ObjectiveTo investigate the present situation of input, output, outcome and impact of all registered community-based rehabilitation stations in Inner Mongolia in China, and analyze how the input predict the output, outcome and impact. MethodsFrom March 1st to April 30th, 2025, a questionnaire survey was conducted on all registered community-based rehabilitation stations in Inner Mongolia, covering four dimensions: input, output, outcome and impact. A total of 1 365 questionnaires were distributed. The input included four items: laws and policies, human resources, equipment and facilities, and rehabilitation information management. The output included two items: technical paths and benefits/effectiveness. The outcome included three items: coverage rates, rehabilitation interventions and functional results. The impact included two items: health and sustainability. Each item contained several questions, all of which were described in a positive way. Each question was scored from one to five. A lower score indicated that the situation of the community-based rehabilitation station was more in line with the content described in the question. Regression analysis was performed using the total score of each item of input dimension as independent variables, and the total scores of the output, outcome and impact dimensions as dependent variables. ResultsA total of 1 262 valid questionnaires were collected. The mean values of input, output, outcome and impact of community-based rehabilitation stations were 1.827 to 1.904, with coefficient of variation of 45.892% to 49.239%. The regression analysis showed that, rehabilitation information management, human resources, and laws and policies significantly predicted the output dimension (R² = 0.910, P < 0.001). Meanwhile, all four items in the input dimension predicted both the outcome (R² = 0.850, P < 0.001) and impact dimensions (R² = 0.833, P < 0.001). ConclusionInput, output, outcome and impact of the community-based rehabilitation stations in Inner Mongolia were generally in line with the content of the questions, although some imbalances were observed. Additionally, the input of community-based rehabilitation stations could significantly predict their output, outcome and impact.

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.

The genome tagging project (GTP) plays a pivotal role in addressing a critical gap in the understanding of protein functions. Within this framework, we successfully generated a human influenza hemagglutinin-tagged sperm-specific protein 411 (HA-tagged Ssp411) mouse model. This model is instrumental in probing the expression and function of Ssp411. Our research revealed that Ssp411 is expressed in the round spermatids, elongating spermatids, elongated spermatids, and epididymal spermatozoa. The comprehensive examination of the distribution of Ssp411 in these germ cells offers new perspectives on its involvement in spermiogenesis. Nevertheless, rigorous further inquiry is imperative to elucidate the precise mechanistic underpinnings of these functions. Ssp411 is not detectable in metaphase II (MII) oocytes, zygotes, or 2-cell stage embryos, highlighting its intricate role in early embryonic development. These findings not only advance our understanding of the role of Ssp411 in reproductive physiology but also significantly contribute to the overarching goals of the GTP, fostering groundbreaking advancements in the fields of spermiogenesis and reproductive biology.
Animals ; Female ; Humans ; Male ; Mice ; Spermatids/metabolism* ; Spermatogenesis/physiology* ; Spermatozoa/metabolism* ; Thioredoxins/genetics*

OBJECTIVE: To investigate the clinical characteristics and prognosis of peripheral T-cell lymphoma, not otherwise specified (PTCL-NOS). METHODS: Clinical data of 10 patients with PTCL-NOS in Gansu Provincial Hospital from May 2016 to June 2023 were collected. The treatment outcomes were evaluated, and the factors affecting prognosis were analyzed. RESULTS: The median age of onset for the 10 patients was 60.7 (47-75) years, with 7 males and 3 females. Nine cases received chemotherapy, while one case died suddenly after diagnosis, and the median course of chemotherapy was 6.9 (1-13) courses. Assessing the efficacy, 3 patients achieved complete remission (CR) while 7 patients showed progression. Age, sex, lactate dehydrogenase (LDH) level, Ki-67 and the presence of hemophagocytic lymphohistocytosis (HLH) were not statistically correlated with CR rate ( P >0.05). Patients with IPI score 3-5, and Ann Arbor stage III-IV had statistically lower CR rates (both P <0.05). Age, B symptoms, LDH level ,hemoglobin, Ki-67 index and PLR value were not statistically correlated with overall survival (OS) time ( P >0.05). Male, platelet <150×10⁹/L, IPI score 3-5, Ann Arbor stage III-IV, presence of HLH, NLR≥4.05, and LMR <2.81 were statistically correlated with shorter OS (all P <0.05). Among the 10 patients, 3 cases have survived and are still in CR status, while 7 cases have died, with a median survival time of 7.5 (1-85) months. CONCLUSIONS Patients with IPI score 3-5 and Ann Arbor stage III-IV have low CR rate and poor prognosis. The OS of patients who are male, with platelet <150×10⁹/L, IPI score 3-5, Ann Arbor stage III-IV, complication of HLH, NLR≥4.05, and LMR <2.81 is short, and prognosis is poor.
Humans ; Lymphoma, T-Cell, Peripheral/diagnosis* ; Male ; Prognosis ; Middle Aged ; Female ; Aged

OBJECTIVE: This study aimed to explore the association between body mass index (BMI) and mortality based on the 10-year population-based multicenter prospective study. METHODS: A general population-based multicenter prospective study was conducted at four sites in rural China between 2013 and 2023. Multivariate Cox proportional hazards models and restricted cubic spline analyses were used to assess the association between BMI and mortality. Stratified analyses were performed based on the individual characteristics of the participants. RESULTS: Overall, 19,107 participants with a sum of 163,095 person-years were included and 1,910 participants died. The underweight (< 18.5 kg/m ²) presented an increase in all-cause mortality (adjusted hazards ratio [ aHR] = 2.00, 95% confidence interval [ CI]: 1.66-2.41), while overweight (≥ 24.0 to < 28.0 kg/m ²) and obesity (≥ 28.0 kg/m ²) presented a decrease with an aHR of 0.61 (95% CI: 0.52-0.73) and 0.51 (95% CI: 0.37-0.70), respectively. Overweight ( aHR = 0.76, 95% CI: 0.67-0.86) and mild obesity ( aHR = 0.72, 95% CI: 0.59-0.87) had a positive impact on mortality in people older than 60 years. All-cause mortality decreased rapidly until reaching a BMI of 25.7 kg/m ² ( aHR = 0.95, 95% CI: 0.92-0.98) and increased slightly above that value, indicating a U-shaped association. The beneficial impact of being overweight on mortality was robust in most subgroups and sensitivity analyses. CONCLUSION This study provides additional evidence that overweight and mild obesity may be inversely related to the risk of death in individuals older than 60 years. Therefore, it is essential to consider age differences when formulating health and weight management strategies.
Humans ; Body Mass Index ; China/epidemiology* ; Male ; Female ; Middle Aged ; Prospective Studies ; Rural Population/statistics & numerical data* ; Aged ; Follow-Up Studies ; Adult ; Mortality ; Cause of Death ; Obesity/mortality* ; Overweight/mortality*

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 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.