Minocycline and the risk of acute psychiatric events in adolescence: A self-controlled case series

BACKGROUND: Minocycline has neurological anti-inflammatory properties and has been hypothesised to have antipsychotic effects. AIM: The aim of this study was to investigate, using routinely collected United Kingdom primary health care data, whether adolescent men and women are more or less likely to receive an urgent psychiatric referral during treatment for acne with minocycline compared with periods of non-treatment. METHOD: A self-controlled case series using United Kingdom Clinical Practice Research Datalink to calculate the incidence rate ratio of urgent psychiatric referrals for individuals, comparing periods during which minocycline was prescribed with unexposed periods, adjusted for age. RESULTS: We found 167 individuals who were at the time exposed to minocycline for a mean of 99 days and who received an urgent psychiatric referral. There was no difference in psychiatric referral risk during periods of exposure compared with periods of non-exposure: incidence rate ratio first 6 weeks of exposure 1.96, 95% confidence interval 0.82-4.71, p=0.132; incidence rate ratio remaining exposure period=1.97, 95% confidence interval 0.86-4.47, p=0.107. CONCLUSIONS: We found no evidence in support of a protective effect of minocycline against severe psychiatric symptoms in adolescence

BiOnt: Deep Learning using Multiple Biomedical Ontologies for Relation Extraction

Successful biomedical relation extraction can provide evidence to researchers and clinicians about possible unknown associations between biomedical entities, advancing the current knowledge we have about those entities and their inherent mechanisms. Most biomedical relation extraction systems do not resort to external sources of knowledge, such as domain-specific ontologies. However, using deep learning methods, along with biomedical ontologies, has been recently shown to effectively advance the biomedical relation extraction field. To perform relation extraction, our deep learning system, BiOnt, employs four types of biomedical ontologies, namely, the Gene Ontology, the Human Phenotype Ontology, the Human Disease Ontology, and the Chemical Entities of Biological Interest, regarding gene-products, phenotypes, diseases, and chemical compounds, respectively. We tested our system with three data sets that represent three different types of relations of biomedical entities. BiOnt achieved, in F-score, an improvement of 4.93 percentage points for drug-drug interactions (DDI corpus), 4.99 percentage points for phenotype-gene relations (PGR corpus), and 2.21 percentage points for chemical-induced disease relations (BC5CDR corpus), relatively to the state-of-the-art. The code supporting this system is available at https://github.com/lasigeBioTM/BiOnt.Comment: ECIR 202

Evaluation of pooling operations in convolutional architectures for drug-drug interaction extraction

Background: Deep Neural Networks (DNN), in particular, Convolutional Neural Networks (CNN), has recently achieved state-of-art results for the task of Drug-Drug Interaction (DDI) extraction. Most CNN architectures incorporate a pooling layer to reduce the dimensionality of the convolution layer output, preserving relevant features and removing irrelevant details. All the previous CNN based systems for DDI extraction used max-pooling layers. Results: In this paper, we evaluate the performance of various pooling methods (in particular max-pooling, average-pooling and attentive pooling), as well as their combination, for the task of DDI extraction. Our experiments show that max-pooling exhibits a higher performance in F1-score (64.56%) than attentive pooling (59.92%) and than average-pooling (58.35%). Conclusions: Max-pooling outperforms the others alternatives because is the only one which is invariant to the special pad tokens that are appending to the shorter sentences known as padding. Actually, the combination of max-pooling and attentive pooling does not improve the performance as compared with the single max-pooling technique.Publication of this article was supported by the Research Program of the Ministry of Economy and Competitiveness - Government of Spain, (DeepEMR project TIN2017-87548-C2-1-R) and the TEAM project (Erasmus Mundus Action 2-Strand 2 Programme) funded by the European Commission

Extraction of pharmacokinetic evidence of drug-drug interactions from the literature

