Association between secondhand smoke exposure and serum sex hormone concentrations among US female adults: a cross-sectional analysis using data from the National Health and Nutrition Examination Survey, 2013-2016.

OBJECTIVE: To estimate the association between secondhand smoke (SHS) exposure and serum sex hormone concentrations in female adults (never smokers and former smokers). DESIGN: Cross-sectional analysis. SETTING: US National Health and Nutrition Examination Survey, 2013-2016. OUTCOME MEASURES: Serum sex hormone measures included total testosterone (TT) and oestradiol (E2), sex hormone-binding globulin (SHBG), the ratio of TT and E2 and free androgen index (FAI). Isotope dilution-liquid chromatography tandem mass spectrometry was used to measure serum TT and E2. SHBG was measured using immunoassay. The ratio of TT and E2 and FAI were calculated. SHS exposure was defined as serum cotinine concentration of 0.05-10 ng/mL. PARTICIPANTS: A total of 622 female participants aged ≥20 years were included in the analysis. RESULTS: For never smokers, a doubling of serum cotinine concentration was associated with a 2.85% (95% CI 0.29% to 5.47%) increase in TT concentration and a 6.29% (95% CI 0.68% to 12.23%) increase in E2 in fully adjusted models. The never smokers in the highest quartile (Q4) of serum cotinine level exhibited a 10.30% (95% CI 0.78% to 20.72%) increase in TT concentration and a 27.75% (95% CI 5.17% to 55.17%) increase in E2 compared with those in the lowest quartile (Q1). For former smokers, SHBG was reduced by 4.36% (95% CI -8.47% to -0.07%, p for trend=0.049) when the serum cotinine level was doubled, and the SHBG of those in Q4 was reduced by 17.58% (95% CI -31.33% to -1.07%, p for trend=0.018) compared with those in Q1. CONCLUSION: SHS was associated with serum sex hormone concentrations among female adults. In never smokers, SHS was associated with increased levels of TT and E2. In former smokers, SHS was associated with decreased SHBG levels.

A ratiometric electrochemical biosensor based on ARGET ATRP for detection of HER2 gene.

Human epidermal growth factor receptor 2 (HER2), a common proto-oncogene, is overexpressed in a subset of breast cancer patients. It is essential to track HER2 expression for early breast cancer diagnosis. Herein, a ratiometric electrochemical biosensor for detection of HER2 based on activators generated by electron transfer for atom transfer radical polymerisation (AGET ATRP) and hairpin DNA was developed. Specifically, hairpin DNA was first self-assembled on the gold electrode by Au-S bond. Upon capturing HER2, the stem-loop structure of hairpin DNA was unfolded, the signal value of methylene blue (MB) decreased as it moved away from the electrode surface. cDNA was linked with HER2 by complementary base pairing to introduce amino group. Then, the initiator 2-bromo-2-methylpropionic acid (BMP) were connected to the amino group on the cDNA to activate ARGET ATRP. The detection performance of biosensors for HER2 was explored by the ratio signal between two signal molecules. Under optimal conditions, this ratiometric electrochemical biosensor shows good selectivity and stability with a wide detection range of 1-1 × 106 pM and a detection limit of 78.47 fM. Furthermore, the biosensor exhibits satisfactory anti-interference ability due to the hairpin DNA and dual signal system, and has promising application prospects in the detection of other DNA disease markers.

Associations of screen-based sedentary activities with all cause dementia, Alzheimer's disease, vascular dementia: a longitudinal study based on 462,524 participants from the UK Biobank.

