Membrane proteins are integral components of cellular membranes, accounting for approximately 30% of the mammalian proteome and serving as targets for 60% of FDA-approved drugs. They are critical to both physiological functions and disease mechanisms. Their functional protein-protein interactions form the basis for many physiological processes, such as signal transduction, material transport, and cell communication. Membrane protein interactions are characterized by membrane environment dependence, spatial asymmetry, weak interaction strength, high dynamics, and a variety of interaction sites. Therefore, in situ analysis is essential for revealing the structural basis and kinetics of these proteins. This paper introduces currently available in situ analytical techniques for studying membrane protein interactions and evaluates the characteristics of each. These techniques are divided into two categories: label-based techniques (e.g., co-immunoprecipitation, proximity ligation assay, bimolecular fluorescence complementation, resonance energy transfer, and proximity labeling) and label-free techniques (e.g., cryo-electron tomography, in situ cross-linking mass spectrometry, Raman spectroscopy, electron paramagnetic resonance, nuclear magnetic resonance, and structure prediction tools). Each technique is critically assessed in terms of its historical development, strengths, and limitations. Based on the authors’ relevant research, the paper further discusses the key issues and trends in the application of these techniques, providing valuable references for the field of membrane protein research. Label-based techniques rely on molecular tags or antibodies to detect proximity or interactions, offering high specificity and adaptability for dynamic studies. For instance, proximity ligation assay combines the specificity of antibodies with the sensitivity of PCR amplification, while proximity labeling enables spatial mapping of interactomes. Conversely, label-free techniques, such as cryo-electron tomography, provide near-native structural insights, and Raman spectroscopy directly probes molecular interactions without perturbing the membrane environment. Despite advancements, these methods face several universal challenges: (1) indirect detection, relying on proximity or tagged proxies rather than direct interaction measurement; (2) limited capacity for continuous dynamic monitoring in live cells; and (3) potential artificial influences introduced by labeling or sample preparation, which may alter native conformations. Emerging trends emphasize the multimodal integration of complementary techniques to overcome individual limitations. For example, combining in situ cross-linking mass spectrometry with proximity labeling enhances both spatial resolution and interaction coverage, enabling high-throughput subcellular interactome mapping. Similarly, coupling fluorescence resonance energy transfer with nuclear magnetic resonance and artificial intelligence (AI) simulations integrates dynamic structural data, atomic-level details, and predictive modeling for holistic insights. Advances in AI, exemplified by AlphaFold’s ability to predict interaction interfaces, further augment experimental data, accelerating structure-function analyses. Future developments in cryo-electron microscopy, super-resolution imaging, and machine learning are poised to refine spatiotemporal resolution and scalability. In conclusion, in situ analysis of membrane protein interactions remains indispensable for deciphering their roles in health and disease. While current technologies have significantly advanced our understanding, persistent gaps highlight the need for innovative, integrative approaches. By synergizing experimental and computational tools, researchers can achieve multiscale, real-time, and perturbation-free analyses, ultimately unraveling the dynamic complexity of membrane protein networks and driving therapeutic discovery.

