Improving gut health and growth in early life: a protocol for an individually randomised, two-arm, open-label, controlled trial of a synbiotic in infants in Kaffrine District, Senegal.

INTRODUCTION: Infants exposed to enteropathogens through poor sanitation and hygiene can develop a subclinical disorder of the gut called environmental enteric dysfunction (EED), characterised by abnormal intestinal histology and permeability. EED can contribute to stunting through reduced digestion and absorption of nutrients, increased susceptibility to infections, increased systemic inflammation and inhibition of growth hormones. EED can be apparent by age 12 weeks, highlighting the need for early intervention. Modulating the early life gut microbiota using synbiotics may improve resistance against colonisation of the gut by enteropathogens, reduce EED and improve linear growth. METHODS AND ANALYSIS: An individually randomised, two-arm, open-label, controlled trial will be conducted in Kaffrine District, Senegal. Infants will be recruited at birth and randomised to either receive a synbiotic containing two Bifidobacterium strains and one Lactobacillus strain, or no intervention, during the first 6 months of life. The impact of the intervention will be evaluated primarily by comparing length-for-age z-score at 12 months of age in infants in the intervention and control arms of the trial. Secondary outcome variables include biomarkers of intestinal inflammation, intestinal integrity and permeability, gut microbiota profiles, presence of enteropathogens, systemic inflammation, growth hormones, epigenetic status and episodes of illness during follow-up to age 24 months. DISCUSSION: This trial will contribute to the evidence base on the use of a synbiotic to improve linear growth by preventing or ameliorating EED in a low-resource setting. TRIAL REGISTRATION NUMBER: PACTR202102689928613.

Egocentric Image Captioning for Privacy-Preserved Passive Dietary Intake Monitoring.

Camera-based passive dietary intake monitoring is able to continuously capture the eating episodes of a subject, recording rich visual information, such as the type and volume of food being consumed, as well as the eating behaviors of the subject. However, there currently is no method that is able to incorporate these visual clues and provide a comprehensive context of dietary intake from passive recording (e.g., is the subject sharing food with others, what food the subject is eating, and how much food is left in the bowl). On the other hand, privacy is a major concern while egocentric wearable cameras are used for capturing. In this article, we propose a privacy-preserved secure solution (i.e., egocentric image captioning) for dietary assessment with passive monitoring, which unifies food recognition, volume estimation, and scene understanding. By converting images into rich text descriptions, nutritionists can assess individual dietary intake based on the captions instead of the original images, reducing the risk of privacy leakage from images. To this end, an egocentric dietary image captioning dataset has been built, which consists of in-the-wild images captured by head-worn and chest-worn cameras in field studies in Ghana. A novel transformer-based architecture is designed to caption egocentric dietary images. Comprehensive experiments have been conducted to evaluate the effectiveness and to justify the design of the proposed architecture for egocentric dietary image captioning. To the best of our knowledge, this is the first work that applies image captioning for dietary intake assessment in real-life settings.

Evaluation of Acceptability, Functionality, and Validity of a Passive Image-Based Dietary Intake Assessment Method in Adults and Children of Ghanaian and Kenyan Origin Living in London, UK.

BACKGROUND: Accurate estimation of dietary intake is challenging. However, whilst some progress has been made in high-income countries, low- and middle-income countries (LMICs) remain behind, contributing to critical nutritional data gaps. This study aimed to validate an objective, passive image-based dietary intake assessment method against weighed food records in London, UK, for onward deployment to LMICs. METHODS: Wearable camera devices were used to capture food intake on eating occasions in 18 adults and 17 children of Ghanaian and Kenyan origin living in London. Participants were provided pre-weighed meals of Ghanaian and Kenyan cuisine and camera devices to automatically capture images of the eating occasions. Food images were assessed for portion size, energy, nutrient intake, and the relative validity of the method compared to the weighed food records. RESULTS: The Pearson and Intraclass correlation coefficients of estimates of intakes of food, energy, and 19 nutrients ranged from 0.60 to 0.95 and 0.67 to 0.90, respectively. Bland-Altman analysis showed good agreement between the image-based method and the weighed food record. Under-estimation of dietary intake by the image-based method ranged from 4 to 23%. CONCLUSIONS: Passive food image capture and analysis provides an objective assessment of dietary intake comparable to weighed food records.

Food/Non-Food Classification of Real-Life Egocentric Images in Low- and Middle-Income Countries Based on Image Tagging Features.

Malnutrition, including both undernutrition and obesity, is a significant problem in low- and middle-income countries (LMICs). In order to study malnutrition and develop effective intervention strategies, it is crucial to evaluate nutritional status in LMICs at the individual, household, and community levels. In a multinational research project supported by the Bill & Melinda Gates Foundation, we have been using a wearable technology to conduct objective dietary assessment in sub-Saharan Africa. Our assessment includes multiple diet-related activities in urban and rural families, including food sources (e.g., shopping, harvesting, and gathering), preservation/storage, preparation, cooking, and consumption (e.g., portion size and nutrition analysis). Our wearable device ("eButton" worn on the chest) acquires real-life images automatically during wake hours at preset time intervals. The recorded images, in amounts of tens of thousands per day, are post-processed to obtain the information of interest. Although we expect future Artificial Intelligence (AI) technology to extract the information automatically, at present we utilize AI to separate the acquired images into two binary classes: images with (Class 1) and without (Class 0) edible items. As a result, researchers need only to study Class-1 images, reducing their workload significantly. In this paper, we present a composite machine learning method to perform this classification, meeting the specific challenges of high complexity and diversity in the real-world LMIC data. Our method consists of a deep neural network (DNN) and a shallow learning network (SLN) connected by a novel probabilistic network interface layer. After presenting the details of our method, an image dataset acquired from Ghana is utilized to train and evaluate the machine learning system. Our comparative experiment indicates that the new composite method performs better than the conventional deep learning method assessed by integrated measures of sensitivity, specificity, and burden index, as indicated by the Receiver Operating Characteristic (ROC) curve.

