This paper summarizes Professor HAN Mingxiang's clinical experience in the use of Zeqi （Euphorbia HelioscopiaL.）. It is believed that Zeqi （Euphorbia HelioscopiaL.） has the effects of promoting qi， relieving water retention and swelling， resolving phlegm， stopping cough， dissipating masses， activating blood， removing stasis， and detoxifying. In clinical practice， Zeqi （Euphorbia HelioscopiaL.） is flexibly applied in the treatment of skin diseases， respiratory diseases， tumors， etc. For instance， in treating psoriasis with the pathogenesis of damp-heat toxin， a compound prescription of Zeqi Decoction （泽漆汤） is formulated. For bronchial asthma with kidney deficiency and water retention， Zeqi Decoction is commonly combined with Wuling Powder （五苓散） in adjusted doses. For lung nodules with a combination of deficiency， phlegm， stasis， and toxin， a Lung Nodule Prescription is proposed. For advanced lung cancer with both qi and yin deficiency and toxin accumulation， Qiyu Sanlong Decoction （芪玉三龙汤） is suggested， and for cancer-related ascites with qi deficiency and water retention， Wuling Powder combined with Zeqi （Euphorbia HelioscopiaL.）is chosen.

This paper summarizes Professor HAN Mingxiang's clinical experience in treating chronic obstructive pulmonary disease （COPD）. He believes that the key pathomechanism of COPD in the acute exacerbation stage is the invasion of external pathogens triggering latent illness， while lung qi deficiency is the primary mechanism in the stable stage. The core pathological factors throughout disease progression are deficiency， phlegm， and blood stasis. Treatment emphasizes a staged and syndrome-based approach. During the acute exacerbation stage， for wind-cold invading the lung syndrome， the self-formulated Sanzi Wenfei Decoction （三子温肺汤） is used to relieve the exterior， dispel cold， warm the lung， and resolve phlegm. For phlegm-dampness obstructing the lung syndrome， Huatan Jiangqi Fomulation （化痰降气方） is prescribed to warm the lung， transform phlegm， descend qi， and calm wheezing. For phlegm-heat obstructing the lung syndrome， Qingfei Huatan Fomulation （清肺化痰方） is applied to clear heat， resolve phlegm， moisten the lung， and stop coughing. For phlegm and blood stasis interlocking syndrome， Qibai Pingfei Fomulation （芪白平肺方） is used to tonify qi， resolve phlegm， and activate blood circulation to remove stasis. During the stable stage， for lung qi deficiency syndrome， Shenqi Wenfei Decoction （参芪温肺汤） is employed to warm the lung， tonify qi， resolve phlegm， and eliminate turbidity. For lung-spleen qi deficiency syndrome， Shenqi Buzhong Decoction （参芪补中汤） is utilized to strengthen the spleen， tonify qi， and reinforce metal （lung） from earth （spleen）. For lung-kidney deficiency syndrome， Shenqi Tiaoshen Fomulation （参芪调肾方） is prescribed to tonify the lung， warm yang， and regulate kidney function to calm wheezing. These strategies provide insights into the traditional Chinese medicine treatment of COPD.

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.

Ischemic stroke (IS) is a globally life-threatening disease. Presently, few therapeutic medicines are available for treating IS, and rt-PA is the only drug approved by the US Food and Drug Administration (FDA) in the US. In fact, many agents showing excellent neuroprotection but no blood flow-improving activity in animals have not achieved ideal clinical efficacy, while thrombolytic drugs only improving blood flow without neuroprotection have limited their wider application. To address these challenges and meet the huge unmet clinical need, we have designed and identified a novel compound AAPB with dual effects of neuroprotection and cerebral blood flow improvement. AAPB significantly reduced cerebral infarction and neural function deficit in tMCAO rats, pMCAO rats, and IS rhesus monkeys, as well as displayed exceptional safety profiles and excellent pharmacokinetic properties in rats and dogs. AAPB has now entered phase I of clinical trials fighting IS in China.

