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.

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.

Objective:To explore the efficacy of different second-line treatment strategies in the real world after progression of first-line immunotherapy and its combination therapies in patients with driver gene-negative advanced non-small cell lung cancer (NSCLC) .Methods:A retrospective analysis was conducted on the clinical data of 93 driver gene-negative advanced NSCLC patients who received first-line immunotherapy and its combination therapies from January 1, 2018 to December 31, 2023 at the First Affiliated Hospital of Xinxiang Medical University and Xinxiang Central Hospital. Patients were categorized into immune checkpoint inhibitors (ICIs) -resistant ( n=43) and ICIs-responsive ( n=50) groups according to whether progression free survival (PFS) exceeded 6 months after first-line treatment. Patients were categorized into ICIs-treated ( n=55) and non-ICIs-treated ( n=38), anti-angiogenic-treated ( n=51) and non-anti-angiogenic-treated ( n=42) groups according to the different second-line treatment strategies after progression of first-line immunotherapy and its combination therapies. The median PFS2 (mPFS2) and median overall survival (mOS) 2 after second-line treatment of each group were compared. The Kaplan-Meier method was used for survival analysis. Results:The mPFS2 and mOS2 of 93 advanced NSCLC patients who progressed after first-line ICIs treatment were 4.9 months (95% CI: 4.1-5.7 months) and 14.7 months (95% CI: 11.2-18.2 months). The mPFS2 of patients in the first-line ICIs-responsive and ICIs-resistant groups were 6.0 and 3.8 months, respectively, with no statistically significant difference ( χ2=2.00, P=0.157), and the mOS2 were 25.3 and 11.3 months, respectively, with a statistically significant difference ( χ2=12.13, P<0.001). The mPFS2 of patients in the second-line ICIs-treated group and the non-ICIs-treated group were 5.2 and 4.6 months, respectively, with no statistically significant difference ( χ2=0.16, P=0.687). The mOS2 were 15.1 and 12.7 months, respectively, with no statistically significant difference ( χ2=0.01, P=0.930). The mPFS2 of patients in the second-line anti-angiogenic-treated and non-anti-angiogenic-treated groups were 4.5 and 6.0 months, respectively, with no statistically significant difference ( χ2=0.41, P=0.525), the mOS2 were 14.7 and 16.8 months, respectively, with no statistically significant difference ( χ2=0.01, P=0.943) . Conclusions:After progression of first-line ICIs therapy in patients with driver gene-negative advanced NSCLC, first-line ICIs-responsive patients have significantly longer OS after second-line treatment compared with ICIs-resistant patients. The efficacy of second-line therapy in patients after progression of first-line ICIs therapy does not show significant differences due to the type of treatment strategies.

Terahertz(THz)technology,as an emerging tool for characterizing biomacromolecules,has rapidly developed over the past few decades,demonstrating significant application potential in multiple fields.Defensive qi is a vital energy in the human body,responsible for defense functions by warming the skin,regulating the opening and closing of the striae and interstices,and resisting the invasion of external pathogens.Defensive qi is one of the core concepts for disease prevention and health maintenance in traditional Chinese medicine(TCM).However,due to the high abstraction and complexity of TCM theory,the scientific connotation and mechanism of action of defensive qi remained incompletely elucidated.THz waves have the unique physical properties of molecular vibration sensitivity,penetrability,and thermal radiation effects,and are highly sensitive to biological tissues,which make THz technology a promising tool for defensive qi research.By analyzing the characteristics of THz waves,this paper explores the feasibility of applying THz technology to investigate the circulation patterns of defensive qi,the prevention and treatment of disorders related defensive qi,the pathological essence of defensive qi diseases,the material basis of defensive qi,the screening of defensive qi disorders,and the development of novel drugs targeting defensive qi disorders.This approach provides a new perspective and methodology for the research of TCM fundamental theory.

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.

ObjectiveThis study aims to reveal the mechanism of liver and kidney injuries caused by Dictamni Cortex and its interrelationship by metabonomics analysis of liver and kidney via ultra-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry （UPLC-Q-TOF-MS）. MethodsThe content of the marker compounds of Dictamni Cortex was measured by high-performance liquid chromatography （HPLC） to carry out quality control. Sprague Dawley （SD） rats were randomly divided into a blank group （normal saline）， an administration group （0.9， 2.7， 8.1 g·kg^-1）， and a high-dose withdrawal control group， with eight rats in each group. Continuous administration was performed once daily for 28 days. The liver and kidney injuries caused by each administration group were assessed by organ indices， pathological observations， and serum and plasma biochemical indices measured by enzyme-linked immunosorbent assay （ELISA）. The potential biomarkers of liver and kidney injuries caused by Dictamni Cortex were screened， and pathway enrichment analysis and correlation analysis were performed based on UPLC-Q-TOF-MS. ResultsCompared with the blank group， both the medium- and low-dose groups showed insignificant damage to the liver and kidney of rats. The high-dose group exhibited the most serious damage， and the level of liver and kidney function indices ［alanine aminotransferase （ALT）， aspartate aminotransferase （AST）， creatinine （Cr）， and blood urea nitrogen （BUN）］ and serum inflammatory indices （［interleukin 1β （IL-1β）， IL-6， and tumor necrosis factor-α （TNF-α）］ in the serum were significantly changed （P<0.01）. The liver and kidney metabolism pathways and differential metabolites were quite different. Among them， phenylalanine metabolism， niacin and nicotinamide metabolism， and glycerophospholipid metabolism were common pathways. Correlation analysis of differential metabolites showed that there were significant correlations among disorders of 4′-Phosphopantothenoylcysteine， PC （16∶0/15∶0）， phenylethylamine， arachidonic acid， and linoleic acid in liver and kidney tissue. ConclusionThe decoction of Dictamni Cortex can cause liver and kidney injuries， and its mechanism may be related to oxidative stress and lipid metabolism disorders. The correlation of differential metabolites indicates the interaction between liver and kidney injuries.