Objective To investigate the relationship between brain white matter lesions (WML) and triglyceride glucose (TyG) index in non-diabetic elderly individuals based on magnetic resonance imaging. Methods A total of 523 non-diabetic elderly individuals aged ≥ 60 years were selected from Jinan, Shandong Province, China from June 2018 to December 2019. According to the quartiles of TyG index, there were 133 participants in the first quartile (Q1) group, 127 in the second quartile (Q2) group, 132 in the third quartile (Q3) group, and 131 in the fourth quartile (Q4) group. All participants underwent brain magnetic resonance imaging to evaluate paraventricular, deep, and total WML volumes, as well as Fazekas scores. Results Compared with Q1, Q2, and Q3 groups, Q4 group showed significant increase in periventricular, deep, and total WML volumes (P < 0.05). The proportion of participants with a Fazekas score ≥ 2 in the periventricular, deep, and total WML was higher in the Q4 group compared with the Q1 and Q2 groups (P < 0.05). The proportion of participants with a Fazekas score ≥ 2 in deep WML was higher in Q4 group than in Q3 group (P < 0.05). TyG index was significantly positively correlated with periventricular, deep, and total WML volumes (r = 0.401, 0.405, and 0.445, P < 0.001). After adjusting for confounding factors, TyG index was still significantly positively correlated with periventricular, deep, and total WML volumes (P < 0.001). Logistic regression analysis showed that compared with Q1 group, the risk of Fazekas score ≥ 2 in periventricular WML was 1.950-fold (95% confidence interval [CI]: 1.154-3.294, P = 0.013) in Q3 group and 3.411-fold (95% CI: 1.984-5.863, P < 0.001) in Q4 group, the risk of Fazekas score ≥ 2 in total WML was 2.529-fold (95%CI: 1.444-4.430, P = 0.001) in Q3 group and 4.486-fold (95%CI: 2.314-8.696, P < 0.001) in Q4 group. The risk of Fazekas score ≥ 2 in deep WML was 2.953-fold (95%CI: 1.708-5.106, P < 0.001) in Q4 group compared with Q1 group. Conclusion Increased TyG index is an independent risk factor for WML in non-diabetic elderly individuals.

Objective To analyze the risk factors associated with the occurrence of rectal neuroendocrine tumors (RNETs) and construct a risk prediction model. Methods Clinical data of patients who underwent electronic colonoscopy were collected. The clinical information on patients with and without RNETs were compared, and potential risk factors for RNETs were identified. Binary logistic regression was performed to analyze the relevant risk factors and construct a risk prediction model. Results Among 164 patients, 66 were diagnosed with RNETs, and 98 who did not have such a condition were randomly selected. Univariate logistic regression analysis revealed that age, fatty liver, anxiety and depression, total cholesterol, triglyceride levels, and carcinoembryonic antigen (CEA) were significant factors influencing the occurrence of RNETs (P<0.05). Multivariate logistic regression analysis identified age (P=0.015), anxiety and depression (P=0.031), cholesterol level (P=0.009), fatty liver (P=0.001), and CEA (P<0.001) as independent risk factors for RNETs. The participants were randomly divided into training and test sets at a 7:3 ratio. The training set was used to construct a nomogram-based risk prediction model, and the testing set was used for internal validation. The area under the curve values for the training and testing sets were 0.843 and 0.772, respectively (P>0.05). These findings indicate a good discriminative performance. The calibration curves for the training and testing sets were in good agreement with the 45° standard line, which suggests that the predicted probabilities were consistent with the actual outcomes. Decision curve analysis showed that the model provided a high net benefit within a threshold range of 0.2 to 0.7 for clinical decision making. Conclusion Young age, fatty liver, high CEA levels, high cholesterol levels, and anxiety and depression are independent risk factors for RNETs. The nomogram model constructed based on these risk factors exhibits a strong capability to predict the occurrence of RNETs, and clinical intervention can be considered based on the predicted probability values.

