Association of anaesthesia provider sex with perioperative complications: a two-centre retrospective cohort study.

BACKGROUND: Previous studies suggested that surgeon sex is associated with differential patient outcomes. Whether this also applies to anaesthesia providers is unclear. We hypothesised that female sex of the primary anaesthesia provider is associated with lower risk of perioperative complications. METHODS: The first case for all adult patients undergoing anaesthesia care between 2008 and 2022 at two academic healthcare networks in the USA was included in this retrospective cohort study. The primary exposure was the sex of the anaesthesia provider who spent the most time in the operating theatre during the case. The primary outcome was intraoperative complications, defined as hypotension (mean arterial blood pressure <55 mm Hg for ≥5 cumulative minutes) or hypoxaemia (oxygen saturation <90% for >2 consecutive minutes). The co-primary outcome was 30-day adverse postoperative events (including complications, readmission, and mortality). Analyses were adjusted for a priori defined confounders. RESULTS: Among 364,429 included patients, 57,550 (15.8%) experienced intraoperative complications and 55,168 (15.1%) experienced adverse postoperative events. Care by female compared with male anaesthesia providers was associated with lower risk of intraoperative complications (adjusted odds ratio [aOR] 0.95, 95% confidence interval [CI] 0.94-0.97, P<0.001), which was magnified among non-trainees (aOR 0.84, 95% CI 0.82-0.87, P-for-interaction <0.001). Anaesthesia provider sex was not associated with the composite of adverse postoperative events (aOR 1.00, 95% CI 0.98-1.02, P=0.88). CONCLUSIONS: Care by a female anaesthesia provider was associated with a lower risk of intraoperative complications, which was magnified among non-trainees. Future studies should investigate underlying mechanisms.

The Role and Applications of Artificial Intelligence in the Treatment of Chronic Pain.

PURPOSE OF REVIEW: This review aims to explore the interface between artificial intelligence (AI) and chronic pain, seeking to identify areas of focus for enhancing current treatments and yielding novel therapies. RECENT FINDINGS: In the United States, the prevalence of chronic pain is estimated to be upwards of 40%. Its impact extends to increased healthcare costs, reduced economic productivity, and strain on healthcare resources. Addressing this condition is particularly challenging due to its complexity and the significant variability in how patients respond to treatment. Current options often struggle to provide long-term relief, with their benefits rarely outweighing the risks, such as dependency or other side effects. Currently, AI has impacted four key areas of chronic pain treatment and research: (1) predicting outcomes based on clinical information; (2) extracting features from text, specifically clinical notes; (3) modeling 'omic data to identify meaningful patient subgroups with potential for personalized treatments and improved understanding of disease processes; and (4) disentangling complex neuronal signals responsible for pain, which current therapies attempt to modulate. As AI advances, leveraging state-of-the-art architectures will be essential for improving chronic pain treatment. Current efforts aim to extract meaningful representations from complex data, paving the way for personalized medicine. The identification of unique patient subgroups should reveal targets for tailored chronic pain treatments. Moreover, enhancing current treatment approaches is achievable by gaining a more profound understanding of patient physiology and responses. This can be realized by leveraging AI on the increasing volume of data linked to chronic pain.

Learning, visualizing and exploring 16S rRNA structure using an attention-based deep neural network.

Recurrent neural networks with memory and attention mechanisms are widely used in natural language processing because they can capture short and long term sequential information for diverse tasks. We propose an integrated deep learning model for microbial DNA sequence data, which exploits convolutional neural networks, recurrent neural networks, and attention mechanisms to predict taxonomic classifications and sample-associated attributes, such as the relationship between the microbiome and host phenotype, on the read/sequence level. In this paper, we develop this novel deep learning approach and evaluate its application to amplicon sequences. We apply our approach to short DNA reads and full sequences of 16S ribosomal RNA (rRNA) marker genes, which identify the heterogeneity of a microbial community sample. We demonstrate that our implementation of a novel attention-based deep network architecture, Read2Pheno, achieves read-level phenotypic prediction. Training Read2Pheno models will encode sequences (reads) into dense, meaningful representations: learned embedded vectors output from the intermediate layer of the network model, which can provide biological insight when visualized. The attention layer of Read2Pheno models can also automatically identify nucleotide regions in reads/sequences which are particularly informative for classification. As such, this novel approach can avoid pre/post-processing and manual interpretation required with conventional approaches to microbiome sequence classification. We further show, as proof-of-concept, that aggregating read-level information can robustly predict microbial community properties, host phenotype, and taxonomic classification, with performance at least comparable to conventional approaches. An implementation of the attention-based deep learning network is available at https://github.com/EESI/sequence_attention (a python package) and https://github.com/EESI/seq2att (a command line tool).

