Objective: To analyze the temporal variation patterns of sickness absenteeism among primary and secondary school students in Shanghai, so as to explore models suitable for predicting peaks and intensity of absenteeism rates. Methods: The seasonal and trend decomposition using loess (STL) method was used to analyze the seasonal and long term trend changes in sickness absenteeism among primary and secondary school students from September 1 in 2010 to June 30 in 2018, in Shanghai. A hierarchical clustering method based on Dynamic Time Warping (DTW) was employed to classify absenteeism symptoms with similar temporal patterns. Based on historical data, the study constructed and evaluated different time series algorithms and machine learning models to optimize the accuracy of predicting the trend of sickness absenteeism. Results: During the research period, the average new absenteeism rate due to illness was 16.86 per 10 000 person day for every academic year, and the trend of sickness absenteeism exhibited both seasonality and a long term upward trend, reaching its highest point in the 2017 academic year (22.47 per 10 000 person day). The symptoms of absenteeism were divided into three categories: high incidence in winter and spring (respiratory symptoms, fever and general discomfort, etc.), high incidence in summer (eye symptoms, nosebleeds, etc.) and those without obvious seasonality (skin symptoms, accidental injuries, etc.).The constructed time series models effectively predicted the trend of absenteeism due to illness, although the accuracy of predicting peak intensity was relatively low. Among them, the multi layer perceptron (MLP) model performed the best, with an root mean squared error (RMSE) of 8.96 and an mean absolute error (MAE) of 4.37, reducing 36.51% and 39.02% compared to the baseline model. Conclusion Time series models and machine learning algorithms could effectively predict the trend of sickness absenteeism, and corresponding prevention and control measures can be taken for absenteeism caused by different symptoms during peak periods.

The interaction between m⁶A-methylated RNA and chromatin modification remains largely unknown. We found that targeted inhibition of bromodomain-containing protein 4 (BRD4) by siRNA or its inhibitor (JQ1) significantly decreases mRNA m⁶A levels and suppresses the malignancy of breast cancer (BC) cells via increased expression of demethylase AlkB homolog 5 (ALKBH5). Mechanistically, inhibition of BRD4 increases the mRNA stability of ALKBH5 via enhanced binding between its 3' untranslated regions (3'UTRs) with RNA-binding protein RALY. Further, BRD4 serves as a scaffold for ubiquitin enzymes tripartite motif containing-21 (TRIM21) and ALKBH5, resulting in the ubiquitination and degradation of ALKBH5 protein. JQ1-increased ALKBH5 then demethylates mRNA of extra spindle pole bodies like 1 (ESPL1) and reduces binding between ESPL1 mRNA and m⁶A reader insulin like growth factor 2 mRNA binding protein 3 (IGF2BP3), leading to decay of ESPL1 mRNA. Animal and clinical studies confirm a critical role of BRD4/ALKBH5/ESPL1 pathway in BC progression. Further, our study sheds light on the crosstalks between histone modification and RNA methylation.