ObjectiveTo investigate the quality markers of Xiaoqinglong granules（XQLG） for treating bronchial asthma using the analytic hierarchy process（AHP）-entropy weight method（EWM）， network pharmacology and high performance liquid chromatography（HPLC） content determination. MethodsEffectiveness， testability and peculiarity component data of XQLG in treating bronchial asthma were constructed through database retrieval， literature review， and network pharmacology. Subsequently， AHP-EWM was used to quantitatively identify and weight the control layer and element layer， the relevant compounds were selected as candidate quality markers based on comprehensive scores. Further comparison of reference substances and establishment of HPLC content determination method were used to determine the potential quality markers of XQLG， which were verified by molecular docking with disease targets. ResultsA total of 13 components， including glycyrrhizic acid， paeoniflorin， schisandrol A， isoliquiritigenin， 6-gingerol， ephedrine， liquiritin， albiflorin， liquiritigenin， 6-shogaol， pseudoephedrine， cinnamic acid and cinnamaldehyde， were identified as potential quality markers of XQLG by AHP-EWM. Quantitative analysis indicated that all aforementioned quality markers could be detected in 13 batches of XQLG， indicating that it had stable testability as a quality marker. Among these 13 batches of samples， ephedrine and paeoniflorin exhibited good consistency in content， while pseudoephedrine and cinnamaldehyde showed poor consistency. Molecular docking analysis revealed that the 13 compounds exhibited binding energies with the core targets -2.11 kcal·mol^-1， indicating that the 13 compounds could spontaneously bind to the disease targets， which may be the material basis for the treatment of bronchial asthma with XQLG. ConclusionIn this study， 13 compounds were screened by AHP-EWM combined with network pharmacology and HPLC as quality markers for the treatment of bronchial asthma by XQLG， laying the foundation for enhancing the quality standards of this preparation.

Background::Carotid intima-media thickness (IMT) and diameter, stiffness, and wave reflections, are independent and important clinical biomarkers and risk predictors for cardiovascular diseases. The purpose of the present study was to establish nationwide reference values of carotid properties for healthy Chinese adults and to explore potential clinical determinants.Methods::A total of 3053 healthy Han Chinese adults (1922 women) aged 18-79 years were enrolled at 28 collaborating tertiary centers throughout China between April 2021 and July 2022. The real-time tracking of common carotid artery walls was achieved by the radio frequency (RF) ultrasound system. The IMT, diameter, compliance coefficient, β stiffness, local pulse wave velocity (PWV), local systolic blood pressure, augmented pressure (AP), and augmentation index (AIx) were then automatically measured and reported. Data were stratified by age groups and sex. The relationships between age and carotid property parameters were analyzed by Jonckheere-Terpstra test and simple linear regressions. The major clinical determinants of carotid properties were identified by Pearson’s correlation, multiple linear regression, and analyses of covariance.Results::All the parameters of carotid properties demonstrated significantly age-related trajectories. Women showed thinner IMT, smaller carotid diameter, larger AP, and AIx than men. The β stiffness and PWV were significantly higher in men than women before forties, but the differences reversed after that. The increase rate of carotid IMT (5.5 μm/year in women and 5.8 μm/year in men) and diameter (0.03 mm/year in both men and women) were similar between men and women. For the stiffness and wave reflections, women showed significantly larger age-related variations than men as demonstrated by steeper regression slopes (all P for age by sex interaction <0.05). The blood pressures, body mass index (BMI), and triglyceride levels were identified as major clinical determinants of carotid properties with adjustment of age and sex. Conclusions::The age- and sex-specific reference values of carotid properties measured by RF ultrasound for healthy Chinese adults were established. The blood pressures, BMI, and triglyceride levels should be considered for clinical application of corresponding reference values.

Aim To study the main active ingredients,key targets and pathways of Cigu Xiaozhi Decoction(CXD)based on network pharmacology,and to ana-lyze and verify the mechanism of CXD on the transfor-mation of"inflammatory cancer"in non-alcoholic steatohepatitis(NASH)by animal experiments.Meth-ods The potential targets and signaling pathways of CXD in the treatment of NASH"inflammatory carcino-ma"were predicted based on network pharmacology.The mouse model of NASH was induced by methionine-choline deficiency diet(MCD),and CXD and epider-mal growth factor receptor(EGFR)inhibitors were given for 28 days.The mice were killed after the inter-vention,and the liver histopathology of each group was observed by hematoxylin-eosin method(HE).The rel-ative expression levels of EGFR,phosphatidylinositol 3-kinase(PI3K)and protein kinase B(AKT)in liver tissue of mice in each group were detected by Western blot.The contents of interleukin-6(IL-6),interleu-kin-1 β(IL-1β)and tumor necrosis factor-α(TNF-α)in serum were detected by enzyme-linked immunosor-bent assay(ELISA).Results A total of 284 poten-tial active components,159 potential therapeutic tar-gets and 20 key targets of CXD were identified by net-work pharmacological screening.CXD could affect multiple biological processes such as cell proliferation and inflammatory response,involving multiple signa-ling pathways such as tumor and PI3K/AKT.Animal experiments showed that CXD could reduce the levels of IL-6,IL-1β and TNF-α in serum of NASH mice.The relative expression of PI3K and AKT protein in liv-er tissue decreased,and the relative expression of EG-FR protein was increased.Conclusion CXD can reg-ulate EGFR/PI3K/AKT signaling pathway by partici-pating in biological processes such as cell proliferation and inflammatory response,and improve liver tissue injury in NASH mice.

