Hypoxemia is a common pathological state characterized by low oxygen saturation in the blood. This condition compromises mucosal barrier integrity particularly in the gut and oral cavity. However, the mechanisms underlying this association remain unclear. This study used periodontitis as a model to investigate the role of platelet activation in oral mucosal immunopathology under hypoxic conditions. Hypoxia upregulated methyltransferase-like protein 4 (METTL4) expression in platelets, resulting in N⁶-methyladenine modification of mitochondrial DNA (mtDNA). This modification impaired mitochondrial transcriptional factor A-dependent cytosolic mtDNA degradation, leading to cytosolic mtDNA accumulation. Excess cytosolic mt-DNA aberrantly activated the cGAS-STING pathway in platelets. This resulted in excessive platelet activation and neutrophil extracellular trap formation that ultimately exacerbated periodontitis. Targeting platelet METTL4 and its downstream pathways offers a potential strategy for managing oral mucosa immunopathology. Further research is needed to examine its broader implications for mucosal inflammation under hypoxic conditions.
DNA, Mitochondrial/metabolism* ; Mouth Mucosa/pathology* ; Hypoxia/immunology* ; Methyltransferases/metabolism* ; Blood Platelets/metabolism* ; Animals ; Periodontitis/immunology* ; Humans ; Platelet Activation ; Mice

Acute kidney injury (AKI) is a critical clinical condition characterized by rapid renal function decline, with high morbidity, mortality, and healthcare costs. Traditional Chinese medicine (TCM) has shown potential effects on mitigating oxidative stress and programmed cell death in AKI models. Scutellaria barbata D. Don (SB) and Scleromitrion diffusum (Willd.) R. J. Wang (SD), a classic TCM herbal pair exhibited anti-inflammatory and antioxidant activities. Using advanced chromatographic separation technology, we enriched the effective fractions of water extracts from SB-SD, obtaining self-assembled herbal nanoparticles (SB and SD nanoparticles, SSNPs) rich in flavonoids and terpenoids. These SSNPs demonstrated robust antioxidant properties in vitro and mitigated AKI progression in vivo by activating the nuclear factor erythroid 2-related factor 2 (Nrf2) signaling pathway. Oral administration of SSNPs in mice resulted in absorption into the bloodstream, formation of a protein corona, reduced macrophage phagocytosis, and enhanced bioavailability and renal targeting. Furthermore, we investigated the self-assembly principle of SSNPs using representative flavonoids and terpenoids. Kinetic studies and in situ transmission electron microscopy (in situ TEM) revealed that these compounds self-assemble via supramolecular forces like hydrogen bonding and π-π interactions, forming stable nanostructures. This study elucidates the renoprotective effects and mechanisms of SB and SD, and provides a novel approach for the development of TCM-based nanomedicines, highlighting the potential of nano-TCM in AKI treatment.

Public health laboratory testing involves a wide range of sample types,complex matrices,diverse target analytes with varying concentrations,and multiple application contexts with different analytical requirements.As a critical step in public health laboratory analysis and testing,sample pretreatment plays a decisive role in ensuring the reproducibility and efficiency of the analytical methods.It directly affects the accuracy,sensitivity,and reliability of testing results,as well as the feasibility of downstream analyses.Traditional sample pretreatment techniques face persistent challenges,including low efficiency,limited throughput,restricted universal applicability,high organic solvent consumption,and poor compatibility with downstream analytical procedures.These limitations constrain their capacity to meet the evolving demands of research and practice in public health and preventive medicine.In recent years,technological advances have focused on improving efficiency and automation,enhancing selectivity and sensitivity,facilitating online testing capabilities,and promoting environmental sustainability.Sample pretreatment techniques in public health laboratory testing have been undergoing progressive upgrades,and numerous novel technologies have emerged.The paper provides a comprehensive review of new technologies and applications in the field.We focused on the development of new materials,the application of artificial intelligence,connections for online processing,and the approaches tailored to the demands of specific testing settings.We also discussed sample processing for omics analyses and mass spectrometry imaging methods relevant to public health laboratory testing.These advances are expected to support the development of greener and higher-throughput sample pretreatment and foster innovation in the public health laboratory testing system.

Objective To systematically review the risk prediction models for intradialytic hypotension in maintenance hemodialysis patients,with a view to provide references for clinical practice.Methods PubMed,Embase,Web of Science,Cochrane Library,CINAHL,CNKI,VIP,Wanfang and CBM were searched from inception to May 29,2023.2 reviewers independently screened the literature,extracted information and assessed methodological quality using the Prediction Model Risk of Bias Assessment Tool.Results A total of 20 studies and 25 models were included with the sample size of 68～9 292 cases and the incidence of outcome events of 2.1～51％.Baseline systolic blood pressure,age,ultrafiltration rate,diabetes and dialysis duration were the top 5 predictors of repeated reporting of the models.20 models reported the area under the curve of ranging from 0.649 to 0.969,and 5 models reported calibration metrics.There were 9 internal validations and 4 combined internal and external validation models.The overall applicability of the 20 studies was good,but all had a high risk of bias,mainly in data analysis.Conclusion Research on risk prediction models for intradialytic hypotension in maintenance hemodialysis patients is still in the developmental stage.Future studies should improve the research design and reporting process,and validation studies of existing models should be carried out to further evaluate the effectiveness and feasibility in clinical practice.

