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TIN-X (Target Importance and Novelty eXplorer) is an interactive visualization tool for illuminating associations between diseases and potential drug targets and is publicly available at newdrugtargets.org. TIN-X uses natural language processing to identify disease and protein mentions within PubMed content using previously published tools for named entity recognition (NER) of gene/protein and disease names. Target data is obtained from the Target Central Resource Database (TCRD). Two important metrics, novelty and importance, are computed from this data and when plotted as log(importance) vs. log(novelty), aid the user in visually exploring the novelty of drug targets and their associated importance to diseases. TIN-X Version 3.0 has been significantly improved with an expanded dataset, modernized architecture including a REST API, and an improved user interface (UI). The dataset has been expanded to include not only PubMed publication titles and abstracts, but also full-text articles when available. This results in approximately 9-fold more target/disease associations compared to previous versions of TIN-X. Additionally, the TIN-X database containing this expanded dataset is now hosted in the cloud via Amazon RDS. Recent enhancements to the UI focuses on making it more intuitive for users to find diseases or drug targets of interest while providing a new, sortable table-view mode to accompany the existing plot-view mode. UI improvements also help the user browse the associated PubMed publications to explore and understand the basis of TIN-X’s predicted association between a specific disease and a target of interest. While implementing these upgrades, computational resources are balanced between the webserver and the user’s web browser to achieve adequate performance while accommodating the expanded dataset. Together, these advances aim to extend the duration that users can benefit from TIN-X while providing both an expanded dataset and new features that researchers can use to better illuminate understudied proteins.

Abstract Background Birth defects are functional and structural abnormalities that impact about 1 in 33 births in the United States. They have been attributed to genetic and other factors such as drugs, cosmetics, food, and environmental pollutants during pregnancy, but for most birth defects there are no known causes. Methods To further characterize associations between small molecule compounds and their potential to induce specific birth abnormalities, we gathered knowledge from multiple sources to construct a reproductive toxicity Knowledge Graph (ReproTox-KG) with a focus on associations between birth defects, drugs, and genes. Specifically, we gathered data from drug/birth-defect associations from co-mentions in published abstracts, gene/birth-defect associations from genetic studies, drug- and preclinical-compound-induced gene expression changes in cell lines, known drug targets, genetic burden scores for human genes, and placental crossing scores for small molecules. Results Using ReproTox-KG and semi-supervised learning (SSL), we scored >30,000 preclinical small molecules for their potential to cross the placenta and induce birth defects, and identified >500 birth-defect/gene/drug cliques that can be used to explain molecular mechanisms for drug-induced birth defects. The ReproTox-KG can be accessed via a web-based user interface available at https://maayanlab.cloud/reprotox-kg . This site enables users to explore the associations between birth defects, approved and preclinical drugs, and all human genes. Conclusions ReproTox-KG provides a resource for exploring knowledge about the molecular mechanisms of birth defects with the potential of predicting the likelihood of genes and preclinical small molecules to induce birth defects.

Abstract Identifying novel autophagy (ATG) associated genes in humans remains an important task for understanding this fundamental physiological process. Machine learning (ML) can highlight potentially “missing pieces” linking core ATG genes with understudied, “dark” genes by mining functional genomic data. Here, a set of 103 (out of 288 genes from the Autophagy Database) was used as training set, based on ATG‐associated terms annotated from 3 secondary sources: GO (gene ontology), Kyoto Encyclopedia of Genes and Genomes pathway, and UniProt keywords, as additional confirmation of their importance in ATG. As negative labels, an OMIM list of genes associated with monogenic diseases was used (after excluding the 288 ATG‐associated genes). Data related to these genes from 17 different sources were compiled and used to derive a trained MetaPath/XGBoost (MPxgb) ML model for distinguishing ATG and non‐ATG genes (10‐fold cross‐validated, 100‐times randomized models, median area under the curve = 0.994 ± 0.008). Sixteen ATG‐relevant variables explained 64% of the total model gain. Overall, 23% of the top 251 predicted genes are annotated in the Autophagy Database, whereas 193 genes (77%) are not. In 2019, we suggested that some of these 193 genes may represent “ATG dark genes.” A literature search in 2022 for those top 20 predicted ATG dark genes found that 9 were subsequently reported as ATG genes during the intervening 3.5 years. A post‐factum evaluation of data leakage (the presence of ATG‐associated terms in the top 40 ML features) confirms that 7 out of these 9 genes and 2 out of 3 other recently validated predictions from the bottom 20 are novel. Those genes with the largest number of ATG features would be most likely to yield valuable experimental insights. Modern high‐throughput testing would be capable of spanning the full 193 ATG genes list reported here. Our analysis demonstrates that ML can guide genomics research to gain a more complete functional and pathway annotation of complex processes. Key points –A knowledge‐graph based machine learning model was designed for predicting unknown autophagy genes via mining functional genomic data. –Literature search validated predicted genes. –Our machine learning models could be generalized and applied to other genomic libraries to uncover dark genes for various functions.

