Doylebond0158

Trial Registry ( ChiCTR1800019551 ). Registered 18 November 2018.

In Japan, 55.5% of breast cancer survivors (BCSs) are of working age, so various perspectives regarding return to work (RTW) after cancer diagnosis need to be considered. Therefore, this study aimed to clarify the risk factors for resignation and taking sick leave (SL) among BCSs in continued employment at the time of diagnosis.

A web-based retrospective cross-sectional survey was conducted on BCSs using data from a 2018 Japanese national research project (Endo-Han) commissioned by the Ministry of Health, Labour and Welfare of Japan. The subjects were women aged 18-69 years who had been diagnosed with breast cancer for the first time at least 1 year previously. The risk factors for resignation and taking SL after breast cancer diagnosis, including age at diagnosis, education level, cancer stage, surgery, chemotherapy, radiotherapy, employment status, and occupational type, were then analyzed using a logistic regression model.

In total, 40 (14.9%) of 269 BCSs quit their jobs at least 1 year after being diagnosed with breast cancer. The results of the multivariable analysis indicated that lower education level (odds ratio [OR] 3.802; 95% confidence interval [CI] 1.233-11.729), taking SL (OR 2.514; 95%CI 1.202-5.261), and younger age at diagnosis (OR 0.470; 95%CI 0.221-0.998) were predictors of resignation. Of 229 patients who continued working, SL was taken by 72 (31.4%). In addition, undergoing surgery was found to be a predictor of taking SL (OR 8.311; 95%CI 1.007-68.621).

In total, 40 (14.9%) of 269 BCSs quit their jobs at least 1 year after being diagnosed with breast cancer. The results of this study indicated that younger age, lower education level, and taking SL were predictors of resignation after breast cancer diagnosis.

In total, 40 (14.9%) of 269 BCSs quit their jobs at least 1 year after being diagnosed with breast cancer. The results of this study indicated that younger age, lower education level, and taking SL were predictors of resignation after breast cancer diagnosis.

Although Myanmar is moving to attain UHC in 2030, health care utilization indicators are still low, especially among women. Women's health outcomes are determined by the lack of access to health care, and many factors influence this condition. The objective of the present work was to identify the association between women's empowerment and barriers to accessing health care among currently married women in Myanmar.

We performed a secondary analysis using the Myanmar Demographic and Health Survey (2015-16), including 7759 currently married women aged 15-49 years. The outcome variable, barriers to accessing health care, were asked about in terms of whether the respondent faced barriers to getting permission to go, getting money to go, the distance to the health facility, and not wanting to go alone. The variables were recoded into zero, one, and more than one barrier. After performing the exploratory factor analysis for women's empowerment indicators (decision-making power and disagreement to justification tld contribute to the policy formulation for reducing health inequity issues by increasing women's empowerment.

When women are more empowered, they tend to face fewer barriers when accessing health care services. This finding could contribute to the policy formulation for reducing health inequity issues by increasing women's empowerment.

This study aimed to establish a deep learning system for detecting the active and inactive phases of thyroid-associated ophthalmopathy (TAO) using magnetic resonance imaging (MRI). This system could provide faster, more accurate, and more objective assessments across populations.

A total of 160 MRI images of patients with TAO, who visited the Ophthalmology Clinic of the Ninth People's Hospital, were retrospectively obtained for this study. Of these, 80% were used for training and validation, and 20% were used for testing. The deep learning system, based on deep convolutional neural network, was established to distinguish patients with active phase from those with inactive phase. The accuracy, precision, sensitivity, specificity, F1 score and area under the receiver operating characteristic curve were analyzed. Besides, visualization method was applied to explain the operation of the networks.

Network A inherited from Visual Geometry Group network. The accuracy, specificity and sensitivity were 0.863±0.055, 0.896±0.042 and 0.750±0.136 respectively. Due to the recurring phenomenon of vanishing gradient during the training process of network A, we added parts of Residual Neural Network to build network B. After modification, network B improved the sensitivity (0.821±0.021) while maintaining a good accuracy (0.855±0.018) and a good specificity (0.865±0.021).

The deep convolutional neural network could automatically detect the activity of TAO from MRI images with strong robustness, less subjective judgment, and less measurement error. This system could standardize the diagnostic process and speed up the treatment decision making for TAO.

The deep convolutional neural network could automatically detect the activity of TAO from MRI images with strong robustness, less subjective judgment, and less measurement error. selleck inhibitor This system could standardize the diagnostic process and speed up the treatment decision making for TAO.

it is important to investigate the relationship between disordered eating in male adolescents and smoking and alcohol consumption as they are risk factors to other diseases as well. For example, high levels of alcohol accompanied by the acidic damage and nutritional deficit exhibited in people with disordered eating habits - due to induced vomiting - has been shown to increase risk of esophageal cancer. Considering the very few studies done on disordered eating prevailing in males and the prevalence of smoking and drinking habits, our study aims to investigate the correlation between inappropriate eating habits and one's dependence on cigarettes, waterpipes, and alcohol all the while focusing on male adolescents.

This was a cross-sectional observational study that enrolled 389 male students (13-17 years of age) drawn from five Lebanese schools between October and December 2019.

The MANCOVA analysis was performed taking the addiction scales as the dependent variables and the EAT-26 score as an independent variable, adjusting for the covariates (age, BMI and household crowding index).