Lesion Search with Self-supervised Learning

Content-based image retrieval (CBIR) with self-supervised learning (SSL) accelerates clinicians' interpretation of similar images without manual annotations. We develop a CBIR from the contrastive learning SimCLR and incorporate a generalized-mean (GeM) pooling followed by L2 normalization to classify lesion types and retrieve similar images before clinicians' analysis. Results have shown improved performance. We additionally build an open-source application for image analysis and retrieval. The application is easy to integrate, relieving manual efforts and suggesting the potential to support clinicians' everyday activities.Comment: ICLR 2023 Tiny Pape

ALS Longitudinal Studies With Frequent Data Collection at Home: Study Design and Baseline Data

Objective: To design an ALS clinical study in which patients are remotely recruited, screened, enrolled and then assessed via daily data collection at home by themselves or caregivers. Methods: This observational, natural-history study included two academic medical centers, one providing overall clinical management and the other overseeing computing and web-services design and management. Both healthy and ALS subjects were recruited on the Internet via advertisement on governmental and foundation websites as well as through Facebook and Google paid advertisements. Individuals underwent screening and enrollment remotely, including signing an electronic informed consent form. Participants were then provided self-measurement equipment and instructed on their use through a series of web-based videos. The equipment included a handgrip dynamometer, spirometer with smartphone connection, electrical impedance myography device, and an activity tracker. ALS Functional Rating Scale-Revised data were also collected. Subjects were asked to collect data daily for three months and twice-weekly for the subsequent six months. Results: One hundred and eleven ALS patients and 30 healthy individuals enrolled in the study from across 41 states (74 men, 62 women). Baseline median ALSFRS-R score was 33. Seventy two percent of the ALS patients sent equipment and 88% of the healthy subjects sent equipment were able to complete a first set of measurements. Expected baseline differences between the ALS patients and healthy participants were identified for all measures. Conclusions: It is possible to design and institute an at-home based study in ALS patients, using a number of state-of-the-art approaches, including web-based consenting and training and Internet-connected measurement devices

Making Study Populations Visible through Knowledge Graphs

Treatment recommendations within Clinical Practice Guidelines (CPGs) are largely based on findings from clinical trials and case studies, referred to here as research studies, that are often based on highly selective clinical populations, referred to here as study cohorts. When medical practitioners apply CPG recommendations, they need to understand how well their patient population matches the characteristics of those in the study cohort, and thus are confronted with the challenges of locating the study cohort information and making an analytic comparison. To address these challenges, we develop an ontology-enabled prototype system, which exposes the population descriptions in research studies in a declarative manner, with the ultimate goal of allowing medical practitioners to better understand the applicability and generalizability of treatment recommendations. We build a Study Cohort Ontology (SCO) to encode the vocabulary of study population descriptions, that are often reported in the first table in the published work, thus they are often referred to as Table 1. We leverage the well-used Semanticscience Integrated Ontology (SIO) for defining property associations between classes. Further, we model the key components of Table 1s, i.e., collections of study subjects, subject characteristics, and statistical measures in RDF knowledge graphs. We design scenarios for medical practitioners to perform population analysis, and generate cohort similarity visualizations to determine the applicability of a study population to the clinical population of interest. Our semantic approach to make study populations visible, by standardized representations of Table 1s, allows users to quickly derive clinically relevant inferences about study populations.Comment: 16 pages, 4 figures, 1 table, accepted to the ISWC 2019 Resources Track (https://iswc2019.semanticweb.org/call-for-resources-track-papers/

Automated detection of medication administration errors in neonatal intensive care

AbstractObjectiveTo improve neonatal patient safety through automated detection of medication administration errors (MAEs) in high alert medications including narcotics, vasoactive medication, intravenous fluids, parenteral nutrition, and insulin using the electronic health record (EHR); to evaluate rates of MAEs in neonatal care; and to compare the performance of computerized algorithms to traditional incident reporting for error detection.MethodsWe developed novel computerized algorithms to identify MAEs within the EHR of all neonatal patients treated in a level four neonatal intensive care unit (NICU) in 2011 and 2012. We evaluated the rates and types of MAEs identified by the automated algorithms and compared their performance to incident reporting. Performance was evaluated by physician chart review.ResultsIn the combined 2011 and 2012 NICU data sets, the automated algorithms identified MAEs at the following rates: fentanyl, 0.4% (4 errors/1005 fentanyl administration records); morphine, 0.3% (11/4009); dobutamine, 0 (0/10); and milrinone, 0.3% (5/1925). We found higher MAE rates for other vasoactive medications including: dopamine, 11.6% (5/43); epinephrine, 10.0% (289/2890); and vasopressin, 12.8% (54/421). Fluid administration error rates were similar: intravenous fluids, 3.2% (273/8567); parenteral nutrition, 3.2% (649/20124); and lipid administration, 1.3% (203/15227). We also found 13 insulin administration errors with a resulting rate of 2.9% (13/456). MAE rates were higher for medications that were adjusted frequently and fluids administered concurrently. The algorithms identified many previously unidentified errors, demonstrating significantly better sensitivity (82% vs. 5%) and precision (70% vs. 50%) than incident reporting for error recognition.ConclusionsAutomated detection of medication administration errors through the EHR is feasible and performs better than currently used incident reporting systems. Automated algorithms may be useful for real-time error identification and mitigation