Biofortified and fortified maize consumption reduces prevalence of low milk retinol, but does not increase vitamin A stores of breastfeeding Zambian infants with adequate reserves: a randomized controlled trial.

BACKGROUND: Replacement of conventional staples with biofortified or industrially fortified staples in household diets may increase maternal breast milk retinol content and vitamin A intakes from complementary foods, improving infant total body stores (TBS) of vitamin A. OBJECTIVES: To determine whether biofortified or industrially fortified maize consumption by Zambian women and their breastfeeding infants could improve milk retinol concentration and infant TBS. METHODS: We randomly assigned 255 lactating women and their 9-mo-old infants to a 90-d intervention providing 0 µg retinol equivalents (RE)/d as conventional maize or ∼315 µg RE/d to mothers and ∼55 µg RE/d to infants as provitamin A carotenoid-biofortified maize or retinyl palmitate-fortified maize. Outcomes were TBS, measured by retinol isotope dilution in infants (primary), and breast milk retinol, measured by HPLC in women (secondary). RESULTS: The intervention groups were comparable at baseline. Loss to follow-up was 10% (n = 230 mother-infant pairs). Women consumed 92% of the intended 287 g/d and infants consumed 82% of the intended 50 g/d maize. The baseline geometric mean (GM) milk retinol concentration was 1.57 µmol/L (95% CI: 1.45, 1.69 µmol/L), and 24% of women had milk retinol <1.05 µmol/L. While mean milk retinol did not change in the biofortified arm (ß: 0.11; 95% CI: -0.02, 0.24), the intervention reduced low milk retinol (RR: 0.42; 95% CI: 0.21, 0.85). Fortified maize increased mean milk retinol (ß: 0.17; 95% CI: 0.04, 0.30) and reduced the prevalence of low milk retinol (RR: 0.46; 95% CI: 0.25, 0.82). The baseline GM TBS was 178 µmol (95% CI: 166, 191 µmol). This increased by 24 µmol (± 136) over the 90-d intervention period, irrespective of treatment group. CONCLUSIONS: Both biofortified and fortified maize consumption improved milk retinol concentration. This did not translate into greater infant TBS, most likely due to adequate TBS at baseline. This trial was registered at clinicaltrials.gov as NCT02804490.

Development and Validation of an Objective, Passive Dietary Assessment Method for Estimating Food and Nutrient Intake in Households in Low- and Middle-Income Countries: A Study Protocol.

Malnutrition is a major concern in low- and middle-income countries (LMIC), but the full extent of nutritional deficiencies remains unknown largely due to lack of accurate assessment methods. This study seeks to develop and validate an objective, passive method of estimating food and nutrient intake in households in Ghana and Uganda. Household members (including under-5s and adolescents) are assigned a wearable camera device to capture images of their food intake during waking hours. Using custom software, images captured are then used to estimate an individual's food and nutrient (i.e., protein, fat, carbohydrate, energy, and micronutrients) intake. Passive food image capture and assessment provides an objective measure of food and nutrient intake in real time, minimizing some of the limitations associated with self-reported dietary intake methods. Its use in LMIC could potentially increase the understanding of a population's nutritional status, and the contribution of household food intake to the malnutrition burden. This project is registered at clinicaltrials.gov (NCT03723460).

Emergence of HBV resistance to lamivudine (3TC) in HIV/HBV co-infected patients in The Gambia, West Africa.

BACKGROUND: Lamivudine (3TC) is a potent inhibitor of both Hepatitis B virus (HBV) and Human Immunodeficiency Virus (HIV) replication and is part of first-line highly active antiretroviral therapy (HAART) in the Gambia. Unfortunately, the effectiveness of 3TC against HBV is limited by the emergence of resistant strains. AIM: The aim of this retrospective study was to characterise 3TC-resistant mutations in HBV from co-infected patients receiving HAART, by generating HBV polymerase sequence data and viral loads from HBV genotype E infected patients, both at initiation and during a course of 3TC therapy. METHOD: Samples from 21 HBV chronic carriers co-infected with HIV-1 (n = 18), HIV-2 (n = 2) and HIV-dual (n = 1) receiving HAART for a period of 6-52 months were analysed for the emergence of 3TC-resistance mutations. FINDINGS: Sixteen out of 21 HBV/HIV co-infected patients responded well to HAART treatment maintaining suppression of HBV viraemia to low (≤ 104 copies/mL) (n = 5) or undetectable levels (< 260 copies/ml) (n = 11). Out of the 5 non-responders, 3 had developed 3TC-resistant HBV strains showing mutations in the YMDD motif at position 204 of the RT domain of the HBV polymerase. One patient showed the M204V+ L180M+ V173L+ triple mutation associated with a vaccine escape phenotype, which could be of public health concern in a country with a national HBV vaccination programme. All except one patient was infected with HBV genotype E. CONCLUSIONS: Our findings confirm the risk of 3TC mutations in HAART patients following monotherapy. This is a novel study on 3TC resistance in HBV genotype E patients and encourage the use of tenofovir (in association with 3TC), which has not shown unequivocally documented HBV resistance to date, as part of first-line therapy in HIV/HBV co-infected patients in West Africa.HBV- hepatitis B infection; HIV- human immunodeficiency virus; HAART- antiretroviral therapy.