BACKGROUND: Current drug treatments for dementia aren't effective. Studying gene-environment interactions can help develop personalized early intervention strategies for Alzheimer's disease (AD). However, no studies have examined the relationship between screen-based sedentary activities and genetic susceptibility to AD risk, and further understanding of the causal relationship is also crucial. METHODS: This study included 462,524 participants from the UK Biobank with a follow-up of 13.6 years. Participants' screen-based sedentary activities time was categorized into three groups based on recorded time: ≥ 4 h/day, 2-3 h/day, and ≤ 1 h/day. Cox proportional risk models were used to analyze the association between computer use/TV viewing groups and the risk of all-cause dementia, AD and vascular dementia (VD). Generalized linear model (GLM) were used to examine the relationship between screen-based sedentary activities and brain structure. Bidirectional Mendelian randomization (MR) was used to validate the causal relationship between TV viewing and AD. RESULTS: Compared to TV viewing ≤ 1 h/day, 1)TV viewing 2-3 h/day was correlated with a higher risk of all-cause dementia (HR = 1.09, 95% CI:1.01-1.18, P < 0.05), and TV viewing ≥ 4 h/day was associated with a higher risk of all-cause dementia (HR = 1.29, 95% CI: 1.19-1.40, P < 0.001), AD (HR = 1.25, 95% CI:1.1-1.42, P < 0.001), and VD (HR = 1.24, 95% CI: 1.04-1.49, P < 0.05); 2) TV viewing ≥ 4 h/day was correlated with a higher AD risk at intermediate (HR = 1.34, 95% CI: 1.03-1.75, P < 0.001) and high AD genetic risk score (AD-GRS) (HR = 2.18, 95% CI: 1.65-2.87, P < 0.001);3) TV viewing 2-3 h/day [ß = (-94.8), 95% CI: (-37.9) -(-151.7), P < 0.01] and TV viewing ≥ 4 h/day [ß = (-92.94), 95% CI: (-17.42) -(-168.46), P < 0.05] were correlated with a less hippocampus volume. In addition, a causal effect of TV viewing times was observed on AD analyzed using MR Egger (OR = 5.618, 95%CI:1.502-21.013, P < 0.05). CONCLUSIONS: There was a causal effect between TV viewing time and AD analyzed using bidirectional MR, and more TV viewing time exposure was correlated with a higher AD risk. Therefore, it is recommended that people with intermediate and high AD-GRS should control their TV viewing time to be less than 4 h/ day or even less than 1 h/day.

Associations of Unhealthy Lifestyle and Nonalcoholic Fatty Liver Disease With Cardiovascular Healthy Outcomes.

BACKGROUND: The role of nonalcoholic fatty liver disease (NAFLD) as a mediator in the association between various unhealthy lifestyles and major adverse cardiovascular events and all-cause death remains unclear. METHODS AND RESULTS: This study used data from the UK Biobank, with follow-up until the end of 2021. It involved the calculation of unweighted and weighted lifestyle scores using the Cox model to classify participants on the basis of these scores. Additionally, the research assessed the mediation effect proportion of NAFLD using the difference method and examined the interaction and joint effects of lifestyle and NAFLD on health outcomes. Among the 134 616 enrolled participants, 4024 had records of major adverse cardiovascular events, while among the 130 144 participants included in the analysis of all-cause death, 6697 deaths occurred. The proportions of the association between overall lifestyle and major adverse cardiovascular events mediated by NAFLD were 19.4% and 21.7% (95% CI, 16.2-22.6 and 17.8-25.7) for scores 1 and 2, respectively, and those for all-cause death were 14.1% and 10.1% (95% CI, 11.3-17.1 and 7.9-12.2). After fully adjusting for traditional cardiovascular risk factors, the mediating effects declined across both outcomes. The associations between overall lifestyle and outcomes were stronger among those of the non-NAFLD group, and significant interactions were observed between overall lifestyle and NAFLD status. The joint analysis revealed that patients with NAFLD with unhealthy lifestyle had the highest risk of major adverse cardiovascular events and all-cause death. CONCLUSIONS: Improving lifestyle and addressing metabolic risk factors are essential for cardiovascular risk management in patients with NAFLD.

The role of hypertension in bone mineral density among males older than 50 years and postmenopausal females: evidence from the US National Health and Nutrition Examination Survey, 2005-2010.