Jessica Binder et al. developed a machine learning model to discover potential drug targets for Alzheimer’s disease. They validated their 20 top candidates in several in vitro models, and highlight FRRS1, CTRAM, SCGB3A1, FAM92B/CIBAR2, and TMEFF2 as potential AD risk genes.

Abstract Background LINCS, "Library of Integrated Network-based Cellular Signatures", and IDG, "Illuminating the Druggable Genome", are both NIH projects and consortia that have generated rich datasets for the study of the molecular basis of human health and disease. LINCS L1000 expression signatures provide unbiased systems/omics experimental evidence. IDG provides compiled and curated knowledge for illumination and prioritization of novel drug target hypotheses. Together, these resources can support a powerful new approach to identifying novel drug targets for complex diseases, such as Parkinson's disease (PD), which continues to inflict severe harm on human health, and resist traditional research approaches. Results Integrating LINCS and IDG, we built the Knowledge Graph Analytics Platform (KGAP) to support an important use case: identification and prioritization of drug target hypotheses for associated diseases. The KGAP approach includes strong semantics interpretable by domain scientists and a robust, high performance implementation of a graph database and related analytical methods. Illustrating the value of our approach, we investigated results from queries relevant to PD. Approved PD drug indications from IDG’s resource DrugCentral were used as starting points for evidence paths exploring chemogenomic space via LINCS expression signatures for associated genes, evaluated as target hypotheses by integration with IDG. The KG-analytic scoring function was validated against a gold standard dataset of genes associated with PD as elucidated, published mechanism-of-action drug targets, also from DrugCentral. IDG's resource TIN-X was used to rank and filter KGAP results for novel PD targets, and one, SYNGR3 (Synaptogyrin-3), was manually investigated further as a case study and plausible new drug target for PD. Conclusions The synergy of LINCS and IDG, via KG methods, empowers graph analytics methods for the investigation of the molecular basis of complex diseases, and specifically for identification and prioritization of novel drug targets. The KGAP approach enables downstream applications via integration with resources similarly aligned with modern KG methodology. The generality of the approach indicates that KGAP is applicable to many disease areas, in addition to PD, the focus of this paper.

Abstract Background One of the most successful approaches to develop new small molecule therapeutics has been to start from a validated druggable protein target. However, only a small subset of potentially druggable targets has attracted significant research and development resources. The Illuminating the Druggable Genome (IDG) project develops resources to catalyze the development of likely targetable, yet currently understudied prospective drug targets. A central component of the IDG program is a comprehensive knowledge resource of the druggable genome. Results As part of that effort, we have developed a framework to integrate, navigate, and analyze drug discovery data based on formalized and standardized classifications and annotations of druggable protein targets, the Drug Target Ontology (DTO). DTO was constructed by extensive curation and consolidation of various resources. DTO classifies the four major drug target protein families, GPCRs, kinases, ion channels and nuclear receptors, based on phylogenecity, function, target development level, disease association, tissue expression, chemical ligand and substrate characteristics, and target-family specific characteristics. The formal ontology was built using a new software tool to auto-generate most axioms from a database while supporting manual knowledge acquisition. A modular, hierarchical implementation facilitate ontology development and maintenance and makes use of various external ontologies, thus integrating the DTO into the ecosystem of biomedical ontologies. As a formal OWL-DL ontology, DTO contains asserted and inferred axioms. Modeling data from the Library of Integrated Network-based Cellular Signatures (LINCS) program illustrates the potential of DTO for contextual data integration and nuanced definition of important drug target characteristics. DTO has been implemented in the IDG user interface Portal, Pharos and the TIN-X explorer of protein target disease relationships. Conclusions DTO was built based on the need for a formal semantic model for druggable targets including various related information such as protein, gene, protein domain, protein structure, binding site, small molecule drug, mechanism of action, protein tissue localization, disease association, and many other types of information. DTO will further facilitate the otherwise challenging integration and formal linking to biological assays, phenotypes, disease models, drug poly-pharmacology, binding kinetics and many other processes, functions and qualities that are at the core of drug discovery. The first version of DTO is publically available via the website http://drugtargetontology.org/ , Github ( http://github.com/DrugTargetOntology/DTO ), and the NCBO Bioportal ( http://bioportal.bioontology.org/ontologies/DTO ). The long-term goal of DTO is to provide such an integrative framework and to populate the ontology with this information as a community resource.

