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Many individuals with early psychosis experience impairments in social and occupational function. Identification of modifiable predictors of function such as cognitive performance has the potential to inform effective treatments. Our aim was to estimate the strength of the relationship between psychosocial function in early psychosis and different domains of cognitive and social cognitive performance. We conducted a systematic review and meta-analysis of peer-reviewed, cross-sectional, and longitudinal studies examining cognitive predictors of psychosocial function. Literature searches were conducted in PsycINFO, PubMed, and reference lists of relevant articles to identify studies for inclusion. Of the 2565 identified, 46 studies comprising 3767 participants met inclusion criteria. Separate meta-analyses were conducted for 9 cognitive domains. Pearson correlation values between cognitive variables and function were extracted. All cognitive domains were related to psychosocial function both cross-sectionally and longitudinally. Importantly, these associations remained significant even after the effects of symptom severity, duration of untreated psychosis, and length of illness were accounted for. Overall, general cognitive ability and social cognition were most strongly associated with both concurrent and long-term function. Associations demonstrated medium effect sizes. These findings suggest that treatments targeting cognitive deficits, in particular those focusing on social cognition, are likely to be important for improving functional outcomes in early psychosis. [Abstract copyright: © The Author(s) 2021. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center.All rights reserved. For permissions, please email: journals.permissions@oup.com.]

Prematurity is the leading cause of neonatal morbidity and mortality worldwide. Premature infants often require extended hospital stays, with increased risk of developing infection compared with term infants. A picture is emerging of wide-ranging deleterious consequences resulting from innate immune system activation in the newborn infant. Those who survive infection have been exposed to a stimulus that can impose long-lasting alterations into later life. In this review, we discuss sepsis-driven alterations in integrated neuroendocrine and metabolic pathways and highlight current knowledge gaps in respect of neonatal sepsis. We review established biomarkers for sepsis and extend the discussion to examine emerging findings from human and animal models of neonatal sepsis that propose novel biomarkers for early identification of sepsis. Future research in this area is required to establish a greater understanding of the distinct neonatal signature of early and late-stage infection, to improve diagnosis, curtail inappropriate antibiotic use, and promote precision medicine through a biomarker-guided empirical and adjunctive treatment approach for neonatal sepsis. There is an unmet clinical need to decrease sepsis-induced morbidity in neonates, to limit and prevent adverse consequences in later life and decrease mortality.

Background Evidence is accruing to suggest that microbiota-gut-brain signalling plays a regulatory role in cardiorespiratory physiology. Chronic intermittent hypoxia (CIH), modelling human sleep apnoea, affects gut microbiota composition and elicits cardiorespiratory morbidity. We investigated if treatment with prebiotics ameliorates cardiorespiratory dysfunction in CIH-exposed rats. Methods Adult male rats were exposed to CIH (96 cycles/day, 6.0% O2 at nadir) for 14 consecutive days with and without prebiotic supplementation (fructo- and galacto-oligosaccharides) beginning two weeks prior to gas exposures. Findings CIH increased apnoea index and caused hypertension. CIH exposure had modest effects on the gut microbiota, decreasing the relative abundance of Lactobacilli species, but had no effect on microbial functional characteristics. Faecal short-chain fatty acid (SCFA) concentrations, plasma and brainstem pro-inflammatory cytokine concentrations and brainstem neurochemistry were unaffected by exposure to CIH. Prebiotic administration modulated gut microbiota composition and diversity, altering gut-metabolic (GMMs) and gut-brain (GBMs) modules and increased faecal acetic and propionic acid concentrations, but did not prevent adverse CIH-induced cardiorespiratory phenotypes. Interpretation CIH-induced cardiorespiratory dysfunction is not dependant upon changes in microbial functional characteristics and decreased faecal SCFA concentrations. Prebiotic-related modulation of microbial function and resultant increases in faecal SCFAs were not sufficient to prevent CIH-induced apnoea and hypertension in our model. Our results do not exclude the potential for microbiota-gut-brain axis involvement in OSA-related cardiorespiratory morbidity, but they demonstrate that in a relatively mild model of CIH, sufficient to evoke classic cardiorespiratory dysfunction, such changes are not obligatory for the development of morbidity, but may become relevant in the elaboration and maintenance of cardiorespiratory morbidity with progressive disease. Funding Department of Physiology and APC Microbiome Ireland, University College Cork, Ireland. APC Microbiome Ireland is funded by Science Foundation Ireland, through the Government's National Development Plan.

