On the need for an international effort to capture, share and use crystallization screening data

Development of an ontology for the description of crystallization experiments and results is proposed

AI-defined cardiac anatomy Improves Risk Stratification of Hybrid Perfusion Imaging

Background: CTAC improves perfusion quantification of hybrid myocardial perfusion imaging (MPI) by correcting for attenuation artifacts. AI can automatically measure coronary artery calcium (CAC) from CTAC to improve risk prediction but could potentially derive additional anatomic features. Objectives: We evaluated artificial intelligence (AI)-based derivation of cardiac anatomy from CT attenuation correction (CTAC) and assessed its added prognostic utility.Methods: We considered consecutive patients without known coronary artery disease who underwent SPECT/CT MPI at 3 separate centers. Previously validated AI models were used to segment CAC and cardiac structures (left atrium[LA], left ventricle[LV], right atrium[RA], and right ventricle[RV] volume and LV mass) from CTAC. We evaluated associations with major adverse cardiovascular events (MACE), which included death, myocardial infarction, unstable angina, or revascularization.Results: In total, 7,613 patients were included with median age 64. During median follow-up of 2.4 (interquartile range 1.3 – 3.4) years, MACE occurred in 1,045 (13.7%) patients. Fully automated AI processing took an average of 6.2 ± 0.2 seconds for CAC and 15.8 ± 3.2 seconds for cardiac volumes and LV mass. Patients in the highest quartile of LV mass and LA, LV, RA, and RV volume were at significantly increased risk of MACE compared to patients in the lowest quartile, with a hazard ratio ranging from 1.46 to 3.31. The addition of all CT-based volumes and CT-based LV mass improved continuous net reclassification index by 23.1%.Conclusions: AI can automatically derive LV mass and cardiac chamber volumes from CT attenuation imaging, significantly improving cardiovascular risk assessment for hybrid perfusion imaging.<br/

First-in-human study of TK-positive oncolytic vaccinia virus delivered by adipose stromal vascular fraction cells.

BACKGROUND: ACAM2000, a thymidine kinase (TK)-positive strain of vaccinia virus, is the current smallpox vaccine in the US. Preclinical testing demonstrated potent oncolytic activity of ACAM2000 against several tumor types. This Phase I clinical trial of ACAM2000 delivered by autologous adipose stromal vascular fraction (SVF) cells was conducted to determine the safety and feasibility of such a treatment in patients with advanced solid tumors or acute myeloid leukemia (AML). METHODS: Twenty-four patients with solid tumors and two patients with AML participated in this open-label, non-randomized dose-escalation trial. All patients were treated with SVF derived from autologous fat and incubated for 15 min to 1 h with ACAM2000 before application. Six patients received systemic intravenous application only, one patient received intra-tumoral application only, 15 patients received combination intravenous with intra-tumoral deployment, 3 patients received intravenous and intra-peritoneal injection and 1 patient received intravenous, intra-tumoral and intra-peritoneal injections. Safety at each dose level of ACAM2000 (1.4 × 10 RESULTS: No serious toxicities (\u3e grade 2) were reported. Seven patients reported an adverse event (AE) in this study: self-limiting skin rashes, lasting 7 to 18 days-an expected adverse reaction to ACAM2000. No AEs leading to study discontinuation were reported. Viral DNA was detected in all patients\u27 blood samples immediately following treatment. Interestingly, in 8 patients viral DNA disappeared 1 day and re-appeared 1 week post treatment, suggesting active viral replication at tumor sites, and correlating with longer survival of these patients. No major increase in cytokine levels or correlation between cytokine levels and skin rashes was noted. We were able to assess some initial efficacy signals, especially when the ACAM2000/SVF treatment was combined with checkpoint inhibition. CONCLUSIONS: Treatment with ACAM2000/SVF in patients with advanced solid tumors or AML is safe and well tolerated, and several patients had signals of an anticancer effect. These promising initial clinical results merit further investigation of therapeutic utility. Trial registration Retrospectively registered (ISRCTN#10201650) on October 22, 2018

Time and event-specific deep learning for personalized risk assessment after cardiac perfusion imaging

