Objective: To investigate the role and mechanism of the heat-clearing and detoxifying drug Laggerae Herba in regulating the nuclear factor-erythroid 2-related factor-2(Nrf2)/solute carrier family 7 member 11 (SLC7A11)/glutathione peroxidase 4 (GPX4) signaling pathway to inhibit ferroptosis and improve chronic heart failure induced by transverse aortic arch constriction in mice. Methods: Twenty-four male ICR mice were divided into the sham (n=6) and transverse aortic arch constriction groups (n=18) according to the random number table method. The transverse aortic arch constriction group underwent transverse aortic constriction surgery to establish models. After modeling, the transverse aortic arch constriction group was further divided into the model, captopril, and Laggerae Herba groups according to the random number table method, with six mice per group. The captopril (15 mg/kg) and Laggerae Herba groups (1.95 g/kg) received the corresponding drugs by gavage, whereas the sham operation and model groups were administered the same volume of ultrapure water by gavage once a day for four consecutive weeks. After treatment, the cardiac function indexes of mice in each group were detected using ultrasound. The heart mass and tibia length were measured to calculate the ratio of heart weight to tibia length. Hematoxylin and eosin staining were used to observe the pathological changes in myocardial tissue. Masson staining was used to observe the degree of myocardial fibrosis. Wheat germ agglutinin staining was used to observe the degree of myocardial cell hypertrophy. Prussian blue staining was used to observe the iron deposition in myocardial tissue. An enzyme-linked immunosorbent assay was used to detect the amino-terminal pro-brain natriuretic peptide (NT-proBNP) and glutathione (GSH) contents in mice serum. Colorimetry was used to detect the malondialdehyde (MDA) content in mice serum. Western blotting was used to detect the Nrf2, GPX4, SLC7A11, and ferritin heavy chain 1 (FTH1) protein expressions in mice cardiac tissue. Results: Compared with the sham group, in the model group, the ejection fraction (EF) and fractional shortening (FS) of mice decreased, the left ventricular end-systolic volume (LVESV) and left ventricular end-systolic diameter (LVESD) increased, the left ventricular anterior wall end-systolic thickness (LVAWs) and left ventricular posterior wall end-systolic thickness (LVPWs) decreased, the ratio of heart weight to tibia length increased, the myocardial tissue morphology changed, myocardial fibrosis increased, the cross-sectional area of myocardial cells increased, iron deposition appeared in myocardial tissue, the serum NT-proBNP and MDA levels increased, the GSH level decreased, and Nrf2, GPX4, SLC7A11, and FTH1 protein expressions in cardiac tissue decreased (P<0.05). Compared with the model group, in the captopril and Laggerae Herba groups, the EF, FS, and LVAWs increased, the LVESV and LVESD decreased, the ratio of heart weight to tibia length decreased, the myocardial cells were arranged neatly, the degree of myocardial fibrosis decreased, the cross-sectional area of myocardial cells decreased, the serum NT-proBNP level decreased, and the GSH level increased. Compared with the model group, the LVPWs increased, the iron deposition in myocardial tissue decreased, the serum MDA level decreased, and Nrf2, GPX4, SLC7A11, and FTH1 protein expressions in cardiac tissue increased (P<0.05) in the Laggerae Herba group. Conclusion Laggerae Herba improves the cardiac function of mice with chronic heart failure caused by transverse aortic arch constriction, reduces the pathological remodeling of the heart, and reduces fibrosis. Its mechanism may be related to Nrf2/SLC7A11/GPX4 pathway-mediated ferroptosis.