Background Prescription medication (PM) misuse/abuse has emerged as a national crisis in the United States, and social media has been suggested as a potential resource for performing active monitoring. However, automating a social media-based monitoring system is challenging—requiring advanced natural language processing (NLP) and machine learning methods. In this paper, we describe the development and evaluation of automatic text classification models for detecting self-reports of PM abuse from Twitter. Methods We experimented with state-of-the-art bi-directional transformer-based language models, which utilize tweet-level representations that enable transfer learning (e.g., BERT, RoBERTa, XLNet, AlBERT, and DistilBERT), proposed fusion-based approaches, and compared the developed models with several traditional machine learning, including deep learning, approaches. Using a public dataset, we evaluated the performances of the classifiers on their abilities to classify the non-majority “abuse/misuse” class. Results Our proposed fusion-based model performs significantly better than the best traditional model (F1-score [95% CI]: 0.67 [0.64–0.69] vs. 0.45 [0.42–0.48]). We illustrate, via experimentation using varying training set sizes, that the transformer-based models are more stable and require less annotated data compared to the other models. The significant improvements achieved by our best-performing classification model over past approaches makes it suitable for automated continuous monitoring of nonmedical PM use from Twitter. Conclusions BERT, BERT-like and fusion-based models outperform traditional machine learning and deep learning models, achieving substantial improvements over many years of past research on the topic of prescription medication misuse/abuse classification from social media, which had been shown to be a complex task due to the unique ways in which information about nonmedical use is presented. Several challenges associated with the lack of context and the nature of social media language need to be overcome to further improve BERT and BERT-like models. These experimental driven challenges are represented as potential future research directions.

There is clear evidence of physiological effects of the gut microbiota on whole-body function in health and disease. Microbiota-gut-brain axis signalling is recognised as a key player in behavioural disorders such as depression and anxiety. Recent evidence suggests that the gut microbiota affects neurocontrol networks responsible for homeostatic functions that are essential for life. We consider the evidence suggesting the potential for the gut microbiota to shape cardiorespiratory homeostasis. In various animal models of disease, there is an association between cardiorespiratory morbidity and perturbed gut microbiota, with strong evidence in support of a role of the gut microbiota in the control of blood pressure. Interventions that target the gut microbiota or manipulate the gut-brain axis, such as short-chain fatty acid supplementation, prevent hypertension in models of obstructive sleep apnoea. Emerging evidence points to a role for the microbiota-gut-brain axis in the control of breathing and ventilatory responsiveness, relevant to cardiorespiratory disease. There is also evidence for an association between the gut microbiota and disease severity in people with asthma and cystic fibrosis. There are many gaps in the knowledge base and an urgent need to better understand the mechanisms by which gut health and dysbiosis contribute to cardiorespiratory control. Nevertheless, there is a growing consensus that manipulation of the gut microbiota could prove an efficacious adjunctive strategy in the treatment of common cardiorespiratory diseases, which are the leading causes of morbidity and mortality.

The rapidly evolving COVID-19 pandemic presents challenges for actively monitoring its transmission. In this study, we extend a social media mining approach used in the US to automatically identify personal reports of COVID-19 on Twitter in England, UK. The findings indicate that natural language processing and machine learning framework could help provide an early indication of the chronological and geographical distribution of COVID-19 in England.

The rapidly evolving outbreak of COVID-19 presents challenges for actively monitoring its spread. In this study, we assessed a social media mining approach for automatically analyzing the chronological and geographical distribution of users in the United States reporting personal information related to COVID-19 on Twitter. The results suggest that our natural language processing and machine learning framework could help provide an early indication of the spread of COVID-19.

Background Social media data are being increasingly used for population-level health research because it provides near real-time access to large volumes of consumer-generated data. Recently, a number of studies have explored the possibility of using social media data, such as from Twitter, for monitoring prescription medication abuse. However, there is a paucity of annotated data or guidelines for data characterization that discuss how information related to abuse-prone medications is presented on Twitter. Objective This study discusses the creation of an annotated corpus suitable for training supervised classification algorithms for the automatic classification of medication abuse–related chatter. The annotation strategies used for improving interannotator agreement (IAA), a detailed annotation guideline, and machine learning experiments that illustrate the utility of the annotated corpus are also described. Methods We employed an iterative annotation strategy, with interannotator discussions held and updates made to the annotation guidelines at each iteration to improve IAA for the manual annotation task. Using the grounded theory approach, we first characterized tweets into fine-grained categories and then grouped them into 4 broad classes—abuse or misuse, personal consumption, mention, and unrelated. After the completion of manual annotations, we experimented with several machine learning algorithms to illustrate the utility of the corpus and generate baseline performance metrics for automatic classification on these data. Results Our final annotated set consisted of 16,443 tweets mentioning at least 20 abuse-prone medications including opioids, benzodiazepines, atypical antipsychotics, central nervous system stimulants, and gamma-aminobutyric acid analogs. Our final overall IAA was 0.86 (Cohen kappa), which represents high agreement. The manual annotation process revealed the variety of ways in which prescription medication misuse or abuse is discussed on Twitter, including expressions indicating coingestion, nonmedical use, nonstandard route of intake, and consumption above the prescribed doses. Among machine learning classifiers, support vector machines obtained the highest automatic classification accuracy of 73.00% (95% CI 71.4-74.5) over the test set (n=3271). Conclusions Our manual analysis and annotations of a large number of tweets have revealed types of information posted on Twitter about a set of abuse-prone prescription medications and their distributions. In the interests of reproducible and community-driven research, we have made our detailed annotation guidelines and the training data for the classification experiments publicly available, and the test data will be used in future shared tasks.

