ObjectiveThis paper aims to investigate the role of NDC80 kinetochore complex component （NUF2） and magnesium homeostasis in ovarian cancer cell apoptosis， as well as the regulatory mechanism of gypenoside L （Gyp-L） on NUF2 and magnesium homeostasis. MethodsOvarian cancer OVCAR3 cells were divided into a blank control group， a low-concentration Gyp-L group （50 µmol·L^-1）， a high-concentration Gyp-L group （100 µmol·L^-1）， and a cisplatin （15 µmol·L^-1） group. The migration， proliferation， and apoptosis capabilities of OVCAR3 cells were evaluated through cell scratch assays， clonal experiments， and terminal-deoxynucleotidyl transferase-mediated dUTP-biotin nick end labeling assay （TUNEL） staining. Differentially expressed genes of ovarian cancer were screened by using the Gene Expression Omnibus （GEO） database. The interaction relationships of differentially expressed genes and proteins were analyzed via the Search Tool for Recurring Instances of Neighbouring Genes （STRING） database. The prognostic survival analysis was performed by using the Tumor Immune Estimation Resource （TIMER） database， and the differential expression levels of genes were validated with the Gene Expression Profiling Interactive Analysis （GEPIA） database. The mRNA expression levels of NUF2， magnesium homeostasis-related indicators， such as magnesium transporter 1 （MAGT1）， non-imprinted in Prader-Willi/Angelman syndrome 1 （NIPA1）， NIPA-like domain containing 1 （NIPAL1）， as well as apoptosis-related indicators B cell lymphoma-2 （Bcl-2） and Bcl-2-associated X protein （Bax） in OVCAR3 cells， were detected by real-time quantitative polymerase chain reaction （Real-time PCR）. The protein expression levels of NUF2， MAGT1， NIPA1， NIPAL1， Bcl-2， and Bax in OVCAR3 cells were quantitatively analyzed by ProteinSimple WES. A model of overexpression of NUF2 was constructed， and Gyp-L intervention was performed. The molecular mechanism by which Gyp-L induces ovarian cancer cell apoptosis by regulating NUF2 and influencing magnesium homeostasis was quantitatively analyzed and detected through cell cloning， TUNEL staining， Real-time PCR， and ProteinSimple WES. Finally， the Mg²⁺ content and protein synthesis efficiency were detected by immunofluorescence. ResultsGyp-L significantly inhibited the migration and proliferation capabilities of OVCAR3 cells and promoted their apoptosis （P<0.05）. Overexpression of NUF2 markedly increased the expression levels of MAGT1， NIPA1， NIPAL1， and Bcl-2， while reducing the expression level of Bax （P<0.05）. It also significantly elevated intracellular Mg²⁺ content and protein synthesis efficiency and simultaneously inhibited apoptosis （P<0.05）. Gyp-L could reverse the magnesium homeostasis imbalance and apoptosis inhibition caused by the overexpression of NUF2， downregulating the expression levels of NUF2， MAGT1， NIPA1， NIPAL1， and Bcl-2 （P<0.05）， while upregulating the expression level of Bax （P<0.05）. ConclusionGyp-L can inhibit the occurrence of ovarian cancer， and its mechanism may involve inhibiting the expression of NUF2 to maintain magnesium homeostasis and inducing apoptosis of ovarian cancer cells.

Plant-derived extracellular vesicles (PDEVs), describe a group of nanoparticles released by plants. These particles are characterized by a lipid bilayer structure containing various proteins, lipids, nucleic acids, and unique metabolites. Although the study on PDEVs is relatively new, having only been around for ten years, they have shown promising development prospects in both basic research and clinical transformation areas. Evidence suggests that PDEVs have excellent application prospects in regulating inflammation and treating tumors. Their distinctive, vesicle-mimicking architecture and stellar biocompatibility render them prime candidates for ferrying various anti-cancer agents, including RNA, proteins, and conventional chemotherapy drugs. Increasingly, studies have shown that PDEVs can be engineered as an innovative platform for combination cancer immunotherapy. Consequently, this paper provides an extensive summary of current developments in engineering methods and strategies for PDEVs in cancer treatment and combined cancer immune therapeutics. The essential characteristics of PDEVs, including the biogenesis process and components, as well as their anti-tumor activity and mechanism, are summarized. Finally, the in vivo safety of PDEVs as delivery vectors and the challenges of scale-up production and clinical transformation are discussed.

