Objective To investigate the expression levels of selenoprotein genes in the patients with coronavirus disease 2019 (COVID-19) and the possible regulatory mechanisms.Methods The dataset GSE177477 was obtained from the Gene Expression Omnibus,consisting of a symptomatic group (n=11),an asymptomatic group (n=18),and a healthy control group (n=18).The dataset was preprocessed to screen the differentially expressed genes (DEG) related to COVID-19,and gene ontology functional annotation and Kyoto encyclopedia of genes and genomes enrichment analysis were performed for the DEGs.The protein-protein interaction network of DEGs was established,and multivariate Logistic regression was employed to analyze the effects of selenoprotein genes on the presence/absence of symptoms in the patients with COVID-19.Results Compared with the healthy control,the symptomatic COVID-19 patients presented up-regulated expression of GPX1,GPX4,GPX6,DIO2,TXNRD1,SELENOF,SELENOK,SELENOS,SELENOT,and SELENOW and down-regulated expression of TXNRD2 and SELENON (all P<0.05).The asymptomatic patients showcased up-regulated expression of GPX2,SELENOI,SELENOO,SELENOS,SELENOT,and SELENOW and down-regulated expression of SELP (all P<0.05).The results of multivariate Logistic regression analysis showed that the abnormally high expression of GPX1 (OR=0.067,95%CI=0.005-0.904,P=0.042) and SELENON (OR=56.663,95%CI=3.114-856.999,P=0.006) was the risk factor for symptomatic COVID-19,and the abnormally high expression of SELP was a risk factor for asymptomatic COVID-19 (OR=15.000,95%CI=2.537-88.701,P=0.003).Conclusions Selenoprotein genes with differential expression are involved in the regulation of COVID-19 development.The findings provide a new reference for the prevention and treatment of COVID-19.
目的 探讨硒蛋白基因在新型冠状病毒感染(COVID-19)患者中的表达水平及其可能的调控机制。方法 从基因表达综合数据库获取数据集GSE177477,样本由有症状组(n=11)、无症状组(n=18)和健康对照组(n=18)构成。对数据集进行预处理,筛选出与COVID-19相关的差异表达基因,并进行基因本体功能注释和京都基因与基因组百科全书富集分析,建立差异表达硒蛋白基因的蛋白质-蛋白质相互作用网络,采用多因素Logistic回归分析硒蛋白基因对COVID-19患者是否出现症状的影响。结果 与健康对照组比较,有症状的COVID-19患者中GPX1、GPX4、GPX6、DIO2、TXNRD1、SELENOF、SELENOK、SELENOS、SELENOT、SELENOW基因表达均升高,TXNRD2、SELENON基因表达均下降(P均<0.05);无症状的COVID-19患者中GPX2、SELENOI、SELENOO、SELENOS、SELENOT、SELENOW基因表达均升高,SELP基因表达下降(P均<0.05)。多因素Logistic回归分析结果显示,GPX1(OR=0.067,95%CI=0.005~0.904,P=0.042)、SELENON(OR=56.663,95%CI=3.114~856.999,P=0.006)基因的异常高表达是有症状COVID-19患者的影响因素,SELP基因的异常高表达是无症状COVID-19患者的危险因素(OR=15.000,95%CI=2.537~88.701,P=0.003)。结论 硒蛋白基因的差异表达参与调控COVID-19疾病的发生发展,为COVID-19的预防和治疗提供新的参考依据。.

UNASSIGNED: Cerebral small vessel disease (CSVD) is a common neurodegenerative condition in the elderly, closely associated with cognitive impairment. Early identification of individuals with CSVD who are at a higher risk of developing cognitive impairment is crucial for timely intervention and improving patient outcomes.
UNASSIGNED: The aim of this study is to construct a predictive model utilizing LASSO regression and binary logistic regression, with the objective of precisely forecasting the risk of cognitive impairment in patients with CSVD.
UNASSIGNED: The study utilized LASSO regression for feature selection and logistic regression for model construction in a cohort of CSVD patients. The model\'s validity was assessed through calibration curves and decision curve analysis (DCA).
UNASSIGNED: A nomogram was developed to predict cognitive impairment, incorporating hypertension, CSVD burden, apolipoprotein A1 (ApoA1) levels, and age. The model exhibited high accuracy with AUC values of 0.866 and 0.852 for the training and validation sets, respectively. Calibration curves confirmed the model\'s reliability, and DCA highlighted its clinical utility. The model\'s sensitivity and specificity were 75.3 and 79.7% for the training set, and 76.9 and 74.0% for the validation set.
UNASSIGNED: This study successfully demonstrates the application of machine learning in developing a reliable predictive model for cognitive impairment in CSVD. The model\'s high accuracy and robust predictive capability provide a crucial tool for the early detection and intervention of cognitive impairment in patients with CSVD, potentially improving outcomes for this specific condition.