Background Although studies report that more than 90% of pregnant women utilize digital sources to supplement their maternal healthcare, little is known about the kinds of information that women seek from their peers during pregnancy. To date, most research has used self-report measures to elucidate how and why women to turn to digital sources during pregnancy. However, given that these measures may differ from actual utilization of online health information, it is important to analyze the online content pregnant women generate. Objective To apply machine learning methods to analyze online pregnancy forums, to better understand how women seek information from a community of online peers during pregnancy. Methods Data from seven WhatToExpect.com “birth club” forums (September 2018; January-June 2018) were scraped. Forum posts were collected for a one-year period, which included three trimesters and three months postpartum. Only initial posts from each thread were analyzed (n = 262,238). Automatic natural language processing (NLP) methods captured 50 discussed topics, which were annotated by two independent coders and grouped categorically. Results The largest topic categories were maternal health (45%), baby-related topics (29%), and people/relationships (10%). While pain was a popular topic all throughout pregnancy, individual topics that were dominant by trimester included miscarriage (first trimester), labor (third trimester), and baby sleeping routine (postpartum period). Conclusion More than just emotional or peer support, pregnant women turn to online forums to discuss their health. Dominant topics, such as labor and miscarriage, suggest unmet informational needs in these domains. With misinformation becoming a growing public health concern, more attention must be directed toward peer-exchange outlets.

Introduction Adverse effects of medications taken during pregnancy are traditionally studied through post-marketing pregnancy registries, which have limitations. Social media data may be an alternative data source for pregnancy surveillance studies. Objective The objective of this study was to assess the feasibility of using social media data as an alternative source for pregnancy surveillance for regulatory decision making. Methods We created an automated method to identify Twitter accounts of pregnant women. We identified 196 pregnant women with a mention of a birth defect in relation to their baby and 196 without a mention of a birth defect in relation to their baby. We extracted information on pregnancy and maternal demographics, medication intake and timing, and birth defects. Results Although often incomplete, we extracted data for the majority of the pregnancies. Among women that reported birth defects, 35% reported taking one or more medications during pregnancy compared with 17% of controls. After accounting for age, race, and place of residence, a higher medication intake was observed in women who reported birth defects. The rate of birth defects in the pregnancy cohort was lower (0.44%) compared with the rate in the general population (3%). Conclusions Twitter data capture information on medication intake and birth defects; however, the information obtained cannot replace pregnancy registries at this time. Development of improved methods to automatically extract and annotate social media data may increase their value to support regulatory decision making regarding pregnancy outcomes in women using medications during their pregnancies.

Introduction Adverse drug reactions (ADRs) are associated with significant health-related and financial burden, and multiple sources are currently utilized to actively discover them. Social media has been proposed as a potential resource for monitoring ADRs, but drug-specific analytical studies comparing social media with other sources are scarce. Objectives Our objective was to develop methods to compare ADRs mentioned in social media with those in traditional sources: the US FDA Adverse Event Reporting System (FAERS), drug information databases (DIDs), and systematic reviews. Methods A total of 10,188 tweets mentioning adalimumab collected between June 2014 and August 2016 were included. ADRs in the corpus were extracted semi-automatically and manually mapped to standardized concepts in the Unified Medical Language System. ADRs were grouped into 16 biologic categories for comparisons. Frequencies, relative frequencies, disproportionality analyses, and rank ordering were used as metrics. Results There was moderate agreement between ADRs in social media and traditional sources. “Local and injection site reactions” was the top ADR in Twitter, DIDs, and systematic reviews by frequency, ranked frequency, and index ranking. The next highest ADR in Twitter—fatigue—ranked fifth and seventh in FAERS and DIDs. Conclusion Social media posts often express mild and symptomatic ADRs, but rates are measured differently in scientific sources. ADRs in FAERS are reported as absolute numbers, in DIDs as percentages, and in systematic reviews as percentages, risk ratios, or other metrics, which makes comparisons challenging; however, overlap is substantial. Social media analysis facilitates open-ended investigation of patient perspectives and may reveal concepts (e.g. anxiety) not available in traditional sources. Electronic supplementary material The online version of this article (10.1007/s40264-018-0707-6) contains supplementary material, which is available to authorized users.