ObjectiveTo explore the mechanism of the immunotoxicity induced by dictamnine （DIC） in rats and the recovery effect after drug withdrawal by ultra-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry， thereby providing a theoretical basis for elucidating the toxic mechanism of DIC. MethodsSD rats were randomized into blank （normal saline）， DIC （10 mg·kg^-1）， and DIC withdrawal （recovery period） groups （n=8）. The rats were continuously treated for 7 days， once a day， and the body weight and organ weight were recorded. The levels of interleukin-1 （IL-1）， IL-6， and tumor necrosis factor-α （TNF-α） in the serum and immunoglobulin A （IgA）， immunoglobulin G （IgG）， and immunoglobulin M （IgM） in the spleen were determined by enzyme-linked immunosorbent assay. Hematoxylin-eosin staining was used to observe the pathological changes in the spleen. ultra performance liquid chromatography quadrupole time-of-flight mass spectrometry （UPLC-Q-TOF-MS） was employed to screen the potential biomarkers of immune inflammation caused by DIC， and pathway enrichment analysis and correlation analysis were performed. The mRNA levels of IL-1β， TNF-α， lysophosphatidylcholine acyltransferase 2 （LPCAT2）， and farnesoid X receptor （FXR） in the serum were determined by Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR）. ResultsCompared with the blank group， the DIC group showed elevated levels of IL-1β， IL-6， and TNF-α in the serum （P<0.01）， and the DIC withdrawal group showcased lowered levels of IL-1β， IL-6， and TNF-α in the serum （P<0.01）. The levels of IgA， IgG， and IgM in the spleen of rats in the DIC group were decreased （P<0.01）， while those in the DIC withdrawal group were recovered （P<0.05， P<0.01）. Untargeted metabolomics of the serum and spleen screened out 14 common differential metabolites and 14 common metabolic pathways. The Spearman correlation analysis between differential metabolites and inflammatory factors identified PC （32∶0）， LysoPC （20∶4/0∶0）， LysoPC （P-18∶0/0∶0）， taurochenodeoxycholic acid， taurocholic acid， LysoPC ［20∶5（5Z，8Z，11Z，14Z，17Z）/0∶0］， chenodeoxycholic acid， arachidonic acid， LysoPC （18∶0/0∶0）， LysoPC （15∶0/0∶0）， LysoPC （16∶0/0∶0）， and LysoPC （17∶0/0∶0） as the biomarkers of immunotoxicity induced by DIC in SD rats. In the process of immunotoxicity caused by DIC， lipid metabolism disorders such as glycerophospholipid metabolism， primary bile acid metabolism， and arachidonic acid metabolism were enriched， which was consistent with the DIC-induced inflammatory factors and pathological characteristics of the spleen. Compared with the blank group， the DIC group exhibited up-regulated mRNA levels of IL-1β， TNF-α， LPCAT2， and FXR （P<0.01）， and the up-regulation was decreased in the withdrawal group （P<0.01）. ConclusionDIC can lead to immune and inflammatory disorders. DIC withdrawal can regulate the expression of biomarkers related to serum and spleen metabolites， regulate the inflammatory metabolic pathway， reduce the inflammation level， and alleviate the metabolic disorders， thus attenuating the potential toxicity induced by DIC.

Objective: To explore the association between mental health and muscle strength among Chinese adolescents aged 13- 18, providing a theoretical foundation and intervention strategies for mental health promotion. Methods: Data were obtained from the 2019 Chinese National Survey on Students Constitution and Health, including 98 631 Chinese adolescents aged 13- 18. Psychological distress was assessed by using the Kessler Psychological Distress Scale (K10), and mental well being was measured with the Warwick-Edinburgh Mental Well being Scale (WEMWBS). Based on the gender and age specific Z scores of various test items [grip strength, standing long jump, pull ups (for males), and sit ups (for females)], muscle strength index (MSI) was constructed to evaluate the comprehensive level of muscle strength in adolescents. According to the Dual factor Model (DFM) of mental health, participants were categorized into four groups:troubled, symptomatic but content, vulnerable, and complete mental health. Gender differences were analyzed by using Chi-square tests, trends were tested with Cochran-Armitage tests, and multinomial Logistic regression models were applied to assess associations between muscle strength and mental health among adolescents. Results: In 2019, 37.4% of Chinese adolescents aged 13-18 were reported of high mental distress, and 59.9% were reported of low mental well being. Boys had significantly lower rates of high mental distress (35.3%) and low mental well being (55.6%) compared to girls (39.4%, 64.3%), and the differences were of statistical significance ( χ 2=176.13, 780.42, both P <0.05). In 2019, the rate of complete mental health among adolescents showed a downward trend with increasing age ( χ 2 trend = 258.47) and a gradual upward trend with increasing muscle strength levels ( χ 2 trend =123.14)，and both boys and girls exhibited similar trends ( χ 2 trend =103.83, 168.46; 57.00 , 67.34) (all P <0.05). The results of the unordered multiclass Logistic regression model showed that after controlling for confounding factors such as age and gender, when the completely pathological group as a reference, for every 1 unit increase in MSI in adolescents, the likelihood of being in a completely mental health state increased by 29% ( OR = 1.29); for every unit increase in the Z-score for pull ups, the likelihood of being in a completely mental health state increased by 6% ( OR =1.06) among boys; for every 1 unit increase in sit up Z score, the likelihood of being in a completely mental health state increased by 19% ( OR =1.19) among girls (all P <0.05). Conclusions The mental health status of Chinese adolescents is not good enough. Muscle strength is positively associated with mental health.

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

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.