Assessment of artificial intelligence (AI) reporting methodology in glioma MRI studies using the Checklist for AI in Medical Imaging (CLAIM)

Purpose: The Checklist for Artificial Intelligence in Medical Imaging (CLAIM) is a recently released guideline designed for the optimal reporting methodology of artificial intelligence (AI) studies. Gliomas are the most common form of primary malignant brain tumour and numerous outcomes derived from AI algorithms such as grading, survival, treatment-related effects and molecular status have been reported. The aim of the study is to evaluate the AI reporting methodology for outcomes relating to gliomas in magnetic resonance imaging (MRI) using the CLAIM criteria. Methods: A literature search was performed on three databases pertaining to AI augmentation of glioma MRI, published between the start of 2018 and the end of 2021 Results: A total of 4308 articles were identified and 138 articles remained after screening. These articles were categorised into four main AI tasks: grading (n= 44), predicting molecular status (n= 50), predicting survival (n= 25) and distinguishing true tumour progression from treatment-related effects (n= 10). The average CLAIM score was 20/42 (range: 10–31). Studies most consistently reported the scientific background and clinical role of their AI approach. Areas of improvement were identified in the reporting of data collection, data management, ground truth and validation of AI performance. Conclusion: AI may be a means of producing high-accuracy results for certain tasks in glioma MRI; however, there remain issues with reporting quality. AI reporting guidelines may aid in a more reproducible and standardised approach to reporting and will aid in clinical integration

A Hybrid Monitor Assisted Fault Injection Environment

Coordinated Science Laboratory was formerly known as Control Systems LaboratoryJoint Services Electronics Program / N00014-91-J-1116Tandem Computers, Inc.Department of the Navy, Office of the Chief of Naval Research / N00014-91-J-111

Early respiratory diagnosis: benefits of enhanced lung function assessment.

INTRODUCTION: The National Health Service for England Long Term Plan identifies respiratory disease as one of its priority workstreams. To assist with earlier and more accurate diagnosis of lung disease they recommend improvement in delivery of quality-assured spirometry. However, there is a likelihood that patients will present with abnormal gas exchange when spirometry results are normal and therefore there will be a proportion of patients whose time to diagnosis is still protracted. We wished to determine the incidence rate of this occurring within our Trust. METHODS: A retrospective review of all patients attending the lung function laboratory for their first pulmonary function assessment from June 2006 to December 2020 was undertaken. Forced expiratory volume in 1 s/forced vital capacity (FEV1/FVC) >-1.64 standardised residual (SR) was used to confirm no obstructive lung function abnormality and FVC >-1.64 SR to confirm no suggestion of a restrictive lung function abnormality. Lung gas transfer for carbon monoxide (TLCO) and transfer coefficient of the lung for carbon monoxide (KCO) <-1.64 SR confirmed the presence of a gas exchange abnormality. Spirometry and gas transfer reference values generated by the Global Lung Initiative were used to determine normality. RESULTS: Of 12 835 eligible first visits with normal FEV1/FVC and FVC, 4856 (37.8%) were identified as having an abnormally low TLCO and 3302 (25.7%) presenting with an abnormally low KCO. Of 3494 with FEV1/FVC SR <-1.64, 3316 also had a ratio of <0.70, meaning 178 (5%) of patients in this cohort would have been misclassified as having obstructive lung disease using the 0.70 cut-off recommended by the Global Initiative for Chronic Obstructive Lung Disease for diagnosing obstructive lung disease. DISCUSSION: In conclusion, to assist with ensuring more accurate and timely diagnosis of lung disease and enhance patients' diagnostic pathway, we recommend the performance of lung gas transfer measurements alongside spirometry in all healthcare settings. To assess and monitor gas transfer at the earliest opportunity we recommend this is implemented into new models being developed within community hubs. This will increase the identification of lung function abnormalities and provide patients with a definitive diagnosis earlier

The Prospect of Detecting Volcanic Signatures on an ExoEarth Using Direct Imaging

The James Webb Space Telescope (JWST) has provided the first opportunity to study the atmospheres of terrestrial exoplanets and estimate their surface conditions. Earth-sized planets around Sun-like stars are currently inaccessible with JWST however, and will have to be observed using the next generation of telescopes with direct imaging capabilities. Detecting active volcanism on an Earth-like planet would be particularly valuable as it would provide insight into its interior, and provide context for the commonality of the interior states of Earth and Venus. In this work we used a climate model to simulate four exoEarths over eight years with ongoing large igneous province eruptions with outputs ranging from 1.8-60 Gt of sulfur dioxide. The atmospheric data from the simulations were used to model direct imaging observations between 0.2-2.0 $\mu$m, producing reflectance spectra for every month of each exoEarth simulation. We calculated the amount of observation time required to detect each of the major absorption features in the spectra, and identified the most prominent effects that volcanism had on the reflectance spectra. These effects include changes in the size of the O$_3$, O$_2$, and H$_2$O absorption features, and changes in the slope of the spectrum. Of these changes, we conclude that the most detectable and least ambiguous evidence of volcanism are changes in both O$_3$ absorption and the slope of the spectrum.Comment: 13 pages, 5 figures, 4 tables, Accepted for publication in AJ (September 26, 2023

Rare coding variants in RCN3 are associated with blood pressure

Background: While large genome-wide association studies have identified nearly one thousand loci associated with variation in blood pressure, rare variant identification is still a challenge. In family-based cohorts, genome-wide linkage scans have been successful in identifying rare genetic variants for blood pressure. This study aims to identify low frequency and rare genetic variants within previously reported linkage regions on chromosomes 1 and 19 in African American families from the Trans-Omics for Precision Medicine (TOPMed) program. Genetic association analyses weighted by linkage evidence were completed with whole genome sequencing data within and across TOPMed ancestral groups consisting of 60,388 individuals of European, African, East Asian, Hispanic, and Samoan ancestries. Results: Associations of low frequency and rare variants in RCN3 and multiple other genes were observed for blood pressure traits in TOPMed samples. The association of low frequency and rare coding variants in RCN3 was further replicated in UK Biobank samples (N = 403,522), and reached genome-wide significance for diastolic blood pressure (p = 2.01 × 10- 7). Conclusions: Low frequency and rare variants in RCN3 contributes blood pressure variation. This study demonstrates that focusing association analyses in linkage regions greatly reduces multiple-testing burden and improves power to identify novel rare variants associated with blood pressure traits