UNASSIGNED: To investigate the effect of body mass index (BMI) on progesterone (P) level on trigger day in gonadotropin-releasing hormone antagonist (GnRH-ant) cycles.
UNASSIGNED: This study was a retrospective cohort study. From October 2017 to April 2022, 412 in-vitro fertilization (IVF)/intracytoplasmic sperm injection (ICSI) patients who were treated with GnRH-ant protocol for controlled ovarian hyperstimulation (COH) in the reproductive center of our hospital were selected as the research objects. Patients were divided into three groups according to BMI level: normal weight group (n = 230):18.5 kg/m2≤BMI < 24 kg/m2; overweight group (n = 122): 24 kg/m2≤BMI < 28 kg/m2; Obesity group (n = 60): BMI ≥ 28 kg/m2. Variables with p < .10 in univariate analysis (BMI, basal FSH, basal P, FSH days, Gn starting dose and E2 level on trigger day) and variables that may affect P level on trigger day (infertility factors, basal LH, total FSH, HMG days and total HMG) were included in the multivariate logistic regression model to analyze the effect of BMI on P level on trigger day of GnRH-ant protocol.
UNASSIGNED: After adjustment for confounding factors, compared with that in normal weight patients, the risk of serum P elevation on trigger day was significantly lower in overweight and obese patients (OR = 0.434 and 0.199, respectively, p < .05).
UNASSIGNED: The risk of P elevation on trigger day in GnRH-ant cycles decreased with the increase of BMI, and BMI could be used as one of the predictors of P level on trigger day in GnRH-ant cycles.

BACKGROUND: Perioperative myocardial injury (PMI) with different cut-off values has showed to be associated with different prognostic effect after cardiac surgery. Machine learning (ML) method has been widely used in perioperative risk predictions during cardiac surgery. However, the utilization of ML in PMI has not been studied yet. Therefore, we sought to develop and validate the performances of ML for PMI with different cut-off values in cardiac surgery with cardiopulmonary bypass (CPB).
METHODS: This was a second analysis of a multicenter clinical trial (OPTIMAL) and requirement for written informed consent was waived due to the retrospective design. Patients aged 18-70 undergoing elective cardiac surgery with CPB from December 2018 to April 2021 were enrolled in China. The models were developed using the data from Fuwai Hospital and externally validated by the other three cardiac centres. Traditional logistic regression (LR) and eleven ML models were constructed. The primary outcome was PMI, defined as the postoperative maximum cardiac Troponin I beyond different times of upper reference limit (40x, 70x, 100x, 130x) We measured the model performance by examining the area under the receiver operating characteristic curve (AUROC), precision-recall curve (AUPRC), and calibration brier score.
RESULTS: A total of 2983 eligible patients eventually participated in both the model development (n = 2420) and external validation (n = 563). The CatboostClassifier and RandomForestClassifier emerged as potential alternatives to the LR model for predicting PMI. The AUROC demonstrated an increase with each of the four cutoffs, peaking at 100x URL in the testing dataset and at 70x URL in the external validation dataset. However, it\'s worth noting that the AUPRC decreased with each cutoff increment. Additionally, the Brier loss score decreased as the cutoffs increased, reaching its lowest point at 0.16 with a 130x URL cutoff. Moreover, extended CPB time, aortic duration, elevated preoperative N-terminal brain sodium peptide, reduced preoperative neutrophil count, higher body mass index, and increased high-sensitivity C-reactive protein levels were identified as risk factors for PMI across all four cutoff values.
CONCLUSIONS: The CatboostClassifier and RandomForestClassifer algorithms could be an alternative for LR in prediction of PMI. Furthermore, preoperative higher N-terminal brain sodium peptide and lower high-sensitivity C-reactive protein were strong risk factor for PMI, the underlying mechanism require further investigation.

