Search

BackgroundThe integration of high technology into health care systems is intended to provide new treatment options and improve the quality, safety, and efficiency of care. Robotic-assisted surgery is an example of high technology integration in health care, which has become ubiquitous in many surgical disciplines. ObjectiveThis study aims to understand and measure current robotic-assisted surgery processes in a systematic, quantitative, and replicable manner to identify latent systemic threats and opportunities for improvement based on our observations and to implement and evaluate interventions. This 5-year study will follow a human factors engineering approach to improve the safety and efficiency of robotic-assisted surgery across 4 US hospitals. MethodsThe study uses a stepped wedge crossover design with 3 interventions, introduced in different sequences at each of the hospitals over four 8-month phases. Robotic-assisted surgery procedures will be observed in the following specialties: urogynecology, gynecology, urology, bariatrics, general, and colorectal. We will use the data collected from observations, surveys, and interviews to inform interventions focused on teamwork, task design, and workplace design. We intend to evaluate attitudes toward each intervention, safety culture, subjective workload for each case, effectiveness of each intervention (including through direct observation of a sample of surgeries in each observational phase), operating room duration, length of stay, and patient safety incident reports. Analytic methods will include statistical data analysis, point process analysis, and thematic content analysis. ResultsThe study was funded in September 2018 and approved by the institutional review board of each institution in May and June of 2019 (CSMC and MDRH: Pro00056245; VCMC: STUDY 270; MUSC: Pro00088741). After refining the 3 interventions in phase 1, data collection for phase 2 (baseline data) began in November 2019 and was scheduled to continue through June 2020. However, data collection was suspended in March 2020 due to the COVID-19 pandemic. We collected a total of 65 observations across the 4 sites before the pandemic. Data collection for phase 2 was resumed in October 2020 at 2 of the 4 sites. ConclusionsThis will be the largest direct observational study of surgery ever conducted with data collected on 680 robotic surgery procedures at 4 different institutions. The proposed interventions will be evaluated using individual-level (workload and attitude), process-level (perioperative duration and flow disruption), and organizational-level (safety culture and complications) measures. An implementation science framework is also used to investigate the causes of success or failure of each intervention at each site and understand the potential spread of the interventions. International Registered Report Identifier (IRRID)DERR1-10.2196/25284

BackgroundMany important applications in bioinformatics, including sequence alignment and protein family profiling, employ sequence weighting schemes to mitigate the effects of non-independence of homologous sequences and under- or over-representation of certain taxa in a dataset. These schemes aim to assign high weights to sequences that are 'novel' compared to the others in the same dataset, and low weights to sequences that are over-represented.ResultsWe formalise this principle by rigorously defining the evolutionary 'novelty' of a sequence within an alignment. This results in new sequence weights that we call 'phylogenetic novelty scores'. These scores have various desirable properties, and we showcase their use by considering, as an example application, the inference of character frequencies at an alignment column-important, for example, in protein family profiling. We give computationally efficient algorithms for calculating our scores and, using simulations, show that they are versatile and can improve the accuracy of character frequency estimation compared to existing sequence weighting schemes.ConclusionsOur phylogenetic novelty scores can be useful when an evolutionarily meaningful system for adjusting for uneven taxon sampling is desired. They have numerous possible applications, including estimation of evolutionary conservation scores and sequence logos, identification of targets in conservation biology, and improving and measuring sequence alignment accuracy.

Case-control genome-wide association studies (CC-GWAS) might provide valuable clues to the underlying pathophysiologic mechanisms of complex diseases, such as neurodegenerative disease and cancer. A commonly overlooked complication is that multiple distinct disease states might present with the same set of symptoms and hence share a clinical diagnosis. These disease states can only be distinguished based on a biomarker evaluation that might not be feasible in the whole set of cases in the large number of samples that are typically needed for CC-GWAS. Instead, the biomarkers are measured on a subset of cases. Or an external reliability study estimates the frequencies of the disease states of interest within the clinically diagnosed set of cases. These frequencies often vary by the genetic and/or nongenetic variables. We derive a simple approximation that relates the genetic effect estimates obtained in a traditional logistic regression model with the clinical diagnosis as the outcome variable to the genetic effect estimates in the relationship to the true disease state of interest. We performed simulation studies to assess the accuracy of the approximation that we have derived. We next applied the derived approximation to the analysis of the genetic basis of the innate immune system of Alzheimer's disease.