Standard clinical interpretation of myocardial perfusion imaging (MPI) has proven prognostic value for predicting major adverse cardiovascular events (MACE). However, personalizing predictions to a specific event type and time interval is more challenging. We demonstrate an explainable deep learning model that predicts the time-specific risk separately for all-cause death, acute coronary syndrome (ACS), and revascularization directly from MPI and 15 clinical features. We train and test the model internally using 10-fold hold-out cross-validation (n = 20,418) and externally validate it in three separate sites (n = 13,988) with MACE follow-ups for a median of 3.1 years (interquartile range [IQR]: 1.6, 3.6). We evaluate the model using the cumulative dynamic area under receiver operating curve (cAUC). The best model performance in the external cohort is observed for short-term prediction - in the first six months after the scan, mean cAUC for ACS and all-cause death reaches 0.76 (95% confidence interval [CI]: 0.75, 0.77) and 0.78 (95% CI: 0.78, 0.79), respectively. The model outperforms conventional perfusion abnormality measures at all time points for the prediction of death in both internal and external validations, with improvement increasing gradually over time. Individualized patient explanations are visualized using waterfall plots, which highlight the contribution degree and direction for each feature. This approach allows the derivation of individual event probability as a function of time as well as patient- and event-specific risk explanations that may help draw attention to modifiable risk factors. Such a method could help present post-scan risk assessments to the patient and foster shared decision-making

Unsupervised learning to characterize patients with known coronary artery disease undergoing myocardial perfusion imaging

PURPOSE Patients with known coronary artery disease (CAD) comprise a heterogenous population with varied clinical and imaging characteristics. Unsupervised machine learning can identify new risk phenotypes in an unbiased fashion. We use cluster analysis to risk-stratify patients with known CAD undergoing single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI). METHODS From 37,298 patients in the REFINE SPECT registry, we identified 9221 patients with known coronary artery disease. Unsupervised machine learning was performed using clinical (23), acquisition (17), and image analysis (24) parameters from 4774 patients (internal cohort) and validated with 4447 patients (external cohort). Risk stratification for all-cause mortality was compared to stress total perfusion deficit (< 5%, 5-10%, ≥10%). RESULTS Three clusters were identified, with patients in Cluster 3 having a higher body mass index, more diabetes mellitus and hypertension, and less likely to be male, have dyslipidemia, or undergo exercise stress imaging (p < 0.001 for all). In the external cohort, during median follow-up of 2.6 [0.14, 3.3] years, all-cause mortality occurred in 312 patients (7%). Cluster analysis provided better risk stratification for all-cause mortality (Cluster 3: hazard ratio (HR) 5.9, 95% confidence interval (CI) 4.0, 8.6, p < 0.001; Cluster 2: HR 3.3, 95% CI 2.5, 4.5, p < 0.001; Cluster 1, reference) compared to stress total perfusion deficit (≥10%: HR 1.9, 95% CI 1.5, 2.5 p < 0.001; < 5%: reference). CONCLUSIONS Our unsupervised cluster analysis in patients with known CAD undergoing SPECT MPI identified three distinct phenotypic clusters and predicted all-cause mortality better than ischemia alone

Clinical phenotypes among patients with normal cardiac perfusion using unsupervised learning:a retrospective observational study

BACKGROUND: Myocardial perfusion imaging (MPI) is one of the most common cardiac scans and is used for diagnosis of coronary artery disease and assessment of cardiovascular risk. However, the large majority of MPI patients have normal results. We evaluated whether unsupervised machine learning could identify unique phenotypes among patients with normal scans and whether those phenotypes were associated with risk of death or myocardial infarction.METHODS: Patients from a large international multicenter MPI registry (10 sites) with normal perfusion by expert visual interpretation were included in this cohort analysis. The training population included 9849 patients, and external testing population 12,528 patients. Unsupervised cluster analysis was performed, with separate training and external testing cohorts, to identify clusters, with four distinct phenotypes. We evaluated the clinical and imaging features of clusters and their associations with death or myocardial infarction.FINDINGS: Patients in Clusters 1 and 2 almost exclusively underwent exercise stress, while patients in Clusters 3 and 4 mostly required pharmacologic stress. In external testing, the risk for Cluster 4 patients (20.2% of population, unadjusted hazard ratio [HR] 6.17, 95% confidence interval [CI] 4.64-8.20) was higher than the risk associated with pharmacologic stress (HR 3.03, 95% CI 2.53-3.63), or previous myocardial infarction (HR 1.82, 95% CI 1.40-2.36).INTERPRETATION: Unsupervised learning identified four distinct phenotypes of patients with normal perfusion scans, with a significant proportion of patients at very high risk of myocardial infarction or death. Our results suggest a potential role for patient phenotyping to improve risk stratification of patients with normal imaging results.FUNDING: This work was supported by the National Heart, Lung, and Blood Institute at the National Institutes of Health [R35HL161195 to PS]. The REFINE SPECT database was supported by the National Heart, Lung, and Blood Institute at the National Institutes of Health [R01HL089765 to PS]. MCW was supported by the British Heart Foundation [FS/ICRF/20/26002].</p

