FDMine: a graph mining approach to predict and evaluate food-drug interactions

Food-drug interactions (FDIs) arise when nutritional dietary consumption regulates biochemical mechanisms involved in drug metabolism. Towards characterizing the nature of food’s influence on pharmacological treatment, it is essential to detect all possible FDIs. In this study, we propose FDMine, a novel systematic framework that models the FDI problem as a homogenous graph. In this graph, all nodes representing drug, food and food composition are referenced as chemical structures. This homogenous representation enables us to take advantage of reported drug-drug interactions for accuracy evaluation, especially when accessible ground truth for FDIs is lacking. Our dataset consists of 788 unique approved small molecule drugs with metabolism-related drug-drug interactions (DDIs) and 320 unique food items, composed of 563 unique compounds with 179 health effects. The potential number of interactions is 87,192 and 92,143 when two different versions of the graph referred to as disjoint and joint graphs are considered, respectively. We defined several similarity subnetworks comprising food-drug similarity (FDS), drug-drug similarity (DDS), and food-food similarity (FFS) networks, based on similarity profiles. A unique part of the graph is the encoding of the food composition as a set of nodes and calculating a content contribution score to re-weight the similarity links. To predict new FDI links, we applied the path category-based (path length 2 and 3) and neighborhood-based similarity-based link prediction algorithms. We calculated the precision@top (top 1%, 2%, and 5%) of the newly predicted links, the area under the receiver operating characteristic curve, and precision-recall curve. We have performed three types of evaluations to benchmark results using different types of interactions. The shortest path-based method has achieved a precision 84%, 60% and 40% for the top 1%, 2% and 5% of FDIs identified, respectively. We validated the top FDIs predicted using FDMine to demonstrate its applicability and we relate therapeutic anti-inflammatory effects of food items informed by FDIs. We hypothesize that the proposed framework can be used to gain new insights on FDIs. FDMine is publicly available to support clinicians and researchers.

Usefulness of quantitative 99mTc-pyrophosphate SPECT/CT for predicting the prognosis of patients with wild-type transthyretin cardiac amyloidosis

Purpose Wild-type transthyretin-related amyloidosis cardiomyopathy (ATTRwt-CM) is an increasingly recognized cause of heart failure especially in elderly patients. The purpose of the present study was to determine retrospectively whether the quantitative indices of 99mTc-pyrophosphate (PYP) SPECT/CT help to predict the prognosis of ATTRwt-CM patients when compared with other clinical parameters. Materials and methods Sixty-eight patients with biopsy-proven ATTRwt-CM who underwent PYP SPECT/CT were enrolled. Baseline clinical characteristics, echocardiographic parameters, and qualitative and/or quantitative indices of planar and SPECT/CT imaging in PYP scintigraphy for each patient were included. For quantitative analysis of SPECT/CT, the accumulation ratio of PYP in the septum, posterior, anterior, lateral, and apex walls to the cavity pool was calculated as the septal wall-to-cavity ratio (Se/C), lateral wall-to-cavity ratio (La/C), anterior wall-to-cavity ratio (An/C), inferior wall-to-cavity ratio (In/C), and apical wall-to-cavity ratio (Ap/C), respectively. Endpoints for prognostic accuracy evaluation were cardiac death or hospitalization due to heart failure. Event-free survival rate was evaluated through Cox proportional hazards regression analysis, providing estimated hazard ratios (HRs) with 95% confidence intervals (CIs) and Kaplan–Meier curves. Results High-sensitivity cardiac troponin T (hs-cTnT), La/C, age, interventricular septal thickness in diastole, and E/e′ ratio in the septal wall were significantly associated with event-free survival (P < 0.05). For a multivariable Cox proportional hazards analysis, hs-cTnT (HR 1.153; 95% CI 1.034–1.286; P < 0.01), La/C (HR 2.091; 95% CI 1.012–4.322; P = 0.046), and age (HR 1.116; 95% CI 1.007–1.238; P = 0.037) were significant independent prognostic factors. Conclusion This study indicated that the quantitative indices of PYP SPECT/CT can help to predict the prognosis of ATTRwt-CM patients.

Modelling of the reflectance profile of axisymmetrical radiolocation objects using the integral equation method

Based on the first-kind integral equation method for the electric field, the procedure and software for calculating the radar cross-section of axisymmetrical objects, bodies of revolution, are developed. Algorithms are proposed for computation of the matrix of mutual impedances and Green's function of a ring source providing the computation accuracy required for obtaining a stable solution. The method of solution approximation accuracy evaluation by azimuthal harmonics is proposed. Comparison with test examples is carried out and the applicability for solving real-world problems is shown.

A Lagrangian Backward Air Parcel Trajectories Clustering Framework

Many studies concerning atmosphere moisture paths use Lagrangian backward air parcel trajectories to determine the humidity sources for specific locations. Automatically grouping trajectories according to their geographical position simplifies and speeds up their analysis. In this paper, we propose a framework for clustering Lagrangian backward air parcel trajectories, from trajectory generation to cluster accuracy evaluation. We employ a novel clustering algorithm, called DenLAC, to cluster troposphere air currents trajectories. Our main contribution is representing trajectories as a one-dimensional array consisting of each trajectory’s points position vector directions. We empirically test our pipeline by employing it on several Lagrangian backward trajectories initiated from Břeclav District, Czech Republic.