Reactive oxygen species (ROS) are proposed as mediators of chronic intermittent hypoxia (CIH)-induced respiratory plasticity. We sought to determine if NADPH oxidase 2 (NOX2)-derived ROS underpin CIH-induced maladaptive changes in respiratory control. Adult male mice (C57BL/6 J) were assigned to one of three groups: normoxic controls (sham); chronic intermittent hypoxia-exposed (CIH, 12 cycles/hour, 8 h/day for 14 days); and CIH + apocynin (NOX2 inhibitor, 2 mM) given in the drinking water throughout exposure to CIH. In addition, we studied sham and CIH-exposed NOX2-null mice (B6.129S-CybbTM1Din/J). Whole-body plethysmography was used to measure breathing and metabolic parameters. Ventilation (VI/VCO2) during normoxia was unaffected by CIH, but apnoea index was increased, which was prevented by apocynin, but not by NOX2 deletion. The ventilatory response to hypercapnia following exposure to CIH was potentiated in NOX2-null mice. Our results reveal ROS-dependent influences on the control of breathing and point to antioxidant intervention as a potential adjunctive therapeutic strategy in respiratory control disorders.

Family support is a core component of the Early Psychosis Intervention (EPI) model, yet it continues to have relatively low rates of implementation in practice. This paper reports results of a literature review on facilitators and barriers to delivering family interventions in EPI programmes. A search was conducted of 4 electronic databases, Medline, EMBASE, PsycINFO and Joanna Briggs, from 2000 to 2015 using terms related to early onset psychosis, family work and implementation. Four thousand four hundred and two unique studies were identified, 7 of which met inclusion criteria. Barriers and facilitators were coded and aggregated to higher-level themes using a consensus approach. Five of 7 studies examined structured multifamily psychoeducation. Uptake by families was affected by: family/client interest and readiness to participate; ability to access supports; and support needs/preferences. Implementation by programmes was affected by staff access to training and resources to provide family support. A key finding across the identified studies was that families have different needs and preferences regarding the timing, length, intensity and content of the intervention. One size does not fit all and many families do not require the intensive psychoeducational programmes typically provided. The reviewed literature suggests that flexible, tiered approaches to care may better meet family needs and increase rates of uptake of family support. However, more research is needed on the effectiveness of different models of family support in early psychosis and how they can be successfully implemented.

Summary We present GeoBoost2, a natural language-processing pipeline for extracting the location of infected hosts for enriching metadata in nucleotide sequences repositories like National Center of Biotechnology Information’s GenBank for downstream analysis including phylogeography and genomic epidemiology. The increasing number of pathogen sequences requires complementary information extraction methods for focused research, including surveillance within countries and between borders. In this article, we describe the enhancements from our earlier release including improvement in end-to-end extraction performance and speed, availability of a fully functional web-interface and state-of-the-art methods for location extraction using deep learning. Availability and implementation Application is freely available on the web at https://zodo.asu.edu/geoboost2. Source code, usage examples and annotated data for GeoBoost2 is freely available at https://github.com/ZooPhy/geoboost2. Supplementary information Supplementary data are available at Bioinformatics online.

Objective: After years of research, Twitter posts are now recognized as an important source of patient-generated data, providing unique insights into population health. A fundamental step to incorporating Twitter data in pharmacoepidemiological research is to automatically recognize medication mentions in tweets. Given that lexical searches for medication names may fail due to misspellings or ambiguity with common words, we propose a more advanced method to recognize them. Methods: We present Kusuri, an Ensemble Learning classifier, able to identify tweets mentioning drug products and dietary supplements. Kusuri ("medication" in Japanese) is composed of two modules. First, four different classifiers (lexicon-based, spelling-variant-based, pattern-based and one based on a weakly-trained neural network) are applied in parallel to discover tweets potentially containing medication names. Second, an ensemble of deep neural networks encoding morphological, semantical and long-range dependencies of important words in the tweets discovered is used to make the final decision. Results: On a balanced (50-50) corpus of 15,005 tweets, Kusuri demonstrated performances close to human annotators with 93.7% F1-score, the best score achieved thus far on this corpus. On a corpus made of all tweets posted by 113 Twitter users (98,959 tweets, with only 0.26% mentioning medications), Kusuri obtained 76.3% F1-score. There is not a prior drug extraction system that compares running on such an extremely unbalanced dataset. Conclusion: The system identifies tweets mentioning drug names with performance high enough to ensure its usefulness and ready to be integrated in larger natural language processing systems., This is a pre-copy-editing, author-produced PDF of an article accepted for publication in JAMIA following peer review. The definitive publisher-authenticated version is "D. Weissenbacher, A. Sarker, A. Klein, K. O'Connor, A. Magge, G. Gonzalez-Hernandez, Deep neural networks ensemble for detecting medication mentions in tweets, Journal of the American Medical Informatics Association, ocz156, 2019"

