Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 3 appraisals
Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Is iron supplementation likely to impair linear growth and gut microbiota composition of Myanmar young children when complementary feeding is not optimized? A randomized controlled trial.
Iron deficiency, with or without anemia, remains a major public health concern among young children in Myanmar. While iron supplementation is widely recommended, its potential adverse effects on growth and gut microbiota warrant investigation, particularly in contexts of poor complementary feeding. To assess the effects of iron supplementation, with or without complementary feeding recommendations (CFRs), on micronutrient status, linear growth and gut microbiota composition among Myanmar children aged 12-23 months, a 24-week randomized controlled trial was conducted in Ayeyarwady Region of Myanmar. Fourteen clusters (villages/wards) were randomized to receive CFRs or not, and children within the clusters were randomized to receive daily aqueous iron or placebo, creating four arms: Placebo (n = 104), CFR (n = 112), Fe (n = 105) and CFRFe (n = 112) totalling 433 children. All intervention groups (CFR, Fe, CFRFe) improved haemoglobin and reduced anaemia risk compared with Placebo (AOR = 0.31 (0.15-0.62), 0.15 (0.07-0.32), 0.14 (0.07-0.31), respectively). CFR and CFRFe also improved zinc status. However, iron-alone (Fe) increased the stunting risk (AOR= 2.74 (1.04-7.23)), while CFRFe did not. No significant effects were observed on gut microbiota composition. Aqueous iron supplementation may impair linear growth when diets are not optimized; combining supplements with optimized complementary feeding may support healthier outcomes. This article is part of the theme issue 'Biological, biomedical and environmental drivers of stunting'.
17 May 2026
Read appraisal →The Proceedings of the Nutrition Society
Good practices and common pitfalls of machine learning in nutrition research
Machine learning is increasingly being utilised across various domains of nutrition research due to its ability to analyse complex data, especially as large datasets become more readily available. However, at times, this enthusiasm has led to the adoption of machine learning techniques prior to a proper understanding of how they should be applied, leading to non-robust study designs and results of questionable validity. To ensure that research standards do not suffer, key machine learning concepts must be understood by the research community. The aim of this review is to facilitate a better understanding of machine learning in research by outlining good practices and common pitfalls in each of the steps in the machine learning process. Key themes include the importance of generating high-quality data, employing robust validation techniques, quantifying the stability of results, accurately interpreting machine learning outputs, adequately describing methodologies, and ensuring transparency when reporting findings. Achieving this aim will facilitate the implementation of robust machine learning methodologies, which will reduce false findings and make research more reliable, as well as enable researchers to critically evaluate and better interpret the findings of others using machine learning in their work.
4 May 2026
Read appraisal →PloS one
IOTA Tangle-based traceability framework for wheat crop supply chain
Distributed ledger technology (DLT) has emerged as a transformative solution across industries, offering decentralization, transparency, and security. Among DLT architectures, IOTA's Tangle stands out as a scalable, feeless framework well-suited for supply chain applications, particularly in critical sectors like agriculture. This work leverages IOTA's Tangle to design a secure and traceable wheat supply chain system, addressing challenges such as opacity, fraud, and inefficiency. The proposed solution is an IOTA-based framework designed for enhancing supply chain traceability of wheat, offering trustworthy, transparent, secure and scalable services. Based on smart contracts, a transaction processing system was developed to enhance transparency and traceability and automate tasks. Furthermore, we used various technologies for increased visibility and operational efficiency throughout the supply chain, including IPFS (InterPlanetary File System) for storing extensive data such as stakeholder certifications, AI (Artificial Intelligence) for predicting crop yields, and Self-Sovereign Identity (SSI) for enhanced security. In addition, a government-provided mobile app helps silo managers and police officers verify transportation credentials and prevent contraband. The implementation of this system is expected to enhance supply chain resilience and transparency, particularly in Tunisia, thereby supporting the country's goal of food autonomy.
6 Apr 2026
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