Automated extraction of genes associated with antibiotic resistance from the biomedical literature.

The detection of bacterial antibiotic resistance phenotypes is important when carrying out clinical decisions for patient treatment. Conventional phenotypic testing involves culturing bacteria which requires a significant amount of time and work. Whole-genome sequencing is emerging as a fast alternative to resistance prediction, by considering the presence/absence of certain genes. A lot of research has focused on determining which bacterial genes cause antibiotic resistance and efforts are being made to consolidate these facts in knowledge bases (KBs). KBs are usually manually curated by domain experts to be of the highest quality. However, this limits the pace at which new facts are added. Automated relation extraction of gene-antibiotic resistance relations from the biomedical literature is one solution that can simplify the curation process. This paper reports on the development of a text mining pipeline that takes in English biomedical abstracts and outputs genes that are predicted to cause resistance to antibiotics. To test the generalisability of this pipeline it was then applied to predict genes associated with Helicobacter pylori antibiotic resistance, that are not present in common antibiotic resistance KBs or publications studying H. pylori. These genes would be candidates for further lab-based antibiotic research and inclusion in these KBs. For relation extraction, state-of-the-art deep learning models were used. These models were trained on a newly developed silver corpus which was generated by distant supervision of abstracts using the facts obtained from KBs. The top performing model was superior to a co-occurrence model, achieving a recall of 95%, a precision of 60% and F1-score of 74% on a manually annotated holdout dataset. To our knowledge, this project was the first attempt at developing a complete text mining pipeline that incorporates deep learning models to extract gene-antibiotic resistance relations from the literature. Additional related data can be found at https://github.com/AndreBrincat/Gene-Antibiotic-Resistance-Relation-Extraction.

Adverse reactions to Pfizer-BioNTech vaccination of healthcare workers at Malta's state hospital.

BACKGROUND: The long-term control of COVID-19 depends on an effective global vaccination strategy. Protecting healthcare workers (HCWs) from serious infection is critical. Malta, a European country, initiated the vaccination roll-out using Pfizer-BioNTech COVID-19 vaccine targeting HCWs. This study determined vaccination adverse effects (AEs) in this cohort. METHOD: An online survey was disseminated to all HCWs via work email (29/3/21 to 9/4/21) to gather AEs regarding pain, redness and swelling at injection site, fever, chills, fatigue, muscle/joint pains, headache, vomiting and diarrhoea severity following each dose (Likert scale). Descriptive, comparative and multiple binary regression analyses were performed. RESULTS: A response of 30.30% (n = 1480) was achieved with the commonest AEs being pain at injection site (88.92% CI 95%: 87.21-90.42), mostly mild (51%) and moderate (43%). Fatigue was reported by 72.97% (CI 95%: 70.65-75.17), 42% were mild and 41% were moderate. Females reported significantly (P ≤ .05, respectively) more pain (OR: 1.90), redness (OR: 2.49), swelling at injection site (OR: 1.33), fever (OR: 1.74), chills (OR: 2.32), fatigue (OR: 2.43), muscle (OR: 1.54) and joint pains (OR: 2.01), headache (OR: 2.07) and vomiting (OR: 3.43) when adjusted for age and HCW role. Localised AEs were reported following both vaccine doses unlike systemic AEs that were mostly reported after second doses. CONCLUSION: Vaccination benefits outweigh the minor AEs experienced, with females exhibiting a higher susceptibility. The general low vaccination AEs observed within the HCW cohort is encouraging and should help in allaying vaccine hesitancy among the population.

The fastest national COVID vaccination in Europe - Malta's strategies.

BACKGROUND: COVID-19 vaccines reduce morbidity and mortality, but mass vaccination faces multiple challenges leading to different vaccination rates in different countries. Malta, a small European country, has achieved a very rapid vaccination rollout. This paper presents a narrative review of Malta's vaccination strategy and its impact on the country's COVID-19 situation. METHODS: Data was obtained through a literature review of Maltese newspapers and from Malta's COVID-19 government dashboard. A comprehensive summary of vaccination operations was provided by Malta's COVID-19 vaccination team. RESULTS: Malta comprised part of the European Commission joint procurement and obtained the maximum vaccines that were eligible from all manufacturers. Four tier priority population groups were set up, with both vaccine doses (where applicable) allocated and stored for each individual. Multiple hubs were set up to simultaneously administer first and eventually second doses accordingly. To date (August 9, 2021) 398,128 of the population are fully vaccinated and 405,073 received the first dose, with both morbidity and mortality declining progressively as vaccination coverage progressed. CONCLUSION: Malta has successfully implemented a COVID-19 strategy that rapidly covered a substantial proportion of the population over a short period of time, with herd immunity reached by end of May 2021. Low population vaccination hesitancy and high vaccine doses availability were two major factors in this success.