Phenotypic assays have a proven track record for generating leads that become first-in-class therapies. Whole cell assays that inform on a phenotype or mechanism also possess great potential in drug repositioning studies by illuminating new activities for the existing pharmacopeia. The National Center for Advancing Translational Sciences (NCATS) pharmaceutical collection (NPC) is the largest reported collection of approved small molecule therapeutics that is available for screening in a high-throughput setting. Via a wide-ranging collaborative effort, this library was analyzed in the Open Innovation Drug Discovery (OIDD) phenotypic assay modules publicly offered by Lilly. The results of these tests are publically available online at www.ncats.nih.gov/expertise/preclinical/pd2 and via the PubChem Database (https://pubchem.ncbi.nlm.nih.gov/) (AID 1117321). Phenotypic outcomes for numerous drugs were confirmed, including sulfonylureas as insulin secretagogues and the anti-angiogenesis actions of multikinase inhibitors sorafenib, axitinib and pazopanib. Several novel outcomes were also noted including the Wnt potentiating activities of rotenone and the antifolate class of drugs, and the anti-angiogenic activity of cetaben.

Objectives: This investigation aimed to examine the effects of the COVID-19 pandemic on treatment delays and the severity of initial disease presentation in cholesteatoma patients treated in the prepandemic and pandemic periods., Methods:  This retrospective cohort study was of patients who underwent primary surgical management of cholesteatoma between October 2018 and December 2021, split between the prepandemic (October 2018 to February 2020) and pandemic (April 2020 to December 2021) time periods. Data collected included time from referral to otology clinic visit, time of diagnosis, and time of surgical interventions. The extent of cholesteatoma disease and surgery, hearing loss levels, and the need for additional surgical intervention were also considered. The datasets were compared using the Wilcoxon rank-sum test., Results:  Eighty-six patients met inclusion criteria, 36 of whom were treated prepandemic and 50 after March 2020. Of the examined variables, only time from diagnosis to surgery and case duration were significantly different between the two cohorts, with the pandemic cohort experiencing less time between initial diagnosis and surgery (51.4 days prepandemic vs. 38.4 days pandemic, p = 0.02) and shorter case duration (221.0 minutes prepandemic vs. 171.8 minutes pandemic, p = 0.0008). There was no difference between the severity of presentation in the prepandemic and pandemic populations., Conclusions:  There was no significant difference in disease severity or delays in treatment when comparing the prepandemic to the pandemic population. There was a quicker time from referral to surgery and decreased surgical times during the pandemic., Competing Interests: Human subjects: Consent for treatment and open access publication was obtained or waived by all participants in this study. Louisiana State University Institutional Review Board issued approval 2055. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024, Shires et al.)

Social distancing is the core policy response to coronavirus disease 2019 (COVID-19). But, as federal, state and local governments begin opening businesses and relaxing shelter-in-place orders worldwide, we lack quantitative evidence on how policies in one region affect mobility and social distancing in other regions and the consequences of uncoordinated regional policies adopted in the presence of such spillovers. To investigate this concern, we combined daily, county-level data on shelter-in-place policies with movement data from over 27 million mobile devices, social network connections among over 220 million Facebook users, daily temperature and precipitation data from 62,000 weather stations, and county-level census data on population demographics to estimate the geographic and social network spillovers created by regional policies across the United States. Our analysis shows that the contact patterns of people in a given region are significantly influenced by the policies and behaviors of people in other, sometimes distant, regions. When just one-third of a state's social and geographic peer states adopt shelter-in-place policies, it creates a reduction in mobility equal to the state's own policy decisions. These spillovers are mediated by peer travel and distancing behaviors in those states. A simple analytical model calibrated with our empirical estimates demonstrated that the "loss from anarchy" in uncoordinated state policies is increasing in the number of noncooperating states and the size of social and geographic spillovers. These results suggest a substantial cost of uncoordinated government responses to COVID-19 when people, ideas, and media move across borders., Competing Interests: Competing interest statement: D.H., J.A., D.G., and D.E. were previously employees, interns, or contractors of Facebook, and J.A. has and, until recently, D.E. had a significant financial interest in Facebook. While this paper was under editorial review, S.A. and D.E. received a grant from Facebook for other research.

