Elizabeth Phillips

Globally, approximately 100,000 patients receive hematopoietic stem cell transplants (HCTs) annually, with increasing use for curative intent for many diseases (eg, leukemia, lymphoma).1 There is increasing recognition that antibiotic utilization may have a unique impact on this patient population by affecting both infection- and transplant-related outcomes.2,3 From an infection perspective, utilizing appropriate antibiotics is critically important: among patients with HCTs, there is a growing incidence of multidrug-resistant (MDR) infections, and potentially treatable infection is the primary cause of death in up to 20% of patients following allogenic HCT.3 Simultaneously, a growing body of literature suggests that decreased gastrointestinal microbiome diversity (which is associated with broad-spectrum antibiotic utilization) may worsen therapy response, acute graft-versus-host disease outcomes, and overall survival among patients with HCTs.2 Social and structural determinants of health have also proven to be important determinants of health outcomes among patients with cancer generally.

A label of betalactam (BL) allergy is estimated in around 10% of the population in their medical records. Second-line choices carry significant negative consequences, including reduced efficacy, effectiveness, and safety. This study aimed to develop a new highly specific score constructed by selecting variables assisted by artificial intelligence to identify low-risk BL-allergic patients.
In this study, derivation and validation of the BL-predictor score were performed on a retrospective cohort of 2207 patients who underwent penicillin allergy testing at Málaga University Hospital (Spain). The development of the BL-predictor encompassed expert drafting and a two-step variable selection process consisting of univariate analysis and variable filtering, followed by stepwise logistic regression with resampling. To assess the efficiency, a multicentric retrospective external validation was performed in 4261 patients from six populations: Salamanca and Madrid, Spain; Nashville, United States of America; Verona, Italy; Paris, France; and Copenhagen, Denmark.
The definitive questionnaire consisted of eight items and risk points were computed from the logistic regression model as follows: +1 for reactions after first dose or in less than 1 h (ITEM-1), +2 for anaphylaxis (ITEM-2); +1 for previous reaction with the culprit (ITEM-3); -1 for resolution in > 24 h (ITEM-4); +2 for spontaneous resolution (ITEM-5); -2 for unknown symptoms (ITEM-6); -2 for reaction occurred > 5 years (ITEM-7), and -1 for another reported drug allergy (ITEM-8). After establishing a threshold of ≤ 0 points to classify individuals with low risk, internal validation showed a specificity of 86% and a negative predictive value (NPV) of 83%. Overall multicenter external validation showed a specificity of 93%, which implies a 25% increase in specificity compared to the previously published BL decision tool.
This score would simplify diagnostic procedures in low-risk patients, enabling rapid delabeling, potentially in non-specialty settings, and reducing diagnostic costs and the negative consequences associated with incorrect antibiotic allergy labels.

Mammarenaviruses are classified into Old World and New World viruses (Old World arenaviruses [OWAs] and New World arenaviruses [NWAs]). Characterization of antigens recognized by human T cells is essential for identifying immunodominant targets, informing vaccine design, and performing immunological assessments. Here, we select the Lassa virus (LASV) as a prototype OWA to map the human CD4 T cell epitope repertoire. We then calculate conservation in different LASV lineages and other representative OWAs and NWAs and define conserved T cell epitope regions (CTERs) using Lassa as the OWA prototype and Junin as the NWA prototype. We show that these CTERs are able to broadly cross-recognize other OWA or NWA sequences. We validate our findings in humans immunized with an experimental glycoprotein complex (GPC) LASV vaccine or infected with lymphocytic choriomeningitis mammarenavirus (LCMV), as well as in the mouse model immunized with a stabilized GPC vaccine candidate. Our results on mammarenavirus-specific T cell immunity contribute to guiding the development of next-generation mammarenavirus vaccines.

Background
Inaccurate multiple antibiotic allergy labels to first-line agents (penicillins, cephalosporins, and sulfonamides) restrict optimal therapy and often require sequential evaluations across multiple visits, delaying delabeling and increasing loss to follow-up.

Objective
To conduct a comparative effectiveness study of the Multiple Antibiotic Allergy Evaluation Strategy (MAAES), comparing its effectiveness, safety, and patient-reported outcomes (PRO) to traditional sequential evaluation (non-MAAES).

Methods
This retrospective cohort study (2014–2024) compared same-day multi-antibiotic testing (MAAES) with non-MAAES in patients with ≥2 low-risk labels. Multivariable regression adjusted for baseline differences and testing era. Inverse probability of treatment weighting (IPTW) and a restricted post-2020 subcohort analysis addressed selection bias and temporal shifts. Outcomes included delabeling efficacy, time to complete delabeling, loss to follow-up, relabeling, adverse events (AEs), and PRO.

Results
Baseline differences were present between the MAAES and non-MAAES groups, including penicillin anaphylaxis (1.4%vs.6.6%, p=0.002), angioedema (12.4%vs.19.1%, p=0.049), maculopapular rash (13.1%vs.24.3%, p=0.002), and unknown sulfonamide history (9.5%vs.3.7%, p=0.03). MAAES removed 97.4% of labels vs 79.6% for non-MAAES (adjusted odds ratio(aOR) 9.41, 95%CI 5.22-16.95, p<0.001). MAAES achieved faster complete delabeling (log-rank χ2=42.4, p<0.001), with 84.9%vs.21.1% delabeled at the initial visit (restricted mean survival time difference: 2.1 months). Results remained robust in post-2020 and IPTW models. Loss to follow-up (1.3%vs.17.2%; aOR 0.06, 95%CI 0.03–0.13; p<0.001) and AEs (<1%vs.2.8%; aOR 0.26, 95%CI 0.09–0.78; p=0.02) were significantly lower with MAAES. Favorable PRO was sustained, with no significant differences between groups.