Human beings are inevitably exposed to toxic substances as a result of influences of potential contamination factors in the environment,food and medicines,which poses a threat to human health.In order to effectively screen and prevent the exposure or intake of such substances,it is neces-sary to develop in vitro assays for the detection and toxicity evaluation of toxic substances.High content screening(HCS)has been recognized as an important tool for toxicity testing and risk assessment of compounds due to its high throughput and automation advantages,and has been widely used in in vitro toxicology research.In this review,we described the system components of HCS and its workflow in toxicity screening and toxicity evaluation by focusing on cases of their application in toxicity detection and evaluation studies,including the cytotoxicity,hepatotoxicity,nephrotoxicity,genotoxicity,neurotox-icity,cardiotoxicity,and developmental toxicity.In addition,the applications and developments of machine learning in HCS were explored,especially to the advantages of supervised and unsupervised machine learning strategies for high throughput image screening and data analysis.Finally,the future applications of HCS in toxicity screening and evaluation are outlined,especially in terms of binding new models and gene editing technology.

Objective To investigate the factors influencing prognosis in patients with acute myocardial infarction complicated with cardiogenic shock undergoing primary percutaneous coronary intervention(PPCI).Methods Patients with acute myocardial infarction complicated with cardiogenic shock who underwent PPCI at our hospital between January 2015 and December 2019 were enrolled.Clinical baseline characteristics,coronary angiography and PCI-related parameters,and mechanical support information were collected.The patients were followed up for one year and divided into survival and death groups based on their survival status within one year.Differences in various factors between the two groups were compared.Results A total of 40 patients were enrolled,including 26 in the survival group and 14 in the death group.There were no differences in baseline data,diagnosis,risk factors,and comorbidities between the two groups.The survival group had a lower heart rate and higher blood pressure trend at admission compared to the death group.Myocardial enzymes were significantly lower in the survival group compared to the death group(median CK peak:496.00(198.25,2 830.00)U/L vs.3 040.00(405.75,5 626.53)U/L,P=0.003;median CK-MB peak:52.65(31.75,219.50)U/L vs.306.00(27.25,489.63)U/L,P=0.006).When comparing coronary angiography and PCI-related indicators between the two groups,the survival group had a higher rate of complete revascularization compared to the control group(53.85％vs.21.43％,P=0.048).The survival group had a higher proportion of extracorporeal membrane oxygenation(ECMO)combined with intra-aortic balloon pump(IABP)support compared to the control group[38.46％vs.7.14％,P=0.034].Conclusions Survival in patients with acute myocardial infarction complicated with cardiogenic shock undergoing PPCI is associated with lower level of myocardial enzymes,ECMO combined with IABP support and complete revascularization.