OBJECTIVE: The investigation of the correlation between ecological factors and the genetic characteristics or metabolites of plants offers valuable insights into the regional causes of genetic and metabolic diversity. Here, Gastrodia elata, a medicinal plant, is employed as a model to explore the environmental factors that influence its genetic characteristics and metabolic accumulations. METHODS: A total of 23 G. elata populations from six cultispecies and 11 cultivated regions were selected based on the predictions of the global geographic information system. The genetic characteristics of these populations were evaluated using highly polymorphic simple sequence repeat markers. Additionally, the metabolic accumulations and antioxidant capacity of mature tubers were measured employing colorimetry and high performance liquid chromatography (HPLC). Ecological data of each region were obtained from the WorldClim-global climate database and harmonized world soil database. To assess the influence of ecological factors on the genetic characteristics and metabolic profiles of G. elata, Pearson's correlation analysis was conducted. RESULTS: Genetic variation among G. elata populations exceeded that within populations. Genetic diverisity, distance and structure manifested regional and species-specific patterns. Metabolic profiling and antioxidant capacity exhibited regional variations. Notably, the Lueyang region demonstrated that a content range of total polysaccharide, total protein, and phenolic glycosides was 9.34%-189.67% higher than the average. Similarly, in the Hubei region, total phenolic content, p-hydroxybenzyl alcohol content, and antioxidant indicators were observed to be higher than the average levels, by 106.57%, 136.47% and 12.50%-91.14%, respectively. Furthermore, ecological factors had a significant comprehensive impact on G. elata genetic characteristics (r > 0.256 and P < 0.05). Multivariate metabolite accumulations in G. elata were influenced by dominant ecological factors. Temperature notably impacted the accumulation of total protein (|r| > 0.528 and P < 0.05). Moisture, encompassing precipitation and soil content, significantly affected the production of phenolic glycosides (|r| > 0.503 and P < 0.05). CONCLUSION The genetic characteristics of G. elata manifested regional and species-specific patterns, with the metabolic accumulations and antioxidant capacity of mature tubers exhibited regional variations. Specifically, multivariate ecological factors comprehensively influenced genetic characteristics. Temperature and moisture played pivotal roles in regulating the accumulations of proteins and phenolic glycosides, respectively. These findings underscore the significant impact of ecological factors on the shaping of G. elata, highlighting their crucial role in enhancing the quality of Chinese medicinal materials.

OBJECTIVE: To investigate the frequency and related factors of ineffective triggering (IT) and double triggering (DT) in patients with acute brain injury undergoing invasive mechanical ventilation. METHODS: A retrospective cohort study was conducted using data from a single-center observational trial. Patients with acute brain injury [traumatic brain injury, stroke, and post-craniotomy for brain tumors] undergoing mechanical ventilation in the intensive care unit (ICU) of Beijing Tiantan Hospital, Capital Medical University between June 2017 and July 2019 were retrospectively analyzed. Demographic and clinical data were collected. Respiratory parameters and waveforms during the first 3 days of mechanical ventilation were recorded, with 15-minute waveform segments collected 4 times daily. Airway occlusion pressure (P_0.1) was measured via end-expiratory hold at the end of each recording. IT and DT were identified based on airway pressure, flow, and esophageal pressure waveforms, and the ineffective triggering index (ITI) and DT incidence were calculated. Multivariate Logistic regression was used to identify factors associated with IT and DT. RESULTS: A total of 94 patients with acute brain injury were ultimately enrolled, including 19 cases of traumatic brain injury (20.2%), 39 cases of stroke (41.5%), and 36 cases of post-craniotomy for brain tumor (38.3%). Supratentorial injury was observed in 49 patients (52.1%), while infratentorial injury was identified in 45 patients (47.9%). A total of 94 patients with 1 018 datasets were analyzed; 684 (67.2%) datasets were on pressure support ventilation (PSV), and 334 (32.8%) were on mandatory ventilation. IT was detected in 810 (79.6%) datasets, with a median incidence of 2.1% (0.3%, 12.0%). Datasets demonstrating IT were characterized by lower P_0.1, higher tidal volume (VT), reduced respiratory rate (RR), and decreased minute ventilation (MV) compared to those without IT. The proportion of datasets exhibiting IT was higher during PSV than in mandatory ventilation [83.8% (573/684) vs. 71.0% (237/334), P < 0.05], while, the prevalence of ITI ≥ 10% was lower [23.8% (163/684) vs. 33.5% (112/334), P < 0.05]. DT was detected in 305 datasets (30%), with a median incidence of 0.6% (0.4%, 1.3%). Datasets exhibiting DT were characterized by higher VT, reduced RR, and lower pressure support levels. The incidence of DT was lower in PSV compared to mandatory ventilation modes [0% (0%, 0.3%) vs. 0% (0%, 0.5%), P < 0.05]. The post-craniotomy for brain tumors group exhibited higher ITI, lower RR, reduced MV, and a greater proportion of infratentorial lesions, compared to the TBI group. The infratentorial lesion group demonstrated higher ITI and incidence of DT compared to the supratentorial lesion group [ITI: 3.1% (0.7%, 17.8%) vs. 1.5% (0%, 8.3%), incidence of DT: 0% (0%, 0.5%) vs. 0% (0%, 0%), both P < 0.05]. After adjusting for confounding factors through multivariate logistic regression analysis, infratentorial lesion [odds ratio (OR) = 2.029, 95% confidence interval (95%CI) was 1.465-2.811, P < 0.001], lower P_0.1 (OR = 0.714, 95%CI was 0.616-0.827, P < 0.001), and mandatory ventilation (OR = 1.613, 95%CI was 1.164-2.236, P = 0.004) were independently associated with IT. Additionally, infratentorial lesion (OR = 1.618, 95%CI was 1.213-2.157, P = 0.001), large tidal volume (OR = 1.222, 95%CI was 1.137-1.314, P < 0.001), lower pressure support levels (OR = 0.876, 95%CI was 0.829-0.925, P < 0.001), and mandatory ventilation (OR = 2.750, 95%CI was 1.983-3.814, P < 0.001) were independently associated with DT. CONCLUSION IT and DT were common in patients with acute brain injury. Infratentorial lesions and mandatory ventilation were independently associated with both IT and DT.
