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Participant motion during functional magnetic resonance image (fMRI) acquisition produces spurious signal fluctuations that can confound measures of functional connectivity. Without mitigation, motion artifact can bias statistical inferences about relationships between connectivity and individual differences. To counteract motion artifact, this protocol describes the implementation of a validated, high-performance denoising strategy that combines a set of model features, including physiological signals, motion estimates, and mathematical expansions, to target both widespread and focal effects of subject movement. This protocol can be used to reduce motion-related variance to near zero in studies of functional connectivity, providing up to a 100-fold improvement over minimal-processing approaches in large datasets. Image denoising requires 40 min to 4 h of computing per image, depending on model specifications and data dimensionality. The protocol additionally includes instructions for assessing the performance of a denoising strategy. Associated software implements all denoising and diagnostic procedures, using a combination of established image-processing libraries and the eXtensible Connectivity Pipeline (XCP) software.

Diagnostic errors are costly and they can contribute to adverse patient outcomes, including avoidable deaths. Differential diagnosis (DDX) generators are electronic tools that may facilitate the diagnostic process. We conducted a systematic review and meta-analysis to investigate the efficacy and utility of DDX generators. We undertook a comprehensive search of the literature including 16 databases from inception to May 2015 and specialist patient safety databases. We also searched the reference lists of included studies. Article screening, selection and data extraction were independently conducted by 2 reviewers. 36 articles met the eligibility criteria and the pooled accurate diagnosis retrieval rate of DDX tools was high with high heterogeneity (pooled rate = 0.70, 95% CI = 0.63 to 0.77; I2 = 97%, p<0.0001). DDX generators did not demonstrate improved diagnostic retrieval compared to clinicians but small improvements were seen in the before and after studies where clinicians had the opportunity to revisit their diagnoses following DDX generator consultation. Clinical utility data generally indicated high levels of user satisfaction and significant reductions in time taken to use for newer web-based tools. Lengthy differential lists and their low relevance were areas of concern and have the potential to increase diagnostic uncertainty. Data on the number of investigations ordered and on cost-effectiveness remain inconclusive. DDX generators have the potential to improve diagnostic practice among clinicians. However, the high levels of heterogeneity, the variable quality of the reported data and the minimal benefits observed for complex cases suggest caution. Further research needs to be undertaken in routine clinical settings with greater consideration of enablers and barriers which are likely to impact on DDX use before their use in routine clinical practice can be recommended.

Variations in paraspinal muscle cross-sectional area (CSA) and composition, particularly of the multifidus muscle, have been of interest with respect to risk of, and recovery from, low back pain problems. Several investigators have reported on the reliability of such muscle measurements using various protocols and image analysis programs. However, there is no standard protocol for tissue segmentation, nor has there been an investigation of reliability or agreement of measurements using different software. The purpose of this study was to provide a detailed muscle measurement protocol and determine the reliability and agreement of associated paraspinal muscle composition measurements obtained with 2 commonly used image analysis programs: OsiriX and ImageJ. This was a measurement reliability study. Lumbar magnetic resonance images of 30 individuals were randomly selected from a cohort of patients with various low back conditions. Muscle CSA and composition measurements were acquired from axial T2-weighted magnetic resonance images of the multifidus muscle, the erector spinae muscle, and the 2 muscles combined at L4-L5 and S1 for each participant. All measurements were repeated twice using each software program, at least 5 days apart. The assessor was blinded to all earlier measurements. The intrarater reliability and standard error of measurement (SEM) were comparable for most measurements obtained using OsiriX or ImageJ, with reliability coefficients (intraclass correlation coefficients [ICCs]) varying between .77 and .99 for OsiriX and .78 and .99 for ImageJ. There was similarly excellent agreement between muscle composition measurements using the 2 software applications (inter-software ICCs = .81-.99). The high degree of inter-software measurement reliability may not generalize to protocols using other commercial or custom-made software. The proposed method to investigate paraspinal muscle CSA, composition, and side-to-side asymmetry was highly reliable, with excellent agreement between the 2 software programs.