Background: Hypertension is a significant chronic disease that has been linked with bone mineral density (BMD) in various studies. However, the conclusions are contradictory. The purpose of our study was to identify the bone mineral density (BMD) of postmenopausal females and males older than 50 years with hypertension. Methods: This cross-sectional study of 4,306 participants from the 2005-2010 US National Health and Nutrition Examination Survey explored the relationship between BMD and hypertension. Participants who had a mean systolic blood pressure (SBP) ≥140 mmHg, or a mean diastolic blood pressure (DBP) ≥90 mmHg, or were taking any prescribed medicine for high blood pressure were defined as having hypertension. BMD values were measured at the femoral neck and lumbar vertebrae as the primary outcome. Weight general linear model was used to describe the status of BMD in patients with hypertension. Weighted multivariate regression analysis was conducted to demonstrate the association between hypertension and BMD. Weighted restricted cubic spline (RCS) was used to assess the relationship between BMD and SBP and DBP. Results: Our study found that there was a positive association between hypertension and lumbar BMD and the lumbar BMD was significantly higher in the presence of hypertension than in the control group in both males (1.072 vs. 1.047 g/cm2) and females (0.967 vs. 0.938 g/cm2; both p < 0.05), but a similar pattern was not found in the femoral neck. Meanwhile, lumbar BMD was positively associated with SBP and negatively associated with DBP both in males and females. The prevalence of low bone mass and osteoporosis at the lumbar vertebrae was lower in male patients with hypertension than in the control group. However, no difference was observed among postmenopausal females between the hypertension and control groups. Conclusions: Hypertension was associated with higher BMD at the lumbar vertebrae in both males older than 50 years and postmenopausal females.

Differentiating glioblastoma from primary central nervous system lymphoma of atypical manifestation using multiparametric magnetic resonance imaging: A comparative study.

Background: The aim of this study is to evaluate the diagnostic efficiency of magnetic resonance imaging (MRI) of single parameters, unimodality, and bimodality in distinguishing glioblastoma (GBM) from atypical primary central nervous system lymphoma (PCNSL) based on diffusion-weighted imaging (DWI), dynamic susceptibility contrast (DSC) enhancement, diffusion tensor imaging (DTI), and proton magnetic resonance spectroscopy (1H-MRS) findings. Methods: The cohort included 108 patients pathologically diagnosed with GBM and 54 patients pathologically diagnosed with PCNSL. Pretreatment morphological MRI, DWI, DSC, DTI and MRS were all performed on each patient. The quantitative parameters of multimodal MRI were measured and compared between the patients in the GBM and atypical PCNSL groups, and those parameters showing a significant difference (p < 0.05) between patients in the GBM and atypical PCNSL groups were used to develop one-parameters, unimodality, and bimodality models. We evaluated the efficiency of different models in distinguishing GBM from atypical PCNSL by performing receiver operating characteristic analysis (ROC). Results: Atypical PCNSL had lower minimum apparent diffusion coefficient (ADCmin), mean ADC (ADCmean), relative ADC (rADC), mean relative cerebral blood volume (rCBVmean), maximum rCBV (rCBVmax), fractional anisotropy (FA), axial diffusion coefficient (DA) and radial diffusion coefficient (DR) values and higher choline/creatine (Cho/Cr) and lipid/creatine (Lip/Cr) ratios than GBM (all p < 0.05). The rCBVmax, DTI and DSC + DTI data were optimal models of single-parameter, unimodality and bimodality for differentiation of GBM from atypical PCNSL, yielding areas under the curves (AUCs) of 0.905, 0.954, and 0.992, respectively. Conclusions: Models of single-parameter, unimodality and bimodality based on muti multiparameter functional MRI may help to discriminate GBM from atypical PCNSL.

Machine learning models for predicting survival in patients with ampullary adenocarcinoma.

