METHODS: Ethnic Chinese mothers intending to breastfeed their healthy infants were recruited post-delivery between August and October 2017 then, at 1 and 6 months, they were telephone interviewed about their experience. For every participant going to a CC after the birth, another mother going home ("home") for her confinement was recruited. Chi-square test was used to compare groups and multiple logistic regression was used to assess the effect of confinement place on exclusive breastfeeding.
RESULTS: Of 187 mothers, 88 (47%) went to CCs. Significantly more were primipara and fewer had previous breastfeeding experience. Response rates for the 1- and 6- month interviews were 88% (CC) versus 97% (home); and 77% (CC) versus 87% (home) respectively. Exclusive breastfeeding rates were similar between the groups: 62% (CC) versus 56% (home) at 1 month (p = 0.4); and 37% (CC) versus 42% (home) at 6 months (p = 0.5). Multiple logistic regression did not show that CCs were a factor affecting exclusive breastfeeding rates at 1 month, (adjusted odds ratio [aOR] 1.7, 95% confidence interval [CI] 0.9, 3.3), or 6 months (aOR 0.9, 95% CI 0.4, 1.7). However, significantly more CC participants only fed expressed breast milk. Despite 66% of CC participants reporting that their centre supported breastfeeding, only 6 (8%) CC participants compared to 66 (69%) of home participants roomed-in with their baby (p
METHODS: Breast cancer MRI images were classified into BA, BF, BPT, BTA, MDC, MLC, MMC, and MPC using a proposed Deep Learning model with additional 5 fine-tuned Deep learning models consisting of Xception, InceptionV3, VGG16, MobileNet and ResNet50 trained on ImageNet database. The dataset was collected from Kaggle depository for breast cancer detection and classification. That Dataset was boosted using GAN technique. The images in the dataset have 4 magnifications (40X, 100X, 200X, 400X, and Complete Dataset). Thus we evaluated the proposed Deep Learning model and 5 pre-trained models using each dataset individually. That means we carried out a total of 30 experiments. The measurement that was used in the evaluation of all models includes: F1-score, recall, precision, accuracy.
RESULTS: The classification F1-score accuracies of Xception, InceptionV3, ResNet50, VGG16, MobileNet, and Proposed Model (BCCNN) were 97.54%, 95.33%, 98.14%, 97.67%, 93.98%, and 98.28%, respectively.
CONCLUSION: Dataset Boosting, preprocessing and balancing played a good role in enhancing the detection and classification of breast cancer of the proposed model (BCCNN) and the fine-tuned pre-trained models' accuracies greatly. The best accuracies were attained when the 400X magnification of the MRI images due to their high images resolution.
RESEARCH AIM: This study describes the infant feeding experiences of women living with HIV in Malaysia.
METHODS: From August to October 2021, a nationwide, community-based qualitative study was conducted among women living with HIV and who received care from the Malaysian Ministry of Health. Using purposive sampling, participants who met the inclusion criteria were recruited. Interview and focus group transcripts were coded based on a secondary thematic analysis.
RESULTS: Six in-depth interviews and five focus group discussions were conducted among 32 participants. Study participants were mostly Malay secondary school graduates in their 30s and 40s. Due to the fear of vertical transmission, which was explained by healthcare providers to the participants, none of the women breastfed their infants. The three primary themes that emerged from analyzing the women's infant feeding experiences were (1) a human milk substitute was the only option and was encouraged; (2) feeding infants with a human milk substitute made the women feel incomplete as mothers; and (3) the women encountered difficulties in obtaining the subsidized human milk substitute.
CONCLUSION: Women living with HIV in Malaysia have been advised to provide human milk substitutes to their infants in fear of HIV transmission.
METHODS: Studying breast cancer, we established genome-scale DNA methylation profiles of prospectively collected buffy coat samples (n = 702) from a case-control study nested within the EPIC-Heidelberg cohort using reduced representation bisulphite sequencing (RRBS).
RESULTS: We observed cancer-specific DNA methylation events in buffy coat samples. Increased DNA methylation in genomic regions associated with SURF6 and REXO1/CTB31O20.3 was linked to the length of time to diagnosis in the prospectively collected buffy coat DNA from individuals who subsequently developed breast cancer. Using machine learning methods, we piloted a DNA methylation-based classifier that predicted case-control status in a held-out validation set with 76.5% accuracy, in some cases up to 15 years before clinical diagnosis of the disease.
CONCLUSIONS: Taken together, our findings suggest a model of gradual accumulation of cancer-associated DNA methylation patterns in peripheral blood, which may be detected long before clinical manifestation of cancer. Such changes may provide useful markers for risk stratification and, ultimately, personalized cancer prevention.