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At least 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Structural polymorphism of amyloid fibrils in ATTR amyloidosis revealed by cryo-electron microscopy

ATTR amyloidosis is caused by the deposition of transthyretin in the form of amyloid fibrils in virtually every organ of the body, including the heart. This systemic deposition leads to a phenotypic variability that has not been molecularly explained yet. In brain amyloid conditions, previous studies suggest an association between clinical phenotype and the molecular structures of their amyloid fibrils. Here we investigate whether there is such an association in ATTRv amyloidosis patients carrying the mutation I84S. Using cryo-electron microscopy, we determined the structures of cardiac fibrils extracted from three ATTR amyloidosis patients carrying the ATTRv-I84S mutation, associated with a consistent clinical phenotype. We found that in each ATTRv-I84S patient, the cardiac fibrils exhibited different local conformations, and these variations can co-exist within the same fibril. Our finding suggests that one amyloid disease may associate with multiple fibril structures in systemic amyloidoses, calling for further studies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Impaired neuron differentiation in GBA-associated Parkinson’s disease is linked to cell cycle defects in organoids

The mechanisms underlying Parkinson’s disease (PD) etiology are only partially understood despite intensive research conducted in the field. Recent evidence suggests that early neurodevelopmental defects might play a role in cellular susceptibility to neurodegeneration. To study the early developmental contribution of GBA mutations in PD we used patient-derived iPSCs carrying a heterozygous N370S mutation in the GBA gene. Patient-specific midbrain organoids displayed GBA-PD relevant phenotypes such as reduction of GCase activity, autophagy impairment, and mitochondrial dysfunction. Genome-scale metabolic (GEM) modeling predicted changes in lipid metabolism which were validated with lipidomics analysis, showing significant differences in the lipidome of GBA-PD. In addition, patient-specific midbrain organoids exhibited a decrease in the number and complexity of dopaminergic neurons. This was accompanied by an increase in the neural progenitor population showing signs of oxidative stress-induced damage and premature cellular senescence. These results provide insights into how GBA mutations may lead to neurodevelopmental defects thereby predisposing to PD pathology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Subnetwork representation learning for discovering network biomarkers in predicting lymph node metastasis in early oral cancer

Cervical lymph node metastasis is the leading cause of poor prognosis in oral tongue squamous cell carcinoma and also occurs in the early stages. The current clinical diagnosis depends on a physical examination that is not enough to determine whether micrometastasis remains. The transcriptome profiling technique has shown great potential for predicting micrometastasis by capturing the dynamic activation state of genes. However, there are several technical challenges in using transcriptome data to model patient conditions: (1) An Insufficient number of samples compared to the number of genes, (2) Complex dependence between genes that govern the cancer phenotype, and (3) Heterogeneity between patients between cohorts that differ geographically and racially. We developed a computational framework to learn the subnetwork representation of the transcriptome to discover network biomarkers and determine the potential of metastasis in early oral tongue squamous cell carcinoma. Our method achieved high accuracy in predicting the potential of metastasis in two geographically and racially different groups of patients. The robustness of the model and the reproducibility of the discovered network biomarkers show great potential as a tool to diagnose lymph node metastasis in early oral cancer.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

The effect of protein mutations on drug binding suggests ensuing personalised drug selection

The advent of personalised medicine promises a deeper understanding of mechanisms and therefore therapies. However, the connection between genomic sequences and clinical treatments is often unclear. We studied 50 breast cancer patients belonging to a population-cohort in the state of Qatar. From Sanger sequencing, we identified several new deleterious mutations in the estrogen receptor 1 gene (ESR1). The effect of these mutations on drug treatment in the protein target encoded by ESR1, namely the estrogen receptor, was achieved via rapid and accurate protein–ligand binding affinity interaction studies which were performed for the selected drugs and the natural ligand estrogen. Four nonsynonymous mutations in the ligand-binding domain were subjected to molecular dynamics simulation using absolute and relative binding free energy methods, leading to the ranking of the efficacy of six selected drugs for patients with the mutations. Our study shows that a personalised clinical decision system can be created by integrating an individual patient’s genomic data at the molecular level within a computational pipeline which ranks the efficacy of binding of particular drugs to variant proteins.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Informing selection of drugs for COVID-19 treatment through adverse events analysis