Objective To monitor the susceptibility of clinical isolates to antimicrobial agents in tertiary hospitals in major regions of China in 2022.Methods Clinical isolates from 58 hospitals in China were tested for antimicrobial susceptibility using a unified protocol based on disc diffusion method or automated testing systems.Results were interpreted using the 2022 Clinical &Laboratory Standards Institute(CLSI)breakpoints.Results A total of 318 013 clinical isolates were collected from January 1,2022 to December 31,2022,of which 29.5％were gram-positive and 70.5％were gram-negative.The prevalence of methicillin-resistant strains in Staphylococcus aureus,Staphylococcus epidermidis and other coagulase-negative Staphylococcus species(excluding Staphylococcus pseudintermedius and Staphylococcus schleiferi)was 28.3％,76.7％and 77.9％,respectively.Overall,94.0％of MRSA strains were susceptible to trimethoprim-sulfamethoxazole and 90.8％of MRSE strains were susceptible to rifampicin.No vancomycin-resistant strains were found.Enterococcus faecalis showed significantly lower resistance rates to most antimicrobial agents tested than Enterococcus faecium.A few vancomycin-resistant strains were identified in both E.faecalis and E.faecium.The prevalence of penicillin-susceptible Streptococcus pneumoniae was 94.2％in the isolates from children and 95.7％in the isolates from adults.The resistance rate to carbapenems was lower than 13.1％in most Enterobacterales species except for Klebsiella,21.7％-23.1％of which were resistant to carbapenems.Most Enterobacterales isolates were highly susceptible to tigecycline,colistin and polymyxin B,with resistance rates ranging from 0.1％to 13.3％.The prevalence of meropenem-resistant strains decreased from 23.5％in 2019 to 18.0％in 2022 in Pseudomonas aeruginosa,and decreased from 79.0％in 2019 to 72.5％in 2022 in Acinetobacter baumannii.Conclusions The resistance of clinical isolates to the commonly used antimicrobial agents is still increasing in tertiary hospitals.However,the prevalence of important carbapenem-resistant organisms such as carbapenem-resistant K.pneumoniae,P.aeruginosa,and A.baumannii showed a downward trend in recent years.This finding suggests that the strategy of combining antimicrobial resistance surveillance with multidisciplinary concerted action works well in curbing the spread of resistant bacteria.

Objective To investigate the distribution and antimicrobial resistance profiles of the common pathogens isolated from urine from 2015 to 2021 in the CHINET Antimicrobial Resistance Surveillance Program.Methods The bacterial strains were isolated from urine and identified routinely in 51 hospitals across China in the CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021.Antimicrobial susceptibility was determined by Kirby-Bauer method,automatic microbiological analysis system and E-test according to the unified protocol.Results A total of 261 893 nonduplicate strains were isolated from urine specimen from 2015 to 2021,of which gram-positive bacteria accounted for 23.8％(62 219/261 893),and gram-negative bacteria 76.2％(199 674/261 893).The most common species were E.coli(46.7％),E.faecium(10.4％),K.pneumoniae(9.8％),E.faecalis(8.7％),P.mirabilis(3.5％),P.aeruginosa(3.4％),SS.agalactiae(2.6％),and E.cloacae(2.1％).The strains were more frequently isolated from inpatients versus outpatients and emergency patients,from females versus males,and from adults versus children.The prevalence of ESBLs-producing strains in E.coli,K.pneumoniae and P.mirabilis was 53.2％,52.8％and 37.0％,respectively.The prevalence of carbapenem-resistant strains in E.coli,K.pneumoniae,P.aeruginosa and A.baumannii was 1.7％,18.5％,16.4％,and 40.3％,respectively.Lower than 10％of the E.faecalis isolates were resistant to ampicillin,nitrofurantoin,linezolid,vancomycin,teicoplanin and fosfomycin.More than 90％of the E.faecium isolates were ressitant to ampicillin,levofloxacin and erythromycin.The percentage of strains resistant to vancomycin,linezolid or teicoplanin was＜2％.The E.coli,K.pneumoniae,P.aeruginosa and A.baumannii strains isolated from ICU inpatients showed significantly higher resistance rates than the corresponding strains isolated from outpatients and non-ICU inpatients.Conclusions E.coli,Enterococcus and K.pneumoniae are the most common pathogens in urinary tract infection.The bacterial species and antimicrobial resistance of urinary isolates vary with different populations.More attention should be paid to antimicrobial resistance surveillance and reduce the irrational use of antimicrobial agents.