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Risk analysis for outpatient experimental infection as a pathway for affordable RSV vaccine development.
Controlled human infection models (CHIMs) are an important tool for accelerating clinical development of vaccines. CHIM costs are driven by quarantine facilities but may be reduced by performing CHIM in the outpatient setting. Furthermore, outpatient CHIMs offer benefits beyond costs, such as a participant-friendly approach and increased real-world aspect. We analyze safety, logistic and ethical risks of respiratory syncytial virus (RSV) CHIM in the outpatient setting. A review of the literature identified outpatient CHIMs involving respiratory pathogens. RSV transmission risk was assessed using data from our inpatient and outpatient RSV CHIMs (EudraCT 020-004137-21). Fifty-nine outpatient CHIMs using RSV, Streptococcus pneumoniae, rhinovirus, and an ongoing Bordetella Pertussis outpatient CHIM were included. One transmission event was recorded. In an inpatient RSV CHIM, standard droplet and isolation measures were sufficient to limit RSV transmission and no symptomatic third-party transmission was measured in the first outpatient RSV CHIM. Logistic and ethical advantages support outpatient CHIM adoption. We propose a framework for outpatient RSV CHIM with risk mitigation strategies to enhance affordable vaccine development.
Transformers and large language models are efficient feature extractors for electronic health record studies
Abstract Background Free-text data is abundant in electronic health records, but challenges in accurate and scalable information extraction mean less specific clinical codes are often used instead. Methods We evaluated the efficacy of feature extraction using modern natural language processing methods (NLP) and large language models (LLMs) on 938,150 hospital antibiotic prescriptions from Oxfordshire, UK. Specifically, we investigated inferring the type(s) of infection from a free-text “indication” field, where clinicians state the reason for prescribing antibiotics. Clinical researchers labelled a subset of the 4000 most frequent unique indications (representing 692,310 prescriptions) into 11 categories describing the infection source or clinical syndrome. Various models were then trained to determine the binary presence/absence of these infection types and also any uncertainty expressed by clinicians. Results We show on separate internal (n = 2000 prescriptions) and external test datasets (n = 2000 prescriptions), a fine-tuned domain-specific Bio+Clinical BERT model performs best across the 11 categories (average F1 score 0.97 and 0.98 respectively) and outperforms traditional regular expression (F1 = 0.71 and 0.74) and n-grams/XGBoost (F1 = 0.86 and 0.84) models. A zero-shot OpenAI GPT4 model matches the performance of traditional NLP models without the need for labelled training data (F1 = 0.71 and 0.86) and a fine-tuned GPT3.5 model achieves similar performance to the fine-tuned BERT-based model (F1 = 0.95 and 0.97). Infection sources obtained from free-text indications reveal specific infection sources 31% more often than ICD-10 codes. Conclusions Modern transformer-based models have the potential to be used widely throughout medicine to extract information from structured free-text records, to facilitate better research and patient care.
The validity of test-negative design for assessment of typhoid conjugate vaccine protection: comparison of estimates by different study designs using data from a cluster-randomised controlled trial.
BACKGROUND: Typhoid fever remains a substantial public health challenge in low-income and middle-income countries. By 2023, typhoid conjugate vaccines (TCVs) had been introduced in six countries globally, with more than 50 million doses distributed. Now that TCVs are being deployed, there is a need for observational studies to assess vaccine effectiveness in the field. We aimed to evaluate the validity of different observational study designs in estimating vaccine protection. METHODS: We compared different observational and experimental study designs for assessing vaccine effectiveness by re-analysing data from the TyVAC Bangladesh trial, a participant-blinded and observer-blinded cluster-randomised controlled trial done in Mirpur, Dhaka, Bangladesh. 150 geographical clusters were randomly assigned (1:1) to receive either TCV or Japanese encephalitis vaccine. Eligible children aged 9 months to 15 years were offered a single dose of the vaccine randomly assigned to their cluster of residence, and baseline vaccination was done between April 15 and May 15, 2018. We compared estimates of vaccine effectiveness from the cluster-randomised controlled trial analysis-which assessed the risk of blood-culture-confirmed typhoid fever among recipients of TCV versus recipients of Japanese encephalitis vaccine-with estimates from cohort study and test-negative case-control study design (TND) analyses, which compared recipients of TCV with non-vaccinees in the 75 geographical clusters where TCV was administered. We further conducted negative-control exposure (NCE) and negative-control outcome (NCO) analyses as bias indicators. FINDINGS: 41 344 (67%) of 62 025 