Coronavirus disease 2019 (COVID-19) is an ongoing pandemic and there is an urgent need for safe and effective drugs for COVID-19 treatment. Since developing a new drug is time consuming, many approved or investigational drugs have been repurposed for COVID-19 treatment in clinical trials. Therefore, selection of safe drugs for COVID-19 patients is vital for combating this pandemic. Our goal was to evaluate the safety concerns of drugs by analyzing adverse events reported in post-market surveillance. We collected 296 drugs that have been evaluated in clinical trials for COVID-19 and identified 28,597,464 associated adverse events at the system organ classes (SOCs) level in the FDA adverse events report systems (FAERS). We calculated Z-scores of SOCs that statistically quantify the relative frequency of adverse events of drugs in FAERS to quantitatively measure safety concerns for the drugs. Analyzing the Z-scores revealed that these drugs are associated with different significantly frequent adverse events. Our results suggest that this safety concern metric may serve as a tool to inform selection of drugs with favorable safety profiles for COVID-19 patients in clinical practices. Caution is advised when administering drugs with high Z-scores to patients who are vulnerable to associated adverse events.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Clinical knowledge extraction via sparse embedding regression (KESER) with multi-center large scale electronic health record data

The increasing availability of electronic health record (EHR) systems has created enormous potential for translational research. However, it is difficult to know all the relevant codes related to a phenotype due to the large number of codes available. Traditional data mining approaches often require the use of patient-level data, which hinders the ability to share data across institutions. In this project, we demonstrate that multi-center large-scale code embeddings can be used to efficiently identify relevant features related to a disease of interest. We constructed large-scale code embeddings for a wide range of codified concepts from EHRs from two large medical centers. We developed knowledge extraction via sparse embedding regression (KESER) for feature selection and integrative network analysis. We evaluated the quality of the code embeddings and assessed the performance of KESER in feature selection for eight diseases. Besides, we developed an integrated clinical knowledge map combining embedding data from both institutions. The features selected by KESER were comprehensive compared to lists of codified data generated by domain experts. Features identified via KESER resulted in comparable performance to those built upon features selected manually or with patient-level data. The knowledge map created using an integrative analysis identified disease-disease and disease-drug pairs more accurately compared to those identified using single institution data. Analysis of code embeddings via KESER can effectively reveal clinical knowledge and infer relatedness among codified concepts. KESER bypasses the need for patient-level data in individual analyses providing a significant advance in enabling multi-center studies using EHR data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2023

Federated benchmarking of medical artificial intelligence with MedPerf

Medical artificial intelligence (AI) has tremendous potential to advance healthcare by supporting and contributing to the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving both healthcare provider and patient experience. Unlocking this potential requires systematic, quantitative evaluation of the performance of medical AI models on large-scale, heterogeneous data capturing diverse patient populations. Here, to meet this need, we introduce MedPerf, an open platform for benchmarking AI models in the medical domain. MedPerf focuses on enabling federated evaluation of AI models, by securely distributing them to different facilities, such as healthcare organizations. This process of bringing the model to the data empowers each facility to assess and verify the performance of AI models in an efficient and human-supervised process, while prioritizing privacy. We describe the current challenges healthcare and AI communities face, the need for an open platform, the design philosophy of MedPerf, its current implementation status and real-world deployment, our roadmap and, importantly, the use of MedPerf with multiple international institutions within cloud-based technology and on-premises scenarios. Finally, we welcome new contributions by researchers and organizations to further strengthen MedPerf as an open benchmarking platform.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Question-answering system extracts information on injection drug use from clinical notes