Chest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. Artificial intelligence (AI) has the potential to aid in rapid evaluation of CT scans for differentiation of COVID-19 findings from other clinical entities. Here we show that a series of deep learning algorithms, trained in a diverse multinational cohort of 1280 patients to localize parietal pleura/lung parenchyma followed by classification of COVID-19 pneumonia, can achieve up to 90.8% accuracy, with 84% sensitivity and 93% specificity, as evaluated in an independent test set (not included in training and validation) of 1337 patients. Normal controls included chest CTs from oncology, emergency, and pneumonia-related indications. The false positive rate in 140 patients with laboratory confirmed other (non COVID-19) pneumonias was 10%. AI-based algorithms can readily identify CT scans with COVID-19 associated pneumonia, as well as distinguish non-COVID related pneumonias with high specificity in diverse patient populations. Chest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. Here, the authors present a multinational study on the application of deep learning algorithms for COVID-19 diagnosis against multiple lung conditions as controls.

The social-ecological systems framework (SESF) is arguably the most comprehensive conceptual framework for diagnosing interactions and outcomes in social-ecological systems (SES). This article systematically reviews the literature applying and developing the SESF and discusses methodological challenges for its continued use and development. Six types of research approaches using the SESF are identified, as well as the context of application, types of data used, and commonly associated concepts. The frequency of how each second-tier variable is used across articles is analyzed. A summary list of indicators used to measure each second-tier variable is provided. Articles suggesting modifications to the framework are summarized and linked to the specific variables. The discussion reflects on the results and focuses on methodological challenges for applying the framework. First, how the SESF is historically related to commons and collective action research. This affects its continued development in relation to inclusion criteria for variable modification and discourse in the literature. The framework may evolve into separate modified versions for specific resource use sectors (e.g., forestry, fisheries, food production, etc.), and a general framework would aggregate the generalizable commonalities between them. Methodological challenges for applying the SESF are discussed related to research design, transparency, and cross-case comparison. These are referred to as “methodological gaps” that allow the framework to be malleable to context but create transparency, comparability, and data abstraction issues. These include the variable-definition gap, variable-indicator gap, the indicator-measurement gap, and the data transformation gap. A benefit of the framework has been its ability to be malleable and multipurpose, bringing a welcomed pluralism of methods, data, and associated concepts. However, pluralism creates challenges for synthesis, data comparison, and mutually agreed-upon methods for modifications. Databases are a promising direction forward to help solve this problem. In conclusion, future research is discussed by reflecting on the different ways the SESF may continue to be a useful tool through (1) being a general but adaptable framework, (2) enabling comparison, and (3) as a diagnostic tool for theory building.

Extracellular vesicles (EVs) are phospholipid bilayer membrane-enclosed structures containing RNAs, proteins, lipids, metabolites, and other molecules, secreted by various cells into physiological fluids. EV-mediated transfer of biomolecules is a critical component of a variety of physiological and pathological processes. Potential applications of EVs in novel diagnostic and therapeutic strategies have brought increasing attention. However, EV research remains highly challenging due to the inherently complex biogenesis of EVs and their vast heterogeneity in size, composition, and origin. There is a need for the establishment of standardized methods that address EV heterogeneity and sources of pre-analytical and analytical variability in EV studies. Here, we review technologies developed for EV isolation and characterization and discuss paths toward standardization in EV research. Despite the substantial recent progress made in extracellular vesicle (EV) research, our understanding of the functional and mechanistic biology of EVs and their relevance to specific pathophysiological states remains limited.Detailed characterization of the molecular composition of EVs and EV subpopulations remains a challenge.Alternative, similar, or identical experimental approaches may often lead to substantially different EV profiling results in different laboratories.Standard protocols for specimen procurement, collection, preprocessing, EV isolation, analytical characterization, and data analysis/interpretation need to be developed for specialized applications and analytical workflows, optimized, documented, cross-evaluated by several laboratories, and disseminated to further accelerate progress toward further understanding of EV biology and development of novel EV-based diagnostic and therapeutic approaches.