Objective: The aim of this study was to predict the long-term survival probability of patients with ampullary adenocarcinoma (AAC), which would provide a theoretical basis for the long-term care of these patients. Methods: Data on patients with AAC during 2004-2015 were obtained from the Surveillance, Epidemiology, and End Results database, which were split at a 7:3 ratio into two independent cohorts: training and testing cohorts. Differences in survival between the two groups were tested using the Kaplan-Meier estimator and log-rank test methods. We constructed six survival analysis methods: the American Joint Committee on Cancer TNM stage, Cox Proportional Hazards regression, CoxTime, DeepSurv, XGBoost Survival Embeddings, and Random Survival Forest. The performances of these models were evaluated using the C-index, receiver operating characteristic (ROC), and calibration curves. Results: This study included 2,935 patients with AAC. Univariate Cox regression analyses of the training cohort indicated that race, marital status at diagnosis, scope of regional lymph node surgery, tumor grade, summary stage, American Joint Committee on Cancer stage, TNM stage T, and TNM stage N were important factors affecting survival (P â€‹< â€‹0.05). The results of the C-index indicated that DeepSurv performed the best among the six models, with the highest C-index of 0.731. The areas under the ROC curves of the DeepSurv model at the 1-year, 3-year, 5-year, and 10-year time points were 0.823, 0.786, 0.803, and 0.813, respectively. The calibration curve indicated that DeepSurv performed well, with good calibration. Conclusions: Machine learning models such as DeepSurv have a stronger performance in the survival analysis of patients with AAC.

Factors Associated With Lymph Node Yield and Effects of Lymph Node Density on Survival of Patients With Pulmonary Sarcomatoid Carcinoma.

OBJECTIVE: The objective of this study was to identify factors associated with lymph node yield (LNY) during surgeries for pulmonary sarcomatoid carcinoma (PSC) and to determine effects of lymph node density (LND) on the overall survival (OS) of patients with PSC. MATERIALS AND METHODS: The SEER Research Plus database was searched for data on patients with PSC from 1988 to 2018. Poisson regression was used of all patients with PSC to identify relevant factors associated with LNY. Univariate and multivariate Cox regression analyses were adopted for lymph node (LN)-positive patients to evaluate the impact of LND on OS. The 5-year OS rates of patients with PSC were compared based on their LN status and LND. RESULTS: There were 545 eligible patients in the study sample, 175 of which were LN-positive. These patients had significantly lower 5-year OS than those with no positive LNs ( P <0.001). Poisson regression analysis indicated relevant factors increasing LNY included higher diagnosis age, non-Hispanic American Indian or Alaska Native races, larger tumor, pleomorphic carcinoma histology, and more advanced disease stages. The Cox regression analysis indicated higher LND ( P =0.022) was probably associated with a worse prognosis for LN-positive patients. The group with LND ≥0.12 had a higher risk of death than the group with LND <0.12 ( P <0.001) among LN-positive patients with PSC. CONCLUSIONS: Patients with PSC with high LND experienced worse outcomes than those with low LND. Further risk stratification of patients with PSC may help to improve survival benefits based on prognostic indicators of LND.

Metformin Protects Cardiovascular Health in People With Diabetes.

Background: Metformin is the most commonly used drug for patients with diabetes, but there is still some controversy about whether it has a protective effect on cardiovascular health. We therefore used the National Health and Nutritional Examination Survey (NHANES) database to analyze the impact of metformin use on cardiovascular health in patients with diabetes. Methods: We extracted the demographic data and laboratory test results of all people with diabetes in the NHANES database from January 2017 to March 2020. The outcomes were seven indicators of cardiovascular health from the American Heart Association, each was scored as 0, 1, and 2 to represent poor, moderate, and ideal health statuses, respectively. The scores for the indicators (excluding diet and glycemic status) were summed, and the sum score was then considered to indicate unhealthy (0-5) or healthy (>5). Multivariate logistic regression analysis was used, and subgroup analyses were performed by age, alcohol consumption, education, and marital status. Results: This study included 1,356 patients with diabetes, among which 606 were taking metformin. After adjusting for all included variables, oral metformin in patients with diabetes had a protective effect on the cardiovascular health of patients (OR = 0.724, 95% CI = 0.573-0.913, P = 0.007). Subgroup analysis indicated that metformin protects the cardiovascular health of people with diabetes more clearly in those who are young (OR = 0.655, 95% CI = 0.481-0.892, P = 0.007), married (OR = 0.633, 95% CI = 0.463-0.863, P = 0.003), and drink alcohol (OR = 0.742, 95% CI = 0.581-0.946, P = 0.016). Conclusion: This study found that metformin has a protective effect on the cardiovascular health of patients with diabetes. The study findings support the general applicability of metformin.