Humans ; Respiration, Artificial/methods* ; Retrospective Studies ; Brain Injuries/therapy* ; Intensive Care Units ; Male ; Female ; Middle Aged ; Brain Injuries, Traumatic/therapy* ; Logistic Models ; Aged ; Adult

OBJECTIVE: To investigate the potential mechanisms of sepsis pathogenesis in intensive care unit (ICU), with a specific focus on the role of skin microbiota, and to evaluate the causal relationships between skin microbiota and ICU sepsis using Mendelian randomization (MR). METHODS: A two-sample MR analysis was performed using skin microbiota genome-wide association study (GWAS) summary data from German population cohorts as exposures, combined with ICU sepsis susceptibility and 28-day mortality GWAS summary data from the IEU OpenGWAS database as outcomes. The primary causal effect estimates were generated using the inverse variance weighted (IVW) method, supplemented by validation through MR-Egger and weighted median approaches. Heterogeneity and pleiotropy tests, along with sensitivity analyses, were conducted to evaluate the robustness of the results. RESULTS: Regarding risk of ICU sepsis, IVW analysis showed that order Pseudomonadales [odds ratio (OR) = 0.93, 95% confidence interval (95%CI) was 0.88-0.98], family Flavobacteriaceae (OR = 0.93, 95%CI was 0.90-0.96), and genus Acinetobacter (OR = 0.96, 95%CI was 0.93-0.99) were significantly negatively correlated with the risk of ICU sepsis (all P < 0.05). There was a significant positive correlation between the risk of ICU sepsis and the presence of β-Proteobacteria (OR = 1.05, 95%CI was 1.00-1.11) and Actinobacteria (OR = 1.05, 95%CI was 1.00-1.11), both P < 0.05. Regarding 28-day mortality of ICU sepsis, IVW analysis showed that phylum Bacteroidetes (OR = 0.92, 95%CI was 0.86-0.99), family Streptococcaceae (OR = 0.92, 95%CI was 0.85-0.98), family Flavobacteriaceae (OR = 0.90, 95%CI was 0.83-0.97), genus Streptococcus (OR = 0.92, 95%CI was 0.86-0.99), ASV016 [Enhydrobacter] (OR = 0.92, 95%CI was 0.87-0.98), and ASV042 [Acinetobacter] (OR = 0.92, 95%CI was 0.88-0.97) were significantly negatively correlated with the 28-day mortality of ICU sepsis (all P < 0.05); family Moraxellaceae (OR = 1.09, 95%CI was 1.00-1.18) and ASV008 [Staphylococcus] (OR = 1.08, 95%CI was 1.03-1.14) was significantly positively correlated with the 28-day mortality of ICU sepsis (both P < 0.05). Sensitivity analysis and MR-PRESSO showed no heterogeneity, pleiotropy, or horizontal pleiotropy between skin microbiota and ICU sepsis risk and 28-day mortality rate. Analysis of confounding factors showed that single nucleotide polymorphisms (SNPs) associated with relevant skin bacteria could independently and causally affect the risk of ICU sepsis or ICU sepsis related mortality rate, independent of other confounding factors. The Steiger test results indicated that the established causal relationship was not due to reverse causality. CONCLUSIONS Skin microbiota composition may influence both sepsis susceptibility and 28-day mortality in ICU settings. Family Flavobacteriaceae demonstrated protective effects against sepsis onset and mortality. These findings provide new perspectives for early detection and management strategies.