age-eligible children in the study area received the TCV or Japanese encephalitis vaccine during the baseline vaccination campaign. Among the 62 025 age-eligible children, 5582 blood-culture specimens were collected by passive surveillance, including 2546 (46%) specimens from the 75 TCV clusters. The estimated vaccine efficacy was 89% (95% CI 81-93) in the cluster-randomised controlled trial analysis, 79% (70-86) by the cohort design, 88% (79-93) by the TND when pan-negatives were used as test-negative controls, and 90% (75-96) by the TND when specimens positive for pathogens other than Salmonella enterica serotype Typhi were used as test-negative controls. Using NCE analysis, Japanese encephalitis vaccination was associated with an increased risk of typhoid fever compared with non-vaccinees in the 75 Japanese encephalitis clusters in the cohort design (incidence rate ratio 1·98 [95% CI 1·56-2·52]), but no significant association between Japanese encephalitis vaccination and typhoid fever was found with the TND. Similarly, an increased risk of non-typhoid infections was observed in the cohort NCO analyses when comparing vaccinees with non-vaccinees in both Japanese encephalitis vaccine clusters and TCV clusters, but not in the TND NCO analyses. INTERPRETATION: Our findings suggests that the TND provides reliable estimates of TCV effectiveness, whereas the cohort design can bias vaccine effectiveness estimates, possibly due to unmeasured confounding effects, such as health-care-seeking behaviours. NCE and NCO approaches are useful tools for identifying such biases. FUNDING: The Bill & Melinda Gates Foundation.
Silaproline-bearing nirmatrelvir derivatives are potent inhibitors of the SARS-CoV-2 main protease highlighting the value of silicon-derivatives in structure-activity-relationship studies.
Nirmatrelvir is a substrate-related inhibitor of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) main protease (Mpro) that is clinically used in combination with ritonavir to treat COVID-19. Derivatives of nirmatrelvir, modified at the substrate P2-equivalent position, have been developed to fine-tune inhibitor properties and are now in clinical use. We report the synthesis of nirmatrelvir derivatives with a (R)-4,4-dimethyl-4-silaproline (silaproline) group at the P2-equivalent position. Mass spectrometry (MS)-based assays demonstrate that silaproline-bearing nirmatrelvir derivatives efficiently inhibit isolated recombinant Mpro, albeit with reduced potency compared to nirmatrelvir. Investigations with SARS-CoV-2 infected VeroE6 cells reveal that the silaproline-bearing inhibitors with a CF3 group at the P4-equivalent position inhibit viral progression, implying that incorporating silicon atoms into Mpro inhibitors can yield in vivo active inhibitors with appropriate optimization. MS and crystallographic studies show that the nucleophilic active site cysteine residue of Mpro (Cys145) reacts with the nitrile group of the silaproline-bearing inhibitors. Substituting the electrophilic nitrile group for a non-activated terminal alkyne shifts the inhibition mode from reversible covalent inhibition to irreversible covalent inhibition. One of the two prochiral silaproline methyl groups occupies space in the S2 pocket that is unoccupied in Mpro:nirmatrelvir complex structures, highlighting the value of sila-derivatives in structure-activity-relationship (SAR) studies. The combined results highlight the potential of silicon-containing molecules for inhibition of Mpro and, by implication, other nucleophilic cysteine enzymes.
A systems biology approach to define SARS-CoV-2 correlates of protection.
Correlates of protection (CoPs) for SARS-CoV-2 have yet to be sufficiently defined. This study uses the machine learning platform, SIMON, to accurately predict the immunological parameters that reduced clinical pathology or viral load following SARS-CoV-2 challenge in a cohort of 90 non-human primates. We found that anti-SARS-CoV-2 spike antibody and neutralising antibody titres were the best predictors of clinical protection and low viral load in the lung. Since antibodies to SARS-CoV-2 spike showed the greatest association with clinical protection and reduced viral load, we next used SIMON to investigate the immunological features that predict high antibody titres. It was found that a pre-immunisation response to seasonal beta-HCoVs and a high frequency of peripheral intermediate and non-classical monocytes predicted low SARS-CoV-2 spike IgG titres. In contrast, an elevated T cell response as measured by IFNγ ELISpot predicted high IgG titres. Additional predictors of clinical protection and low SARS-CoV-2 burden included a high abundance of peripheral T cells. In contrast, increased numbers of intermediate monocytes predicted clinical pathology and high viral burden in the throat. We also conclude that an immunisation strategy that minimises pathology post-challenge did not necessarily mediate viral control. This would be an important finding to take forward into the development of future vaccines aimed at limiting the transmission of SARS-CoV-2. These results contribute to SARS-CoV-2 CoP definition and shed light on the factors influencing the success of SARS-CoV-2 vaccination.
