in economically developed regions（per capita GDP ≥ 92 059 Yuan）than that in economically less-developed regions（per capita GDP <92 059 Yuan）（ χ2=6.240， P=0.012）. Conclusions:The proportion of Gram-negative bacteria in bloodstream infections shows an increasing trend，and Escherichia coli is ranked in the top，while the trend of CRKP decreases continuously with time. Decreasing trends are noted in ESBLs-producing Escherichia coli and Klebsiella pneumoniae. Low prevalence of carbapenem resistance in Escherichia coli and high prevalence in CRAB complex have been observed. The composition ratio and antibacterial spectrum of bloodstream infections in different regions of China are slightly different，and the proportion of main drug resistant bacteria in provincial hospitals is higher than those in municipal hospitals.

Ankylosing spondylitis (AS) combined with lower cervical fracture is often categorized into unstable fracture, with a high incidence of neurological injury and a high rate of disability and morbidity. As factors such as shoulder occlusion may affect the accuracy of X-ray imaging diagnosis, it is often easily misdiagnosed at the primary diagnosis. Non-operative treatment has complications such as bone nonunion and the possibility of secondary neurological damage, while the timing, access and choice of surgical treatment are still controversial. Currently, there are no clinical practice guidelines for the treatment of AS combined with lower cervical fracture with or without dislocation. To this end, the Spinal Trauma Group of Orthopedics Branch of Chinese Medical Doctor Association organized experts to formulate Clinical guidelines for the treatment of ankylosing spondylitis combined with lower cervical fracture in adults ( version 2024) in accordance with the principles of evidence-based medicine, scientificity and practicality, in which 11 recommendations were put forward in terms of the diagnosis, imaging evaluation, typing and treatment, etc, to provide guidance for the diagnosis and treatment of AS combined with lower cervical fracture.

Objective To observe the efficacy and safety of Morinda officinalis oligosaccharides in the continuation treatment of mild and moderate depression.Methods An open,single-arm,multi-center design was adopted in our study.Adult patients with mild and moderate depression who had received acute treatment of Morinda officinalis oligosaccharides were enrolled and continue to receive Morinda officinalis oligosaccharides capsules for 24 weeks,the dose remained unchanged during continuation treatment.The remission rate,recurrence rate,recurrence time,and the change from baseline to endpoint of Hamilton Depression Scale(HAMD),Hamilton Anxiety Scale(HAMA),Clinical Global Impression-Severity(CGI-S)and Arizona Sexual Experience Scale(ASEX)were evaluated.The incidence of treatment-related adverse events was reported.Results The scores of HAMD-17 at baseline and after treatment were 6.60±1.87 and 5.85±4.18,scores of HAMA were 6.36±3.02 and 4.93±3.09,scores of CGI-S were 1.49±0.56 and 1.29±0.81,scores of ASEX were 15.92±4.72 and 15.57±5.26,with significant difference(P＜0.05).After continuation treatment,the remission rate was 54.59％(202 cases/370 cases),and the recurrence rate was 6.49％(24 cases/370 cases),the recurrence time was(64.67±42.47)days.The incidence of treatment-related adverse events was 15.35％(64 cases/417 cases).Conclusion Morinda officinalis oligosaccharides capsules can be effectively used for the continuation treatment of mild and moderate depression,and are well tolerated and safe.