BackgroundImpostor syndrome is increasingly presented in the media and lay literature as a key behavioral health condition impairing professional performance and contributing to burnout. However, there is no published review of the evidence to guide the diagnosis or treatment of patients presenting with impostor syndrome.PurposeTo evaluate the evidence on the prevalence, predictors, comorbidities, and treatment of impostor syndrome.Data SourcesMedline, Embase, and PsycINFO (January 1966 to May 2018) and bibliographies of retrieved articles.Study SelectionEnglish-language reports of evaluations of the prevalence, predictors, comorbidities, or treatment of impostor syndrome.Data ExtractionTwo independent investigators extracted data on study variables (e.g., study methodology, treatments provided); participant variables (e.g., demographics, professional setting); diagnostic tools used, outcome variables (e.g., workplace performance, reductions in comorbid conditions); and pre-defined quality variables (e.g., human subjects approval, response rates reported).Data SynthesisIn total, 62 studies of 14,161 participants met the inclusion criteria (half were published in the past 6 years). Prevalence rates of impostor syndrome varied widely from 9 to 82% largely depending on the screening tool and cutoff used to assess symptoms and were particularly high among ethnic minority groups. Impostor syndrome was common among both men and women and across a range of age groups (adolescents to late-stage professionals). Impostor syndrome is often comorbid with depression and anxiety and is associated with impaired job performance, job satisfaction, and burnout among various employee populations including clinicians. No published studies evaluated treatments for this condition.LimitationsStudies were heterogeneous; publication bias may be present.ConclusionsClinicians and employers should be mindful of the prevalence of impostor syndrome among professional populations and take steps to assess for impostor feelings and common comorbidities. Future research should include evaluations of treatments to mitigate impostor symptoms and its common comorbidities.

ObjectiveThe purpose of this article was to perform a systematic review and meta-analysis regarding the diagnostic test accuracy of chest CT for detecting coronavirus disease 2019 (COVID-19).MethodsPubMed, Embase, Web of Science, and CNKI were searched up to March 12, 2020. We included studies providing information regarding diagnostic test accuracy of chest CT for COVID-19 detection. The methodological quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Sensitivity and specificity were pooled.ResultsSixteen studies (n = 3186 patients) were included. The risks of bias in all studies were moderate in general. Pooled sensitivity was 92% (95% CI = 86–96%), and two studies reported specificity (25% [95% CI = 22–30%] and 33% [95% CI = 23–44%], respectively). There was substantial heterogeneity according to Cochran’s Q test (p < 0.01) and Higgins I2 heterogeneity index (96% for sensitivity). After dividing the studies into two groups based on the study site, we found that the sensitivity of chest CT was great in Wuhan (the most affected city by the epidemic) and the sensitivity values were very close to each other (97%, 96%, and 99%, respectively). In the regions other than Wuhan, the sensitivity varied from 61 to 98%.ConclusionChest CT offers the great sensitivity for detecting COVID-19, especially in a region with severe epidemic situation. However, the specificity is low. In the context of emergency disease control, chest CT provides a fast, convenient, and effective method to early recognize suspicious cases and might contribute to confine epidemic.Key Points• Chest CT has a high sensitivity for detecting COVID-19, especially in a region with severe epidemic, which is helpful to early recognize suspicious cases and might contribute to confine epidemic.

The COVID-19 pandemic has highlighted that new diagnostic technologies are essential for controlling disease transmission. Here, we develop SHINE (Streamlined Highlighting of Infections to Navigate Epidemics), a sensitive and specific diagnostic tool that can detect SARS-CoV-2 RNA from unextracted samples. We identify the optimal conditions to allow RPA-based amplification and Cas13-based detection to occur in a single step, simplifying assay preparation and reducing run-time. We improve HUDSON to rapidly inactivate viruses in nasopharyngeal swabs and saliva in 10 min. SHINE’s results can be visualized with an in-tube fluorescent readout — reducing contamination risk as amplification reaction tubes remain sealed — and interpreted by a companion smartphone application. We validate SHINE on 50 nasopharyngeal patient samples, demonstrating 90% sensitivity and 100% specificity compared to RT-qPCR with a sample-to-answer time of 50 min. SHINE has the potential to be used outside of hospitals and clinical laboratories, greatly enhancing diagnostic capabilities. The COVID-19 pandemic has highlighted the need for user-friendly diagnostic techniques. Here, the authors present SHINE, a streamlined and optimised Cas13-based method with accompanying smartphone app for visual diagnosis.

Neutral-atomic and molecular outflows are a common occurrence in galaxies, near and far. They operate over the full extent of their galaxy hosts, from the innermost regions of galactic nuclei to the outermost reaches of galaxy halos. They carry a substantial amount of material that would otherwise have been used to form new stars. These cool outflows may have a profound impact on the evolution of their host galaxies and environments. This article provides an overview of the basic physics of cool outflows, a comprehensive assessment of the observational techniques and diagnostic tools used to characterize them, a detailed description of the best-studied cases, and a more general discussion of the statistical properties of these outflows in the local and distant universe. The remaining outstanding issues that have not yet been resolved are summarized at the end of the review to inspire new research directions.