Conclusions
A consolidated multi-drug evaluation strategy substantially improves effectiveness of first-line antibiotic allergy delabeling while maintaining excellent safety profiles and PRO.

Paramyxoviruses comprise a diverse family of viruses that threaten global human health through direct infection and zoonotic transmission. Understanding adaptive immune responses to these viruses is critical for characterizing host-pathogen interactions and evaluating vaccine performance. Here, we systematically map human CD4+ T cell epitopes across Nipah and measles viruses, two prototypic members of the Paramyxoviridae family. We identify broad epitope repertoires, including 186 Nipah and 288 measles epitopes recognized in multiple donors. Epitopes are characterized for HLA binding and inferred restrictions, and broader HLA binding correlates with immunodominance. We observe overlapping T cell targets between viruses, with N and F proteins immunodominant in both and L additionally dominant in Nipah. We define conserved T cell epitope regions (CTERs) in Nipah virus that encompass 17% of the proteome, capture over 50% of T cell responses, show high conservation across different Henipaviruses, and elicit broadly cross-reactivity, supporting broad population coverage.
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•Systematic mapping reveals broad CD4+ T cell epitopes in Nipah and measles•HLA binding promiscuity correlates with epitope immunodominance•Conserved T cell epitope regions (CTERs) capture over 50% of Nipah T cell responses•CTERs elicit cross-reactive T cells across diverse Paramyxoviruses
Tarke et al. systematically map CD4+ T cell epitopes in measles and Nipah viruses, revealing broad and overlapping immune targets. They define conserved T cell epitope regions (CTERs) that drive cross-reactive T cell responses across Paramyxoviruses, providing a framework for vaccine strategies that enhance population coverage and pandemic preparedness.

Large language models (LLMs) have emerged as transformative technologies, revolutionising natural language understanding and generation across various domains, including medicine. In this study, we investigated the capabilities, limitations, and generalisability of Generative Pre-trained Transformer (GPT) models in analysing unstructured patient notes from large healthcare datasets to identify immune-related adverse events (irAEs) associated with the use of immune checkpoint inhibitor (ICI) therapy.
We evaluated the performance of GPT-3.5, GPT-4, and GPT-4o models on manually annotated datasets of patients receiving ICI therapy, sampled from two electronic health record (EHR) systems and seven clinical trials. A zero-shot prompt was designed to exhaustively identify irAEs at both the patient level (main analysis) and the note level (secondary analysis). The LLM-based system followed a multi-label classification approach to identify any combination of irAEs associated with individual patients or clinical notes. System evaluation was conducted for each available irAE as well as for broader categories of irAEs classified at the organ level.
Our analysis included 442 patients across three institutions. The most common irAEs manually identified in the patient datasets included pneumonitis (N = 64), colitis (N = 56), rash (N = 32), and hepatitis (N = 28). The GPT models demonstrated generalisable abilities in identifying irAEs across EHRs and clinical trial reports. Overall, the models achieved relatively high sensitivity and specificity but only moderate positive predictive values, reflecting a potential bias towards overpredicting irAE outcomes. GPT-4o achieved the highest F1 and micro-averaged F1 scores for both patient-level and note-level evaluations. Highest performance was observed in the haematological (F1 range = 1.0–1.0), gastrointestinal (F1 range = 0.81–0.85), and musculoskeletal and rheumatologic (F1 range = 0.67–1.0) irAE categories. Error analysis uncovered substantial limitations of GPT models in handling textual causation, where adverse events should not only be accurately identified in clinical text but also causally linked to immune checkpoint inhibitors.
This study demonstrated that GPT models can automate the detection of immune related adverse events in varied healthcare datasets, reducing the burden on physicians and other healthcare professionals by limiting the need for manual review. This capability will accelerate the generation of safety insights across large healthcare datasets and facilitate the characterisation of patient-level drivers of toxicities, thus enhancing safety monitoring and ultimately improving patient care.
National Institutes of Health, Roche, National Health and Medical Research Council of Australia, Stevens-Johnson Syndrome Foundation, Angela Anderson Research Fund, Larry L Hillblom Foundation and UCSF Research Allocation Program.

Immune checkpoint inhibitors are used in a wide range of cancers, offering durable responses for a substantial subset of patients. However, immune-related adverse events, the most clinically consequential checkpoint inhibitor–associated adverse reactions, pose a key challenge in practice, affecting virtually any organ system, resulting in treatment interruption, morbidity, or mortality. Patient education, early recognition, and effective management are essential to limit complications and maintain continuity of immunotherapy. Achieving this requires well-informed multidisciplinary teams who can identify, evaluate, and manage immune-related adverse events promptly. This review summarizes the most clinically significant immune-related adverse events and highlights the key principles of multidisciplinary diagnosis and management most relevant to the practicing allergist-immunologist to optimize patient outcomes.