Drug-drug interaction (DDI) is a major cause of morbidity and mortality and a subject of intense scientific interest. Biomedical literature mining can aid DDI research by extracting evidence for large numbers of potential interactions from published literature and clinical databases. Though DDI is investigated in domains ranging in scale from intracellular biochemistry to human populations, literature mining has not been used to extract specific types of experimental evidence, which are reported differently for distinct experimental goals. We focus on pharmacokinetic evidence for DDI, essential for identifying causal mechanisms of putative interactions and as input for further pharmacological and pharmacoepidemiology investigations. We used manually curated corpora of PubMed abstracts and annotated sentences to evaluate the efficacy of literature mining on two tasks: first, identifying PubMed abstracts containing pharmacokinetic evidence of DDIs; second, extracting sentences containing such evidence from abstracts. We implemented a text mining pipeline and evaluated it using several linear classifiers and a variety of feature transforms. The most important textual features in the abstract and sentence classification tasks were analyzed. We also investigated the performance benefits of using features derived from PubMed metadata fields, various publicly available named entity recognizers, and pharmacokinetic dictionaries. Several classifiers performed very well in distinguishing relevant and irrelevant abstracts (reaching F10.93, MCC0.74, iAUC0.99) and sentences (F10.76, MCC0.65, iAUC0.83). We found that word bigram features were important for achieving optimal classifier performance and that features derived from Medical Subject Headings (MeSH) terms significantly improved abstract classification. We also found that some drug-related named entity recognition tools and dictionaries led to slight but significant improvements, especially in classification of evidence sentences. Based on our thorough analysis of classifiers and feature transforms and the high classification performance achieved, we demonstrate that literature mining can aid DDI discovery by supporting automatic extraction of specific types of experimental evidence.National Institutes of Health, National Library of Medicine Program, grant 01LM011945-01 "BLR: Evidence-based Drug-Interaction Discovery: In-Vivo, In-Vitro and Clinical," a grant from the Indiana University Collaborative Research Program 2013, "Drug-Drug Interaction Prediction from Large-scale Mining of Literature and Patient Records," as well as a grant from the joint program between the Fundação Luso-Americana para o Desenvolvimento (Portugal) and National Science Foundation (USA), 2012-2014, "Network Mining For Gene Regulation And Biochemical Signaling.

Clonal chromosomal mosaicism and loss of chromosome Y in elderly men increase vulnerability for SARS-CoV-2

The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, COVID-19) had an estimated overall case fatality ratio of 1.38% (pre-vaccination), being 53% higher in males and increasing exponentially with age. Among 9578 individuals diagnosed with COVID-19 in the SCOURGE study, we found 133 cases (1.42%) with detectable clonal mosaicism for chromosome alterations (mCA) and 226 males (5.08%) with acquired loss of chromosome Y (LOY). Individuals with clonal mosaic events (mCA and/or LOY) showed a 54% increase in the risk of COVID-19 lethality. LOY is associated with transcriptomic biomarkers of immune dysfunction, pro-coagulation activity and cardiovascular risk. Interferon-induced genes involved in the initial immune response to SARS-CoV-2 are also down-regulated in LOY. Thus, mCA and LOY underlie at least part of the sex-biased severity and mortality of COVID-19 in aging patients. Given its potential therapeutic and prognostic relevance, evaluation of clonal mosaicism should be implemented as biomarker of COVID-19 severity in elderly people. Among 9578 individuals diagnosed with COVID-19 in the SCOURGE study, individuals with clonal mosaic events (clonal mosaicism for chromosome alterations and/or loss of chromosome Y) showed an increased risk of COVID-19 lethality

Examining the potential preventative effects of minocycline prescribed for acne on the incidence of severe mental illnesses:A historical cohort study

BACKGROUND: Animal studies suggest that the antibiotic and microglial activation inhibitor, minocycline, is likely to have a protective effect against the emergence of psychosis but evidence from human studies is lacking. The aim of this study is to examine the effects of exposure to minocycline during adolescence on the later incidence of severe mental illness (SMI). METHODS: A historical cohort study using electronic primary care data was conducted to assess the association between exposure to minocycline during adolescence and incidence of SMI. The Incidence Rate Ratio (IRR) was measured using Poisson regression adjusted for age, gender, time of exposure, socioeconomic deprivation status, calendar year and co-medications. RESULTS: Early minocycline prescription (n=13,248) did not affect the incidence of SMI compared with non-prescription of minocycline (n=14,393), regardless of gender or whether or not the data were filtered according to a minimum exposure period (minimum period: IRR 0.96; 95% CI 0.68–1.36; p=0.821; no minimum period: IRR 1.08; 95% CI 0.83–1.42; p=0.566). CONCLUSION: Exposure to minocycline for acne treatment during adolescence appears to have no effect on the incidence of SMI

Minocycline and the risk of acute psychiatric events in adolescence:A self-controlled case series

BACKGROUND: Minocycline has neurological anti-inflammatory properties and has been hypothesised to have antipsychotic effects. AIM: The aim of this study was to investigate, using routinely collected United Kingdom primary health care data, whether adolescent men and women are more or less likely to receive an urgent psychiatric referral during treatment for acne with minocycline compared with periods of non-treatment. METHOD: A self-controlled case series using United Kingdom Clinical Practice Research Datalink to calculate the incidence rate ratio of urgent psychiatric referrals for individuals, comparing periods during which minocycline was prescribed with unexposed periods, adjusted for age. RESULTS: We found 167 individuals who were at the time exposed to minocycline for a mean of 99 days and who received an urgent psychiatric referral. There was no difference in psychiatric referral risk during periods of exposure compared with periods of non-exposure: incidence rate ratio first 6 weeks of exposure 1.96, 95% confidence interval 0.82-4.71, p=0.132; incidence rate ratio remaining exposure period=1.97, 95% confidence interval 0.86-4.47, p=0.107. CONCLUSIONS: We found no evidence in support of a protective effect of minocycline against severe psychiatric symptoms in adolescence