OBJECTIVE: To explore the feasibility of incorporating simple bedside indicators into death predictive model for elderly critically ill patients based on interpretability machine learning algorithms, providing a new scheme for clinical disease assessment. METHODS: Elderly critically ill patients aged ≥ 65 years who were hospitalized in the intensive care unit (ICU) of Tacheng People's Hospital of Ili Kazak Autonomous Prefecture from June 2017 to May 2020 were retrospectively selected. Basic parameters including demographic characteristics, basic vital signs and fluid intake and output within 24 hours after admission, as well acute physiology and chronic health evaluation II (APACHE II), Glasgow coma score (GCS) and sequential organ failure assessment (SOFA) were also collected. According to outcomes in hospital, patients were divided into survival group and death group. Four datasets were constructed respectively, namely baseline dataset (B), including age, body temperature, heart rate, pulse oxygen saturation, respiratory rate, mean arterial pressure, urine output volume, infusion volume, and crystal solution volume; B+APACHE II dataset (BA), B+GCS dataset (BG), and B+SOFA dataset (BS). Then three machine learning algorithms, Logistic regression (LR), extreme gradient boosting (XGboost) and gradient boosting decision tree (GBDT) were used to develop the corresponding mortality predictive models within four datasets. The feature importance histogram of each prediction model was drawn by SHapley additive explanation (SHAP) method. The area under curve (AUC), accuracy and F1 score of each model were compared to determine the optimal prediction model and then illuminate the nomogram. RESULTS: A total of 392 patients were collected, including 341 in the survival group and 51 in the death group. There were statistically significant differences in heart rate, pulse oxygen saturation, mean arterial pressure, infusion volume, crystal solution volume, and etiological distribution between the two groups. The top three causes of death were shock, cerebral hemorrhage, and chronic obstructive pulmonary disease. Among the 12 prognostic models trained by three machine learning algorithms, overall performance of prognostic models based on B dataset was behind, whereas the LR model trained by BA dataset achieved the best performance than others with AUC of 0.767 [95% confidence interval (95%CI) was 0.692-0.836], accuracy of 0.875 (95%CI was 0.837-0.903) and F1 score of 0.190. The top 3 variables in this model were crystal solution volume with first 24 hours, heart rate and mean arterial pressure. The nomogram of the model showed that the total score between 150 and 230 were advisable. CONCLUSION The interpretable machine learning model including simple bedside parameters combined with APACHE II score could effectively identify the risk of death in elderly patients with critically illness.
Humans ; Critical Illness ; Machine Learning ; Aged ; Algorithms ; Intensive Care Units ; Retrospective Studies ; APACHE ; Prognosis ; Organ Dysfunction Scores ; Hospital Mortality ; Male ; Female

Thyroid nodule, especially thyroid cancer, is one of the most common endocrine surgical diseases at present. Its incidence is increasing, and the demand for surgery is also increasing.The neck scar caused by traditional surgery cannot satisfy the patients′ demand for aesthetics.Therefore, endoscopic techniques, including da Vinci robotic surgery, have gradually become a hot spot for clinical exploration.This article reviews the application and advances of endoscopic techniques in thyroid surgery.

ObjectiveTo understand the epidemic characteristics of influenza in Beijing from 2023 to 2024, and to provide a scientific basis for the prevention and control of influenza. MethodsData on influenza-like illness (ILI) from secondary level and above hospitals, etiology surveillance data, and influenza clusters outbreaks data from 2023‒2024 were used to analyze the epidemic trend and pathogenic characteristics of influenza. Furthermore, an influenza comprehensive index was used to categorize the epidemic intensity at the severity level. ResultsA total of 2 065 857 ILI cases were reported in 2023‒2024 epidemic season, and the percentage of ILI was 3.67%. The age group of 5‒14 years accounted for the highest proportion of ILI (30.48%). A total of 41 766 throat swabs from ILI were detected, with a positive rate of 17.28%.A (H3N2) (51.86%) and B Victoria (41.93%) were the most prevalent subtypes of influenza virus. Clustered influenza outbreaks occurred mainly in primary schools (57.78%) and middle schools (35.55%), mainly caused by the influenza A (H3N2) subtype (85.93%). According to the influenza comprehensive index (I), the period of influenza activity and above (I>0.5) lasted for a total of 37 weeks, accounting for 71.15% of the entire influenza season. ConclusionCompared with previous years, the epidemic level of influenza in Beijing was increased in 2023‒2024, and the peak time became earlier. The comprehensive index method can objectively evaluate the level of influenza epidemic and provide suggestions for the future prevention and control of influenza in Beijing.

