Reliability of scored patient generated subjective global assessment for nutritional status among HIV infected adults in TASO, Kampala

Objective: Establish the reliability of the scored Patient Generated-Subjective Global Assessment (PG-SGA) in determining nutritional status among Antiretroviral Therapy (ART) naive HIV-infected adults. Methods: A descriptive, cross sectional study among outpatient medical clinics, in The AIDS Support Organization (TASO), Mulago Centre, Kampala, Uganda. The study group (n=217) consisted of male (n=60) and female (n=157) HIV patients (18-67 years). Purposive sampling was used. Anthropometry (weight, height, BMI), nutritional history (body weight, dietary intake, gastrointestinal symptoms, functional capacity and infections), and clinical status were assessed. Sensitivity and specificity of PG-SGA were determined using Receiver Operating Characteristic (ROC) curve. Data collection was done from April-May 2008. Results: Only 12% of the subjects were underweight and over half (58.2%) had normal weight. The PG-SGA had low sensitivity (69.2%) and specificity (57.1%) at categorizing the risk for malnutrition indicated by BMI< 18.5. Conclusion: There was a high prevalence of malnutrition among the study group. Overall, this study indicated the PG-SGA could not adequately discriminate between underweight and normal patients. The tool was not reliable enough for determining nutritional status in this population

Reliability of scored patient generated subjective global assessment for nutritional status among HIV infected adults in TASO, Kampala

OBJECTIVE: Establish the reliability of the scored Patient Generated-Subjective Global Assessment (PG-SGA) in determining nutritional status among Antiretroviral Therapy (ART) naive HIV-infected adults. METHODS: A descriptive, cross sectional study among outpatient medical clinics, in The AIDS Support Organization (TASO), Mulago Centre, Kampala, Uganda. The study group (n=217) consisted of male (n=60) and female (n=157) HIV patients (18–67 years). Purposive sampling was used. Anthropometry (weight, height, BMI), nutritional history (body weight, dietary intake, gastrointestinal symptoms, functional capacity and infections), and clinical status were assessed. Sensitivity and specificity of PG-SGA were determined using Receiver Operating Characteristic (ROC) curve. Data collection was done from April–May 2008. RESULTS: Only 12% of the subjects were underweight and over half (58.2%) had normal weight. The PG-SGA had low sensitivity (69.2%) and specificity (57.1%) at categorizing the risk for malnutrition indicated by BMI< 18.5. CONCLUSION: There was a high prevalence of malnutrition among the study group. Overall, this study indicated the PG-SGA could not adequately discriminate between underweight and normal patients. The tool was not reliable enough for determining nutritional status in this population

Addressing neglected tropical diseases in Africa: A gender perspective

Abstract This article delves into the interplay of neglected tropical diseases (NTDs) and Sustainable Development Goals (SDGs) within Africa, spotlighting gender disparities in NTD programs. NTDs, impacting marginalized communities, impose considerable physical, mental, and social burdens. The article underscores NTDs as equity markers for SDGs, spotlighting gender‐based imbalances in disease susceptibility, treatment accessibility, and health‐seeking tendencies. Gender's influence on NTD risks is elucidated, emphasizing the heightened susceptibility of women due to socioeconomic constraints, cultural dynamics, and gender norms. The article also highlights the absence of gender considerations in NTD programs, advocating for gender‐integrated strategies, enhanced data collection, and collaborative partnerships to rectify these inequities. By embracing a gender‐equity approach, the article underscores the necessity of gender‐balanced NTD efforts for comprehensive health, sustainable development, and gender parity, demanding cohesive actions across sectors

Using machine learning for image-based analysis of sweetpotato root sensory attributes

The sweetpotato breeding process involves assessing different phenotypic traits, such as the sensory attributes, to decide which varieties to progress to the next stage during the breeding cycle. Sensory attributes like appearance, taste, colour and mealiness are important for consumer acceptability and adoption of new varieties. Therefore, measuring these sensory attributes is critical to inform the selection of varieties during breeding. Current methods using a trained human panel enable screening of different sweetpotato sensory attributes. Despite this, such methods are costly and time-consuming, leading to low throughput, which remains the biggest challenge for breeders. In this paper, we describe an approach to apply machine learning techniques with image-based analysis to predict flesh-colour and mealiness sweetpotato sensory attributes. The developed models can be used as highthroughput methods to augment existing approaches for the evaluation of flesh-colour and mealiness for different sweetpotato varieties. The work involved capturing images of boiled sweetpotato cross-sections using the DigiEye imaging system, data pre-processing for background elimination and feature extraction to develop machine learning models to predict the flesh-colour and mealiness sensory attributes of different sweetpotato varieties. For flesh-colour the trained Linear Regression and Random Forest Regression models attained 2 values of 0.92 and 0.87, respectively, against the ground truth values given by a human sensory panel. In contrast, the Random Forest Regressor and Gradient Boosting model attained 2 values of 0.85 and 0.80, respectively, for the prediction of mealiness. The performance of the models matched the desirable 2 threshold of 0.80 for acceptable comparability to the human sensory panel showing that this approach can be used for the prediction of these attributes with high accuracy. The machine learning models were deployed and tested by the sweetpotato breeding team at the International Potato Center in Uganda. This solution can automate and increase throughput for analysing flesh-colour and mealiness sweetpotato sensory attributes. Using machine learning tools for analysis can inform and quicken the selection of promising varieties that can be progressed for participatory evaluation during breeding cycles and potentially lead to increased chances of adoption of the varieties by consumers