Oxford open immunology最新文献

{"title":"Patients know best: DESTINIES Patient Panel appraisal of a novel, risk-stratified approach to COVID-19 disease surveillance in the adult immunosuppressed.","authors":"Meredith Leston, Ingrid de Groot, José M Ordóñez-Mena, F D Richard Hobbs, Mark Joy, Lennard Y W Lee, Benjamin W Teh, Iain McInnes, Simon de Lusignan","doi":"10.1093/oxfimm/iqag015","DOIUrl":"https://doi.org/10.1093/oxfimm/iqag015","url":null,"abstract":"<p><p>The immunosuppressed are poorly represented in disease surveillance and pharmacovigilance dataflows, proving especially consequential during the COVID-19 pandemic. This mixed methods study recruited a panel of immunosuppressed patients (DESTINIES Patient Panel) to quality assure a new digital phenotype for COVID-19 risk in the adult immunosuppressed (DESTINIES phenotype) prior to its implementation in real-world surveillance. Applying their lived experiences, the DESTINIES Patient Panel met to critique the strategy and structure of the provisional phenotype before voting on its adoption in UK disease surveillance. They did so anonymously. This convening was hosted and recorded on Zoom; Google Forms collected panellist feedback and their final ratifying vote. Feedback was inductively content analysed. 33 patients that adhered to the DESTINIES phenotype definition of immunosuppression submitted their feedback and final vote. Despite obtaining expressions of interest, no person with drug-managed HIV participated; this was a significant limitation given their unique positioning in the phenotype. However, 88% of panellists approved the DESTINIES phenotype for adoption in the UK, 91% agreed it represented a step forward for COVID-19 disease surveillance, and 91% agreed to see it trialled in other surveillance pathways (RSV, pneumonia etc.). Panellist feedback was catalogued by themes of 'Abandonment', 'Accessibility', 'Accuracy', and 'Omission'. The patient voice is often absent in the dataflows purporting to be in their interest. This is an oversight; certain limitations captured in this exercise were unlikely to have surfaced without patient engagement. The DESTINIES phenotype is made more robust by the collective criticism of expert clinicians and patients alike.</p>","PeriodicalId":74384,"journal":{"name":"Oxford open immunology","volume":"7 1","pages":"iqag015"},"PeriodicalIF":0.0,"publicationDate":"2026-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13651694/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149167698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An open-label feasibility study of remdesivir in long COVID: results from the ERASE LC trial.","authors":"Mark A Faghy, Jade Chynoweth, Anton Barnett, Lindsay Skipper, Victoria Allgar, Helen Neilens, Kayle-Anne Sands, Amber Lord, Helen Hambly, Paigan J Aspinall, Chris Rollinson, W David Strain, Rebecca Owen, Callum Thomas, Marie J Grigg, Sascha H Kranen, Kinan Mokbel, Karen M Knapp, Hairil Abdul Razak, Ruth Ashton, Ian Maidment, Tom Bewick","doi":"10.1093/oxfimm/iqag014","DOIUrl":"10.1093/oxfimm/iqag014","url":null,"abstract":"<p><p>Treatment of acute COVID-19 with anti-viral and immunomodulator medications demonstrates a reduced risk of long-term outcomes. To date, remdesivir, an intravenous (IV) antiviral that prevents RNA transcription, has not been evaluated in people with Long COVID (LC). This study assessed the feasibility of a five‑day IV remdesivir intervention for individuals with LC. Seventy-three participants aged <b>≥</b> 18 years with a confirmed LC diagnosis were recruited across two sites in the United Kingdom to receive a five‑day course of IV remdesivir. Primary feasibility outcomes included recruitment, treatment completion, acceptability, and safety. Exploratory patient-reported [e.g. Fatigue Assessment Scale (FAS)] and clinical outcomes [e.g. 6-minute walk test (6MWT)] were also collected. Of 106 individuals screened, 73 were enrolled and 71 (97%) completed the 5-day IV remdesivir regimen. Follow-up assessments were completed by 70 participants (96%), with high completion rates observed across clinical assessments, patient-reported outcomes and symptom tracking. No severe adverse reactions were reported. Improvements were also observed in several exploratory patient-reported and clinical outcomes. We demonstrated the feasibility and acceptability of administering IV remdesivir in people with LC. Analysis of secondary outcomes suggests therapeutic promise, but rigorous evaluation in large-scale randomised controlled trials is essential to determine the true efficacy and clinical utility of intravenous remdesivir in this population. <i>Trial registration</i>: ClinicalTrials.gov NCT05911906.