Objective To investigate the mechanism underlying gasdermin E(GSDME)-mediated pyroptosis in radiation-induced intestinal injury and to find out whether gasdermin(GSDM)family members regulate pyroptosis through similar signaling pathways.Methods Human normal colon epithelial cells(NCM460)and human colon cancer cells(HT-29)were exposed to radiation of different doses and durations before pyroptosis indicators were evaluated by observing pyroptotic bubbles,cell survival,and the cleavage of pyroptosis execution proteins.HT-29 cells overexpressing GSDME were subjected to radiation,followed by enrichment analysis of pyroptosis-related differentially expressed genes using RNA-seq.Results Radiation induced substantial pyroptosis in NCM460 cells.Overexpression of GSDME in HT-29 cells resulted in substantial radiation-induced pyroptosis.The pyroptosis state of human intestinal cells was simulated in the HT-29 model cell line.Overexpressions of GSDME-N and GSDMD-N resulted in the expression of more than 50％ of the differentially expressed genes in the pyroptosis state.Sequencing analysis showed that the genes in the pyroptosis state were mainly overrepresented in immune response,inflammatory response,and Rapl signaling pathway.Conclusion GSDME activation can mediate radiation-induced pyroptosis by producing GSDME-N fragments.GSDM family members participate in pyroptosis in a similar mode of regulation.Furthermore,radiation-induced activation of GSDME/D may regulate pyroptosis through immune response,inflammatory response,and Rap1 signaling pathway.

Objective:The activation of astrocytes is an important process in the formation of chronic pain.This study aims to observe the activation of A1 reactive astrocytes in the medullary dorsal horn in the rat model of trigeminal neuralgia,and to explore the mechanism of central sensitization caused by A1 reactive astrocyte. Methods:The adult male rats were randomly divided into a sham group and a chronic constriction injury of infraorbital nerve(ION-CCI)group.The facial mechanical pain threshold and thermal withdrawal latency were measured before the operation and on the 1st,3rd,7th,10th,and 14th day after the operation.After pain behavior observation,the expression of glial fibrillary acidic protein(GFAP)in the medullary dorsal horn was observed by immunohistochemistry and immunofluorescence colocalization of GFAP,complement 3(C3)/S100A10,and 4',6-diamidino-2-phenylindole(DAPI)was analyzed.Primary astrocytes were cultured and randomly divided into a naive group and a DHK group.The DHK group was treated with 1 mmol/L of astrocyte activation inhibitor dihydrokainic acid(DHK).Fura-2/AM was used to stain the astrocytes and the calcium wave of the 2 groups under the stimulation of high potassium was recorded and compared.The expression of C3 was detected by Western blotting. Results:The facial mechanical pain threshold and thermal withdrawal latency of the ION-CCI group were significantly lower than those of the sham group(both P<0.05).There were a large number of GFAP positive astrocytes in the medullary dorsal horn of the ION-CCI group.The fluorescence intensity of GFAP in the ION-CCI group was higher than that in the sham group(P<0.05).GFAP and C3/S100A10 were co-expressed in astrocytes.Compared with the sham group,the fluorescence intensity of C3 and the protein expression of C3 in the ION-CCI group were increased(both P<0.05).The expression of C3 in ION-CCI group was significantly increased(P<0.05).Compared with the naive group,the C3 protein expression was significantly decreased in the DHK group(P<0.05).The intensity of calcium fluorescence was increased after high potassium stimulation in both groups.Furthermore,the peak and increase amplitude of calcium fluorescence in the naive group were much higher than those in the DHK group(both P<0.05). Conclusion:A1 reactive astrocytes in the medullary dorsal horn of trigeminal neuralgia model rats are increased significantly,which may participate in central sensitization of trigeminal neuralgia by impacting astrocyte calcium wave.

Objective This study aimed to explore the relationships between residential greenness and cardiometabolic risk factors among rural adults in Xinjiang Uygur Autonomous Region(Xinjiang)and thus provide a theoretical basis and data support for improving the health of residents in this region. Methods We recruited 9,723 adult rural residents from the 51st Regiment of the Third Division of the Xinjiang Production and Construction Corps in September 2016.The normalized difference vegetation index(NDVI)was used to estimate residential greenness.The generalized linear mixed model(GLMM)was used to examine the association between residential greenness and cardiometabolic risk factors. Results Higher residential greenness was associated with lower cardiometabolic risk factor prevalence.After adjustments were made for age,sex,education,and marital status,for each interquartile range(IQR)increase of NDVI500-m,the risk of hypertension was reduced by 10.3%(OR=0.897,95%CI=0.836-0.962),the risk of obesity by 20.5%(OR=0.795,95%CI=0.695-0.910),the risk of type 2 diabetes by 15.1%(OR=0.849,95%CI=0.740-0.974),and the risk of dyslipidemia by 10.5%(OR=0.895,95%CI=0.825-0.971).Risk factor aggregation was reduced by 20.4%(OR=0.796,95%CI=0.716-0.885)for the same.Stratified analysis showed that NDVI500-m was associated more strongly with hypertension,dyslipidemia,and risk factor aggregation among male participants.The association of NDVI500-m with type 2 diabetes was stronger among participants with a higher education level.PM10 and physical activity mediated 1.9%-9.2%of the associations between NDVI500-m and obesity,dyslipidemia,and risk factor aggregation. Conclusion Higher residential greenness has a protective effect against cardiometabolic risk factors among rural residents in Xinjiang.Increasing the area of green space around residences is an effective measure to reduce the burden of cardiometabolic-related diseases among rural residents in Xinjiang.