Does getting defensive get you anywhere?-Seasonal balancing selection, temperature, and parasitoids shape real-world, protective endosymbiont dynamics in the pea aphid.

Facultative, heritable endosymbionts are found at intermediate prevalence within most insect species, playing frequent roles in their hosts' defence against environmental pressures. Focusing on Hamiltonella defensa, a common bacterial endosymbiont of aphids, we tested the hypothesis that such pressures impose seasonal balancing selection, shaping a widespread infection polymorphism. In our studied pea aphid (Acyrthosiphon pisum) population, Hamiltonella frequencies ranged from 23.2% to 68.1% across a six-month longitudinal survey. Rapid spikes and declines were often consistent across fields, and we estimated that selection coefficients for Hamiltonella-infected aphids changed sign within this field season. Prior laboratory research suggested antiparasitoid defence as the major Hamiltonella benefit, and costs under parasitoid absence. While a prior field study suggested these forces can sometimes act as counter-weights in a regime of seasonal balancing selection, our present survey showed no significant relationship between parasitoid wasps and Hamiltonella prevalence. Field cage experiments provided some explanation: parasitoids drove modest ~10% boosts to Hamiltonella frequencies that would be hard to detect under less controlled conditions. They also showed that Hamiltonella was not always costly under parasitoid exclusion, contradicting another prediction. Instead, our longitudinal survey - and two overwintering studies - showed temperature to be the strongest predictor of Hamiltonella prevalence. Matching some prior lab discoveries, this suggested that thermally sensitive costs and benefits, unrelated to parasitism, can shape Hamiltonella dynamics. These results add to a growing body of evidence for rapid, seasonal adaptation in multivoltine organisms, suggesting that such adaptation can be mediated through the diverse impacts of heritable bacterial endosymbionts.

16S rRNA sequence embeddings: Meaningful numeric feature representations of nucleotide sequences that are convenient for downstream analyses.

Advances in high-throughput sequencing have increased the availability of microbiome sequencing data that can be exploited to characterize microbiome community structure in situ. We explore using word and sentence embedding approaches for nucleotide sequences since they may be a suitable numerical representation for downstream machine learning applications (especially deep learning). This work involves first encoding ("embedding") each sequence into a dense, low-dimensional, numeric vector space. Here, we use Skip-Gram word2vec to embed k-mers, obtained from 16S rRNA amplicon surveys, and then leverage an existing sentence embedding technique to embed all sequences belonging to specific body sites or samples. We demonstrate that these representations are meaningful, and hence the embedding space can be exploited as a form of feature extraction for exploratory analysis. We show that sequence embeddings preserve relevant information about the sequencing data such as k-mer context, sequence taxonomy, and sample class. Specifically, the sequence embedding space resolved differences among phyla, as well as differences among genera within the same family. Distances between sequence embeddings had similar qualities to distances between alignment identities, and embedding multiple sequences can be thought of as generating a consensus sequence. In addition, embeddings are versatile features that can be used for many downstream tasks, such as taxonomic and sample classification. Using sample embeddings for body site classification resulted in negligible performance loss compared to using OTU abundance data, and clustering embeddings yielded high fidelity species clusters. Lastly, the k-mer embedding space captured distinct k-mer profiles that mapped to specific regions of the 16S rRNA gene and corresponded with particular body sites. Together, our results show that embedding sequences results in meaningful representations that can be used for exploratory analyses or for downstream machine learning applications that require numeric data. Moreover, because the embeddings are trained in an unsupervised manner, unlabeled data can be embedded and used to bolster supervised machine learning tasks.

Renin-angiotensin-aldosterone system inhibitors in pediatric focal segmental glomerulosclerosis.

