Predictors of urinary heavy metal concentrations among pregnant women in Jinan, China.

BACKGROUND: Toxic heavy metal exposure and insufficiency or excess of essential heavy metals may have negative effects on pregnant women's health and fetal growth. To date, the predictors of pregnant women's heavy metal exposure levels remain unclear and vary with different regions. The study intended to explore potential predictors of exposure to heavy metals individually and high co-exposure to heavy metal mixtures. METHODS: We recruited 298 pregnant women in first trimester from prenatal clinics in Jinan, Shandong Province, China, and collected spot urine samples and questionnaire data on their demographic characteristics, lifestyle habits, consumption of food and dietary supplement, and residential environment. All urine samples were analyzed for seven heavy metals: cobalt (Co), molybdenum (Mo), strontium (Sr), arsenic (As), cadmium (Cd), lead (Pb) and mercury (Hg). RESULTS: Factors associated with single heavy metal concentration were as follows: a) urinary As, Sr and Cd increased with women's age respectively; b) pregnant women with higher monthly household income per capita had lower Sr and Mo levels; c) pregnant women with intermittent folic acid supplementation and those not taking tap water as domestic drinking water had lower Sr concentrations; d) Cd was positively linked with consumption frequency of rice; e) Hg was adversely related to consumption frequency of egg and the women who took purified water as domestic drinking water had lower Hg exposure. In addition, pregnant women's age was positively associated with odds of high co-exposure to Co, As, Sr, Mo, Cd and Pb; while those with an educational level of college had lower odds of high exposure to such a metal mixture compared with those whose educational levels were lower than high school. CONCLUSION: Predictors of single urinary heavy metal concentration included pregnant women's age (As, Sr and Cd), monthly household income per capita (Sr and Mo), folic acid supplementation (Sr), rice consumption frequency (Cd), egg consumption frequency (Hg) and the type of domestic drinking water (Sr and Hg). Pregnant women with older age, lower educational level tended to have high co-exposure to Co, As, Sr, Mo, Cd and Pb.

Weakly Supervised Breast Lesion Detection in Dynamic Contrast-Enhanced MRI.

Currently, obtaining accurate medical annotations requires high labor and time effort, which largely limits the development of supervised learning-based tumor detection tasks. In this work, we investigated a weakly supervised learning model for detecting breast lesions in dynamic contrast-enhanced MRI (DCE-MRI) with only image-level labels. Two hundred fifty-four normal and 398 abnormal cases with pathologically confirmed lesions were retrospectively enrolled into the breast dataset, which was divided into the training set (80%), validation set (10%), and testing set (10%) at the patient level. First, the second image series S2 after the injection of a contrast agent was acquired from the 3.0-T, T1-weighted dynamic enhanced MR imaging sequences. Second, a feature pyramid network (FPN) with convolutional block attention module (CBAM) was proposed to extract multi-scale feature maps of the modified classification network VGG16. Then, initial location information was obtained from the heatmaps generated using the layer class activation mapping algorithm (Layer-CAM). Finally, the detection results of breast lesion were refined by the conditional random field (CRF). Accuracy, sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC) were utilized for evaluation of image-level classification. Average precision (AP) was estimated for breast lesion localization. Delong's test was used to compare the AUCs of different models for significance. The proposed model was effective with accuracy of 95.2%, sensitivity of 91.6%, specificity of 99.2%, and AUC of 0.986. The AP for breast lesion detection was 84.1% using weakly supervised learning. Weakly supervised learning based on FPN combined with Layer-CAM facilitated automatic detection of breast lesion.

The effect of comprehensive nursing on the recovery speed and prognosis of elderly colon cancer patients.

To explore the effect of comprehensive nursing on the recovery speed and prognosis of elderly colon cancer patients. A total of 110 elderly patients undergoing colon cancer surgery in our hospital from January 2018 to January 2020 were selected and divided into a control group (n=55, routine care) and an observation group (n=55, comprehensive nursing) according to the random number table method. The post-care stress state index score, surgical tolerance, postoperative recovery, postoperative complications, satisfaction, gastrointestinal quality of life index and other conditions were compared. The Numerical Rating Scale (NRS), Hamilton Depression Scale (HAMD) and Hamilton Anxiety Scale (HAMA) and other indicators scores in observation group were lower than those in the control group; the observation group had shorter postoperative hospital stay, bed time, first defecating time, first anal exhaust time; the postoperative complication rate of the observation group was 9.09%, which was lower than 25.45% of the control group; the satisfaction rate of the observation group was 94.55%, which was higher than 78.18% of the control group. All the above differences were statistically significant (P<0.05). The Gastrointestinal Quality of Life Index (GIQLI) score of the observation group was higher than that of the control group, and the difference was statistically significant (inter-group effect: F=172.100, P<0.001). The GIQLI score of both groups increased with time (time effect: F=69.270, P<0.001), and the grouping and time had an interactive effect (interactive effect: F=7.186, P<0.001). The application of comprehensive nursing in elderly patients with colon cancer has a remarkable effect. It can promote the rehabilitation of patients and improve the prognosis, which is worthy of promotion in clinic.