Discrete phylogeography using software such as BEAST considers the sampling location of each taxon as fixed; often to a single location without uncertainty. When studying viruses, this implies that there is no possibility that the location of the infected host for that taxa is somewhere else. Here, we relaxed this strong assumption and allowed for analytic integration of uncertainty for discrete virus phylogeography. We used automatic language processing methods to find and assign uncertainty to alternative potential locations. We considered two influenza case studies: H5N1 in Egypt; H1N1 pdm09 in North America. For each, we implemented scenarios in which 25 per cent of the taxa had different amounts of sampling uncertainty including 10, 30, and 50 per cent uncertainty and varied how it was distributed for each taxon. This includes scenarios that: (i) placed a specific amount of uncertainty on one location while uniformly distributing the remaining amount across all other candidate locations (correspondingly labeled 10, 30, and 50); (ii) assigned the remaining uncertainty to just one other location; thus ‘splitting’ the uncertainty among two locations (i.e. 10/90, 30/70, and 50/50); and (iii) eliminated uncertainty via two predefined heuristic approaches: assignment to a centroid location (CNTR) or the largest population in the country (POP). We compared all scenarios to a reference standard (RS) in which all taxa had known (absolutely certain) locations. From this, we implemented five random selections of 25 per cent of the taxa and used these for specifying uncertainty. We performed posterior analyses for each scenario, including: (a) virus persistence, (b) migration rates, (c) trunk rewards, and (d) the posterior probability of the root state. The scenarios with sampling uncertainty were closer to the RS than CNTR and POP. For H5N1, the absolute error of virus persistence had a median range of 0.005–0.047 for scenarios with sampling uncertainty—(i) and (ii) above—versus a range of 0.063–0.075 for CNTR and POP. Persistence for the pdm09 case study followed a similar trend as did our analyses of migration rates across scenarios (i) and (ii). When considering the posterior probability of the root state, we found all but one of the H5N1 scenarios with sampling uncertainty had agreement with the RS on the origin of the outbreak whereas both CNTR and POP disagreed. Our results suggest that assigning geospatial uncertainty to taxa benefits estimation of virus phylogeography as compared to ad-hoc heuristics. We also found that, in general, there was limited difference in results regardless of how the sampling uncertainty was assigned; uniform distribution or split between two locations did not greatly impact posterior results. This framework is available in BEAST v.1.10. In future work, we will explore viruses beyond influenza. We will also develop a web interface for researchers to use our language processing methods to find and assign uncertainty to alternative potential locations for virus phylogeography.

peer-reviewed To date, Ireland has been a leading light in the provision of youth mental health services. However, cognisant of the efforts of governmental and non-governmental agencies working in youth mental health, there is much to be done. Barriers into care as well as discontinuity of care across the spectrum of services remain key challenges. This editorial provides guidance for the next stage of development in youth mental care and support that will require significant national engagement and resource investment. peer-reviewed

Background: It is increasingly evident that perturbations to the diversity and composition of the gut microbiota have significant consequences for the regulation of integrative physiological systems. There is growing interest in the potential contribution of microbiota-gut-brain signalling to cardiorespiratory control in health and disease. Methods: In adult male rats, we sought to determine the cardiorespiratory effects of manipulation of the gut microbiota following a 4-week administration of a cocktail of antibiotics. We subsequently explored the effects of administration of faecal microbiota from pooled control (vehicle) rat faeces, given by gavage to vehicle- and antibiotic-treated rats. Findings: Antibiotic intervention depressed the ventilatory response to hypercapnic stress in conscious animals, owing to a reduction in the respiratory frequency response to carbon dioxide. Baseline frequency, respiratory timing variability, and the expression of apnoeas and sighs were normal. Microbiota-depleted rats had decreased systolic blood pressure. Faecal microbial transfer to vehicle- and antibiotic-treated animals also disrupted the gut microbiota composition, associated with depressed ventilatory responsiveness to hypercapnia. Chronic antibiotic intervention or faecal microbial transfer both caused significant disruptions to brainstem monoamine neurochemistry, with increased homovanillic acid:dopamine ratio indicative of increased dopamine turnover, which correlated with the abundance of several bacteria of six different phyla. Interpretation: Chronic antibiotic administration and faecal microbial transfer disrupt gut microbiota, brainstem monoamine concentrations and the ventilatory response to hypercapnia. We suggest that aberrant microbiota-gut-brain axis signalling has a modulatory influence on respiratory behaviour during hypercapnic stress. Fund: Department of Physiology and APC Microbiome Ireland, University College Cork, Ireland., This project was funded by the Department of Physiology, and the APC Microbiome Ireland (funded by Science Foundation Ireland (SFI/12/RC/2273)), University College Cork, Ireland.

