An electronic health record cohort of Veterans with amyotrophic lateral sclerosis.

Objective: To assemble and characterize an electronic health record (EHR) dataset for a large cohort of US military Veterans diagnosed with ALS (Amyotrophic Lateral Sclerosis). Methods: An EHR dataset for 19,662 Veterans diagnosed with ALS between January 1, 2000 to December 31, 2020 was compiled from the Veterans Health Administration (VHA) EHR database by a query for ICD9 diagnosis (335.20) or ICD10 diagnosis (G12.21) for Amyotrophic Lateral Sclerosis. Results: The cohort is predominantly male (98.94%) and white (72.37%) with a median age at disease onset of 68 years and median survival from the date of diagnosis of 590 days. With the designation of ALS as a compensable illness in 2009, there was a subsequent increase in the number of Veterans diagnosed per year in the VHA, but no change in median survival. The cohort included a greater-than-expected proportion of individuals whose branch of service at the time of separation was the Army. Conclusions: The composition of the cohort reflects the VHA population who are at greatest risk for ALS. The greater than expected proportion of individuals whose branch of service at the time of separation was the Army suggests the possibility of a branch-specific risk factor for ALS.

COVID-19 outcomes in patients with cancer: Findings from the University of California health system database.

BACKGROUND: The interaction between cancer diagnoses and COVID-19 infection and outcomes is unclear. We leveraged a state-wide, multi-institutional database to assess cancer-related risk factors for poor COVID-19 outcomes. METHODS: We conducted a retrospective cohort study using the University of California Health COVID Research Dataset, which includes electronic health data of patients tested for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at 17 California medical centers. We identified adults tested for SARS-CoV-2 from 2/1/2020-12/31/2020 and selected a cohort of patients with cancer. We obtained demographic, clinical, cancer type, and antineoplastic therapy data. The primary outcome was hospitalization within 30d after the first positive SARS-CoV-2 test. Secondary outcomes were SARS-CoV-2 positivity and severe COVID-19 (intensive care, mechanical ventilation, or death within 30d after the first positive test). We used multivariable logistic regression to identify cancer-related factors associated with outcomes. RESULTS: We identified 409,462 patients undergoing SARS-CoV-2 testing. Of 49,918 patients with cancer, 1781 (3.6%) tested positive. Patients with cancer were less likely to test positive (RR 0.70, 95% CI: 0.67-0.74, p < 0.001). Among the 1781 SARS-CoV-2-positive patients with cancer, BCR/ABL-negative myeloproliferative neoplasms (RR 2.15, 95% CI: 1.25-3.41, p = 0.007), venetoclax (RR 2.96, 95% CI: 1.14-5.66, p = 0.028), and methotrexate (RR 2.72, 95% CI: 1.10-5.19, p = 0.032) were associated with greater hospitalization risk. Cancer and therapy types were not associated with severe COVID-19. CONCLUSIONS: In this large, diverse cohort, cancer was associated with a decreased risk of SARS-CoV-2 positivity. Patients with BCR/ABL-negative myeloproliferative neoplasm or receiving methotrexate or venetoclax may be at increased risk of hospitalization following SARS-CoV-2 infection. Mechanistic and comparative studies are needed to validate findings.

AI Meets Exascale Computing: Advancing Cancer Research With Large-Scale High Performance Computing.

The application of data science in cancer research has been boosted by major advances in three primary areas: (1) Data: diversity, amount, and availability of biomedical data; (2) Advances in Artificial Intelligence (AI) and Machine Learning (ML) algorithms that enable learning from complex, large-scale data; and (3) Advances in computer architectures allowing unprecedented acceleration of simulation and machine learning algorithms. These advances help build in silico ML models that can provide transformative insights from data including: molecular dynamics simulations, next-generation sequencing, omics, imaging, and unstructured clinical text documents. Unique challenges persist, however, in building ML models related to cancer, including: (1) access, sharing, labeling, and integration of multimodal and multi-institutional data across different cancer types; (2) developing AI models for cancer research capable of scaling on next generation high performance computers; and (3) assessing robustness and reliability in the AI models. In this paper, we review the National Cancer Institute (NCI) -Department of Energy (DOE) collaboration, Joint Design of Advanced Computing Solutions for Cancer (JDACS4C), a multi-institution collaborative effort focused on advancing computing and data technologies to accelerate cancer research on three levels: molecular, cellular, and population. This collaboration integrates various types of generated data, pre-exascale compute resources, and advances in ML models to increase understanding of basic cancer biology, identify promising new treatment options, predict outcomes, and eventually prescribe specialized treatments for patients with cancer.

Investigating drug induced changes in single, living lymphocytes based on Raman micro-spectroscopy.

