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Background Low shearing force can induce vascular inflammatory response, which leads to the occurrence and development of hypertension. Estrogen can regulate the immune system and inflammatory response through estrogen receptor mediated pathway, with a protective effect on hypertension. Cardiovascular diseases have become the leading cause of death in China. Objective To investigate the current prevalence and influencing factors of hypertension in women, explore the correlation of shearing force and estradiol with hypertension as well as the effect of estradiol on hypertension under various shearing force, so as to further explore the effect of the interaction between estradiol and shearing force on hypertension. Methods Female patients admitted to the Department of Cardiology of Hunan People's Hospital from January to August 2021 were selected as the study subjects, 288 of whom with essential hypertension were included as the case group, and 296 female non-hypertensive medical examiners in the physical examination department of Hunan People's Hospital were randomly selected as the control group. The self-designed questionnaire was used to investigate the general information of the study subjects, and the fasting venous blood samples of the subjects were taken in the early morning for the examination of laboratory indicators. The color doppler ultrasound was used for ultrasonography on vessels to calculate the shearing force. Multivariate Logistic regression was used to construct the hypertension prediction model and draw the nomogram. Receiver operating characteristic curve (ROC curve) was plotted to evaluate the hypertension prediction model. Stratification analysis was conducted on estradiol in four groups to explore the effect of each estradiol group on the occurrence of hypertension under different shearing force dichotomous groups. The multiplicative interaction model was used to evaluate the interaction between estradiol and shearing force. Results There were significant differences in age, education level, body mass index (BMI), menopause, family history of hypertension, triglyceride (TG), total cholesterol (TC), low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), estradiol, uric acid (UA), creatinine (Scr), urea nitrogen (BUN), shear force, and blood viscosity between the case group and control group (P60 years old〔OR=2.172, 95%CI (1.361, 3.468) 〕, menopause〔OR=14.940, 95%CI (1.938, 115.176) 〕, family history of hypertension〔OR=1.614, 95%CI (1.014, 2.570) 〕, BMI of 24-28 kg/m2〔OR=3.454, 95%CI (2.072, 5.758) 〕, BMI>28 kg/m2〔OR=6.750, 95%CI (2.380, 19.146) 〕, TG of 1.30-1.83 mmol/L〔OR=1.980, 95%CI (1.114, 3.520) 〕, TG>1.83 mmol/L〔OR=2.311, 95%CI (1.308, 4.082) 〕, TC of 4.29-5.12 mmol/L〔OR=2.222, 95%CI (1.250, 3.950) 〕, TC>5.12 mmol/L〔OR=1.884, 95%CI (1.062, 3.341) 〕, LDL-C>2.81 mmol/L〔OR=1.546, 95%CI (1.150, 2.078) 〕, HDL-C>1.17 mmol/L〔OR=0.140, 95%CI (0.065, 0.301) 〕, BUN>5.47 mmol/L〔OR=3.249, 95%CI (2.380, 4.434) 〕, Scr>69 μmol/L〔OR=1.166, 95%CI (1.126, 1.207) 〕, UA>324 μmol/L〔OR=1.040, 95%CI (1.029, 1.050) 〕, WBV4.41 mPa·s〔OR=3.456, 95%CI (2.557, 4.671) 〕, estradiol of 10.60-23.99 ng/L〔OR=0.005, 95%CI (0.001, 0.020) 〕, shearing force >8.21 dyne/cm2〔OR=0.033, 95%CI (0.017, 0.062) 〕were influncing factors for hypertension (P8.21 dyne/cm2 group, estradiol≥15.39 ng/L was significantly associated with reduced risk of hypertension〔OR=0.012, 95%CI (0.005, 0.019), P

Background Sleep duration is strongly associated with hypertension, but its effect on H-type hypertension in female population is not well characterized. Objective The examine the effect of sleep duration on the prevalence of H-type hypertension in female population. Methods A cross-sectional study design was used. One thousand seven hundred and nine female hypertensive inpatients were selected from Department of General Medicine and Geriatric Department of Hunan Provincial People's Hospital from July 2018 to December 2020, among whom those who with serum Hcy≥10 μmol/L and the others were assigned into H-type hypertension and non-H-type hypertension groups, respectively. Binary Logistic regression was used to assess the correlation of between sleep duration and H-type hypertension across three groups with different self-reported mean daily sleep durations in the past month (8 h) , and to further assess the correlation between the two across by age (≤60 and >60) . Results The distribution of age, education level and marital status differed across three groups with different daily sleep durations (P8 h. The distribution of age, education level, living area (urban or rural) , marital status, BMI, exercise status, and daily sleep duration, as well as prevalence of drinking between H-type hypertension group〔973 (56.93%) 〕 and non-H-type hypertension group〔736 (43.07%) 〕 (P

Improving the performance and reducing the training cost of road extraction models in the absence of samples is important for updating road maps. Despite the success of recent road extraction models on standard datasets, they often fail to perform when applied to new datasets or real-world scenarios where labeled samples are not available. In this paper, our focus diverges from the typical quest to pinpoint the optimal road extraction model or evaluate generalization prowess across models. Instead, we propose a method called Asymmetric Consistent Co-Training (ACCT) to train existing road extraction models faster and make them perform better in new scenarios lacking samples. ACCT uses two models with different structures and a supervision module to enhance accuracy through mutual learning. Labeled and unlabeled images are processed by both models to generate road maps from different perspectives. The supervision module ensures consistency between predictions by computing losses based on labeling status. ACCT iteratively adjusts parameters using unlabeled data, improving generalization. Empirical evaluations show that ACCT improves IoU by 2.79% to 10.26% using only 1/8 of the labeled data compared to fully supervised methods. It also reduces parameters by over 49% compared to state-of-the-art semi-supervised methods while maintaining similar accuracy. These results highlight the potential of leveraging large amounts of unlabeled data to enhance road extraction models as data acquisition technology advances. [ABSTRACT FROM AUTHOR]