Micromechanical resonators with ultra-low energy dissipation are essential for a wide range of applications, such as navigation in GPS-denied environments. Routinely implemented in silicon (Si), their energy dissipation often reaches the quantum limits of Si, which can be surpassed by using materials with lower intrinsic loss. This paper explores dissipation limits in 4H monocrystalline silicon carbide-on-insulator (4H-SiCOI) mechanical resonators fabricated at wafer-level, and reports on ultra-high quality-factors (Q) in gyroscopic-mode disk resonators. The SiC disk resonators are anchored upon an acoustically-engineered Si substrate containing a phononic crystal which suppresses anchor loss and promises Q ANCHOR near 1 Billion by design. Operating deep in the adiabatic regime, the bulk acoustic wave (BAW) modes of solid SiC disks are mostly free of bulk thermoelastic damping. Capacitively-transduced SiC BAW disk resonators consistently display gyroscopic m = 3 modes with Q-factors above 2 Million (M) at 6.29 MHz, limited by surface TED due to microscale roughness along the disk sidewalls. The surface TED limit is revealed by optical measurements on a SiC disk, with nanoscale smooth sidewalls, exhibiting Q = 18 M at 5.3 MHz, corresponding to f · Q = 9 · 10 13  Hz, a 5-fold improvement over the Akhiezer limit of Si. Our results pave the path for integrated SiC resonators and resonant gyroscopes with Q-factors beyond the reach of Si.

Background: Representation learning provides new and powerful graph analytical approaches and tools for the highly valued data science challenge of mining knowledge graphs. Since previous graph analytical methods have mostly focused on homogeneous graphs, an important current challenge is extending this methodology for richly heterogeneous graphs and knowledge domains. The biomedical sciences are such a domain, reflecting the complexity of biology, with entities such as genes, proteins, drugs, diseases, and phenotypes, and relationships such as gene co-expression, biochemical regulation, and biomolecular inhibition or activation. Therefore, the semantics of edges and nodes are critical for representation learning and knowledge discovery in real world biomedical problems., Results: In this paper, we propose the edge2vec model, which represents graphs considering edge semantics. An edge-type transition matrix is trained by an Expectation-Maximization approach, and a stochastic gradient descent model is employed to learn node embedding on a heterogeneous graph via the trained transition matrix. edge2vec is validated on three biomedical domain tasks: biomedical entity classification, compound-gene bioactivity prediction, and biomedical information retrieval. Results show that by considering edge-types into node embedding learning in heterogeneous graphs, edge2vec significantly outperforms state-of-the-art models on all three tasks., Conclusions: We propose this method for its added value relative to existing graph analytical methodology, and in the real world context of biomedical knowledge discovery applicability.

Micro- and increasingly, nano-fabrication have enabled the miniaturization of atomic devices, from vapor cells to atom chips for Bose-Einstein condensation. Here we present microfabricated planar devices for thermal atomic beams. Etched microchannels were used to create highly collimated, continuous rubidium atom beams traveling parallel to a silicon wafer surface. Precise, lithographic definition of the guiding channels allowed for shaping and tailoring the velocity distributions in ways not possible using conventional machining. Multiple miniature beams with individually prescribed geometries were created, including collimated, focusing and diverging outputs. A "cascaded" collimator was realized with 40 times greater purity than conventional collimators. These localized, miniature atom beam sources can be a valuable resource for a number of quantum technologies, including atom interferometers, clocks, Rydberg atoms, and hybrid atom-nanophotonic systems, as well as enabling controlled studies of atom-surface interactions at the nanometer scale.