Raman spectroscopy is a powerful tool for label-free, single cell characterization. In many reported studies, a Raman spectrum is acquired from a fraction of the cell volume and used as a representative signature of the whole cell to identify and discriminate between cell populations. It has remained an open question whether this is the most suitable approach since the spectra may not truly represent the cell as a whole and critical biochemical information could therefore be lost. To address this question, we developed a line-scan Raman microscope to acquire Raman images of single lymphocytes exposed to the chemotherapeutic drug doxorubicin for 24 to 96 hours. Principal component analysis was able to separate cells based on their drug-exposure times. Difference spectra on the mean data for the different time-points revealed that changes are related to a decrease in mean nucleic acid content and an increase in mean protein and lipid content. Vertex component analysis was used to extract the pure component spectra of lipids, nucleic acids, and proteins. Quantitative analysis of the data revealed that biochemical changes occurred at both local subcellular (i.e. molecular density) and global cellular (i.e. total observable molecular content) levels. However, significant differences between the trends in the local and global changes were observed. While local nucleic acid content decreased with increasing drug exposure time, the total cellular nucleic acid content remained relatively constant. For protein, local content remained relatively constant for all exposure times while the total protein content in the cell increased ∼3 fold. Lipid content in the entire cell increased ∼5 fold, compared to a smaller increase in lipid at the local level. These results show that valuable information about the biochemical changes throughout the entire cell can be missed if only Raman spectra of localized cell regions are used. These findings are expected to have a major impact on the future development of Raman spectroscopy for cytometry applications.

In vivo stability and photodynamic efficacy of fluorinated bacteriopurpurinimides derived from bacteriochlorophyll-a.

The stable bacteriopurpurinimide (788 nm, epsilon: 38,600 in CH2Cl2), obtained by reducing the corresponding unstable Schiff base (803 nm, epsilon: 50,900 in CH2Cl2) that was isolated by reacting bacteriopurpurin methyl ester with 3,5-bis-(trifluoromethyl)benzylamine, produced promising photosensitizing efficacy. 1H NMR, mass spectrometry, and HPLC analyses confirmed the structures of new bacteriopurpurinimides and the metabolic product. The preliminary in vivo photosensitizing efficacy of this stable bacteriopurpurinimide was determined in C3H mice bearing radiation induced fibrosarcoma tumors as a function of variable drug doses. A drug dose of 1.0 micromol/kg and light exposure of 135 J/cm2 (75 mW/cm2; 24 h postinjection) at 796 nm for 30 min produced a 60% long-term tumor cure (3/5 mice were tumor-free on day 90). Colocalization study of the stable bacteriopurpurinimide with MitoTracker Green confirmed some mitochondrial localization. The fluorescein-exclusion assay and histological staining of CD31 confirmed vascular stasis at various time points post-PDT (post photodynamic therapy). The treatment parameters (time for maximum drug uptake and wavelength for light irradiation) were determined by in vivo reflectance spectroscopy.

Multimodality agents for tumor imaging (PET, fluorescence) and photodynamic therapy. A possible "see and treat" approach.

Methyl 3-(1'-m-iodobenzyloxyethyl)-3-devinylpyropheophorbide-a (2), obtained in a sequence of reactions from pyropheophorbide-a (a chlorophyll-a derivative), was found to be a promising imaging agent and a photosensitizer for photodynamic therapy (PDT). The electrophilic aromatic iodination of the corresponding trimethylstannyl intermediate with Na124I in the presence of an Iodogen bead afforded 124I-labeled photosensitizer 4 with >95% radioactive specificity. In addition to drug-uptake, the light fluence and fluence rate that were used for the light treatment had a significant impact in long-term tumor cure. The iodo photosensitizer 2 (nonlabeled analogue of 4) produced 100% tumor cure (5/5 mice were tumor free on day 60) at a dose of 1.5 micromol/kg and a light dose of 128 J/cm2, 14 mW/cm2 for 2.5 h (lambda(max) 665 nm) at 24 h postinjection. The photosensitizer also showed promising tumor fluorescence and PET imaging ability. Our present work demonstrates the utility of the first 124I-labeled photosensitizer as a "multimodality agent", which could further be improved by using more tumor-avid and/or target-specific photosensitizers.

A first comparative study of purpurinimide-based fluorinated vs. nonfluorinated photosensitizers for photodynamic therapy.

A first report on the synthesis and comparative in vitro-in vivo photosensitizing efficacy of various fluorinated and the corresponding nonfluorinated, purpurinimide-based photosensitizers is discussed. In preliminary in vivo screening, compared with the nonfluorinated analogs, purpurinimides bearing trifluoromethyl substituents showed enhanced photosensitizing efficacy. Among compounds (isomers) with similar lipophilicity, the position of the substituents was found to play a decisive role in biological efficacy.