Background. Injection drug use (IDU) can increase mortality and morbidity. Therefore, identifying IDU early and initiating harm reduction interventions can benefit individuals at risk. However, extracting IDU behaviors from patients’ electronic health records (EHR) is difficult because there is no other structured data available, such as International Classification of Disease (ICD) codes, and IDU is most often documented in unstructured free-text clinical notes. Although natural language processing can efficiently extract this information from unstructured data, there are no validated tools. Methods. Here, to address this gap in clinical information, we design a question-answering (QA) framework to extract information on IDU from clinical notes for use in clinical operations. Our framework involves two main steps: (1) generating a gold-standard QA dataset and (2) developing and testing the QA model. We use 2323 clinical notes of 1145 patients curated from the US Department of Veterans Affairs (VA) Corporate Data Warehouse to construct the gold-standard dataset for developing and evaluating the QA model. We also demonstrate the QA model’s ability to extract IDU-related information from temporally out-of-distribution data. Results. Here, we show that for a strict match between gold-standard and predicted answers, the QA model achieves a 51.65% F1 score. For a relaxed match between the gold-standard and predicted answers, the QA model obtains a 78.03% F1 score, along with 85.38% Precision and 79.02% Recall scores. Moreover, the QA model demonstrates consistent performance when subjected to temporally out-of-distribution data. Conclusions. Our study introduces a QA framework designed to extract IDU information from clinical notes, aiming to enhance the accurate and efficient detection of people who inject drugs, extract relevant information, and ultimately facilitate informed patient care.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Rapid, antibiotic incubation-free determination of tuberculosis drug resistance using machine learning and Raman spectroscopy

Tuberculosis (TB) is the world’s deadliest infectious disease, with over 1.5 million deaths and 10 million new cases reported anually. The causative organism Mycobacterium tuberculosis (Mtb) can take nearly 40 d to culture, a required step to determine the pathogen’s antibiotic susceptibility. Both rapid identification and rapid antibiotic susceptibility testing of Mtb are essential for effective patient treatment and combating antimicrobial resistance. Here, we demonstrate a rapid, culture-free, and antibiotic incubation-free drug susceptibility test for TB using Raman spectroscopy and machine learning. We collect few-to-single-cell Raman spectra from over 25,000 cells of the Mtb complex strain Bacillus Calmette-Guérin (BCG) resistant to one of the four mainstay anti-TB drugs, isoniazid, rifampicin, moxifloxacin, and amikacin, as well as a pan-susceptible wildtype strain. By training a neural network on this data, we classify the antibiotic resistance profile of each strain, both on dried samples and on patient sputum samples. On dried samples, we achieve >98% resistant versus susceptible classification accuracy across all five BCG strains. In patient sputum samples, we achieve ~79% average classification accuracy. We develop a feature recognition algorithm in order to verify that our machine learning model is using biologically relevant spectral features to assess the resistance profiles of our mycobacterial strains. Finally, we demonstrate how this approach can be deployed in resource-limited settings by developing a low-cost, portable Raman microscope that costs <$5,000. We show how this instrument and our machine learning model enable combined microscopy and spectroscopy for accurate few-to-single-cell drug susceptibility testing of BCG.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Pre- and post-treatment image-based dosimetry in 90 Y-microsphere radioembolization using the TOPAS Monte Carlo toolkit

Objective.To evaluate the pre-treatment and post-treatment imaging-based dosimetry of patients treated with 90Y-microspheres, including accurate estimations of dose to tumor, healthy liver and lung. To do so, the Monte Carlo (MC) TOPAS platform is in this work extended towards its utilization in radionuclide therapy. Approach. In this study, five patients treated at the Massachusetts General Hospital were selected for this study. All patients had data for both pre-treatment SPECT-CT imaging using 99mTc-MAA as a surrogate of the 90Y-microspheres treatment and SPECT-CT imaging immediately after the 90Y activity administration. Pre- and post-treatment doses were computed with TOPAS using the SPECT images to localize the source positions and the CT images to account for tissue inhomoegeneities. We compared our results with analytical calculations following the voxel-based MIRD scheme. Main results. TOPAS results largely agreed with the MIRD-based calculations in soft tissue regions: the average difference in mean dose to the liver was 0.14 Gy GBq -1 (2.6%). However, dose distributions in the lung differed considerably: absolute differences in mean doses to the lung ranged from 1.2 to 6.3 Gy GBq -1 and relative differences from 153% to 231%. We also found large differences in the intra-hepatic dose distributions between pre- and post-treatment imaging, but only limited differences in the pulmonary dose. Significance. Doses to lung were found to be higher using TOPAS with respect to analytical calculations which may significantly underestimate dose to the lung, suggesting the use of MC methods for 90Y dosimetry. According to our results, pre-treatment imaging may still be representative of dose to lung in these treatments.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Coxsackievirus B4 sewage-isolate induces pancreatitis after oral infection of mice