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 analyze the epidemiological characteristics and changing trends of non suicidal self injury (NSSI) behaviors among middle school students in Jiading District of Shanghai, from 2015 to 2023, so as to provide a basis for the development of NSSI prevention and control measures among students. Methods: Using a stratified cluster random sampling method, a total of five times for Shanghai Adolescent Health Risk Behavior Surveys were conducted for every two years in Jiading District of Shanghai from 2015 to 2023. A total of 5 231 middle school students from junior high schools and senior high schools were selected for questionnaire surveys. Intergroup comparisons were performed using the x 2 test or the χ 2 trend test, and the JointPoint 5.0 software was used to analyze the changing trends, with the annual percent change (APC) used for evaluation. A binary Logistic regression model was employed to analyze the related factors of NSSI behavior among middle school students. Results: In 2023, the reported NSSI rate among middle school students in Jiading District was 14.2%. The rate was significantly higher among junior high school students (17.1%) than that among senior high school students (11.1%), and higher among females (19.2%) than that among males (10.0%) ( χ 2=10.04, 23.21, both P <0.01). From 2015 to 2023, the overall reported NSSI rate showed an increasing trend, rising from 8.6% in 2015 to 14.2% in 2023 ( χ 2 trend =22.25), with an APC of 6.64% ( t =3.49), and the APC for girls was 9.79 % ( t =3.20) (all P <0.05). Among students reporting NSSI, the proportion experiencing ≥6 episodes increased from 10.8% in 2015 to 19.2% in 2023 ( χ 2 trend =6.57, P <0.05). Multivariate Logistic regression analysis indicated that girls, junior high school students, those with insomnia, depressive emotion and drinkers had higher risks of NSSI, compared to boys, senior high school students, those without insomnia, non depressive emotion students and non drinkers ( OR =1.71, 1.96, 3.44, 4.76, 1.77, all P < 0.05 ). Conclusions The reported rate of NSSI among middle school students in Jiading District of Shanghai, increased annually from 2015 to 2023, and the proportion of repeated NSSI also showed an upward trend. Early intervention measures targeting middle school students, especially junior high school students and females, should be implemented to prevent and control its occurrence and development.

Objective:To investigate the association of malnutrition based on Global Leadership Initiative on Malnutrition (GLIM) criteria with the disease activity and clinical outcomes in hospitalized ulcerative colitis (UC) patients.Methods:Clinical data of 115 patients with UC hospitalized in the Affiliated Hospital of Qingdao University from September 2019 to March 2023 were prospectively analyzed. GLIM and European Society for Clinical Nutrition and Metabolism (ESPEN) 2015 criteria were used for the diagnosis of malnutrition, allowing the analysis of consistency between two diagnostic criteria for malnutrition. The relationship between malnutrition based on GLIM criteria and disease activity and clinical outcome was further investigated. The risk factors of adverse clinical outcomes in UC patients were analyzed using binary logistic regression.Results:GLIM and ESPEN 2015 diagnostic criteria showed high correlation and consistency (AUC=0.875, P<0.001; K=0.809, P<0.001). According to GLIM criteria, the prevalence of malnutrition among hospitalized UC patients was 32.17% (37 cases). Compared with non-malnourished UC patients, the modified Mayo score and C-reactive protein level of malnutrition patients were higher ( P<0.005), and the proportion of patients with severe disease activity was higher ( P=0.005). UC patients in the malnourished group had longer hospital stay ( P<0.001), higher hospitalization costs ( P<0.001), and higher rates of drug escalation/conversion therapy, re-admission and surgery at 12 weeks and 54 weeks ( P<0.05). Binary logistic regression analysis showed that a high Mayo score ( OR=3.606, P=0.016), a high modified Mayo score ( OR=1.346, P=0.009) and malnutrition ( OR=1.430, P=0.012) were independent risk factors for adverse clinical outcomes of hospitalized UC patients at 12 weeks. A high modified Mayo score ( OR=6.491, P=0.011) and malnutrition as per GLIM criteria ( OR=6.693, P=0.033) were independent risk factors for adverse clinical outcomes of hospitalized UC patients at 54 weeks. Conclusions:GLIM and ESPEN 2015 diagnostic criteria show high consistency in the diagnosis of malnutrition in hospitalized UC inpatients. Malnutrition may imply adverse clinical outcomes of hospitalized UC patients, which is an independent risk factor for the adverse clinical outcome of hospitalized UC patients.

Objective:To explore the effects of body mass index (BMI) on the incidence of nocturnal hypertension in patients with hypertension.Methods:Totally 341 hospitalized hypertensive patients treated at the Affiliated Hospital of Jining Medical University from February to May 2023 were selected by convenience sampling. Patients' general information, clinical data, and 24-hour ambulatory blood pressure results were collected. A binomial Logistic regression analysis was conducted to investigate the factors affecting the occurrence of nocturnal hypertension in these patients. The relationship between BMI and the incidence of nocturnal hypertension was examined using threshold effect tests and smooth curve fitting.Results:The binomial Logistic regression analysis indicated that blood phosphate level was a factor influencing the occurrence of nocturnal hypertension in hypertensive patients ( P<0.05). Smooth curve fitting and threshold effect test results showed that the relationship between BMI and the incidence of nocturnal hypertension was curve-correlated, with a turning point at 24.61 kg/m 2. To the left of the turning point, there was no correlation ( P=0.130) ; to the right, there was a correlation ( P=0.016) . Conclusions:When the BMI of hypertensive patients exceeds 24.61 kg/m 2, the likelihood of nocturnal hypertension increases with rising BMI, providing a precise intervention target for weight management-based patient care in hypertension.