The Use of Antibiotics for Ventilator-Associated Pneumonia in the MIMIC-IV Database.

Purpose: By analyzing the clinical characteristics, etiological characteristics and commonly used antibiotics of patients with ventilator-associated pneumonia (VAP) in intensive care units (ICUs) in the intensive care database. This study aims to provide guidance information for the clinical rational use of drugs for patients with VAP. Method: Patients with VAP information were collected from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database, including their sociodemographic characteristics, vital signs, laboratory measurements, complications, microbiology, and antibiotic use. After data processing, the characteristics of the medications used by patients with VAP in ICUs were described using statistical graphs and tables, and experiences were summarized and the reasons were analyzed. Results: This study included 2,068 patients with VAP. Forty-eight patient characteristics, including demographic indicators, vital signs, biochemical indicators, scores, and comorbidities, were compared between the survival and death groups of VAP patients. Cephalosporins and vancomycin were the most commonly used. Among them, fourth-generation cephalosporin (ForGC) combined with vancomycin was used the most, by 540 patients. First-generati49n cephalosporin (FirGC) combined with vancomycin was associated with the highest survival rate (86.7%). More than 55% of patients were infected with Gram-negative bacteria. However, patients with VAP had fewer resistant strains (<25%). FirGC or ForGC combined with vancomycin had many inflammation-related features that differed significantly from those in patients who did not receive medication. Conclusion: Understanding antibiotic use, pathogenic bacteria compositions, and the drug resistance rates of patients with VAP can help prevent the occurrence of diseases, contain infections as soon as possible, and promote the recovery of patients.

Deep transfer learning to quantify pleural effusion severity in chest X-rays.

PURPOSE: The detection of pleural effusion in chest radiography is crucial for doctors to make timely treatment decisions for patients with chronic obstructive pulmonary disease. We used the MIMIC-CXR database to develop a deep learning model to quantify pleural effusion severity in chest radiographs. METHODS: The Medical Information Mart for Intensive Care Chest X-ray (MIMIC-CXR) dataset was divided into patients 'with' or 'without' chronic obstructive pulmonary disease (COPD). The label of pleural effusion severity was obtained from the extracted COPD radiology reports and classified into four categories: no effusion, small effusion, moderate effusion, and large effusion. A total of 200 datasets were randomly sampled to manually check each item and determine whether the tags are correct. A professional doctor re-tagged these items as a verification cohort without knowing their previous tags. The learning models include eight common network structures including Resnet, DenseNet, and GoogleNET. Three data processing methods (no sampling, downsampling, and upsampling) and two loss algorithms (focal loss and cross-entropy loss) were used for unbalanced data. The Neural Network Intelligence tool was applied to train the model. Receiver operating characteristic curves, Area under the curve, and confusion matrix were employed to evaluate the model results. Grad-CAM was used for model interpretation. RESULTS: Among the 8533 patients, 15,620 chest X-rays with clearly marked pleural effusion severity were obtained (no effusion, 5685; small effusion, 4877; moderate effusion, 3657; and large effusion, 1401). The error rate of the manual check label was 6.5%, and the error rate of the doctor's relabeling was 11.0%. The highest accuracy rate of the optimized model was 73.07. The micro-average AUCs of the testing and validation cohorts was 0.89 and 0.90, respectively, and their macro-average AUCs were 0.86 and 0.89, respectively. The AUC of the distinguishing results of each class and the other three classes were 0.95 and 0.94, 0.76 and 0.83, 0.85 and 0.83, and 0.87 and 0.93. CONCLUSION: The deep transfer learning model can grade the severity of pleural effusion.