ABSTRACT Numerous serotypes which belong to the genus Enterovirus (EV) show variability in their virulence and clinical manifestations. They are also known to undergo changes caused by mutations and recombination during their circulation in the environment and the population. Various EV serotypes are prevalent in groundwater, wastewater and surface waters. Our previous studies showed that oral infection induces pancreatitis depending on specific conditions, such as gravidity, in an outbred murine model. Our aim in the present study was to further explore the pancreatic histopathology in an outbred mouse model following oral infection with clinical isolates from a patient who had aseptic meningitis and an isolate from a treated-sewage sample recovered from the residential area of the patient. The isolates were identified as coxsackievirus B4 (CVB4) in tissue culture. The CVB4 sewage-isolate induced pancreatitis after oral infection. In contrast, pancreatitis was absent following infection with the clinical isolates. Comparison of polyprotein sequences showed that the treated-sewage strains differed from the patient's isolates by 9 and 11 amino acids. We conclude that the isolates of clinical and environmental origin differed in their pathogenic properties and showed genetic variation.

Benkoova, Brigita↗
DOE OSTI2025

Artemether-Lumefantrine Treatment Selects Plasmodium falciparum Multidrug Resistance 1 ( pfmdr1 ) Increased Copy Number Among African Malaria Infections

Abstract Background Decreased efficacy of artemether-lumefantrine, the globally most used antimalarial, has recently emerged in Africa. Methods An efficacy trial was carried out based on directly observed artemether-lumefantrine therapy at Bengo, Northern Angola. One-hundred Plasmodium falciparum uncomplicated malaria patients (2–10 years old) were enrolled, hospitalized for the treatment period, and followed up for 42 days. Polymerase chain reaction (PCR) correction was performed with pfmsp1/2 plus glurp, with analysis considering 2 or 3 coincident markers. Infections were tested by quantitative PCR (qPCR) for pfmdr1 copy number (pfmdr1×N), a potential P. falciparum marker of lumefantrine resistance previously identified in the region. In vitro clone mixtures were built and used to determine the relation between qPCR copy number scores and actual intrainfection quantitative fractions of pfmdr1×N. Results We observed a significant posttreatment selection of gene amplification, suggesting a role in the parasite in vivo response to this drug. pfmdr1×2 qPCR scores of 1.3, 1.4, and 1.5 were determined to correspond to 15%, 25%, and 35% intrainfection rates. Patients carrying infections with a score ≥1.4 at baseline were linked to decreased artemether-lumefantrine day 42 efficacy (79% vs 97% single-copy pfmdr1). All infections were pfmdr1 N86 carriers and no pfk13 mutations were found. Conclusions Our study suggests pfmdr1×N as a marker of P. falciparum in vivo response to lumefantrine in Africa, while indicating patients carrying infections with a pretreatment pfmdr1×N score ≥1.4 before treatment are a group experiencing decreased artemether-lumefantrine performance.