Androgen Receptor-Target Genes in African American Prostate Cancer Disparities

The incidence and mortality rates of prostate cancer (PCa) are higher in African American (AA) compared to Caucasian American (CA) men. To elucidate the molecular mechanisms underlying PCa disparities, we employed an integrative approach combining gene expression profiling and pathway and promoter analyses to investigate differential transcriptomes and deregulated signaling pathways in AA versus CA cancers. A comparison of AA and CA PCa specimens identified 1,188 differentially expressed genes. Interestingly, these transcriptional differences were overrepresented in signaling pathways that converged on the androgen receptor (AR), suggesting that the AR may be a unifying oncogenic theme in AA PCa. Gene promoter analysis revealed that 382 out of 1,188 genes contained cis-acting AR-binding sequences. Chromatin immunoprecipitation confirmed STAT1, RHOA, ITGB5, MAPKAPK2, CSNK2A,1 and PIK3CB genes as novel AR targets in PCa disparities. Moreover, functional screens revealed that androgen-stimulated AR binding and upregulation of RHOA, ITGB5, and PIK3CB genes were associated with increased invasive activity of AA PCa cells, as siRNA-mediated knockdown of each gene caused a loss of androgen-stimulated invasion. In summation, our findings demonstrate that transcriptional changes have preferentially occurred in multiple signaling pathways converging (“transcriptional convergence”) on AR signaling, thereby contributing to AR-target gene activation and PCa aggressiveness in AAs

Exploring functional pairing between surface glycoconjugates and human galectins using programmable glycodendrimersomes

Precise translation of glycan-encoded information into cellular activity depends critically on highly specific functional pairing between glycans and their human lectin counter receptors. Sulfoglycolipids, such as sulfatides, are important glycolipid components of the biological membranes found in the nervous and immune systems. The optimal molecular and spatial design aspects of sulfated and nonsulfated glycans with high specificity for lectin-mediated bridging are unknown. To elucidate how different molecular and spatial aspects combine to ensure the high specificity of lectin-mediated bridging, a bottom-up toolbox is devised. To this end, negatively surface-charged glycodendrimersomes (GDSs), of different nanoscale dimensions, containing sulfo-lactose groups are self-assembled in buffer from a synthetic sulfatide mimic: Janus glycodendrimer (JGD) containing a 3′-O-sulfo-lactose headgroup. Also prepared for comparative analysis are GDSs with nonsulfated lactose, a common epitope of human membranes. These self-assembled GDSs are employed in aggregation assays with 15 galectins, comprising disease-related human galectins, and other natural and engineered variants from four families, having homodimeric, heterodimeric, and chimera architectures. There are pronounced differences in aggregation capacity between human homodimeric and heterodimeric galectins, and also with respect to their responsiveness to the charge of carbohydrate-derived ligand. Assays reveal strong differential impact of ligand surface charge and density, as well as lectin concentration and structure, on the extent of surface cross-linking. These findings demonstrate how synthetic JGD-headgroup tailoring teamed with protein engineering and network assays can help explain how molecular matchmaking operates in the cellular context of glycan and lectin complexity

Combinatorial CRISPR-Cas9 screens for de novo mapping of genetic interactions.

We developed a systematic approach to map human genetic networks by combinatorial CRISPR-Cas9 perturbations coupled to robust analysis of growth kinetics. We targeted all pairs of 73 cancer genes with dual guide RNAs in three cell lines, comprising 141,912 tests of interaction. Numerous therapeutically relevant interactions were identified, and these patterns replicated with combinatorial drugs at 75% precision. From these results, we anticipate that cellular context will be critical to synthetic-lethal therapies

An Advanced Computational Approach to System of Systems Analysis & Architecting Using Agent-Based Behavioral Model

The goal of this research is to model the evolution of the architecture of an acknowledged SoS that accounts for the ability and willingness of constituent systems to support the SoS capability development. Since DoD Systems of Systems (SoS) development efforts do not typically follow the normal program acquisition process described in DoDI 5000.02, the Wave Model proposed by Dahmann and Rebovich is used as the basis for this research on SoS capability evolution. The Wave Process Model provides a framework for an agent-based modeling methodology, which is used to abstract the non- utopian behavioral aspects of the constituent systems and their interactions with the SoS. In particular, the research focuses on the impact of individual system behavior on the SoS capability and architecture evolution processes.This material is based upon work supported, in whole or in part, by the U.S. Department of Defense through the Office of the Assistant Secretary of Defense for Research and Engineering (ASD(R&E)) under Contract H98230-08- D-0171 (Task Order 0029, RT 044).H98230-08-D-017