Background: There are a huge variety of data sources relevant to chemical, biological and pharmacological research, but these data sources are highly siloed and cannot be queried together in a straightforward way. Semantic technologies offer the ability to create links and mappings across datasets and manage them as a single, linked network so that searching can be carried out across datasets, independently of the source. We have developed an application called PIBAS FedSPARQL that uses semantic technologies to allow researchers to carry out such searching across a vast array of data sources., Results: PIBAS FedSPARQL is a web-based query builder and result set visualizer of bioinformatics data. As an advanced feature, our system can detect similar data items identified by different Uniform Resource Identifiers (URIs), using a text-mining algorithm based on the processing of named entities to be used in Vector Space Model and Cosine Similarity Measures. According to our knowledge, PIBAS FedSPARQL was unique among the systems that we found in that it allows detecting of similar data items. As a query builder, our system allows researchers to intuitively construct and run Federated SPARQL queries across multiple data sources, including global initiatives, such as Bio2RDF, Chem2Bio2RDF, EMBL-EBI, and one local initiative called CPCTAS, as well as additional user-specified data source. From the input topic, subtopic, template and keyword, a corresponding initial Federated SPARQL query is created and executed. Based on the data obtained, end users have the ability to choose the most appropriate data sources in their area of interest and exploit their Resource Description Framework (RDF) structure, which allows users to select certain properties of data to enhance query results., Conclusions: The developed system is flexible and allows intuitive creation and execution of queries for an extensive range of bioinformatics topics. Also, the novel "similar data items detection" algorithm can be particularly useful for suggesting new data sources and cost optimization for new experiments. PIBAS FedSPARQL can be expanded with new topics, subtopics and templates on demand, rendering information retrieval more robust.

The 'druggable genome' encompasses several protein families, but only a subset of targets within them have attracted significant research attention and thus have information about them publicly available. The Illuminating the Druggable Genome (IDG) program was initiated in 2014, has the goal of developing experimental techniques and a Knowledge Management Center (KMC) that would collect and organize information about protein targets from four families, representing the most common druggable targets with an emphasis on understudied proteins. Here, we describe two resources developed by the KMC: the Target Central Resource Database (TCRD) which collates many heterogeneous gene/protein datasets and Pharos (https://pharos.nih.gov), a multimodal web interface that presents the data from TCRD. We briefly describe the types and sources of data considered by the KMC and then highlight features of the Pharos interface designed to enable intuitive access to the IDG knowledgebase. The aim of Pharos is to encourage 'serendipitous browsing', whereby related, relevant information is made easily discoverable. We conclude by describing two use cases that highlight the utility of Pharos and TCRD., (Published by Oxford University Press on behalf of Nucleic Acids Research 2016. This work is written by (a) US Government employee(s) and is in the public domain in the US.)

Objective: To examine the demographic and clinical characteristics of systemic sclerosis (SSc) patients without antinuclear antibodies (ANA) compared to ANA-positive patients., Methods: SSc patients enrolled in the Scleroderma Family Registry and DNA Repository were included. Relevant demographic and clinical data were entered by participating sites or obtained by chart review. ANA and SSc-related antibodies were determined in all investigated patients using commercially available kits at our laboratories., Results: This study included 3249 patients, of whom 208 (6.4%) were ANA negative. The proportion of male patients was higher in the ANA-negative group (OR = 1.65; p = 0.008). ANA-negative patients experienced less vasculopathic manifestations of SSc. The percent predicted diffusing capacity of carbon monoxide (DLCO) was higher in ANA-negative patients (p = 0.03). Pulmonary arterial hypertension (PAH) per right heart catheterization was less common in the ANA-negative group (OR = 0.28; p = 0.03). Furthermore, patients with negative ANA had a lower prevalence of telangiectasias and digital ulcers/pits (OR = 0.59, p = 0.03 and OR = 0.38, p = 0.01, respectively). Although diffuse cutaneous involvement was more common, the modified Rodnan Skin Score (mRSS) was lower in the ANA-negative group (2.4 points lower, p = 0.05). Furthermore, they experienced more malabsorption (p = 0.05). There was no difference in the frequency of pulmonary fibrosis or scleroderma renal crisis. All-cause mortality was not different between the 2 groups (p = 0.28)., Conclusions: In conclusion, the results of this study suggest that SSc patients who are ANA negative constitute a distinct subset of SSc with less vasculopathy (less PAH, digital ulcers, and fewer telangiectasias), a greater proportion of males, and possibly, more frequent lower gastrointestinal involvement., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Recent research has shown that Escherichia coli can persist in aquatic environments, although the characteristics that contribute to their survival remain poorly understood. This study examines periphytic E. coli populations that were continuously present in three temperate freshwater lakes from June to October 2008 in numbers ranging from 2 to 2 × 10(2)  CFU 100 cm(-2) . A crystal violet assay revealed that all tested periphytic E. coli isolates were superior biofilm formers and they formed, on average, 2.5 times as much biofilm as E. coli isolated from humans, 4.5 times as much biofilm as shiga-like toxin-producing E. coli, and 7.5 times as much biofilm as bovine E. coli isolates. Repetitive extragenic palindromic polymerase chain reaction (REP-PCR) DNA fingerprinting analysis demonstrated the genetically diverse nature of the periphytic isolates, with genetic similarity between strains ranging from 40% to 86%. Additionally, the role of curli fibers in biofilm formation was investigated by comparing biofilm formation with curli expression under optimal conditions, although little correlation (R(2)  = 0.095, P = 0.005) was found. The high mean biofilm-forming capacity observed in E. coli isolated from the periphyton suggests that selective pressures may favor E. coli capable of forming biofilms in freshwater environments., (© 2011 Federation of European Microbiological Societies. Published by Blackwell Publishing Ltd. All rights reserved.)