Background Diabetes mellitus is a major global public health issue where self-management is critical to reducing disease burden. Social media has been a powerful tool to understand public perceptions. Public perception of the drugs used for the treatment of diabetes may be useful for orienting interventions to increase adherence. Objective The aim of this study was to explore the public perceptions of anti-diabetic drugs through the analysis of health-related tweets mentioning such medications. Methods This study uses an infoveillance social listening approach to monitor public discourse using Twitter data. We coded 4000 tweets from January 1, 2019 to October 1, 2019 containing key terms related to anti-diabetic drugs by using qualitative content analysis. Tweets were coded for whether they were truly about an anti-diabetic drug and whether they were health-related. Health-related tweets were further coded based on who was tweeting, which anti-diabetic drug was being tweeted about, and the content discussed in the tweet. The main outcome of the analysis was the themes identified by analyzing the content of health-related tweets on anti-diabetic drugs. Results We identified 1664 health-related tweets on 33 anti-diabetic drugs. A quarter (415/1664) of the tweets were confirmed to have been from people with diabetes, 17.9% (298/1664) from people posting about someone else, and 2.7% (45/1664) from health care professionals. However, the role of the tweeter was unidentifiable in two-thirds of the tweets. We identified 13 themes, with the health consequences of the cost of anti-diabetic drugs being the most extensively discussed, followed by the efficacy and availability. We also identified issues that patients may conceal from health care professionals, such as purchasing medications from unofficial sources. Conclusions This study uses an infoveillance approach using Twitter data to explore public perceptions related to anti-diabetic drugs. This analysis gives an insight into the real-life issues that an individual faces when taking anti-diabetic drugs, and such findings may be incorporated into health policies to improve compliance and efficacy. This study suggests that there is a fear of not having access to anti-diabetic drugs due to cost or physical availability and highlights the impact of the sacrifices made to access anti-diabetic drugs. Along with screening for diabetes-related health issues, health care professionals should also ask their patients about any non–health-related concerns regarding their anti-diabetic drugs. The positive tweets about dietary changes indicate that people with type 2 diabetes may be more open to self-management than what the health care professionals believe.

Background In the United States, the rapidly evolving COVID-19 outbreak, the shortage of available testing, and the delay of test results present challenges for actively monitoring its spread based on testing alone. Objective The objective of this study was to develop, evaluate, and deploy an automatic natural language processing pipeline to collect user-generated Twitter data as a complementary resource for identifying potential cases of COVID-19 in the United States that are not based on testing and, thus, may not have been reported to the Centers for Disease Control and Prevention. Methods Beginning January 23, 2020, we collected English tweets from the Twitter Streaming application programming interface that mention keywords related to COVID-19. We applied handwritten regular expressions to identify tweets indicating that the user potentially has been exposed to COVID-19. We automatically filtered out “reported speech” (eg, quotations, news headlines) from the tweets that matched the regular expressions, and two annotators annotated a random sample of 8976 tweets that are geo-tagged or have profile location metadata, distinguishing tweets that self-report potential cases of COVID-19 from those that do not. We used the annotated tweets to train and evaluate deep neural network classifiers based on bidirectional encoder representations from transformers (BERT). Finally, we deployed the automatic pipeline on more than 85 million unlabeled tweets that were continuously collected between March 1 and August 21, 2020. Results Interannotator agreement, based on dual annotations for 3644 (41%) of the 8976 tweets, was 0.77 (Cohen κ). A deep neural network classifier, based on a BERT model that was pretrained on tweets related to COVID-19, achieved an F1-score of 0.76 (precision=0.76, recall=0.76) for detecting tweets that self-report potential cases of COVID-19. Upon deploying our automatic pipeline, we identified 13,714 tweets that self-report potential cases of COVID-19 and have US state–level geolocations. Conclusions We have made the 13,714 tweets identified in this study, along with each tweet’s time stamp and US state–level geolocation, publicly available to download. This data set presents the opportunity for future work to assess the utility of Twitter data as a complementary resource for tracking the spread of COVID-19.

Summary GeoBoost is a command-line software package developed to address sparse or incomplete metadata in GenBank sequence records that relate to the location of the infected host (LOIH) of viruses. Given a set of GenBank accession numbers corresponding to virus GenBank records, GeoBoost extracts, integrates and normalizes geographic information reflecting the LOIH of the viruses using integrated information from GenBank metadata and related full-text publications. In addition, to facilitate probabilistic geospatial modeling, GeoBoost assigns probability scores for each possible LOIH. Availability and implementation Binaries and resources required for running GeoBoost are packed into a single zipped file and freely available for download at https://tinyurl.com/geoboost. A video tutorial is included to help users quickly and easily install and run the software. The software is implemented in Java 1.8, and supported on MS Windows and Linux platforms. Supplementary information Supplementary data are available at Bioinformatics online.