The rational use of medical insurance fund(MIF) plays an important role in promoting the high-quality development of public hospitals, and the supervision of MIF is in a trend of under the rule of law, normalization, professionalization and standardization, and unannounced inspection will become the norm. The authors systematically analyzed three main trends of MIF unannounced inspections, namely, gradually increasing intensity, constantly innovating methods, and increasingly serious consequences. The problems exposed in unannounced inspections were sorted out from five dimensions: form of results, severity, scope of attribution, subjective intention, and regulatory screening ideas. The enlightenment of MIF unannounced inspections to hospital management was explored from four aspects: compliance awareness, organizational system, fine management, and daily supervision. It was proposed that public hospitals should transform their roles and positions, improve the working mechanism of departmental collaboration, and achieve fine management in policy understanding, system formulation, process design, information support, data governance, regulatory implementation, personnel training, and performance matching. At the same time, internal simulated unannounced inspections in hospitals should be regarded as a routine work.

To analyze the factors affecting the cost of hospitalization for patients and provide insights using the intrauterine lesion surgery group (DRG code NE19) as an example.

BACKGROUND:Sepsis is one of the main causes of mortality in intensive care units(ICUs).Early prediction is critical for reducing injury.As approximately 36%of sepsis occur within 24 h after emergency department(ED)admission in Medical Information Mart for Intensive Care(MIMIC-IV),a prediction system for the ED triage stage would be helpful.Previous methods such as the quick Sequential Organ Failure Assessment(qSOFA)are more suitable for screening than for prediction in the ED,and we aimed to find a light-weight,convenient prediction method through machine learning. METHODS:We accessed the MIMIC-IV for sepsis patient data in the EDs.Our dataset comprised demographic information,vital signs,and synthetic features.Extreme Gradient Boosting(XGBoost)was used to predict the risk of developing sepsis within 24 h after ED admission.Additionally,SHapley Additive exPlanations(SHAP)was employed to provide a comprehensive interpretation of the model's results.Ten percent of the patients were randomly selected as the testing set,while the remaining patients were used for training with 10-fold cross-validation. RESULTS:For 10-fold cross-validation on 14,957 samples,we reached an accuracy of 84.1%±0.3%and an area under the receiver operating characteristic(ROC)curve of 0.92±0.02.The model achieved similar performance on the testing set of 1,662 patients.SHAP values showed that the five most important features were acuity,arrival transportation,age,shock index,and respiratory rate. CONCLUSION:Machine learning models such as XGBoost may be used for sepsis prediction using only a small amount of data conveniently collected in the ED triage stage.This may help reduce workload in the ED and warn medical workers against the risk of sepsis in advance.

Objective:To design an electronic nose that can detect and identify urinary volatile organic compounds (VOCs) as marker gases for bladder cancer.Methods:Isopropyl alcohol, ethylbenzene, acetic acid, and ammonia were selected as target gases, and 8 metal oxide gas sensors were used to construct sensor arrays for testing and collecting experimental data, and different characteristics were normalized. Recursive feature elimination (RFE) was used to select the best feature subset, and principal component analysis (PCA) and linear discriminant analysis (LDA) were further introduced to reduce the data dimension and facilitate visual analysis. In addition, three machine learning algorithms, including support vector machine (SVM), random forest (RF), and K-nearest neighbor (KNN), were combined to train and verify the model.Results:When the feature number was 12, the accuracy of the model classification had the best performance. The feature subset consisted of 5 differences, 5 sensitivities, and 2 integrals, and the data was reduced to 12 dimensions. Only PCA couldn’t distinguish the four gases. The LDA classification performance was significantly better than that of PCA, except that isopropyl alcohol and acetic acid had a small overlap area. LDA could distinguish ethylbenzene and ammonia from isopropyl alcohol and acetic acid; the sample points were gathered, which means the clustering performance was also better. The prediction accuracy of SVM, RF, and KNN was 0.85, 0.56, and 0.79, respectively. After model verification, the classification accuracy of PCA+SVM, LDA+RF, and LDA+KNN was 0.97, 0.94, and 0.97, respectively.Conclusions:An electronic nose was designed to detect and identify urinary VOCs marker gases for bladder cancer.

Objective This study aims to analyze the factors influencing benefit finding among cervical cancer patients and their spouses,as well as the interconnections between these factors.The goal is to provide a foundation for developing targeted clinical interventions.Methods Using the convenience sampling method,cervical cancer patients and spouses of 245 pairs who attended or were hospitalized in a tertiary-level hospital in Taiyuan City from October 2022 to July 2023 were selected as study subjects.Data were collected using a general information questionnaire,the Distress Disclosure Index,the Connor-Davidson Resilience Scale,and the Benefit Finding Scale.Univariate analysis,Pearson correlation analysis,and multiple linear regression were employed to scrutinize the data,leading to the establishment of Actor-Partner Interdependence Model.Results Benefit finding scores for cervical cancer patients and their spouses were(65.31±7.94)and(69.87±9.63),respectively.Multiple linear regression revealed that the educational level of patients and their spouses,whether or not they received chemotherapy or radiotherapy,self-disclosure and psychological resilience were the factors that affected patients'benefit finding.Spouse's education level,occupation,self-disclosure,psychological resilience and patients'self-disclosure and psychological resilience were the influencing factors of spouse's benefit finding.The Actor-Partner Interdependence Model analysis indicated that the self-disclosure and psychological resilience of cervical cancer patients positively predicted their own benefit finding and that of their spouses(path coefficients were 0.415,0.501,0.216,and 0.168,respectively,all P＜0.05).However,spouses'self-disclosure and psychological resilience could only positively predict their own benefit finding(path coefficients were 0.188 and 0.254,respectively,all P＜0.05).Conclusion Benefit finding among cervical cancer patients and their spouses is moderate and influenced by various factors.Both self-disclosure and psychological resilience of cervical cancer patients and their spouses have positive subjective effects on their own benefit finding.Healthcare professionals should encourage both parties to engage in healthy interactions about the disease,take steps to increase the level of psychological resilience of both,and ultimately tap into a higher level of benefit finding.