The aim of this study was to investigate the prevalence of spotted fever group rickettsia(SFGR)in wild rodents collected from Fengshan County in the Guangxi Zhuang Autonomous Region,and to determine their species.Wild rodents were captured in cages in Fengshan County,Hechi City,Guangxi Zhuang Autonomous Region.The rodents were identified according to morphological characteristics,and the findings were confirmed through molecular biology methods.Subsequently,spleen samples were collected,and DNA was extracted.The outer membrane protein A(ompA)gene was amplified with semi-nested PCR to determine the species of SFGR in captured wild rodents.After sequencing of the PCR products,homology and phylogenetic analyses of ompA gene sequences were performed.A total of 105 wild rodents belonging to seven species were captured.FGR was detected in six rodent species(Bandicota indica,Leopoldamys edwardsi,Mus caroli,Mus Pahari,Rat-tus andamanensis,and Rattus losea,but not Berylmys bower si),and the total positivity rate was 23.8％.Three Rickettsia species,Candidatus Rickettsia jingxinensis,Rickettsia raoultii,and Rickettsia sibirica,were identified from analysis of the ompA gene sequence.This study revealed the presence of three species of SFGR infecting wild rodents from Fengshan County,Guangxi Zhuang Autonomous Region,thus suggesting that Fengshan County is a natural focus of tick-borne spotted fever.This study highlights the need to strengthen monitoring and prevention measures for rickettsiosis.

Research integrity is the foundation for ensuring the sound and orderly development of scientific and technological innovation. As the main battlefield of clinical medical research, hospitals should effectively fulfill their main responsibilities and do a good job in research integrity management. The China Hospital Research Integrity Alliance, consisting of the first batch of 43 hospitals, was established in November 2021. With the aim of " complementary advantages, resource sharing, and collaborative development", the alliance has carried out construction practices from seven aspects: construction mode, cultural system construction, organizational management, institutional construction, publicity and education, early warning and supervision, and technological empowerment. It has achieved the overall improvement of the research integrity construction ability of member units of the alliance, organic linkage between government and medical institutions, and efficient combination of internal and external resources, which can provide reference for the research integrity construction of medical institutions in China.

Objective To design and implement a high-precision ear pulse wave physiological signal detection device for human centrifuge training to solve the problems in measurement and calibration of pilot ear pulse wave signal during human centrifuge training.Methods The high-precision ear pulse wave physiological signal detection device was composed of an ear pulse wave acquisition sensor,a signal acquisition and control unit and a host signal processing module.The ear pulse wave acquisition sensor had an ear-clip-like shape and consisted of an outer shell,an inner shell and an elastic steel plate;the signal acquisition and control unit was made up of an power supply module,a constant voltage module for the light source,a signal acquisition module,a master control module and a data transmission module,which had its software developed with an embedded system;the host signal processing module divided the signal processing into 2 phases of signal pre-processing and pulse wave signal monitoring and display.The detection performance of the device was verified by using a physiological electrical signal calibrator to test the ear pulse wave signals detected with the device;the effectiveness and stability of the device were validated by implementing human centrifuge training experiments with different loads.Results The voltage measurement error,amplitude-frequency characteristics and common mode rejection ratio detected by this device were all within the permitted ranges of JJG 760-2003 Verification Regulation for Electro Cardiac Monitor and JJG 954-2019 Verification Regulation of Digital Electroencephalographs;the device was capable of detecting the ear pulse wave signals of pilot during human centrifuge training in real time with little interference from motion and stable signal quality.Conclusion The device can accurately clarify the changes in the amplitude of the pilot's ear pulse wave during human centrifuge training and effectively reflect the changes in the pilot's cerebral blood flow under positive acceleration.[Chinese Medical Equipment Journal,2024,45(9):35-40]