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.

AIM To determine the contents of aspartic acid,glutamic acid,serine,glycine,threonine,citrulline,arginine,alanine,γ-amino-butyric acid,tyrosine,valine,phenlalanine,isoleucine,ornithine,leucine,lysine and proline in Gualoupi Injection and its intermediates,and to analyze their change laws.METHODS The OPA-FMOC online derivatization analysis was performed on a 45℃ thermostatic Waters XBridge C18 column(4.6 mm×100 mm,3.5 μm),with the mobile phase comprising of phosphate buffer solution-[methanol-acetonitrile-water(45 : 45 : 10)]flowing at 1 mL/min in a gradient elution manner,and the detection wavelengths were set at 262,338 nm.Principal component analysis and heatmap analysis were adopted in chemical pattern recognition for the corresponding intermediates in ten processes of six batches of samples.RESULTS Seventeen amino acids showed good linear relationships within their own ranges(R2>0.998 0),whose average recoveries were 83.4％-119.5％with the RSDs of 0.91％-7.94％.Different batches of samples in the same process were clustered,and the corresponding intermediates in different processed were clustered into three groups.Alcohol precipitation and cation exchange column demonstrated the biggest influences on amino acid composition.CONCLUSION This experiment can provide important references for the critical factors on quality control of Gualoupi Injection,thus ensure the stability and uniformity of final product.

Objective:To report the results of national surveillance on the distribution and antimicrobial resistance profile of clinical Gram-positive bacteria isolates from bloodstream infections in China in 2022.Methods:The clinical isolates of Gram-positive 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:A total of 3 163 strains of Gram-positive pathogens were collected from 51 member units，and the top five bacteria were Staphylococcus aureus（ n=1 147，36.3%），coagulase-negative Staphylococci（ n=928，29.3%）， Enterococcus faecalis（ n=369，11.7%）， Enterococcus faecium（ n=296，9.4%）and alpha-hemolyticus Streptococci（ n=192，6.1%）. The detection rates of methicillin-resistant Staphylococcus aureus（MRSA）and methicillin-resistant coagulase-negative Staphylococci（MRCNS）were 26.4%（303/1 147）and 66.7%（619/928），respectively. No glycopeptide and daptomycin-resistant Staphylococci were detected. The sensitivity rates of Staphylococcus aureus to cefpirome，rifampin，compound sulfamethoxazole，linezolid，minocycline and tigecycline were all >95.0%. Enterococcus faecium was more prevalent than Enterococcus faecalis. The resistance rates of Enterococcus faecium to vancomycin and teicoplanin were both 0.5%（2/369），and no vancomycin-resistant Enterococcus faecium was detected. The detection rate of MRSA in southern China was significantly lower than that in other regions（ χ2=14.578， P=0.002），while the detection rate of MRCNS in northern China was significantly higher than that in other regions（ χ2=15.195， P=0.002）. The detection rates of MRSA and MRCNS in provincial hospitals were higher than those in municipal hospitals（ χ2=13.519 and 12.136， P<0.001）. The detection rates of MRSA and MRCNS in economically more advanced regions（per capita GDP≥92 059 Yuan in 2022）were higher than those in economically less advanced regions（per capita GDP<92 059 Yuan）（ χ2=9.969 and 7.606， P=0.002和0.006）. Conclusions:Among the Gram-positive pathogens causing bloodstream infections in China， Staphylococci is the most common while the MRSA incidence decreases continuously with time；the detection rate of Enterococcus faecium exceeds that of Enterococcus faecalis. The overall prevalence of vancomycin-resistant Enterococci is still at a low level. The composition ratio of Gram-positive pathogens and resistant profiles varies slightly across regions of China，with the prevalence of MRSA and MRCNS being more pronounced in provincial hospitals and areas with a per capita GDP≥92 059 yuan.