ViPR: an open bioinformatics database and analysis resource for virology research

The Virus Pathogen Database and Analysis Resource (ViPR, www.ViPRbrc.org) is an integrated repository of data and analysis tools for multiple virus families, supported by the National Institute of Allergy and Infectious Diseases (NIAID) Bioinformatics Resource Centers (BRC) program. ViPR contains information for human pathogenic viruses belonging to the Arenaviridae, Bunyaviridae, Caliciviridae, Coronaviridae, Flaviviridae, Filoviridae, Hepeviridae, Herpesviridae, Paramyxoviridae, Picornaviridae, Poxviridae, Reoviridae, Rhabdoviridae and Togaviridae families, with plans to support additional virus families in the future. ViPR captures various types of information, including sequence records, gene and protein annotations, 3D protein structures, immune epitope locations, clinical and surveillance metadata and novel data derived from comparative genomics analysis. Analytical and visualization tools for metadata-driven statistical sequence analysis, multiple sequence alignment, phylogenetic tree construction, BLAST comparison and sequence variation determination are also provided. Data filtering and analysis workflows can be combined and the results saved in personal ‘Workbenches’ for future use. ViPR tools and data are available without charge as a service to the virology research community to help facilitate the development of diagnostics, prophylactics and therapeutics for priority pathogens and other viruses

Approaching the Problem of Time with a Combined Semiclassical-Records-Histories Scheme

I approach the Problem of Time and other foundations of Quantum Cosmology using a combined histories, timeless and semiclassical approach. This approach is along the lines pursued by Halliwell. It involves the timeless probabilities for dynamical trajectories entering regions of configuration space, which are computed within the semiclassical regime. Moreover, the objects that Halliwell uses in this approach commute with the Hamiltonian constraint, H. This approach has not hitherto been considered for models that also possess nontrivial linear constraints, Lin. This paper carries this out for some concrete relational particle models (RPM's). If there is also commutation with Lin - the Kuchar observables condition - the constructed objects are Dirac observables. Moreover, this paper shows that the problem of Kuchar observables is explicitly resolved for 1- and 2-d RPM's. Then as a first route to Halliwell's approach for nontrivial linear constraints that is also a construction of Dirac observables, I consider theories for which Kuchar observables are formally known, giving the relational triangle as an example. As a second route, I apply an indirect method that generalizes both group-averaging and Barbour's best matching. For conceptual clarity, my study involves the simpler case of Halliwell 2003 sharp-edged window function. I leave the elsewise-improved softened case of Halliwell 2009 for a subsequent Paper II. Finally, I provide comments on Halliwell's approach and how well it fares as regards the various facets of the Problem of Time and as an implementation of QM propositions.Comment: An improved version of the text, and with various further references. 25 pages, 4 figure

Silymarin Ascending Multiple Oral Dosing Phase I Study in Noncirrhotic Patients With Chronic Hepatitis C

Silymarin, derived from the milk thistle plant Silybum marianum, is widely used for self-treatment of liver diseases, including hepatitis C virus (HCV), and its antiviral activity has been demonstrated in vitro and in HCV patients administered an intravenous formulation of the major silymarin flavonolignans, silybin A and silybin B. The safety and dose-exposure relationships of higher than customary oral doses of silymarin and its acute effects on serum HCV RNA were evaluated in noncirrhotic HCV patients. Four cohorts of 8 patients with well-compensated, chronic noncirrhotic HCV who failed interferon-based therapy were randomized 3:1 to silymarin or placebo. Oral doses of 140, 280, 560, or 700 mg silymarin were administered every 8 hours for 7 days. Steady-state exposures for silybin A and silybin B increased 11-fold and 38-fold, respectively, with a 5-fold increase in dose, suggesting nonlinear pharmacokinetics. No drug-related adverse events were reported, and no clinically meaningful reductions from baseline serum transaminases or HCV RNA titer were observed. Oral doses of silymarin up to 2.1 g per day were safe and well tolerated. The nonlinear pharmacokinetics of silybin A and silybin B suggests low bioavailability associated with customary doses of silymarin may be overcome with doses above 700 mg