Background Carotid body (peripheral oxygen sensor) sensitisation is pivotal in the development of chronic intermittent hypoxia (CIH)-induced hypertension. We sought to determine if exposure to CIH, modelling human sleep apnoea, adversely affects cardiorespiratory control in guinea-pigs, a species with hypoxia-insensitive carotid bodies. We reasoned that CIH-induced disruption of gut microbiota would evoke cardiorespiratory morbidity. Methods Adult male guinea-pigs were exposed to CIH (6.5% O2 at nadir, 6 cycles.hour−1) for 8 h.day−1 for 12 consecutive days. Findings CIH-exposed animals established reduced faecal microbiota species richness, with increased relative abundance of Bacteroidetes and reduced relative abundance of Firmicutes bacteria. Urinary corticosterone and noradrenaline levels were unchanged in CIH-exposed animals, but brainstem noradrenaline concentrations were lower compared with sham. Baseline ventilation was equivalent in CIH-exposed and sham animals; however, respiratory timing variability, sigh frequency and ventilation during hypoxic breathing were all lower in CIH-exposed animals. Baseline arterial blood pressure was unaffected by exposure to CIH, but β-adrenoceptor-dependent tachycardia and blunted bradycardia during phenylephrine-induced pressor responses was evident compared with sham controls. Interpretation Increased carotid body chemo-afferent signalling appears obligatory for the development of CIH-induced hypertension and elevated chemoreflex control of breathing commonly reported in mammals, with hypoxia-sensitive carotid bodies. However, we reveal that exposure to modest CIH alters gut microbiota richness and composition, brainstem neurochemistry, and autonomic control of heart rate, independent of carotid body sensitisation, suggesting modulation of breathing and autonomic homeostasis via the microbiota-gut-brainstem axis. The findings have relevance to human sleep-disordered breathing. Funding The Department of Physiology, and APC Microbiome Ireland, UCC.

This study aimed to estimate the level of burnout in mental health professionals and to identify specific determinants of burnout in this population. A systematic search of MEDLINE/PubMed, PsychINFO/Ovid, Embase, CINAHL/EBSCO and Web of Science was conducted for original research published between 1997 and 2017. Sixty-two studies were identified as meeting the study criteria for the systematic review. Data on the means, standard deviations, and prevalence of the dimensions of burnout were extracted from 33 studies and included in the meta-analysis (n = 9409). The overall estimated pooled prevalence for emotional exhaustion was 40% (CI 31%–48%) for depersonalisation was 22% (CI 15%–29%) and for low levels of personal accomplishment was 19% (CI 13%–25%). The random effects estimate of the mean scores on the Maslach Burnout Inventory indicate that the average mental health professional has high levels of emotional exhaustion [mean 21.11 (95% CI 19.98, 22.24)], moderate levels of depersonalisation [mean 6.76 (95% CI 6.11, 7.42)] but retains reasonable levels of personal accomplishment [mean 34.60 (95% CI 32.99, 36.21)]. Increasing age was found to be associated with an increased risk of depersonalisation but also a heightened sense of personal accomplishment. Work-related factors such as workload and relationships at work, are key determinants for burnout, while role clarity, a sense of professional autonomy, a sense of being fairly treated, and access to regular clinical supervision appear to be protective. Staff working in community mental health teams may be more vulnerable to burnout than those working in some specialist community teams, e.g., assertive outreach, crisis teams.

Through a semi-automatic analysis of tweets, we show that Twitter users not only express Medication Non-Adherence (MNA) in social media but also their reasons for not complying; further research is necessary to fully extract automatically and analyze this information, in order to facilitate the use of this data in epidemiological studies.

Objective Social media is becoming increasingly popular as a platform for sharing personal health-related information. This information can be utilized for public health monitoring tasks, particularly for pharmacovigilance, via the use of natural language processing (NLP) techniques. However, the language in social media is highly informal, and user-expressed medical concepts are often nontechnical, descriptive, and challenging to extract. There has been limited progress in addressing these challenges, and thus far, advanced machine learning-based NLP techniques have been underutilized. Our objective is to design a machine learning-based approach to extract mentions of adverse drug reactions (ADRs) from highly informal text in social media.Methods We introduce ADRMine, a machine learning-based concept extraction system that uses conditional random fields (CRFs). ADRMine utilizes a variety of features, including a novel feature for modeling words’ semantic similarities. The similarities are modeled by clustering words based on unsupervised, pretrained word representation vectors (embeddings) generated from unlabeled user posts in social media using a deep learning technique.Results ADRMine outperforms several strong baseline systems in the ADR extraction task by achieving an F-measure of 0.82. Feature analysis demonstrates that the proposed word cluster features significantly improve extraction performance.Conclusion It is possible to extract complex medical concepts, with relatively high performance, from informal, user-generated content. Our approach is particularly scalable, suitable for social media mining, as it relies on large volumes of unlabeled data, thus diminishing the need for large, annotated training data sets.

According to the National Institutes of Health (NIH), precision medicine is "an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person." Although the text mining community has explored this realm for some years, the official endorsement and funding launched in 2015 with the Precision Medicine Initiative are beginning to bear fruit. This session sought to elicit participation of researchers with strong background in text mining and/or visualization who are actively collaborating with bench scientists and clinicians for the deployment of integrative approaches in precision medicine that could impact scientific discovery and advance the vision of precision medicine as a universal, accessible approach at the point of care.