There is an urgent need for new drugs against tuberculosis which annually claims 1.7-1.8 million lives. One approach to identify potential leads is to screen in vitro small molecules against Mycobacterium tuberculosis (Mtb). Until recently there was no central repository to collect information on compounds screened. Consequently, it has been difficult to analyze molecular properties of compounds that inhibit the growth of Mtb in vitro. We have collected data from publically available sources on over 300 000 small molecules deposited in the Collaborative Drug Discovery TB Database. A cheminformatics analysis on these compounds indicates that inhibitors of the growth of Mtb have statistically higher mean logP, rule of 5 alerts, while also having lower HBD count, atom count and lower PSA (ChemAxon descriptors), compared to compounds that are classed as inactive. Additionally, Bayesian models for selecting Mtb active compounds were evaluated with over 100 000 compounds and, they demonstrated 10 fold enrichment over random for the top ranked 600 compounds. This represents a promising approach for finding compounds active against Mtb in whole cells screened under the same in vitro conditions. Various sets of Mtb hit molecules were also examined by various filtering rules used widely in the pharmaceutical industry to identify compounds with potentially reactive moieties. We found differences between the number of compounds flagged by these rules in Mtb datasets, malaria hits, FDA approved drugs and antibiotics. Combining these approaches may enable selection of compounds with increased probability of inhibition of whole cell Mtb activity.

Background: HLA sensitization rates in children on extracorporeal membrane oxygenation (ECMO) or ventricular assist devices (VADs) are unknown. In addition, panel-reactive antibody (PRA) assessment is increasingly being performed by Luminex, whose role in comparison to other conventional methods is unclear. We investigated HLA sensitization of mechanically supported children using established PRA methods and then retested stored serum using Luminex to assess its impact on initial PRA results and post-heart transplant (post-HTx) outcomes., Methods: Data on 22 pre-HTx ECMO or VAD patients (0 to 18 years of age) included: age; duration of mechanical support; use of homograft; red cell transfusion volume; PRAs; and outcome. Comparative data were collected from 10 non-supported, age-matched controls., Results: Median age of the 13 ECMO and 9 VAD patients was 1.4 and 192 months (p < 0.001), respectively. Six (27%) device patients and 4 (40%) controls had baseline PRAs >10% (p = 0.7). No ECMO but 6 VAD patients were sensitized after 50 +/- 51 days of support (p = 0.02). Compared with ECMO, VAD patients had higher Class I PRAs according to enzyme-linked immunoassay (p = 0.03). VAD patients had higher final vs initial PRAs for Class I (p = 0.05) and II (p = 0.04) antigens. HLA sensitization was independent of transfusion volume. Only complement-dependent cytotoxicity (CDC) Class I PRAs were different from their respective Luminex values (p = 0.03). Four HTx patients with initially low PRAs but elevated post hoc Luminex assays had no rejection at 3.8 +/- 1.6 years post-HTx., Conclusions: Infants supported with ECMO are at low risk for HLA sensitization. Because it provides full antibody specificity disclosure, Luminex complements more conventional PRA assays by quantitatively identifying potential donor-specific antibodies, which should facilitate the virtual crossmatch process, thereby minimizing post-HTx humoral rejection risk.