</p>","PeriodicalId":74384,"journal":{"name":"Oxford open immunology","volume":"7 1","pages":"iqag014"},"PeriodicalIF":0.0,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13672820/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149217434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Structural homology and binding ability comparisons of epitope pair candidates for molecular mimicry triggering of type 1 diabetes mellitus.","authors":"Ryan Gardner, Joshua Wilkins, Sejal Mistry, Ramkiran Gouripeddi, Julio C Facelli","doi":"10.1093/oxfimm/iqag013","DOIUrl":"10.1093/oxfimm/iqag013","url":null,"abstract":"<p><p>Molecular mimicry is the process by which foreign peptides contain epitopes similar to self-peptides, potentially inducing autoimmune responses. Identifying potential molecular mimics and studying their properties is key to understanding the onset of autoimmune diseases such as type 1 diabetes mellitus (T1DM). Previous work identified pairs of infectious epitopes (E_INF) and T1DM epitopes (E_T1D) that demonstrated sequence homology; however, structural homology and binding ability to HLA molecules were not considered. Correlating sequence homology with structural properties and binding ability is important for translational investigation of potential molecular mimics. This work compares sequence homology with structural homology and binding ability by calculating the structures and binding ability of the epitope pairs identified in previous work. For each pair of E_INF and E_T1D we calculated the root mean square deviations (RMSD) between their predicted isolated structures and their percent (%) overlap when bound to the corresponding HLA molecule using the Boltz-2. Of the 53 epitope pairs considered here, only 6 were found to not exhibit any matching (defined as less than 3 residues overlap). For the other epitopes, the RMSD ranges from 0.13 Å to 1.39 Å with an average of 0.56 Å. The calculated % overlaps between the infectious and T1DM epitopes when bound to the corresponding HLA molecules ranges from 0% to 93.3%. There are 3 pairs that show nearly identical HLA footprints, 15 very similar binding mode, 19 similar binding mode, 11 moderately similar, and only 5 showing a totally different footprint. Most of the E_INF/E_T1D pairs selected by sequence homology show similar structural and binding ability to HLA molecules, indications that may lead to disease onset due to molecular mimicry. These findings suggest that searching for epitope pairs using sequence homology, a much less computationally demanding approach, leads to plausible candidates for molecular mimicry that should be considered for further experimental validation. However, full binding ability calculations at scale may be necessary to advance the <i>in-silico</i> molecular mimicry predictions, which may be useful to select the most promising candidates for experimental studies.</p>","PeriodicalId":74384,"journal":{"name":"Oxford open immunology","volume":"7 1","pages":"iqag013"},"PeriodicalIF":0.0,"publicationDate":"2026-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13602411/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149042314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A call to predoctoral researchers: introducing the Robin Weiss Science Writing Prize-celebrating creativity in science.","authors":"Daniel M Altmann","doi":"10.1093/oxfimm/iqag012","DOIUrl":"10.1093/oxfimm/iqag012","url":null,"abstract":"","PeriodicalId":74384,"journal":{"name":"Oxford open immunology","volume":"7 1","pages":"iqag012"},"PeriodicalIF":0.0,"publicationDate":"2026-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13506046/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148820725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Structural homology of HHV-6B epitopes as candidates for molecular mimicry triggers of the onset type one diabetes mellitus.","authors":"McKay Jones, Julio C Facelli","doi":"10.1093/oxfimm/iqag011","DOIUrl":"10.1093/oxfimm/iqag011","url":null,"abstract":"<p><strong>Objectives: </strong>To study whether HHV-6B can act as a molecular mimic to trigger onset of Type One Diabetes Mellitus (T1DM) by assessing structural and binding similarities between HHV-6B derived epitopes and T1DM autoantigen derived epitopes.</p><p><strong>Methods: </strong>Epitope peptide structures and their interactions with T1DM autoantigens were modeled using Boltz-2, a state-of-the-art artificial intelligence protein structure prediction method.