Objective To investigate the clinical characteristics and prognosis of pneumococcal meningitis(PM),and drug sensitivity of Streptococcus pneumoniae(SP)isolates in Chinese children.Methods A retrospective analysis was conducted on clinical information,laboratory data,and microbiological data of 160 hospitalized children under 15 years old with PM from January 2019 to December 2020 in 33 tertiary hospitals across the country.Results Among the 160 children with PM,there were 103 males and 57 females.The age ranged from 15 days to 15 years,with 109 cases(68.1% )aged 3 months to under 3 years.SP strains were isolated from 95 cases(59.4% )in cerebrospinal fluid cultures and from 57 cases(35.6% )in blood cultures.The positive rates of SP detection by cerebrospinal fluid metagenomic next-generation sequencing and cerebrospinal fluid SP antigen testing were 40% (35/87)and 27% (21/78),respectively.Fifty-five cases(34.4% )had one or more risk factors for purulent meningitis,113 cases(70.6% )had one or more extra-cranial infectious foci,and 18 cases(11.3% )had underlying diseases.The most common clinical symptoms were fever(147 cases,91.9% ),followed by lethargy(98 cases,61.3% )and vomiting(61 cases,38.1% ).Sixty-nine cases(43.1% )experienced intracranial complications during hospitalization,with subdural effusion and/or empyema being the most common complication[43 cases(26.9% )],followed by hydrocephalus in 24 cases(15.0% ),brain abscess in 23 cases(14.4% ),and cerebral hemorrhage in 8 cases(5.0% ).Subdural effusion and/or empyema and hydrocephalus mainly occurred in children under 1 year old,with rates of 91% (39/43)and 83% (20/24),respectively.SP strains exhibited complete sensitivity to vancomycin(100% ,75/75),linezolid(100% ,56/56),and meropenem(100% ,6/6).High sensitivity rates were also observed for levofloxacin(81% ,22/27),moxifloxacin(82% ,14/17),rifampicin(96% ,25/26),and chloramphenicol(91% ,21/23).However,low sensitivity rates were found for penicillin(16% ,11/68)and clindamycin(6% ,1/17),and SP strains were completely resistant to erythromycin(100% ,31/31).The rates of discharge with cure and improvement were 22.5% (36/160)and 66.2% (106/160),respectively,while 18 cases(11.3% )had adverse outcomes.Conclusions Pediatric PM is more common in children aged 3 months to under 3 years.Intracranial complications are more frequently observed in children under 1 year old.Fever is the most common clinical manifestation of PM,and subdural effusion/emphysema and hydrocephalus are the most frequent complications.Non-culture detection methods for cerebrospinal fluid can improve pathogen detection rates.Adverse outcomes can be noted in more than 10% of PM cases.SP strains are high sensitivity to vancomycin,linezolid,meropenem,levofloxacin,moxifloxacin,rifampicin,and chloramphenicol.[Chinese Journal of Contemporary Pediatrics,2024,26(2):131-138]

Objective To investigate the feasibility of developing a grading diagnostic model for schistosomiasis-induced liver fibrosis based on B-mode ultrasonographic images and clinical laboratory indicators. Methods Ultrasound images and clinical laboratory testing data were captured from schistosomiasis patients admitted to the Second People’s Hospital of Duchang County, Jiangxi Province from 2018 to 2022. Patients with grade I schistosomiasis-induced liver fibrosis were enrolled in Group 1, and patients with grade II and III schistosomiasis-induced liver fibrosis were enrolled in Group 2. The machine learning binary classification tasks were created based on patients’radiomics and clinical laboratory data from 2018 to 2021 as the training set, and patients’radiomics and clinical laboratory data in 2022 as the validation set. The features of ultrasonographic images were labeled with the ITK-SNAP software, and the features of ultrasonographic images were extracted using the Python 3.7 package and PyRadiomics toolkit. The difference in the features of ultrasonographic images was compared between groups with t test or Mann-Whitney U test, and the key imaging features were selected with the least absolute shrinkage and selection operator (LASSO) regression algorithm. Four machine learning models were created using the Scikit-learn repository, including the support vector machine (SVM), random forest (RF), linear regression (LR) and extreme gradient boosting (XGBoost). The optimal machine learning model was screened with the receiver operating characteristic curve (ROC), and features with the greatest contributions to the differentiation features of ultrasound images in machine learning models with the SHapley Additive exPlanations (SHAP) method. Results The ultrasonographic imaging data and clinical laboratory testing data from 491 schistosomiasis patients from 2019 to 2022 were included in the study, and a total of 851 radiomics features and 54 clinical laboratory indicators were captured. Following statistical tests (t = −5.98 to 4.80, U = 6 550 to 20 994, all P values < 0.05) and screening of key features with LASSO regression, 44 features or indicators were included for the subsequent modeling. The areas under ROC curve (AUCs) were 0.763 and 0.611 for the training and validation sets of the SVM model based on clinical laboratory indicators, 0.951 and 0.892 for the training and validation sets of the SVM model based on radiomics, and 0.960 and 0.913 for the training and validation sets of the multimodal SVM model. The 10 greatest contributing features or indicators in machine learning models included 2 clinical laboratory indicators and 8 radiomics features. Conclusions The multimodal machine learning models created based on ultrasound-based radiomics and clinical laboratory indicators are feasible for intelligent identification of schistosomiasis-induced liver fibrosis, and are effective to improve the classification effect of one-class data models.