Factors influencing feeding practices of extreme poor infants and young children in families of working mothers in Dhaka slums: A qualitative study

BackgroundNutritional status differs between infants and young children living in slum and non-slum conditions—infants and young children living in City Corporation slums are likely to have worse nutritional status compared to those from non-slums. Furthermore, families in slums tend to engage female labor in cash-earning activities as a survival strategy; hence, a higher percentage of mothers stay at work. However, little is known about feeding practices for infants and young children in families with working mothers in slums. This study aims to understand the factors that determine feeding practices for infants and young children living in families with working mothers in Dhaka slums.MethodsThis study adopted a qualitative approach. Sixteen In-depth Interviews, five Key Informant Interviews, and Focused Group Discussions were conducted with family members, community leaders, and program staff. Method triangulation and thematic analyses were conducted.ResultsFeeding practices for infants and young children in families with working mothers are broadly determined by mothers’ occupation, basis civic facilities, and limited family buying capacity. Although mothers have good nutritional knowledge, they negotiate between work and feeding their infants and young children. Household composition, access to cooking facilities, and poverty level were also found to be significant determining factors.ConclusionThe results suggest a trade-off between mothers’ work and childcare. The absence of alternative care support in homes and/or work places along with societal factors outweighs full benefits of project interventions. Improving alternative childcare support could reduce the burden of feeding practice experienced by working mothers and may improve nutritional outcomes

Mental health in the slums of Dhaka - a geoepidemiological study

Gruebner O, Khan MH, Lautenbach S, et al. Mental health in the slums of Dhaka - a geoepidemiological study. BMC Public Health. 2012;12(1): 177.Background: Urban health is of global concern because the majority of the world's population lives in urban areas. Although mental health problems (e.g. depression) in developing countries are highly prevalent, such issues are not yet adequately addressed in the rapidly urbanising megacities of these countries, where a growing number of residents live in slums. Little is known about the spectrum of mental well-being in urban slums and only poor knowledge exists on health promotive socio-physical environments in these areas. Using a geo-epidemiological approach, the present study identified factors that contribute to the mental well-being in the slums of Dhaka, which currently accommodates an estimated population of more than 14 million, including 3.4 million slum dwellers. Methods: The baseline data of a cohort study conducted in early 2009 in nine slums of Dhaka were used. Data were collected from 1,938 adults (>= 15 years). All respondents were geographically marked based on their households using global positioning systems (GPS). Very high-resolution land cover information was processed in a Geographic Information System (GIS) to obtain additional exposure information. We used a factor analysis to reduce the socio-physical explanatory variables to a fewer set of uncorrelated linear combinations of variables. We then regressed these factors on the WHO-5 Well-being Index that was used as a proxy for self-rated mental wellbeing. Results: Mental well-being was significantly associated with various factors such as selected features of the natural environment, flood risk, sanitation, housing quality, sufficiency and durability. We further identified associations with population density, job satisfaction, and income generation while controlling for individual factors such as age, gender, and diseases. Conclusions: Factors determining mental well-being were related to the socio-physical environment and individual level characteristics. Given that mental well-being is associated with physiological well-being, our study may provide crucial information for developing better health care and disease prevention programmes in slums of Dhaka and other comparable settings

Pitfalls in machine learning‐based assessment of tumor‐infiltrating lymphocytes in breast cancer: a report of the international immuno‐oncology biomarker working group

The clinical significance of the tumor-immune interaction in breast cancer (BC) has been well established, and tumor-infiltrating lymphocytes (TILs) have emerged as a predictive and prognostic biomarker for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2 negative) breast cancer (TNBC) and HER2-positive breast cancer. How computational assessment of TILs can complement manual TIL-assessment in trial- and daily practices is currently debated and still unclear. Recent efforts to use machine learning (ML) for the automated evaluation of TILs show promising results. We review state-of-the-art approaches and identify pitfalls and challenges by studying the root cause of ML discordances in comparison to manual TILs quantification. We categorize our findings into four main topics; (i) technical slide issues, (ii) ML and image analysis aspects, (iii) data challenges, and (iv) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns, or design choices in the computational implementation. To aid the adoption of ML in TILs assessment, we provide an in-depth discussion of ML and image analysis including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial- and routine clinical management of patients with TNBC

Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer

The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state-of-the-art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in-depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple-negative breast cancer

Effects of jute fabric structures on the performance of jute-reinforced polypropylene composites

This paper presents a comparative study among different jute fabric structures in jute-reinforced polypropylene composites. Knitted and woven fabrics of different structures are produced, which are subsequently consolidated into composite materials using a heat-press method. The variation of mechanical properties and water absorption characteristics of different jute fabrics alone are evaluated and described. The effects of fabric structures on the performance of resulting composites are investigated. Composites having twill structure fabrics had the highest value of tensile strength (48 MPa), which was 134% higher than that of composites having plain structure fabrics. Water uptake of composites having rib structure fabrics was found to be 58% less than that of the composites having plain structure fabrics. </jats:p

Room temperature dry and lubricant wear behaviors of Al2O3f/SiCp/Al hybrid metal matrix composites

This study investigates the room temperature sliding wear behaviors of Al2O3f and Sic reinforced aluminum matrix hybrid composites under both the dry and lubricant conditions The effects of fiber orientation of Al2O3f and hybrid ratio of Al2O3f volume fraction to SiCp volume fraction on the wear behaviors are discussed in details The composite specimens with different fiber orientations and hybrid ratios were fabricated by squeeze casting method The tests were carried out using a pin-on-disk friction and wear tester by sliding the pin specimens at a constant speed of 0 36 m/s (570 rpm) against a steel counter disk. The scanning electron microscope (SEM) Images of the worn surfaces and debris were analyzed to understand the modes of wear The results of dry sliding tests showed that the F20P0 unhybrid composites with normal (N)-orientation of fibers had better wear behaviors than those with planer-random (PR)-orientation of fibers However, for hybrid composites, the wear behaviors were better for PR-orientation of fibers On the contrary, the lubricant wear behaviors of F20P0 unhybrid composites with PR-orientation of fibers were superior to those with N-orientation of fibers while the hybrid composites exhibited the reverse lubricant wear behaviors (C) 2009 Elsevier B V All rights reservedX113335sciescopu