Severe acute malnutrition morphological patterns in children under five

Household behavior and vulnerability to acute malnutrition in Kenya

Anticipating those most at-risk of being acutely malnourished significantly shapes decisions that pertain to resource allocation and intervention in times of food crises. Yet, the assumption that household behavior in times of crisis is homogeneous-that households share the same capacity to adapt to external shocks-ostensibly prevails. This assumption fails to explain why, in a given geographical context, some households remain more vulnerable to acute malnutrition relative to others, and why a given risk factor may have a differential effect across households? In an effort to explore how variation in household behavior influences vulnerability to malnutrition, we use a unique household dataset that spans 23 Kenyan counties from 2016 to 2020 to seed, calibrate, and validate an evidence-driven computational model. We use the model to conduct a series of counterfactual experiments on the relationship between household adaptive capacity and vulnerability to acute malnutrition. Our findings suggest that households are differently impacted by given risk factors, with the most vulnerable households typically being the least adaptive. These findings further underscore the salience of household adaptive capacity, in particular, that adaption is less effective for economic vis-à-vis climate shocks. By making explicit the link between patterns of household behavior and vulnerability in the short- to medium-term, we underscore the need for famine early warning to better account for variation in household-level behavior.

Mathematical model and genomics construction of developmental biology patterns using digital image technology

Biological pattern formation ensures that tissues and organs develop in the correct place and orientation within the body. A great deal has been learned about cell and tissue staining techniques, and today’s microscopes can capture digital images. A light microscope is an essential tool in biology and medicine. Analyzing the generated images will involve the creation of unique analytical techniques. Digital images of the material before and after deformation can be compared to assess how much strain and displacement the material responds. Furthermore, this article proposes Development Biology Patterns using Digital Image Technology (DBP-DIT) to cell image data in 2D, 3D, and time sequences. Engineered materials with high stiffness may now be characterized via digital image correlation. The proposed method of analyzing the mechanical characteristics of skin under various situations, such as one direction of stress and temperatures in the hundreds of degrees Celsius, is achievable using digital image correlation. A DBP-DIT approach to biological tissue modeling is based on digital image correlation (DIC) measurements to forecast the displacement field under unknown loading scenarios without presupposing a particular constitutive model form or owning knowledge of the material microstructure. A data-driven approach to modeling biological materials can be more successful than classical constitutive modeling if adequate data coverage and advice from partial physics constraints are available. The proposed procedures include a wide range of biological objectives, experimental designs, and laboratory preferences. The experimental results show that the proposed DBP-DIT achieves a high accuracy ratio of 99,3%, a sensitivity ratio of 98.7%, a specificity ratio of 98.6%, a probability index of 97.8%, a balanced classification ratio of 97.5%, and a low error rate of 38.6%.

Nutritional Status is Associated with Permanent Tooth Eruption in a Group of Brazilian School Children

The present study aimed to investigate the association between nutritional status with delayed tooth eruption (DTE). Oral examination was performed in schoolchildren (8-11 years old), and DTE was defined by absence of dental gingival emergence or when primary tooth was still present in the oral cavity after the expected time. BMI z-score of each child were collected and nutritional status was defined. Chi-square test and binary logistic regression adjusted by age and gender were performed. Odds ratio (OR) and 95% Confidence Interval (95% CI) were calculated. The established alpha was 5%. Among 353 included children, 247 were classified as eutrophic, 16 as underweight, 64 as overweight, and 26 as obese. Underweight was associated as a risk factor to DTE (P = .014; OR = 3.5; 95% CI = 1.3-9.8), and underweight girls had more chance to present DTE than eutrophic girls (P = .048; OR = 4.4; 95% CI = 1.1-17.2) in chi square test. In logistic regression, underweight was associated as a risk factor to DTE (OR = 4.21; CI 95% = 1.42-12.43; P = .009). Underweight children have a higher risk of DTE in permanents.