Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 4 appraisals
Transactions of the Royal Society of Tropical Medicine and Hygiene
Atomic clarity: how structural biology is shaping blood-stage malaria vaccines
Developing an effective malaria vaccine is challenging because Plasmodium parasites use complex strategies to invade host cells. The blood stage, where parasites replicate inside red blood cells, causes disease symptoms and relies on large multiprotein surface complexes that are difficult to target. Understanding their structure is essential for rational vaccine design. Cryogenic electron microscopy (cryoEM) enables atomic-level analysis of dynamic and membrane-associated complexes without crystallization, revealing key processes such as protein trafficking, nutrient uptake, and host cell remodeling. Recent advances in artificial intelligence and deep learning have improved cryoEM data analysis and structural modeling. Together with genomic data, this integrated approach is accelerating progress toward broadly protective malaria vaccines.
9 July 2026
Read appraisal →The Veterinary record
FEC Check: Development of a decision support tool to aid interpretation of gastrointestinal nematode faecal egg counts in sheep
BACKGROUND: Gastrointestinal nematode infections are ubiquitous in grazing livestock worldwide impacting animal health and production. Faecal egg count (FEC) is an accessible diagnostic test that can guide the need for treatment. However, interpretation of FECs can be challenging. METHODS: A prototype decision support tool (DST) was developed using a 'traffic light'-style gradient of potential clinical impact on sheep FEC results. Focus groups were conducted with farmers, livestock advisors and veterinary clinicians to examine the barriers to FEC uptake and provide feedback on the prototype tool. RESULTS: Barriers to uptake for FEC testing included timeliness of reporting, lack of perceived need and knowledge gaps. The DST was well received at all focus groups, with simplicity and ease of use identified as key principles to drive uptake. At the 12 months post-launch, the DST had 1916 users. LIMITATIONS: Engagement with stakeholders with less familiarity with FECs may improve usability for a wider audience. CONCLUSION: The final DST developed here represents a practical resource to improve the interpretation of FEC results reported by farmers and other stakeholders. The initial uptake observed within the first year since launch is promising for the wider adoption of evidence-based parasite management.
10 May 2026
Read appraisal →ChemMedChem
A Novel Triose Phosphate Isomerase Inhibitor With Dual Trypanosomicidal Activity was Identified Using Artificial Intelligence-Based Virtual Screening
Chagas disease and leishmaniasis are neglected protozoan diseases recognized by the World Health Organization as major public health problems. These diseases affect millions of people worldwide, yet effective treatments remain unavailable. Triosephosphate isomerase (TIM), a glycolytic enzyme that exhibits high catalytic efficiency for the isomerization of glyceraldehyde-3-phosphate and dihydroxyacetone-phosphate exclusively in its dimeric form, was subjected to virtual screening. Using a deep neural network for structure-based drug design that predicts binding affinity between small molecules and proteins of known structure, 12.5 million commercially available compounds were screened. From this, 82 compounds were selected for in vitro evaluation. Six compounds inhibited TIM from Trypanosoma cruzi, three of which exhibited anti-T. cruzi activity. Eight compounds demonstrated activity against the parasites T. cruzi and Leishmania infantum. Two compounds showed similar potency against both parasites: 3-(1-acetyl-5-(4-bromophenyl)-4,5-dihydro-1H-pyrazol-3-yl)-4-hydroxy-6-methyl-2H-pyran-2-one (IC50 = 16 ± 3 μM) and 3-[(4-bromophenyl)sulfanyl]-1-(3-nitrophenyl)propan-1-one (IC50 = 12 ± 1 μM). These compounds exhibit favorable selectivity and toxicological profiles, as well as in vivo activity, indicating their potential for future drug development.
2 May 2026
Read appraisal →PloS one
upsML: A high-accuracy machine learning classifier for predicting Plasmodium falciparum var gene upstream groups
Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1), encoded by the hypervariable var gene family, is central to malaria pathogenesis, influencing both disease severity and immune evasion. Classifying var genes into upstream groups (upsA, upsB, upsC, upsE) is important for understanding parasite biology and clinical outcomes, but remains challenging, especially with partial sequences, such as the DBLα tag or RNA-Seq assemblies. We developed upsML, a machine-learning-based classifier trained on 2,530 curated var genes, to accurately assign upstream groups based on sequence features from different partial gene regions. We compared seven methods, including support vector machines, random forests, XGBoost, and HMMER models. Several models in upsML achieve accuracies of 83% for DBLα-tag sequences and 92% for full-length PfEMP1 sequences, thereby significantly outperforming existing tools. Additionally, we developed a model to distinguish internal from subtelomeric var genes, which we applied to a global collection of P. falciparum genomes, revealing a higher frequency of internal var genes in Asia. upsML is available at https://github.com/sii-scRNA-Seq/upsML, providing a robust and efficient resource for large-scale var gene analysis. It can classify var genes from 20 genomes in under one second.
17 Apr 2026
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