OBJECTIVE: Noncarious cervical lesions (NCCLs) are multifactorial and can be caused by the anatomical structure of the teeth, erosion, abrasion and abnormal occlusion. The aim of this case-control study was to explore the risk factors for NCCLs.
METHODS: Cone-beam computed tomography was used to determine whether a wedge-shaped defect existed at the cementoenamel junction. We compared 63 participants with NCCLs with 63 controls without NCCLs, matched for sex, age (±1 year) and toothbrushing-related factors (e.g., type of bristle and brushing patterns, frequency and strength). All participants were asked to complete a questionnaire about self-administered daily diet habits and health condition. Univariate and multivariate logistic regression analyses were conducted to determine the risk factors for NCCLs.
RESULTS: Significant variables in the univariate analysis (i.e., p < .2) included frequency of carbonated beverage consumption, sella-nasion-point B angle (SNB) and Frankfort-mandibular plane angle (FMA). Multivariate logistic regression demonstrated that the consumption frequency of carbonated beverages (odds ratio [OR] = 3.147; 95% confidence interval [CI], 1.039-9.532), FMA (OR = 1.100; 95% CI, 1.004-1.204) and SNB (OR = 0.896; 95% CI, 0.813-0.988) was independent influencing factors. The area under the receiver operating characteristic curve (AUC) value of regression Model 1 (established with the frequency of carbonated beverage consumption, FMA, SNB and sleep bruxism) was 0.700 (95% CI, 0.607-0.792; p < .001), and that of regression Model 2 (established using the frequency of carbonated beverage consumption, FMA and SNB) was 0.704 (95% CI, 0.612-0.796; p < .001).
CONCLUSIONS: The consumption frequency of carbonated beverages and FMA was risk factors for NCCLs; the higher the frequency of carbonated beverage consumption and FMA, the higher was the probability of NCCLs. SNB was a protective factor for NCCL occurrence; the larger the SNB, the lower was the probability of NCCL occurrence. These findings have further clarified the aetiology of NCCLs and provided clinicians with valuable insights into strategies for preventing the loss of dental tissue.

UNASSIGNED: Acute respiratory distress syndrome (ARDS) is a severe complication that can lead to fatalities in multiple trauma patients. Nevertheless, the incidence rate and early prediction of ARDS among multiple trauma patients residing in high-altitude areas remain unknown.
UNASSIGNED: This study included a total of 168 multiple trauma patients who received treatment at Shigatse People\'s Hospital Intensive Care Unit (ICU) between January 1, 2019 and December 31, 2021. The clinical characteristics of the patients and the incidence rate of ARDS were assessed. Univariable and multivariable logistic regression models were employed to identify potential risk factors for ARDS, and the predictive effects of these risk factors were analyzed.
UNASSIGNED: In the high-altitude area, the incidence of ARDS among multiple trauma patients was 37.5% (63/168), with a hospital mortality rate of 16.1% (27/168). Injury Severity Score (ISS) and thoracic injuries were identified as significant predictors for ARDS using the logistic regression model, with an area under the curve (AUC) of 0.75 and 0.75, respectively. Furthermore, a novel predictive risk score combining ISS and thoracic injuries demonstrated improved predictive ability, achieving an AUC of 0.82.
UNASSIGNED: This study presents the incidence of ARDS in multiple trauma patients residing in the Tibetan region, and identifies two critical predictive factors along with a risk score for early prediction of ARDS. These findings have the potential to enhance clinicians\' ability to accurately assess the risk of ARDS and proactively prevent its onset.

UNASSIGNED: Acute abdominal pain (AAP) is a common symptom presented in the emergency department (ED), and it is crucial to have objective and accurate triage. This study aims to develop a machine learning-based prediction model for AAP triage. The goal is to identify triage indicators for critically ill patients and ensure the prompt availability of diagnostic and treatment resources.
UNASSIGNED: In this study, we conducted a retrospective analysis of the medical records of patients admitted to the ED of Wuhan Puren Hospital with acute abdominal pain in 2019. To identify high-risk factors, univariate and multivariate logistic regression analyses were used with thirty-one predictor variables. Evaluation of eight machine learning triage prediction models was conducted using both test and validation cohorts to optimize the AAP triage prediction model.
UNASSIGNED: Eleven clinical indicators with statistical significance (p < 0.05) were identified, and they were found to be associated with the severity of acute abdominal pain. Among the eight machine learning models constructed from the training and test cohorts, the model based on the artificial neural network (ANN) demonstrated the best performance, achieving an accuracy of 0.9792 and an area under the curve (AUC) of 0.9972. Further optimization results indicate that the AUC value of the ANN model could reach 0.9832 by incorporating only seven variables: history of diabetes, history of stroke, pulse, blood pressure, pale appearance, bowel sounds, and location of the pain.
UNASSIGNED: The ANN model is the most effective in predicting the triage of AAP. Furthermore, when only seven variables are considered, including history of diabetes, etc., the model still shows good predictive performance. This is helpful for the rapid clinical triage of AAP patients and the allocation of medical resources.