One of the most important research areas in case-control Genome-Wide Association Studies is to determine how the effect of a genotype varies across the environment or to measure the gene-environment interaction (G × E). We consider the scenario when some of the "healthy" controls actually have the disease and when the frequency of these latent cases varies by the environmental variable of interest. In this scenario, performing logistic regression with the clinically diagnosed disease status as an outcome variable and will result in biased estimates of G × E interaction. Here, we derive a general theoretical approximation to the bias in the estimates of the G × E interaction and show, through extensive simulation, that this approximation is accurate in finite samples. Moreover, we apply this approximation to evaluate the bias in the effect estimates of the genetic variants related to mitochondrial proteins a large-scale prostate cancer study.

Case-control Genome-Wide Association Studies (GWAS) provide a rich resource for studying the genetic architecture of complex diseases. A key is to elucidate how the genetic effects vary by the environment, what is traditionally defined by Gene-Environment interactions (GxE). The overlooked complication is that multiple, distinct pathophysiologic mechanisms may lead to the same clinical diagnosis and often these mechanisms have distinct genetic bases. In this paper, we first show that using the clinically diagnosed status can lead to severely biased estimates of GxE interactions in situations when the frequency of the pathologic diagnosis of interest, as compared to other diagnoses, depends on the environment. We then propose a pseudo-likelihood solution to correct the bias. Finally, we demonstrate our method in extensive simulations and in a GWAS of Alzheimer's disease.

Background: Adolescence is a sensitive stage of oral microbial development that often coincides with the initiation and escalation of alcohol use. Thus, adolescents may be particularly susceptible to alcohol‐induced alterations in the oral microbiome, though minimal research has been done in this area. Understanding the connection between the oral microbiome and alcohol use during adolescence is important to understand fully the biological consequences of alcohol use to mitigate potential adverse outcomes. Methods: Saliva samples were collected from adolescents aged 17–19 who used alcohol heavily (n = 21, 52.4% female) and those who did not use alcohol or any other substances (n = 18, 44.4% female). We utilized 16S rRNA sequencing to examine differences in microbial diversity and composition between the groups. Results: For alpha diversity, evenness was significantly lower in the drinking group than the control group as indicated by Pielou's evenness, Shannon, and Simpson indices. There were no statistically significant findings for beta diversity. Differential abundance analyses revealed higher abundances of Rothia and Corynebacterium in the alcohol‐using group using both centered‐log‐ratio and relative abundance normalization. These genera are known for their high capacity to convert alcohol into acetaldehyde, a toxic metabolite reported to play a role in the neurobiological effects of alcohol. An unclassified Clostridia UCG‐014, Streptobacillus, Comamonas, unclassified Lachnospiraceae, and Parvimonas were also identified as significantly different between groups when using only one of the normalization techniques. Conclusions: This is the first study designed specifically to compare the oral microbiome of adolescents who use alcohol with that of control participants. Our findings reveal distinct alcohol‐related differences in microbial composition and taxon abundance, emphasizing the importance of understanding the impact on the oral microbiome of alcohol use during adolescence. Because the oral microbiome is malleable, this study provides foundational work for future prevention and intervention studies. [ABSTRACT FROM AUTHOR]

