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: This study analyzed the Korea Health Panel Annual Data 2019 to investigate factors related to the use of non-insured Korean medicine (KM) treatment in individuals with chronic diseases. The non-insured KM treatments of interest were herbal decoction (HD) and pharmacopuncture (PA).
UNASSIGNED: Among adults aged 19 or older, 6,159 individuals with chronic diseases who received outpatient KM treatment at least once in 2019 were included. They were divided into three groups according to the KM treatment used (1) basic insured KM non-pharmacological treatment (BT) group (n = 629); (2) HD group (n = 256); (3) PA group (n = 184). Logistic regression analysis was used to explore factors associated with favoring HD or PA use over BT. Potentially relevant candidate factors were classified using the Andersen Behavior Model.
UNASSIGNED: Compared to BT, the 1st to 3rd quartiles of income compared to the 4th quartile (odds ratio 1.50 to 2.06 for HD; 2.03 to 2.83 for PA), health insurance subscribers compared to medical aid (odds ratio 2.51; 13.43), and presence of musculoskeletal diseases (odds ratio 1.66; 1.91) were significantly positively associated with HD and PA use. Moreover, the presence of cardiovascular disease (odds ratio 1.46) and neuropsychiatric disease (odds ratio 1.97) were also significantly positively associated with HD use.
UNASSIGNED: The presence of some chronic diseases, especially musculoskeletal diseases, was significantly positively associated with HD and PA use, while low economic status was significantly negatively associated with HD and PA use, indicating the potential existence of unmet medical needs in this population. Since chronic diseases impose a considerable health burden, the results of this study can be used for reference for future health insurance coverage policies in South Korea.

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: The 2022 Centers for Disease Control\'s \"Clinical Practice Guidelines for Prescribing Opioids for Pain in United States\" called for attention and action toward reducing disparities in untreated and undertreated pain among Black and Latino patients. There is growing evidence for controlled substance safety committees (CSSC) to change prescribing culture, but few have been examined through the lens of health equity. We examined the impact of a primary care CSSC on opioid prescribing, including by patients\' race and sex.
METHODS: We conducted a retrospective cohort study. Our primary outcome was a change in prescribed morphine milligram equivalents (MME) at baseline (2017) and follow-up (2021). We compared the differences in MME by race and sex. We also examined potential intersectional disparities. We used paired t test to compare changes in mean MME\'s and logistic regression to determine associations between patient characteristics and MME changes.
RESULTS: Our cohort included 93 patients. The mean opioid dose decreased from nearly 200 MME to 136.1 MME, P < .0001. Thirty percent of patients had their dose reduced to under 90 MME by follow-up. The reduction rates by race or sex alone were not statistically significant. There was evidence of intersectional disparities at baseline. Black women were prescribed 88.5 fewer MME\'s at baseline compared with their White men counterparts, P = .04.
CONCLUSIONS: Our findings add to the previously documented success of CSSCs in reducing opioid doses for chronic nonmalignant pain to safer levels. We highlight an opportunity for primary care based CSSCs to lead the efforts to identify and address chronic pain management inequities.

OBJECTIVE: To evaluate multiple risk factors of peri-implant bone loss.
METHODS: A case-control study was conducted on patients who had received dental implants treatment from January 2018 to December 2021. Implants with bone loss were included in the case group, and implants with no bone loss were included in the control group. Risk factors including history of periodontitis, abutment connection type, implant surface, diameter, location, three-dimensional position, opposing dentition, adjacent teeth, prosthetic type, retention type and custom abutment were evaluated. A multivariate logistic regression model was used to evaluate these risk factors, providing corresponding odds ratios (ORs) with 95% confidence intervals (CIs).
RESULTS: A total of 776 implants in 479 patients were included in the analysis. The number of implants in the case group and the control group were 84 and 692, respectively. Cement-retained prostheses (OR=2.439, 95%CI=1.241-4.795) and nonplatform switch design (OR=2.055, 95%CI=1.167-3.619) were identified as weak risk factors. Horizontal deviation (OR=4.177, 95%CI=2.265-7.703) was a moderate risk factor. Vertical deviation (OR=10.107, 95%CI=5.280-19.347) and implants located in the mandibular molar region (OR=10.427, 95%CI=1.176-92.461) were considered high risk factors.
CONCLUSIONS: Implants in the molar region, cement retained, non-platform switch design, and poor three-dimensional implant positioning are identified as significant risk factors for peri-implant bone loss.

The aim of the current investigation was to compare the ability of several frailty scores to predict adverse outcomes in hip fracture patients. All adult patients (18 years or older) who suffered a hip fracture due to a fall and underwent surgical fixation were extracted from the 2019 National Inpatient Sample (NIS) Database. A combination of logistic regression and bootstrapping was used to compare the predictive ability of the Orthopedic Frailty Score (OFS), the Nottingham Hip Fracture Score (NHFS), the 11-factor modified Frailty Index (11-mFI) and 5-factor (5-mFI) modified Frailty Index, as well as the Johns Hopkins Frailty Indicator. A total of 227,850 patients were extracted from the NIS. In the prediction of in-hospital mortality and failure-to-rescue (FTR), the OFS surpassed all other frailty measures, approaching an acceptable predictive ability for mortality [AUC (95% CI): 0.69 (0.67-0.72)] and achieving an acceptable predictive ability for FTR [AUC (95% CI): 0.70 (0.67-0.72)]. The NHFS demonstrated the highest predictive ability for predicting any complication [AUC (95% CI): 0.62 (0.62-0.63)]. The 11-mFI exhibited the highest predictive ability for cardiovascular complications [AUC (95% CI): 0.66 (0.64-0.67)] and the NHFS achieved the highest predictive ability for delirium [AUC (95% CI): 0.69 (0.68-0.70)]. No score succeeded in effectively predicting venous thromboembolism or infections. In summary, the investigated frailty scores were most effective in predicting in-hospital mortality and failure-to-rescue; however, they struggled to predict complications.

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.

The purpose of this cross-sectional study was to investigate the relationship between Body Mass Index (BMI), cholesterol, and cancer in United States (US) adults. Data were collected from the 2020 Medical Expenditure Panel Survey (MEPS). Eligible participants were US adults (≥18 years) with data on BMI, cholesterol, and cancer status, who were alive at the end of the data collection period. An adjusted logistic regression model assessed associations between eight possible combinations of BMI and cholesterol status (independent variable) with cancer diagnosis (dependent variable). Among 27,805 individuals in the 2020 MEPS data, 20,818 met the eligibility criteria (weighted N = 252,340,615). Of these 2668 (weighted N = 29,770,359) had cancer and 18,150 (weighted N = 222,570,256) did not have cancer. In the adjusted logistic regression model, underweight and normal weight individuals with high cholesterol were associated with higher odds of cancer (odds ratio, OR = 2.002, and 95% confidence interval, CI = 1.032-3.885, and OR = 1.326 and 95% CI = 1.047-1.681, respectively), while obese individuals with normal cholesterol were associated with lower odds of cancer (OR = 0.681; 95% CI = 0.543-0.853) compared to normal weight individuals with normal cholesterol. This study offers insights into specific groups of individuals who may be prioritized for cancer prevention. Further research is required to investigate these findings in additional subpopulations.