BACKGROUND: Conventional immunosuppressive therapy for primary pediatric focal segmental glomerulosclerosis (FSGS) is potentially toxic and only moderate evidence supports its effectiveness. Renin-angiotensin-aldosterone (RAAS) inhibition monotherapy is anecdotally used in selected patients as an alternative to conventional therapy. METHODS: We performed a retrospective cohort study of children with primary FSGS seen at a tertiary care academic hospital between 1986 and 2008. We classified patients into two groups based upon initial treatment: RAAS inhibition monotherapy (RIM) and conventional therapy (CT). The primary endpoint was progression to end-stage renal disease (ESRD). Secondary endpoints were remission of proteinuria, relapse, and death. RESULTS: The cohort consisted of 67 patients. Mean baseline urine protein/creatinine ratio (Up/c) was 8.0 (5.2, 10.7) mg/mg, and mean baseline estimated glomerular filtration rate (eGFR) was 115.0 (101.8, 128.1) mL/min/1.73 m(2). Patients in the RIM group were more likely to have lower eGFR (100.8 mL/min/1.73 m(2) vs 132.9 mL/min/1.73 m(2), p = 0.01) and less proteinuria (4.4 vs.14.4, p < 0.01). Renal failure occurred in 22.9% of the RIM group vs 40.6% in the CT group (log-rank p = 0.07). After adjustment for African-American race, time period of presentation, baseline age, eGFR, and Up/c, patients in the RIM group had a 0.11 hazard ratio of progressing to renal failure compared with patients in the CT group (p < 0.01). CONCLUSIONS: Children treated initially with RIM may have better outcomes than those treated with CT.

Artificial Intelligence Based on Machine Learning in Pharmacovigilance: A Scoping Review.

INTRODUCTION: Artificial intelligence based on machine learning has made large advancements in many fields of science and medicine but its impact on pharmacovigilance is yet unclear. OBJECTIVE: The present study conducted a scoping review of the use of artificial intelligence based on machine learning to understand how it is used for pharmacovigilance tasks, characterize differences with other fields, and identify opportunities to improve pharmacovigilance through the use of machine learning. DESIGN: The PubMed, Embase, Web of Science, and IEEE Xplore databases were searched to identify articles pertaining to the use of machine learning in pharmacovigilance published from the year 2000 to September 2021. After manual screening of 7744 abstracts, a total of 393 papers met the inclusion criteria for further analysis. Extraction of key data on study design, data sources, sample size, and machine learning methodology was performed. Studies with the characteristics of good machine learning practice were defined and manual review focused on identifying studies that fulfilled these criteria and results that showed promise. RESULTS: The majority of studies (53%) were focused on detecting safety signals using traditional statistical methods. Of the studies that used more recent machine learning methods, 61% used off-the-shelf techniques with minor modifications. Temporal analysis revealed that newer methods such as deep learning have shown increased use in recent years. We found only 42 studies (10%) that reflect current best practices and trends in machine learning. In the subset of 154 papers that focused on data intake and ingestion, 30 (19%) were found to incorporate the same best practices. CONCLUSION: Advances from artificial intelligence have yet to fully penetrate pharmacovigilance, although recent studies show signs that this may be changing.

Emerging Priorities for Microbiome Research.

Microbiome research has increased dramatically in recent years, driven by advances in technology and significant reductions in the cost of analysis. Such research has unlocked a wealth of data, which has yielded tremendous insight into the nature of the microbial communities, including their interactions and effects, both within a host and in an external environment as part of an ecological community. Understanding the role of microbiota, including their dynamic interactions with their hosts and other microbes, can enable the engineering of new diagnostic techniques and interventional strategies that can be used in a diverse spectrum of fields, spanning from ecology and agriculture to medicine and from forensics to exobiology. From June 19-23 in 2017, the NIH and NSF jointly held an Innovation Lab on Quantitative Approaches to Biomedical Data Science Challenges in our Understanding of the Microbiome. This review is inspired by some of the topics that arose as priority areas from this unique, interactive workshop. The goal of this review is to summarize the Innovation Lab's findings by introducing the reader to emerging challenges, exciting potential, and current directions in microbiome research. The review is broken into five key topic areas: (1) interactions between microbes and the human body, (2) evolution and ecology of microbes, including the role played by the environment and microbe-microbe interactions, (3) analytical and mathematical methods currently used in microbiome research, (4) leveraging knowledge of microbial composition and interactions to develop engineering solutions, and (5) interventional approaches and engineered microbiota that may be enabled by selectively altering microbial composition. As such, this review seeks to arm the reader with a broad understanding of the priorities and challenges in microbiome research today and provide inspiration for future investigation and multi-disciplinary collaboration.

Exploring thematic structure and predicted functionality of 16S rRNA amplicon data.