Objective:To analyze the effect of influenza vaccination on influenza cluster epidemic in primary and secondary schools in Beijing during the 2023-2024 surveillance season and provide evidence for the improvement of influenza vaccination strategies.Methods:The incidence data of influenza cluster epidemic and influenza vaccination coverage in the schools in Beijing during 2023-2024 were collected. Descriptive epidemiological methods were used to analyze cluster epidemic characteristics, and χ2 test was used to compare incidence differences between groups, and OR value and vaccine effectiveness [VE=(1- OR)×100%] were calculated. A negative binomial regression model was used to evaluate the association between school vaccination rates and cluster epidemic risk. Joinpoint regression was used to analyze trends in relative risk ( RR) with increasing vaccination coverage and to determine the optimal vaccination threshold. Results:A total of 126 influenza cluster epidemic were reported in 115 primary and secondary schools in Beijing during 2023-2024 with the median size of 15 case, the average attack rate was 36.26% (2 033/5 607). The epidemics mainly occurred in urban area (70, 55.56%). Primary schools were the main setting (78, 61.90%), and influenza A(H3N2) was the predominant subtype (108, 85.71%). The overall influenza vaccination coverage in the primary and secondary students was 54.26%, while the average vaccination in classes affected by the epidemics was 58.57%. The overall protection rate was 47.62%, the protection rate was higher in primary schools (49.65%) than in secondary schools (46.60%). The protection rates against influenza A(H1N1)pdm09 (80.93%) and influenza B (Victoria lineage) (81.65%) were significantly higher than that against influenza A(H3N2) (44.19%). When school vaccination coverage reached ≥76.00%, the epidemic risk decreased by 52.82%.Conclusions:Even the match between influenza vaccine strains and circulating strains is suboptimal, increasing influenza vaccination coverage in schools can effectively reduce the risk for influenza cluster epidemic. In the future, measures such as policy guidance and public health education should be taken to further improve vaccination coverage, thereby establishing herd immunity and reducing the transmission risk of influenza in schools.

Objective:To explore the trend of influenza positive rate in Beijing by using classic autoregressive integrated moving average (ARIMA) model, autoregressive integrated moving average model with exogenous variables (ARIMAX) and vector autoregression model (VAR) to compare the performance of three models in influenza prediction and select the most suitable one for Beijing.Methods:The weekly positive rate of influenza virus nucleic acid test and meteorological data in Beijing from week 1 of 2013 to week 40 of 2024 were collected. The data were divided into four groups with expanding training sets and corresponding testing sets. The training set of the first group was from week 1 of 2013 to week 40 of 2016, and the testing set was from week 41 of 2016 to week 40 of 2017. Subsequent groups extended the training set by one year each time. Data from 2020 to 2023 were excluded due to COVID-19 pandemic. The fourth group used data from the week 1 of 2013 to week 40 of 2023 for training and from the week 41 of 2023 to week 40 of 2024 for testing.Results:The incidence of influenza had seasonality in Beijing with higher incidence in winter and spring. The positive rate of influenza virus was positively correlated with the weekly average atmospheric pressure ( r=0.482, P<0.001) and weekly average wind speed ( r=0.003, P=0.034), and negatively correlated with the weekly average temperature ( r=-0.541, P<0.001). The ARIMAX model incorporating meteorological factors had the best prediction performance, with test set's root mean square error ( RMSE) of 0.115 3 and mean absolute error ( MAE) of 0.076 7 (the RMSE and MAE values for ARIMA and VAR models were 0.117 1 and 0.163 8, and 0.078 6 and 0.122 3, respectively). The prediction results of the optimal model showed that the positive rate of influenza virus would continue to rise in Beijing after October 2024 and reach peak in the second week of 2025, but the peak positive rate would be lower than that of previous influenza season. Conclusions:Compared with the ARIMA model and the VAR model,the ARIMAX model which used meteorological parameters is more suitable for prediction of long-term influenza trend in Beijing. The influenza trend peak was predicted to occur in the second week of 2025, but lower than that in previous influenza season.

