Based on the physiological nature of breast movement in exercising females, a sports bra made of fabric with dynamic moisture transfer properties was developed to improve female thermal comfort. This study aimed to investigate the effects of fabrics with dynamic moisture transfer properties on breast skin temperature, and the thermal physiological and psychological response of women while wearing the sports bra during exercise and recovery. Ten healthy women exercised in random order with two types of sports bra with or without the dynamic moisture transfer properties and then performed a 20-minute short-duration high-intensity exercise and rest to recover under thermoneutral conditions. Heart rate, body core temperature, skin temperature, body mass and thermal psychological subjective sensations were investigated during exercise and recovery. The results indicated that in the running state, the local breast skin temperatures of sports bra made of fabrics with dynamic moisture transfer properties (33.427 ± 0.087℃) are significantly lower than bras without these dynamic moisture transfer properties (33.964 ± 0.055℃) ( P < 0.01). During the exercise and recovery, the thermal psychological subjective sensation for the two types of fabrics were very similar, whereas the body mean skin temperature was revealed to undergo greater decreasing effects in sports bras made of fabrics with dynamic moisture transfer properties than those without the dynamic moisture transfer properties ( P < 0.05). These results provide novel information that usage of fabrics with dynamic moisture properties in sports bras could improve thermoregulation to benefit exercising women’s thermal comfort in terms of decreasing local breast skin temperature.

There is a significant Poisson effect for knitted fabric under tensile deformation. It is an important parameter for practical pattern design, numerical simulation of garment pressure distribution and garment dressing system. However, it is still difficult to measure the fabric Poisson ratio quickly and accurately. In this paper, a method for testing the elastic knitted fabric Poisson ratio and modulus was proposed based on orthotropic theory and strip biaxial tensile test. The fabric’s Poisson ratio and Young’s modulus are identified by linear regression, while the obtained values are validated by uniaxial tensile tests.

Purpose: This study aims to develop a prediction model to categorize the risk of early death among breast cancer patients with bone metastases using machine learning models.Methods: This study examined 16,189 bone metastatic breast cancer patients between 2010 and 2019 from a large oncological database in the United States. The patients were divided into two groups at random in a 90:10 ratio. The majority of patients (n = 14,582, 90%) were served as the training group to train and optimize prediction models, whereas patients in the validation group (n = 1,607, 10%) were utilized to validate the prediction models. Four models were introduced in the study: the logistic regression model, gradient boosting tree model, decision tree model, and random forest model.Results: Early death accounted for 17.4% of all included patients. Multivariate analysis demonstrated that older age; a separated, divorced, or widowed marital status; nonmetropolitan counties; brain metastasis; liver metastasis; lung metastasis; and histologic type of unspecified neoplasms were significantly associated with more early death, whereas a lower grade, a positive estrogen receptor (ER) status, cancer-directed surgery, radiation, and chemotherapy were significantly the protective factors. For the purpose of developing prediction models, the 12 variables were used. Among all the four models, the gradient boosting tree had the greatest AUC [0.829, 95% confident interval (CI): 0.802–0.856], and the random forest (0.828, 95% CI: 0.801–0.855) and logistic regression (0.819, 95% CI: 0.791–0.847) models came in second and third, respectively. The discrimination slopes for the three models were 0.258, 0.223, and 0.240, respectively, and the corresponding accuracy rates were 0.801, 0.770, and 0.762, respectively. The Brier score of gradient boosting tree was the lowest (0.109), followed by the random forest (0.111) and logistic regression (0.112) models. Risk stratification showed that patients in the high-risk group (46.31%) had a greater six-fold chance of early death than those in the low-risk group (7.50%).Conclusion: The gradient boosting tree model demonstrates promising performance with favorable discrimination and calibration in the study, and this model can stratify the risk probability of early death among bone metastatic breast cancer patients.

This study aimed to investigate the quality of life and mental health status and further to identify relevant risk factors among advanced cancer patients with spine metastases. This study prospectively included and analyzed 103 advanced cancer patients with spine metastases. Patient's basic information, lifestyles, comorbidities, tumor characteristics, therapeutic strategies, economic conditions, quality of life, anxiety, and depression were collected. Patient's quality of life was assessed using the Functional Assessment of Cancer Therapy-General Scale (FACT-G), and anxiety and depression were evaluated using the Hospital Anxiety and Depression Scale (HADS). Subgroup analysis was performed based on different age groups, and a multivariate analysis was performed to test the ability of 20 potential risk factors to predict quality of life, anxiety, and depression. The mean total FACT-G score was only 61.38 ± 21.26. Of all included patients, 52.43% had skeptical or identified anxiety and 53.40% suffered from skeptical or identified depression. Patients had an age of 60 or more and &lt;70 years had the lowest FACT-G score (54.91 ± 19.22), highest HADS anxiety score (10.25 ± 4.22), and highest HADS depression score (10.13 ± 4.94). After adjusting all other potential risk factors, age was still significantly associated with quality of life (OR = 0.57, 95%CI: 0.38–0.86, p &lt; 0.01) and depression (OR = 1.55, 95%CI: 1.00–2.42, p = 0.05) and almost significantly associated with anxiety (OR = 1.52, 95%CI: 0.94–2.43, p = 0.08). Besides, preference to eating vegetables, time since knowing cancer diagnosis, surgical treatment at primary cancer, hormone endocrine therapy, and economic burden due to cancer treatments were found to be significantly associated with the quality of life. A number of comorbidities and economic burden due to cancer treatments were significantly associated with anxiety. Advanced cancer patients with spine metastases suffer from poor quality of life and severe anxiety and depression, especially among patients with an age of 60 or more and &lt;70 years. Early mental health care and effective measures should be conducted to advanced cancer patients with spine metastases, and more attention should be paid to take care of patients with an age of 60 or more and &lt;70 years in terms of their quality of life and mental health status.