Humans ; Sepsis/mortality* ; Intensive Care Units ; Mendelian Randomization Analysis ; Microbiota ; Skin/microbiology* ; Genome-Wide Association Study ; Risk Factors ; Skin Microbiome

Objective To explore the effect of different packing contents of solid phase extraction column, repeated use times and solid phase extraction column under pressure on the determination of morphine content in compound liquorice oral solution. Methods The component was performed by high performance liquid chromatography （HPLC） equipped with the C₈ （4.6 mm × 150 mm, 5 μm） chromatographic column. The mobile phase consisted of acetonitrile-2.5 mmol/L sodium heptanesulfonate solution-50 mmol/L potassium dihydrogen phosphate solution （5∶18∶18）. The flow rate was 1.0 ml/min with the 220 nm detection wavelength, 20 μl injection volume and 30℃ column temperature. Results It was found that the SPE column produced of 500 mg had a higher recovery rate of morphine, and the RSD value was small. It was found that SPE column could be reused for up to 3 times under the condition of guaranteeing recovery rate.In the pressurized experiment,at the flow rate of 0.5 ml/min, the measured morphine content was basically consistent with that measured at atmospheric pressure. Conclusion The determination of morphine in compound liquorice oral solution could be carried out by C₁₈ solid phase extraction column of 500 mg. Under pressure, the flow rate was about 0.5 ml/min for morphine recovery, which could achieve high recovery of morphine in compound liquorice oral solution and improve the detection efficiency of morphine.

Objective The study aims to optimize the conditions for constructing orthotopic nude mouse models of liver cancer by injecting human liver tumor cell lines and to explore appropriate timings for drug administration. Methods Human hepatocellular carcinoma Hep3B and hepatoblastoma HepG2 cell lines, which stably expressing the luciferase reporter gene (LUC), were selected. The linear correlation between the luciferase luminescence intensity and the number of liver tumor cells was analyzed using a Small Animal In Vivo Imaging system to verify the luminescent efficiency of the human liver tumor cells. Different concentrations (8×10⁶, 2.4×10⁷, 7.2×10⁷ cells/mL) and resuspension media (PBS, Matrigel) of human liver tumor cell suspensions HepG2-LUC and Hep3B-LUC were orthotopically inoculated into the liver lobes of 5-week-old female BALB/c nude mice (12 groups, 7 mice each) to construct human liver tumor nude mouse orthotopic cancer models. Every 7 days, the weights of mice were recorded, and the growth of orthotopic tumors was monitored using the Small Animal In Vivo Imaging system. On day 35 post-cell inoculation, mouse livers were dissected, and pathological slices were prepared for HE staining to observe histopathological changes in liver tissues. Results The luminescence intensity of human liver tumor cell lines was positively correlated with the number of cells (R²=0.983 1, R²=0.970 5), indicating their suitability for orthotopic model construction. Successful modeling was achieved in the high-concentration groups of HepG2-LUC, the low-, medium-, and high-concentration groups of HepG2-LUC+Matrigel, the medium- and high-concentration groups of Hep3B-LUC, and the low-, medium-, and high-concentration groups of Hep3B-LUC+Matrigel. For both HepG2-LUC+Matrigel and Hep3B-LUC+Matrigel groups, mice in the high-concentration groups exhibited significantly reduced body weight compared to the low- and medium-concentration groups (both with P<0.05). The luminescence intensity of successfully modeled mice increased exponentially over time (R²>0.950 0), and reached a minimum of 1.0×10⁷ p/(s·cm²·sr) by day 14 post-transplantation. Mice in the low- and medium-concentration groups of HepG2-LUC and the low-concentration group of Hep3B-LUC showed no significant pathological changes, while the other groups exhibited evident liver tumors and hepatocyte lesions. Conclusion For the HepG2-LUC cell line, the recommended injection volume is 50 µL with a cell density of 2.4×10⁷ cells/mL, resuspended with Matrigel, followed by drug administration or prognostic measures on day 7 post-modeling. For the Hep3B-LUC cell line, the recommended injection volume is 50 µL with a cell density of 7.2×10⁷ cells/mL, not resuspended with Matrigel, with administration or prognostic measures on day 14 post-modeling.