RenAIssance: A Survey Into AI Text-to-Image Generation in the Era of Large Model.
Text-to-image generation (TTI) refers to the usage of models that could process text input and generate high fidelity images based on text descriptions. Text-to-image generation using neural networks could be traced back to the emergence of Generative Adversial Network (GAN), followed by the autoregressive Transformer. Diffusion models are one prominent type of generative model used for the generation of images through the systematic introduction of noises with repeating steps. As an effect of the impressive results of diffusion models on image synthesis, it has been cemented as the major image decoder used by text-to-image models and brought text-to-image generation to the forefront of machine-learning (ML) research. In the era of large models, scaling up model size and the integration with large language models have further improved the performance of TTI models, resulting the generation result nearly indistinguishable from real-world images, revolutionizing the way we retrieval images. Our explorative study has incentivised us to think that there are further ways of scaling text-to-image models with the combination of innovative model architectures and prediction enhancement techniques. We have divided the work of this survey into five main sections wherein we detail the frameworks of major literature in order to delve into the different types of text-to-image generation methods. Following this we provide a detailed comparison and critique of these methods and offer possible pathways of improvement for future work. In the future work, we argue that TTI development could yield impressive productivity improvements for creation, particularly in the context of the AIGC era, and could be extended to more complex tasks such as video generation and 3D generation.
Long-term trial of protection provided by adenovirus-vectored vaccine expressing the PPRV H protein.
A recombinant, replication-defective, adenovirus-vectored vaccine expressing the H surface glycoprotein of peste des petits ruminants virus (PPRV) has previously been shown to protect goats from challenge with wild-type PPRV at up to 4 months post vaccination. Here, we present the results of a longer-term trial of the protection provided by such a vaccine, challenging animals at 6, 9, 12 and 15 months post vaccination. Vaccinated animals developed high levels of anti-PPRV H protein antibodies, which were virus-neutralising, and the level of these antibodies was maintained for the duration of the trial. The vaccinated animals were largely protected against overt clinical disease from the challenge virus. Although viral genome was intermittently detected in blood samples, nasal and/or ocular swabs of vaccinated goats post challenge, viral RNA levels were significantly lower compared to unvaccinated control animals and vaccinated goats did not appear to excrete live virus. This protection, like the antibody response, was maintained at the same level for at least 15 months after vaccination. In addition, we showed that animals that have been vaccinated with the adenovirus-based vaccine can be revaccinated with the same vaccine after 12 months and showed an increased anti-PPRV antibody response after this boost vaccination. Such vaccines, which provide a DIVA capability, would therefore be suitable for use when the current live attenuated PPRV vaccines are withdrawn at the end of the ongoing global PPR eradication campaign.
Systemic prime mucosal boost significantly increases protective efficacy of bivalent RSV influenza viral vectored vaccine.
Although licensed vaccines against influenza virus have been successful in reducing pathogen-mediated disease, they have been less effective at preventing viral infection of the airways and current seasonal updates to influenza vaccines do not always successfully accommodate viral drift. Most licensed influenza and recently licensed RSV vaccines are administered via the intramuscular route. Alternative immunisation strategies, such as intranasal vaccinations, and "prime-pull" regimens, may deliver a more sterilising form of protection against respiratory viruses. A bivalent ChAdOx1-based vaccine (ChAdOx1-NP + M1-RSVF) encoding conserved nucleoprotein and matrix 1 proteins from influenza A virus and a modified pre-fusion stabilised RSV A F protein, was designed, developed and tested in preclinical animal models. The aim was to induce broad, cross-protective tissue-resident T cells against heterotypic influenza viruses and neutralising antibodies against RSV in the respiratory mucosa and systemically. When administered via an intramuscular prime-intranasal boost (IM-IN) regimen in mice, superior protection was generated against challenge with either RSV A, Influenza A H3N2 or H1N1. These results support further clinical development of a pan influenza & RSV vaccine administered in a prime-pull regimen.
Call for a fairer approach to authorship in publishing biomedical research.
In this Perspective article, we call for a fairer approach to authorship practice in collaborative biomedical research to promote equity and inclusiveness. Current practice does not adequately recognise all contributors involved in different stages of the work and may exacerbate preexisting inequalities. Here, we discuss some key features of contemporary collaborative research practice that complicate authorship decisions. These include the project size, complexity of multidisciplinary team involvement and researchers having varying degrees of expertise and experience. We conclude by making some suggestions to address these concerns.