Fançony, Claudia (ORCID:0000000344211769)↗
DOE OSTI2023

Effects of Substance Use and Antisocial Personality on Neuroimaging-Based Machine Learning Prediction of Schizophrenia

Abstract Background and hypothesis Neuroimaging-based machine learning (ML) algorithms have the potential to aid the clinical diagnosis of schizophrenia. However, literature on the effect of prevalent comorbidities such as substance use disorder (SUD) and antisocial personality (ASPD) on these models’ performance has remained unexplored. We investigated whether the presence of SUD or ASPD affects the performance of neuroimaging-based ML models trained to discern patients with schizophrenia (SCH) from controls. Study design We trained an ML model on structural MRI data from public datasets to distinguish between SCH and controls (SCH = 347, controls = 341). We then investigated the model’s performance in two independent samples of individuals undergoing forensic psychiatric examination: sample 1 was used for sensitivity analysis to discern ASPD (N = 52) from SCH (N = 66), and sample 2 was used for specificity analysis to discern ASPD (N = 26) from controls (N = 25). Both samples included individuals with SUD. Study results In sample 1, 94.4% of SCH with comorbid ASPD and SUD were classified as SCH, followed by patients with SCH + SUD (78.8% classified as SCH) and patients with SCH (60.0% classified as SCH). The model failed to discern SCH without comorbidities from ASPD + SUD (AUC = 0.562, 95%CI = 0.400–0.723). In sample 2, the model’s specificity to predict controls was 84.0%. In both samples, about half of the ASPD + SUD were misclassified as SCH. Data-driven functional characterization revealed associations between the classification as SCH and cognition-related brain regions. Conclusion Altogether, ASPD and SUD appear to have effects on ML prediction performance, which potentially results from converging cognition-related brain abnormalities between SCH, ASPD, and SUD.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Characterizing Sub-Cohorts via Data Normalization and Representation Learning

The process of identifying a cohort of interest is a very challenging task. It requires manually inspecting many patient records of complex structure that might include medical coding errors and missing data. This paper presents a computational pipeline for refining the process of cohort selection based on medical concepts recorded in the electronic health records (EHRs). The pipeline extracts EHR data for a given cohort and normalizes this data using standard vocabularies. Then a stacked denoising autoencoder is used to embed the normalized patient vectors in a low dimensional space, where the patients are subsequently clustered into sub-cohorts. The goal is to represent the cohort in a standard format and abstract variants of sub-populations. As a use-case, we applied the pipeline to 1.8 million Veterans diagnosed with major depressive disorder (MDD), and identified four meaningful sub-cohorts using the features learned by the autoencoder. Then, each sub-cohort was explored using a set of keywords for interpretation.

Rush III, Everett↗
DOE OSTI2021

Mixture Model Framework for Traumatic Brain Injury Prognosis Using Heterogeneous Clinical and Outcome Data

Prognoses of Traumatic Brain Injury (TBI) outcomes are neither easily nor accurately determined from clinical indicators. This is due in part to the heterogeneity of damage inflicted to the brain, ultimately resulting in diverse and complex outcomes. Using a data-driven approach on many distinct data elements may be necessary to describe this large set of outcomes and thereby robustly depict the nuanced differences among TBI patients’ recovery. In this work, we develop a method for modeling large heterogeneous data types relevant to TBI. Our approach is geared toward the probabilistic representation of mixed continuous and discrete variables with missing values. The model is trained on a dataset encompassing a variety of data types, including demographics, blood-based biomarkers, and imaging findings. In addition, it includes a set of clinical outcome assessments at 3, 6, and 12 months post-injury. The model is used to stratify patients into distinct groups in an unsupervised learning setting. We use the model to infer outcomes using input data, and show that the collection of input data reduces uncertainty of outcomes over a baseline approach. In addition, we quantify the performance of a likelihood scoring technique that can be used to self-evaluate the extrapolation risk of prognosis on unseen patients.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Anomaly Detection in Electronic Health Records Across Hospital Networks: Integrating Machine Learning With Graph Algorithms