OBJECTIVE To explore the phenotypic changes and possible mechanisms of lncRNA OIP5-AS1 on the proliferation, migration, apoptosis, invasion and cycle of ovarian epithelial cells IOSE80. METHODS The clinical data of patients were collected from TCGA database and GEO database. After R package analysis, the differential expression of OIP5-AS1 was visualized in the volcanic map. The correlation between survival rate and OIP5-AS1 was analyzed by Kaplan-Meier. The IOSE80 cell model of OIP5-AS1 over expression and silencing was constructed with lentivirus vector. The expression of OIP5-AS1 was verified by RT-qPCR. Cell proliferation was detected by CCK-8. Invasion was detected by Transwell. Cell migration was detected by scratch test. Cell cycle and apoptosis were detected by flow cytometry. Western blotting was used to detect the expression of E-cadherin and N-cadherin, as well as the expression of cyclin-dependent kinase(CDK) and cyclin-G-related kinase(GAK). RESULTS RT-qPCR results showed that IOSE80 cell lines over expressing and silencing OIP5-AS1 were successfully constructed. CCK-8 results showed that overexpressing OIP5-AS1 promoted the proliferation of IOSE80 cells. Scratch test results showed that overexpressing OIP5-AS1 promoted the migration of IOSE80 cells. Transwell results showed that overexpressing OIP5-AS1 would increase the invasiveness of IOSE80 cells. Flow cytometry results showed that overexpression of OIP5-AS1 weakened the apoptosis of IOSE80 cells and promoted the progress of cell cycle. Western blotting results showed that overexpression of OIP5-AS1 downregulated the expression of E-cadherin and upregulated the expression of N-cadherin, while overexpression of OIP5-AS1 increased the expression of CDK and GAK proteins. CONCLUSION LncRNA OIP5-AS1 further interferes with the regulation of IOSE80 cell cycle by up regulating the expression of CDK and GAK, and then indirectly regulates the malignant phenotype of ovarian epithelial cells.

Objective To investigate the application efficiency and potential of CT radiomics in differentiating malignant and benign sub-centimeter solid pulmonary nodules. Methods A retrospective study was performed on the sub-centimeter ( ≤ 10 mm) solid pulmonary nodules detected by enhanced CT in our hospital from March 2020 to January 2023. Malignancy was confirmed by surgical pathology, and benignity was confirmed by surgical pathology or follow-up. Lesions were manually segmented and radiomic features were extracted. The feature dimension was reduced via feature correlation analysis and least absolute shrinkage and selection operator (LASSO). The 5-fold cross validation was used to validate the model. Support vector machine, logistic regression, linear classification support vector machine, gradient boosting, and random forest models were established for CT radiomics. Receiver operating characteristic curves were drawn. Delong test was used to compare the diagnostic performance of the five classifiers. The optimal model was selected and compared to radiologists with medium and high seniority. Results A total of 303 nodules, 136 of which were malignant, were examined. Radiomics models were established after feature extraction and selection. On test set, the areas under the receiver operating characteristic curves of support vector machine, logistic regression, linear classification support vector machine, random forest, and gradient boosting models were 0.922 (95%CI: 0.893, 0.950), 0.910 (95%CI: 0.878, 0.942), 0.905 (95%CI: 0.872, 0.938), 0.899 (95%CI: 0.865, 0.933), and 0.896 (95%CI: 0.862, 0.930), respectively. Delong test indicated no significant differences in the performance of the five radiomics models, and the support vector machine model showed the highest accuracy and F1 score. The support vector machine model showed significantly higher diagnostic accuracy as compared to radiologists (83.8% vs. 55.4%, P < 0.001). Conclusion The radiomics models achieved high diagnostic efficiency and may help to reduce the uncertainty in diagnosis of malignant and benign sub-centimeter solid nodules by radiologists.