Objective To develop the Ego Depletion Scale for Type 2 Diabetes Patients and evaluate its reliability and validity,and to provide a specific assessment tool for evaluating ego-depletion in self-management.Methods Guided by the self-control strength model,the initial scale was constructed through literature review,semi-structured interviews,2 rounds of expert consultation,and a pilot survey.A convenience sampling method was employed to recruit 460 patients with Type 2 Diabetes from the endocrinology department of a tertiary hospital in Wuhan,Hubei Province,between April and July 2024.They were randomly divided into 2 subsets for exploratory factor analysis and confirmatory factor analysis.Results A total of 451 valid questionnaires were collected.Exploratory factor analysis extracted 6 common factors,with a cumulative variance contribution of 73.231％.In confirmatory factor analysis,an item was deleted due to failing to meet the standardized loading value criterion.The revised Ego Depletion Scale for Type 2 Diabetes Patients comprised 6 dimensions and 22 items.The total Cronbach's α coefficient was 0.911;split-half reliability was 0.744;the content validity index was 0.860.Correlation coefficients between the total score and scores of each dimension of the scale and the total score of the Self-Regulatory Fatigue Scale ranged from 0.558 to 0.946(P＜0.001).Conclusion The scale exhibits robust reliability and validity,serving as a scientifically instrument for assessing ego depletion in patients with Type 2 Diabetes.

Objective:To establish a fluorescent quantitative RT-PCR assay based on N_sgRNA of SARS-CoV-2 and preliminarily apply it on real samples.Methods:Recombinant plasmid, specific primers and probes of N_sgRNA were designed and synthesized based on Wuhan-Hu-1/2019_MN908947 and synthesis mechanism of subgenomic RNA (sgRNA). Using recombinant plasmid as amplification templates, the optimal reaction conditions and reaction system were screened according to the Ct value, fluorescence intensity, and shape of amplification curve and was evaluated for sensitivity, reproducibility, and specificity. Meanwhile, the possibility of practical application of the method was explored by testing 172 clinical samples and 256 municipal wastewater samples. Results:A qRT-PCR assay for N_sgRNA in SARS-CoV-2 was initially established. The detection limit of the assay was 20 copies/mL, and the variation coefficients of in-batch (0.002%~0.767%) and batch to batch repetition (0.016%~0.752%) were less than 1%. Only N_sgRNA recombinant plasmid was detected in the specificity assay. So the method is more highly sensitive, specific and reproducible. The RatiosgRNA/ gRNA of clinical samples HK.3, EG.5.1, JN.1 and their sub-lineages and their corresponding urban sewage samples in epidemic period were significantly different ( P<0.05). There is a strong correlation between the median of RatiosgRNA/ gRNA in clinical samples and sewage samples in the same period (correlation coefficient r=1.000, P=0.010). Conclusions:In this study, a qRT-PCR method for detecting N_sgRNA of SARS-CoV-2 was established and the method has the characteristics of higher sensitivity, stronger specificity and better repeatability, and it can be used to detect SARS-CoV-2 infectivity.

ObjectiveTo review the interoceptive assessment tools in terms of structure, content and psychometric properties, based on the framework of the International Classification of Functioning, Disability and Health (ICF). MethodsThe literature on interoceptive evaluation tools was retrieved from databases of CNKI, PubMed, Medline and EBSCO. The principal structures and contents of the assessment tools were analyzed based on the ICF framework, and the quality of the psychometric properties were appraised using COSMIN. ResultsA total of 13 interoceptive assessment tools were ultimately included, involving 16 literature references. There were five interoceptive sensitivity tools, four accuracy tools and four awareness tools. In terms of content, interoceptive sensitivity tools involved 33 categories of body functions, six categories of activities and participation, and one of environmental factors; while interoceptive accuracy tools only involved seven categories of body function, and two of activities and participation items; interoceptive awareness tools involved 30 categories of body function, four categories of activities and participation, and three of environmental factors. In terms of psychometric properties, Body Perception Questionnaire-Short Form (BPQ-SF) was the sensitivity tool with the best reliability and validity (qualified rate of 7/8), followed by Interoceptive Sensitivity Questionnaire (ISQ) (qualified rate of 6/8). Most of the accuracy tools adopted standardized measurement methods, but lacked sufficient reliability and validity verification. The awareness tools were good in reliability and validity (qualified rate above 5/8), especially Multidimensional Assessment of Interoceptive Awareness (MAIA-1) and Body Awareness Questionnaire (BAQ) (qualified rate of 8/8). ConclusionBPQ-SF and ISQ are recommended for interoceptive sensitivity assessment, Water-loading Test and Heart-beat Tracking Task for interoceptive accuracy assessment, and MAIA-1 and BAQ for interoceptive awareness assessment.