In a large hospital system, a network of hospitals relies on electronic health records (EHRs) to make informed decisions regarding their patients in various clinical domains. Consequently, the dependability of the health information technology (HIT) systems responsible for collecting EHR data is of utmost importance for patient safety. Recently, novel methods and tools aimed at identifying anomalies in EHR data to bolster the reliability of HIT systems have been introduced. However, these existing methods and tools primarily concentrate on individual hospitals, which limits our understanding of system-wide anomalous events and their potential impact on patient safety across multiple hospitals. In this article, we introduce a new approach to detecting anomalies in EHR data within a network of hospitals. This is achieved by combining advanced machine learning techniques with graph algorithms to create a tool capable of swiftly identifying and responding to deviations. Our proposed approach employs a combination of five machine learning models, harnessing the unique strengths of each model to provide a more robust detection system. The detected anomalies are then represented as graphs, allowing us to recognize patterns across the hospital network. This aids in identifying anomalies that span multiple medical facilities, potentially indicating broader system-level risks. Extensive real-world testing of our approach demonstrated its ability to offer actionable insights compared to existing methods. Additionally, its scalable design ensures seamless integration into existing HIT infrastructures.

Niu, Haoran [Oak Ridge National Laboratory (ORNL),↗
DOE OSTI2021

Combining glomerular basement membrane and tubular basement membrane assessment improves the prediction of diabetic end‐stage renal disease

Abstract Background To address the prognostic value of combining tubular basement membrane (TBM) and glomerular basement membrane (GBM) thickness in diabetic nephropathy (DN). Methods This retrospective study enrolled 110 patients with type 2 diabetes and biopsy‐proven DN from 2011 to 2018. The pathological findings were confirmed according to the Renal Pathology Society classifications. GBM and TBM thicknesses were determined using the Haas' direct measurement/arithmetic mean method and orthogonal intercept method, respectively. Cox proportional hazard models were used to investigate the hazard ratios (HRs) for the influence of combined GBM and TBM thickness for predicting end‐stage renal disease (ESRD). Results Patients were assigned to three groups according to the median GBM and TBM thickness: GBM lo TBM lo (GBM < 681 nm and TBM < 1200 nm), GBM hi TBM lo /GBM lo TBM hi (GBM ≥ 681 nm and TBM < 1200 nm, or GBM < 681 nm and TBM ≥ 1200 nm), and GBM hi TBM hi (GBM ≥ 681 nm and TBM ≥ 1200 nm). The GBM hi TBM lo /GBM lo TBM hi and GBM hi TBM hi groups displayed poorer renal function, a more severe glomerular classification and interstitial inflammation, and poorer renal survival rates than the GBM lo TBM lo group The GBM hi TBM lo /GBM lo TBM hi and GBM hi TBM hi groups had adjusted HRs of 1.49 (95% confidence interval [CI], 1.21‐9.75) and 3.07 (95% CI, 2.87‐12.78), respectively, compared with the GBM lo TBM lo group. Conclusions TBM thickness enhanced GBM thickness for renal prognosis in patients with type 2 diabetes.

Zhao, Lijun↗
DOE OSTI2022

Pleural homocysteine for malignant pleural effusion: A prospective and double‐blind diagnostic test accuracy study

Abstract Objective To assess the accuracy of pleural fluid homocysteine for discriminating malignant pleural effusion (MPE) and benign pleural effusion (BPE). Methods A total of 194 patients from two cohorts (Hohhot and Changshu) with undiagnosed pleural effusion were prospectively enrolled. Their pleural homocysteine was measured, and its diagnostic accuracy and net benefit for MPE were analyzed by receiver operating characteristic (ROC) curve analysis and decision curve analysis, respectively. Results In the Hohhot cohort ( n = 136) and the Changshu cohort ( n = 58), MPE patients had significantly higher homocysteine levels than BPE patients. The areas under the ROC curves of homocysteine for the diagnosis of MPE were 0.61 ( p = 0.027) and 0.59 ( p = 0.247), respectively. The decision curves of homocysteine were close to the reference line in both the Hohhot cohort and the Changshu cohort. Conclusion The diagnostic accuracy of pleural fluid homocysteine for MPE was low.

Cao, Xi‐Shan↗