High-dimensional biomedical datasets have become easier to collect in the last two decades with the advent of multi-omic and single-cell experiments. These can generate over 1000 measurements per sample or per cell. More recently, focus has been drawn toward the need for longitudinal datasets, with the appreciation that important dynamic changes occur along transitions between health and disease. Analysis of longitudinal omics data comes with many challenges, including type I error inflation and corresponding loss in power when thousands of hypothesis tests are needed. Multivariate analysis can yield approaches with higher statistical power; however, multivariate methods for longitudinal data are currently limited. We propose a multivariate distance-based drift-diffusion framework (MD3F) to tackle the need for a multivariate approach to longitudinal, high-throughput datasets. We show that MD3F can result in surprisingly simple yet valid and powerful hypothesis testing and estimation approaches using generalized linear models. Through simulation and application studies, we show that MD3F is robust and can offer a broadly applicable method for assessing multivariate dynamics in omics data. [ABSTRACT FROM AUTHOR]

Abstract Background Terabyte-scale collections of string-encoded data are expected from consortia efforts such as the Human Microbiome Project http://nihroadmap.nih.gov/hmp. Intra- and inter-project data similarity searches are enabled by rapid k-mer matching strategies. Software applications for sequence database partitioning, guide tree estimation, molecular classification and alignment acceleration have benefited from embedded k-mer searches as sub-routines. However, a rapid, general-purpose, open-source, flexible, stand-alone k-mer tool has not been available. Results Here we present a stand-alone utility, Simrank, which allows users to rapidly identify database strings the most similar to query strings. Performance testing of Simrank and related tools against DNA, RNA, protein and human-languages found Simrank 10X to 928X faster depending on the dataset. Conclusions Simrank provides molecular ecologists with a high-throughput, open source choice for comparing large sequence sets to find similarity.

Objective Given the importance AI in genomics and its potential impact on human health, the American Medical Informatics Association—Genomics and Translational Biomedical Informatics (GenTBI) Workgroup developed this assessment of factors that can further enable the clinical application of AI in this space. Process A list of relevant factors was developed through GenTBI workgroup discussions in multiple in-person and online meetings, along with review of pertinent publications. This list was then summarized and reviewed to achieve consensus among the group members. Conclusions Substantial informatics research and development are needed to fully realize the clinical potential of such technologies. The development of larger datasets is crucial to emulating the success AI is achieving in other domains. It is important that AI methods do not exacerbate existing socio-economic, racial, and ethnic disparities. Genomic data standards are critical to effectively scale such technologies across institutions. With so much uncertainty, complexity and novelty in genomics and medicine, and with an evolving regulatory environment, the current focus should be on using these technologies in an interface with clinicians that emphasizes the value each brings to clinical decision-making. [ABSTRACT FROM AUTHOR]

Aims The microbiome is a critical factor in health throughout human development. The aims of this scoping review are to (i) elucidate the differences between the youth (post-natal day 21–65 for rodents, 2–7 years for non-human primates, and 10–25 years for humans) microbiome with other life stages and (ii) identify youth-specific microbial changes associated with substance use. Methods Peer-reviewed studies published up to May 2023 were identified in PubMed and SCOPUS and included gut and oral microbiome studies from rodents, non-human primates, and humans (N  = 1733). Twenty-six articles were determined eligible based on inclusion criteria (aim 1: n  = 19, aim 2: n  = 7). Results The adolescent and young adult oral and gut microbiomes are distinct compared to other life stages, within both non-human and human models. While there is limited research in this area, the microbiome appears to be vulnerable to substance use exposure earlier in life, including substances commonly initiated and escalated during adolescence and young adulthood (i.e. alcohol, cannabis, and tobacco). Conclusions Studies across the lifespan indicate that adolescence and young adulthood are distinct periods of development, where the microbiome is sensitive to exposures, including substance use. There is a need for more studies focused on the adolescent and young adult microbiome and substance use, as well as focused on the oral microbiome during this developmental period. Understanding the gut and oral microbiome during adolescence and young adulthood may provide insight into the pathophysiology of substance use disorders. [ABSTRACT FROM AUTHOR]