A prospective diagnostic evaluation of accuracy of self-taken and healthcare worker-taken swabs for rapid COVID-19 testing

ObjectivesTo compare self-taken and healthcare worker (HCW)-taken throat/nasal swabs to perform rapid diagnostic tests (RDT) for SARS-CoV-2, and how these compare to RT-PCR. We hypothesised that self-taken samples are non-inferior for use with RDTs and in clinical and research settings could have substantial individual and public health benefit.DesignA prospective diagnostic accuracy evaluation as part of the ‘Facilitating Accelerated Clinical Evaluation of Novel Diagnostic Tests for COVID -19 (FALCON C-19), workstream C (undifferentiated community testing)’.SettingNHS Test and Trace drive-through community PCR testing site (Liverpool, UK). ParticipantsEligible participants 18 years or older with symptoms of COVID-19. 250 participants recruited; one withdrew before analysis.SamplingSelf-administered throat/nasal swab for the Covios® RDT, a trained HCW taken throat/nasal sample for PCR and HCW comparison throat/nasal swab for RDT.Main outcome measuresSensitivity, specificity, and positive and negative predictive values (PPV, NPV) were calculated; comparisons between self-taken and HCW-taken samples used McNemar’s test.ResultsSeventy-five participants (75/249, 30.1%) were positive by RT-PCR. RDTs with self-taken swabs had a sensitivity of 90.5% (67/74, 95% CI: 83.9-97.2), compared to 78.4% (58/74, 95% CI: 69.0-87.8) for HCW-taken swabs (absolute difference 12.2%, 95% CI: 4.7-19.6, p=0.003). Specificity for self-taken swabs was 99.4% (173/174, 95% CI: 98.3-100.0), versus 98.9% (172/174, 95% CI: 97.3-100.0) for HCW-taken swabs (absolute difference 0.6%, 95% CI: 0.5-1.7, p=0.317). The PPV of self-taken RDTs (98.5%, 67/68, 95% CI: 95.7-100.0) and HCW-taken RDTs (96.7%, 58/60, 95% CI 92.1-100.0) were not significantly different (p=0.262). However, the NPV of self-taken swab RDTs was significantly higher (96.1%, 173/180, 95% CI: 93.2-98.9) than HCW-taken RDTs (91.5%, 172/188, 95% CI 87.5-95.5, p=0.003).ConclusionSelf-taken swabs for COVID-19 testing offer substantial individual benefits in terms of convenience, accuracy, and reduced risk of transmitting infection. Our results demonstrate that self-taken throat/nasal samples can be used by lay individuals as part of rapid testing programmes for symptomatic adults.Trial RegistrationIRAS ID:28422, clinical trial ID: NCT04408170SummaryWhat is already known on this topic?Rapid diagnostic tests (RDTs)for SARS-CoV-2 Ag are a cheaper point-of-care alternative to RT-PCR for diagnosing COVID-19 disease.The accuracy of tests can vary dependent on sampling technique, test processing and reading of results.What this study adds?Self-taken throat-nasal swabs for RDTs can be used by symptomatic adults to give reliable results to diagnose SARS-CoV-2.Self-sampling can be implemented with little training and no assistance.

A convolutional neural network highlights mutations relevant to antimicrobial resistance in Mycobacterium tuberculosis

Long diagnostic wait times hinder international efforts to address multi-drug resistance in M. tuberculosis. Pathogen whole genome sequencing, coupled with statistical and machine learning models, offers a promising solution. However, generalizability and clinical adoption have been limited in part by a lack of interpretability and verifiability, especially in deep learning methods. Here, we present a deep convolutional neural network (CNN) that predicts the antibiotic resistance phenotypes of M. tuberculosis isolates. The CNN performs with state-of-the-art levels of predictive accuracy. Evaluation of salient sequence features permits biologically meaningful interpretation and validation of the CNN’s predictions, with promising repercussions for functional variant discovery, clinical applicability, and translation to phenotype prediction in other organisms.

Comparison of Different Multispectral Images to Map Hydrothermal Alteration Zones in Kokap, Kulon Progo

Using remote sensing data for hydrothermal alteration mapping beside saving time and reducing cost leads to increased accuracy. In this study, the result of multispectral remote sensing tehcniques has been compare for manifesting hydrothermal alteration in Kokap, Kulon Progo. Three multispectral images, including ASTER, Landsat 8, and Sentinel-2, were compared in order to find the highest overall accuracy using principle component analysis (PCA) and directed component analysis (DPC). Several subsets band combinations were used as PCA and DPC input to targeting the key mineral of alteration. Multispectral classification with the maximum likelihood algorithm was performed to map the alteration types based on training and testing data and followed by accuracy evaluation. Two alteration zones were succeeded to be mapped: argillic zone and propylitic zone. Results of these image classification techniques were compared with known alteration zones from previous study. DPC combination of band ratio images of 5:2 and 6:7 of Landsat 8 imagery yielded a classification accuracy of 56.4%, which was 5.05% and 10.13% higher than those of the ASTER and Sentinel-2 imagery. The used of DEM together with multispectral images was increase the accuracy of hydrothermal alteration mapping in the study area.