UNASSIGNED: Studies suggest that both depression and disrupted sleep disturbance are linked to cardiovascular disease (CVD). However, the precise role of sleep disturbance in the connection between depression and CVD is poorly understood. Therefore, we sought to examine the associations among these factors and further explore the mediating role of sleep disturbance in the association between depression and CVD.
UNASSIGNED: This study included data from 29,831 adults (≥20 years old). Multifactorial logistic regression analyses were conducted to examine the relationships among depression, sleep disturbance, and CVD. Additionally, bootstrap tests were used to investigate whether the association between depression and CVD was mediated by sleep disturbance.
UNASSIGNED: Our research showed that individuals who experienced depression or sleep disturbance had a notably greater likelihood of developing CVD than those who did not have these issues (depression: OR: 2.21, 95% CI=1.96-2.49; sleep disturbance: OR: 1.74, 95% CI=1.6-1.9). Even after adjusting for potential confounders, depression was still positively associated with the risk of sleep disturbance (OR: 4.07, 95% CI=3.73-4.44). Furthermore, sleep disturbance significantly mediated the association between depression and CVD, with a mediating effect of 18.1%.
UNASSIGNED: Our study demonstrated that depression, sleep disturbance, and CVD are interrelated. The increased risk of CVD among patients with depression may be attributed to the mediating role of sleep disturbance. This finding underscores the importance of interventions focused on sleep disturbances as a means to address the connection between depression and CVD.

BACKGROUND: Schizophrenia is a prevalent mental disorder, leading to severe disability. Currently, the absence of objective biomarkers hinders effective diagnosis. This study was conducted to explore the aberrant spontaneous brain activity and investigate the potential of abnormal brain indices as diagnostic biomarkers employing machine learning methods.
METHODS: A total of sixty-one schizophrenia patients and seventy demographically matched healthy controls were enrolled in this study. The static indices of resting-state functional magnetic resonance imaging (rs-fMRI) including amplitude of low frequency fluctuations (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo), and degree centrality (DC) were calculated to evaluate spontaneous brain activity. Subsequently, a sliding-window method was then used to conduct temporal dynamic analysis. The comparison of static and dynamic rs-fMRI indices between the patient and control groups was conducted using a two-sample t-test. Finally, the machine learning analysis was applied to estimate the diagnostic value of abnormal indices of brain activity.
RESULTS: Schizophrenia patients exhibited a significant increase ALFF value in inferior frontal gyrus, alongside significant decreases in fALFF values observed in left postcentral gyrus and right cerebellum posterior lobe. Pervasive aberrations in ReHo indices were observed among schizophrenia patients, particularly in frontal lobe and cerebellum. A noteworthy reduction in voxel-wise concordance of dynamic indices was observed across gray matter regions encompassing the bilateral frontal, parietal, occipital, temporal, and insular cortices. The classification analysis achieved the highest values for area under curve at 0.87 and accuracy at 81.28% when applying linear support vector machine and leveraging a combination of abnormal static and dynamic indices in the specified brain regions as features.
CONCLUSIONS: The static and dynamic indices of brain activity exhibited as potential neuroimaging biomarkers for the diagnosis of schizophrenia.

BACKGROUND: Predicting vaccination behaviors accurately could provide insights for health care professionals to develop targeted interventions.
OBJECTIVE: The aim of this study was to develop predictive models for influenza vaccination behavior among children in China.
METHODS: We obtained data from a prospective observational study in Wuxi, eastern China. The predicted outcome was individual-level vaccine uptake and covariates included sociodemographics of the child and parent, parental vaccine hesitancy, perceptions of convenience to the clinic, satisfaction with clinic services, and willingness to vaccinate. Bayesian networks, logistic regression, least absolute shrinkage and selection operator (LASSO) regression, support vector machine (SVM), naive Bayes (NB), random forest (RF), and decision tree classifiers were used to construct prediction models. Various performance metrics, including area under the receiver operating characteristic curve (AUC), were used to evaluate the predictive performance of the different models. Receiver operating characteristic curves and calibration plots were used to assess model performance.
RESULTS: A total of 2383 participants were included in the study; 83.2% of these children (n=1982) were <5 years old and 6.6% (n=158) had previously received an influenza vaccine. More than half (1356/2383, 56.9%) the parents indicated a willingness to vaccinate their child against influenza. Among the 2383 children, 26.3% (n=627) received influenza vaccination during the 2020-2021 season. Within the training set, the RF model showed the best performance across all metrics. In the validation set, the logistic regression model and NB model had the highest AUC values; the SVM model had the highest precision; the NB model had the highest recall; and the logistic regression model had the highest accuracy, F1 score, and Cohen κ value. The LASSO and logistic regression models were well-calibrated.
CONCLUSIONS: The developed prediction model can be used to quantify the uptake of seasonal influenza vaccination for children in China. The stepwise logistic regression model may be better suited for prediction purposes.