Objective:To determine incidence of seasonal influenza virus infection in the community and to analyze the factors influencing seasonal influenza virus infection.Methods:This study recruited residents aged 6-59 years to build a cohort in 15 villages/streets in Macheng city in November 2019. Meanwhile, a cross-sectional baseline survey was conducted immediately to collect sera, information on demographics and child protection knowledge, behaviors, as well as attitudes using a questionnaire from the participants enrolled in the cohort (i.e., before the influenza epidemic season). In July 2020, a cross-sectional follow-up survey was conducted to collect sera once again (i.e., after the influenza season). Paired sera from the two cross-sectional surveys were tested for influenza virus-specific antibodies by hemagglutination inhibition (HI) test or micro-neutralization (MN) test using a circulating representative strain of each subtype/lineage of influenza virus as the test antigen. The infections with influenza virus subtype/lineage was confirmed if there was a four-fold or more increase in titers of antibodies against circulating representative strain of the subtype/lineage of influenza virus. Factors influencing infection with influenza A (H3N2) and B/Victoria viruses were analyzed using univariable and multivariable logistic regression.Results:In November 2019, 800 study participants were enrolled in the cohort, including 340 children aged 6-17 years and 460 adults aged 18-59 years; 605 study participants (including 224 children and 381 adults) were followed up in July 2020 and their paired sera were obtained before and after the influenza season. 25.3% (153/605) of the participants were confirmed to be infected with at least one subtype/lineage of seasonal influenza virus by HI and MN tests. The overall incidence of influenza viruses of all subtypes/lineages in children was 44.2% (95% CI: 37.6%-50.8%) which was significantly higher than the incidence of 14.1% in adults (95% CI: 10.7%-17.7%). Children had the highest incidence of influenza A (H3N2) virus infection, followed by B/Victoria. MN or HI antibody titers in A (H3N2)[ OR=0.88 (95% CI: 0.84-0.93)] and B/Victoria[ OR=0.97 (95% CI: 0.95-0.99)] before the influenza season were significantly associated with whether children were infected with that subtype/lineage of influenza virus. Conclusions:The residents aged 6-59 years in Macheng city had a substantial incidence of seasonal influenza virus infection during the influenza season from winter 2019 to spring 2020. Notably, almost half of children aged 6-17 years have been infected with seasonal influenza virus. Higher titers of HI/MN antibodies against seasonal influenza virus before the influenza season would be likely to reduce the risk of infection with influenza A (H3N2) and B/Victoria.

Objective To explore the risk factors for bile leakage after laparoscopic common bile duct exploration(LCBDE)with primary closure.Methods The clinical data of 560 patients with choledocholithiasis who underwent LCBDE with primary closure in Tianjin Hospital of Integrated Traditional Chinese and Western Medicine from September 2021 to September 2023 were retrospectively analyzed,and the patients were divided into the bile leak group and the non-bile leak group according to the occurrence of postoperative bile leakage.The risk factors affecting the occurrence of postoperative bile leakage were analyzed by multivariate analysis.Results A total of 64 cases(11.4% )experienced varying degrees of bile leakage,including 55 cases of grade A bile leakage,7 cases of grade B,and 2 cases of grade C.The thin common bile duct(OR=0.07,P<0.001),history of hypertension(OR=4.56,P<0.001),and high BMI(OR=1.17,P=0.002)were the risk factors for postoperative bile leakage in patients with choledocholithiasis.Conclusion Patients with thin common bile duct,hypertension and obesity are more likely to occur postoperative bile leakage.Patients with choledocholithiasis who have the above high-risk factors should be cautious in choosing LCBDE with primary closure.

Objective To construct an artificial intelligence assisted diagnosis model based on deep learning for dynamically recognizing gastric lesions and their locations under gastroscopy in real time,and to evaluate its ability to detect and recognize gastric lesions and their locations.Methods The gastroscopy videos of 104 patients in our hospital was retrospectively analyzed,and the video frames were manually annotated.The annotated picture frames of lesion category were divided into the training set and the validation set according to the ratio of 8∶2,and the annotated picture frames of location category were divided into the training set and the validation set according to the patient sources at the ratio of 8∶2.These sets were utilized for training and validating the respective models.YoloV4 model was used for the training of lesion recognition,and ResNet152 model was used for the training of location recognition.The accuracy,sensitivity,specificity,positive predictive value,negative predictive value and location recognition accuracy of the auxiliary diagnostic model were evaluated.Results A total of 68 351 image frames were annotated,with 54 872 frames used as the training set,including 41 692 frames for lesion categories and 13 180 frames for location categories.The validation set consisted of 13 479 frames,comprising 10 422 frames for lesion categories and 3 057 frames for location categories.The lesion recognition model achieved an overall accuracy of 98.8%,with a sensitivity of 96.6%,specificity of 99.3%,positive predictive value of 96.3%,and negative predictive value of 99.3% in validation set.Meanwhile,the location recognition model demonstrated an top-5 accuracy of 87.1% .Conclusion The artificial intelligence assisted diagnosis model based on deep learning for real-time dynamic recognition of gastric lesions and their locations under gastroscopy has good ability in lesion detection and location recognition,and has great clinical application prospects.