Summary Background: Natural Language Processing (NLP) methods are increasingly being utilized to mine knowledge from unstructured health-related texts. Recent advances in noisy text processing techniques are enabling researchers and medical domain experts to go beyond the information encapsulated in published texts (e.g., clinical trials and systematic reviews) and structured questionnaires, and obtain perspectives from other unstructured sources such as Electronic Health Records (EHRs) and social media posts. Objectives: To review the recently published literature discussing the application of NLP techniques for mining health-related information from EHRs and social media posts. Methods: Literature review included the research published over the last five years based on searches of PubMed, conference proceedings, and the ACM Digital Library, as well as on relevant publications referenced in papers. We particularly focused on the techniques employed on EHRs and social media data. Results: A set of 62 studies involving EHRs and 87 studies involving social media matched our criteria and were included in this paper. We present the purposes of these studies, outline the key NLP contributions, and discuss the general trends observed in the field, the current state of research, and important outstanding problems. Conclusions: Over the recent years, there has been a continuing transition from lexical and rule-based systems to learning-based approaches, because of the growth of annotated data sets and advances in data science. For EHRs, publicly available annotated data is still scarce and this acts as an obstacle to research progress. On the contrary, research on social media mining has seen a rapid growth, particularly because the large amount of unlabeled data available via this resource compensates for the uncertainty inherent to the data. Effective mechanisms to filter out noise and for mapping social media expressions to standard medical concepts are crucial and latent research problems. Shared tasks and other competitive challenges have been driving factors behind the implementation of open systems, and they are likely to play an imperative role in the development of future systems.

The aim of this study was to investigate whether Ultra High Risk for psychosis (UHR) patients who present with hallucinations alone at identification as UHR are at lower risk of transition to psychosis than UHR patients who present with symptoms other than hallucinations or hallucinations plus other symptoms. Our primary dataset was a retrospective "case-control" study of UHR patients (N=118). The second, independent dataset was a long-term longitudinal follow up study of UHR patients (N=416). We performed a survival analysis using Log-rank test and Cox regression to investigate the relationship between symptom variables and transition to a psychotic disorder. Hallucinations alone at baseline were not significantly associated with a reduced risk of transition to psychosis. In the case control study the presence of hallucinations when found in the absence of any thought disorder and visual hallucinations in the absence of substance misuse was associated with a reduced risk of transition to psychosis. In the longitudinal follow-up dataset perceptual disturbance found in the absence of a disorder of affect or emotion was associated with an increased risk of transition to psychosis.

Introduction Prescription medication overdose is the fastest growing drug-related problem in the USA. The growing nature of this problem necessitates the implementation of improved monitoring strategies for investigating the prevalence and patterns of abuse of specific medications. Objectives Our primary aims were to assess the possibility of utilizing social media as a resource for automatic monitoring of prescription medication abuse and to devise an automatic classification technique that can identify potentially abuse-indicating user posts. Methods We collected Twitter user posts (tweets) associated with three commonly abused medications (Adderall®, oxycodone, and quetiapine). We manually annotated 6400 tweets mentioning these three medications and a control medication (metformin) that is not the subject of abuse due to its mechanism of action. We performed quantitative and qualitative analyses of the annotated data to determine whether posts on Twitter contain signals of prescription medication abuse. Finally, we designed an automatic supervised classification technique to distinguish posts containing signals of medication abuse from those that do not and assessed the utility of Twitter in investigating patterns of abuse over time. Results Our analyses show that clear signals of medication abuse can be drawn from Twitter posts and the percentage of tweets containing abuse signals are significantly higher for the three case medications (Adderall®: 23 %, quetiapine: 5.0 %, oxycodone: 12 %) than the proportion for the control medication (metformin: 0.3 %). Our automatic classification approach achieves 82 % accuracy overall (medication abuse class recall: 0.51, precision: 0.41, F measure: 0.46). To illustrate the utility of automatic classification, we show how the classification data can be used to analyze abuse patterns over time. Conclusion Our study indicates that social media can be a crucial resource for obtaining abuse-related information for medications, and that automatic approaches involving supervised classification and natural language processing hold promises for essential future monitoring and intervention tasks.

Although dietary supplements are widely used and generally are considered safe, some supplements have been identified as causative agents for adverse reactions, some of which may even be fatal. The Food and Drug Administration (FDA) is responsible for monitoring supplements and ensuring that supplements are safe. However, current surveillance protocols are not always effective. Leveraging user-generated textual data, in the form of Amazon.com reviews for nutritional supplements, we use natural language processing techniques to develop a system for the monitoring of dietary supplements. We use topic modeling techniques, specifically a variation of Latent Dirichlet Allocation (LDA), and background knowledge in the form of an adverse reaction dictionary to score products based on their potential danger to the public. Our approach generates topics that semantically capture adverse reactions from a document set consisting of reviews posted by users of specific products, and based on these topics, we propose a scoring mechanism to categorize products as "high potential danger", "average potential danger" and "low potential danger." We evaluate our system by comparing the system categorization with human annotators, and we find that the our system agrees with the annotators 69.4% of the time. With these results, we demonstrate that our methods show promise and that our system represents a proof of concept as a viable low-cost, active approach for dietary supplement monitoring.