</p><p><strong>Results: </strong>Several HHV-6B epitopes studied here demonstrated high structural alignment with T1DM antigen epitopes, but even when structural homology was lacking, in multiple cases the HHV-6B and T1DM epitopes fit into the same binding region of the corresponding HLA molecules, suggesting a plausible structural basis for T cell cross-reactivity. These findings support the hypothesis that HHV-6B may act as a molecular mimic contributing to autoimmune responses in individuals genetically susceptible to T1DM.</p><p><strong>Conclusion: </strong>This study demonstrates that structural modeling is a useful tool for identifying potential mimicry candidates that sequence-based methods may not find, underscoring the importance of integrating structure-based modeling, including docking, into molecular mimicry prediction pipelines for identifying potential epitopes for <i>in vitro</i> studies.</p>","PeriodicalId":74384,"journal":{"name":"Oxford open immunology","volume":"7 1","pages":"iqag011"},"PeriodicalIF":0.0,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13350986/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148427137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Algorithm dependence of patient phenotypes in Long COVID: a patient-led, multi-method clustering of 6031 patients using 162 self-reported symptoms.","authors":"Tessa D Green, Chris McWilliams, Leonardo de Figueiredo, Letícia Soares, Beth Pollack, Alison K Cohen, Tan Zhi-Xuan, Tess Falor, Hannah E Davis","doi":"10.1093/oxfimm/iqag010","DOIUrl":"10.1093/oxfimm/iqag010","url":null,"abstract":"<p><strong>Objectives: </strong>Long COVID, characterized by symptoms that remain or emerge in the months after acute COVID-19 infection, is a multisystemic condition with highly variable patient presentations. Phenotyping studies have reported divergent symptom clusters, increasingly used to design trials and interpret biomarker data. However, robustness of these clusters across analytic methods remains uncertain.</p><p><strong>Methods: </strong>We analyzed data from 6 031 adults with ≥ 90 days of illness from a patient-led international survey. Participants reported presence/absence of 162 symptoms, post-exertional malaise severity and demographics. We applied three unsupervised machine learning approaches to the same symptom matrix, evaluating the resulting clusterings for concordance, robustness to subsampling, and relationship to symptom burden, post-exertional malaise severity, age and gender.</p><p><strong>Results: </strong>Each method produced clinically plausible symptom clusters, but concordance across methods was low. All three approaches identified a high-symptom-burden group enriched for post-exertional malaise severity, and lower-symptom-burden groups with older mean age and a lower proportion of women. Symptom count consistently correlated with higher post-exertional malaise severity and a greater proportion of women. Manifold analysis revealed that the overall symptom space was largely continuous, lacking clear cluster boundaries.</p><p><strong>Conclusions: </strong>The strong dependence of patient clusters on algorithm choice suggests that single-method Long COVID phenotyping may produce incomplete or unstable subgroup definitions. Clustering methods may impose artificial boundaries on a smoothly varying symptom landscape, especially in studies capturing fewer symptoms. Phenotyping efforts should assess clustering robustness and avoid overinterpreting single-method results. Our multi-method analysis highlights the importance of considering the full breadth of patient symptoms when evaluating treatments.</p>","PeriodicalId":74384,"journal":{"name":"Oxford open immunology","volume":"7 1","pages":"iqag010"},"PeriodicalIF":0.0,"publicationDate":"2026-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13284999/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148310626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new patient-led approach to building research infrastructure and evidence generation.","authors":"Oonagh Cousins, Harry Leeming, David Putrino, Janna Gordon","doi":"10.1093/oxfimm/iqag009","DOIUrl":"10.1093/oxfimm/iqag009","url":null,"abstract":"<p><p>Over recent decades, patient and public involvement (PPI) has become a more established element of health research policy, although its implementation is often criticized for tokenism and for underrepresenting marginalized groups. In fields such as complex chronic illness (CCI), where formal research activity has historically been limited, conventional PPI frameworks have had little scope for meaningful application. Within this context, a new wave of patient-led initiatives has emerged that moves beyond participation in existing systems toward the creation of independent infrastructures for knowledge generation, extending the principle of \"nothing about us, without us.