Analysis of microbiome data involves identifying co-occurring groups of taxa associated with sample features of interest (e.g., disease state). Elucidating such relations is often difficult as microbiome data are compositional, sparse, and have high dimensionality. Also, the configuration of co-occurring taxa may represent overlapping subcommunities that contribute to sample characteristics such as host status. Preserving the configuration of co-occurring microbes rather than detecting specific indicator species is more likely to facilitate biologically meaningful interpretations. Additionally, analyses that use taxonomic relative abundances to predict the abundances of different gene functions aggregate predicted functional profiles across taxa. This precludes straightforward identification of predicted functional components associated with subsets of co-occurring taxa. We provide an approach to explore co-occurring taxa using "topics" generated via a topic model and link these topics to specific sample features (e.g., disease state). Rather than inferring predicted functional content based on overall taxonomic relative abundances, we instead focus on inference of functional content within topics, which we parse by estimating interactions between topics and pathways through a multilevel, fully Bayesian regression model. We apply our methods to three publicly available 16S amplicon sequencing datasets: an inflammatory bowel disease dataset, an oral cancer dataset, and a time-series dataset. Using our topic model approach to uncover latent structure in 16S rRNA amplicon surveys, investigators can (1) capture groups of co-occurring taxa termed topics; (2) uncover within-topic functional potential; (3) link taxa co-occurrence, gene function, and environmental/host features; and (4) explore the way in which sets of co-occurring taxa behave and evolve over time. These methods have been implemented in a freely available R package: https://cran.r-project.org/package=themetagenomics, https://github.com/EESI/themetagenomics.

Opportunities and obstacles for deep learning in biology and medicine.

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.

Metagenomic characterization of ambulances across the USA.

BACKGROUND: Microbial communities in our built environments have great influence on human health and disease. A variety of built environments have been characterized using a metagenomics-based approach, including some healthcare settings. However, there has been no study to date that has used this approach in pre-hospital settings, such as ambulances, an important first point-of-contact between patients and hospitals. RESULTS: We sequenced 398 samples from 137 ambulances across the USA using shotgun sequencing. We analyzed these data to explore the microbial ecology of ambulances including characterizing microbial community composition, nosocomial pathogens, patterns of diversity, presence of functional pathways and antimicrobial resistance, and potential spatial and environmental factors that may contribute to community composition. We found that the top 10 most abundant species are either common built environment microbes, microbes associated with the human microbiome (e.g., skin), or are species associated with nosocomial infections. We also found widespread evidence of antimicrobial resistance markers (hits ~ 90% samples). We identified six factors that may influence the microbial ecology of ambulances including ambulance surfaces, geographical-related factors (including region, longitude, and latitude), and weather-related factors (including temperature and precipitation). CONCLUSIONS: While the vast majority of microbial species classified were beneficial, we also found widespread evidence of species associated with nosocomial infections and antimicrobial resistance markers. This study indicates that metagenomics may be useful to characterize the microbial ecology of pre-hospital ambulance settings and that more rigorous testing and cleaning of ambulances may be warranted.

Long-Term Urban Market Dynamics Reveal Increased Bushmeat Carcass Volume despite Economic Growth and Proactive Environmental Legislation on Bioko Island, Equatorial Guinea.

Bushmeat hunting is extensive in west and central Africa as both a means for subsistence and for commercial gain. Commercial hunting represents one of the primary threats to wildlife in the region, and confounding factors have made it challenging to examine how external factors influence the commercial bushmeat trade. Bioko Island, Equatorial Guinea is a small island with large tracts of intact forest that support sizeable populations of commercially valuable vertebrates, especially endemic primates. The island also has a low human population and has experienced dramatic economic growth and rapid development since the mid-1990's. From October 1997 - September 2010, we monitored the largest bushmeat market on Bioko in Malabo, recording over 197,000 carcasses for sale. We used these data to analyze the dynamics of the market in relation to political events, environmental legislation, and rapid economic growth. Our findings suggest that bushmeat hunting and availability increased in parallel with the growth of Equatorial Guinea's GDP and disposable income of its citizens. During this 13-year study, the predominant mode of capture shifted from trapping to shotguns. Consequently, carcass volume and rates of taxa typically captured with shotguns increased significantly, most notably including intensified hunting of Bioko's unique and endangered monkey fauna. Attempts to limit bushmeat sales, including a 2007 ban on primate hunting and trade, were only transiently effective. The hunting ban was not enforced, and was quickly followed by a marked increase in bushmeat hunting compared to hunting rates prior to the ban. Our results emphasize the negative impact that rapid development and unenforced legislation have had on Bioko's wildlife, and demonstrate the need for strong governmental support if conservation strategies are to be successful at preventing extinctions of tropical wildlife.