\" This commentary examines Visible, a patient-founded health technology platform that combines daily energy-management tools with research infrastructure for CCIs. This infrastructure enables in-house data analyses and external collaborations, including app-based data studies, investigator-led research, and integration within clinical trials. We explore the advantages of this dual-purpose model, including greater inclusivity, sustained engagement, and richer longitudinal data. We also describe how embedding research functions within tools that patients find directly useful allows evidence generation and patient support to be mutually reinforcing.</p>","PeriodicalId":74384,"journal":{"name":"Oxford open immunology","volume":"7 1","pages":"iqag009"},"PeriodicalIF":0.0,"publicationDate":"2026-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13242947/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148207142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to: 9 papers to add missing data availability statements.","authors":"","doi":"10.1093/oxfimm/iqag005","DOIUrl":"10.1093/oxfimm/iqag005","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.1093/oxfimm/iqaa003.][This corrects the article DOI: 10.1093/oxfimm/iqab012.][This corrects the article DOI: 10.1093/oxfimm/iqaa006.][This corrects the article DOI: 10.1093/oxfimm/iqad005.][This corrects the article DOI: 10.1093/oxfimm/iqad006.][This corrects the article DOI: 10.1093/oxfimm/iqae010.][This corrects the article DOI: 10.1093/oxfimm/iqae009.][This corrects the article DOI: 10.1093/oxfimm/iqaf005.][This corrects the article DOI: 10.1093/oxfimm/iqag001.].</p>","PeriodicalId":74384,"journal":{"name":"Oxford open immunology","volume":"7 1","pages":"iqag005"},"PeriodicalIF":0.0,"publicationDate":"2026-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13161758/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147935937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing protein immunogenicity prediction via uncertainty weighted deep ensemble.","authors":"Alif Bin Abdul Qayyum, Amir Hossein Rahmati, Xiaoning Qian, Byung-Jun Yoon","doi":"10.1093/oxfimm/iqag008","DOIUrl":"10.1093/oxfimm/iqag008","url":null,"abstract":"<p><p>Recent advances in machine learning (ML) have significantly improved data-driven protein immunogenicity prediction. Although these ML models perform well on natural proteins, their practical utility is limited by the absence of reliable uncertainty estimates for their predictions. The objective of this work is to develop a method that incorporates predictive uncertainty to achieve uncertainty-aware protein immunogenicity prediction. We introduce <i>DUNE</i> (<i>D</i>eep <i>UN</i>certainty-weighted <i>E</i>nsemble), a novel method designed to integrate predictive uncertainty into ML models. DUNE is built to enhance prediction performance by incorporating uncertainty estimates from probabilistic member models in an ensemble into the final prediction. Experimental results demonstrate that incorporating uncertainty estimates through DUNE significantly enhances protein immunogenicity predictive performance. Our proposed DUNE method outperforms existing deterministic single learners and various other deterministic and probabilistic ensemble-based classification strategies. DUNE provides a more reliable and robust framework for protein immunogenicity prediction. By achieving uncertainty-aware prediction, DUNE can improve the trustworthiness and practical utility of ML models in therapeutic antigen design.</p>","PeriodicalId":74384,"journal":{"name":"Oxford open immunology","volume":"7 1","pages":"iqag008"},"PeriodicalIF":0.0,"publicationDate":"2026-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13174275/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147966006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to: Scientific communication and vaccine hesitation: an analysis of the editorial line of a great Brazilian newspaper.","authors":"","doi":"10.1093/oxfimm/iqag006","DOIUrl":"10.1093/oxfimm/iqag006","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.1093/oxfimm/iqaf012.].</p>","PeriodicalId":74384,"journal":{"name":"Oxford open immunology","volume":"7 1","pages":"iqag006"},"PeriodicalIF":0.0,"publicationDate":"2026-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13019307/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147576931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}