Citations

Abstract Immunoassays designed to detect SARS-CoV-2 protein antigens (Ag) are commonly used to diagnose COVID-19. The most widely used tests are lateral flow assays that generate results in approximately 15 minutes for diagnosis at the point-of-care. Higher throughput, laboratory-based SARS-CoV-2 Ag assays have also been developed. The number of commercially available SARS-CoV-2 Ag detection tests has increased rapidly, as has the COVID-19 diagnostic literature. The Infectious Diseases Society of America (IDSA) convened an expert panel to perform a systematic review of the literature and develop best-practice guidance related to SARS-CoV-2 Ag testing. This guideline is an update to the third in a series of frequently updated COVID-19 diagnostic guidelines developed by the IDSA. IDSA's goal was to develop evidence-based recommendations or suggestions that assist clinicians, clinical laboratories, patients, public health authorities, administrators, and policymakers in decisions related to the optimal use of SARS-CoV-2 Ag tests in both medical and nonmedical settings. A multidisciplinary panel of infectious diseases clinicians, clinical microbiologists, and experts in systematic literature review identified and prioritized clinical questions related to the use of SARS-CoV-2 Ag tests. A review of relevant, peer-reviewed published literature was conducted through 1 April 2022. Grading of Recommendations Assessment, Development, and Evaluation (GRADE) methodology was used to assess the certainty of evidence and make testing recommendations. The panel made 10 diagnostic recommendations that address Ag testing in symptomatic and asymptomatic individuals and assess single versus repeat testing strategies. US Food and Drug Administration (FDA) SARS-CoV-2 Ag tests with Emergency Use Authorization (EUA) have high specificity and low to moderate sensitivity compared with nucleic acid amplification testing (NAAT). Ag test sensitivity is dependent on the presence or absence of symptoms and, in symptomatic patients, on timing of testing after symptom onset. In most cases, positive Ag results can be acted upon without confirmation. Results of point-of-care testing are comparable to those of laboratory-based testing, and observed or unobserved self-collection of specimens for testing yields similar results. Modeling suggests that repeat Ag testing increases sensitivity compared with testing once, but no empirical data were available to inform this question. Based on these observations, rapid RT-PCR or laboratory-based NAAT remain the testing methods of choice for diagnosing SARS-CoV-2 infection. However, when timely molecular testing is not readily available or is logistically infeasible, Ag testing helps identify individuals with SARS-CoV-2 infection. Data were insufficient to make a recommendation about the utility of Ag testing to guide release of patients with COVID-19 from isolation. The overall quality of available evidence supporting use of Ag testing was graded as very low to moderate.

LSECs are a unique population of endothelial cells within the liver and are recognized as key regulators of liver homeostasis. LSECs also play a key role in liver disease, as dysregulation of their quiescent phenotype promotes pathological processes within the liver including inflammation, microvascular thrombosis, fibrosis, and portal hypertension. Recent technical advances in single-cell analysis have characterized distinct subpopulations of the LSECs themselves with a high resolution and defined their gene expression profile and phenotype, broadening our understanding of their mechanistic role in liver biology. This article will review 4 broad advances in our understanding of LSEC biology in general: (1) LSEC heterogeneity, (2) LSEC aging and senescence, (3) LSEC role in liver regeneration, and (4) LSEC role in liver inflammation and will then review the role of LSECs in various liver pathologies including fibrosis, DILI, alcohol-associated liver disease, NASH, viral hepatitis, liver transplant rejection, and ischemia reperfusion injury. The review will conclude with a discussion of gaps in knowledge and areas for future research.

Background: Heart disease is linked to worse acute outcomes after coronavirus disease 2019 (COVID-19), although long-term outcomes and prognostic factor data are lacking. We aim to characterize the outcomes and the impact of underlying heart diseases after surviving COVID-19 hospitalization. Methods: We conducted an analysis of the prospective registry HOPE-2 (Health Outcome Predictive Evaluation for COVID-19-2, NCT04778020). We selected patients discharged alive and considered the primary end-point all-cause mortality during follow-up. As secondary main end-points, we included any readmission or any post-COVID-19 symptom. Clinical features and follow-up events are compared between those with and without cardiovascular disease. Factors with p < 0.05 in the univariate analysis were entered into the multivariate analysis to determine independent prognostic factors. Results: HOPE-2 closed on 31 December 2021, with 9299 patients hospitalized with COVID-19, and 1805 died during this acute phase. Finally, 7014 patients with heart disease data were included in the present analysis, from 56 centers in 8 countries. Heart disease (+) patients were older (73 vs. 58 years old), more frequently male (63 vs. 56%), had more comorbidities than their counterparts, and suffered more frequently from post-COVID-19 complications and higher mortality (OR heart disease: 2.63, 95% CI: 1.81–3.84). Vaccination was found to be an independent protector factor (HR all-cause death: 0.09; 95% CI: 0.04–0.19). Conclusions: After surviving the acute phase, patients with underlying heart disease continue to present a more complex clinical profile and worse outcomes including increased mortality. The COVID-19 vaccine could benefit survival in patients with heart disease during follow-up.

AbstractClinical trials of novel salvage therapies have encouraging outcomes for relapsed/refractory transplant‐eligible classic Hodgkin lymphoma (R/R cHL) but comparison with conventional chemotherapy is lacking. Herein, we report the final analysis of a multicenter retrospective cohort of R/R cHL assessing outcomes by type of salvage therapy before autologous stem cell transplant (ASCT). R/R cHL patients who underwent ASCT at 14 institutions across the United States were included. Outcomes were compared among patients receiving conventional chemotherapy, brentuximab vedotin (BV) + chemotherapy, BV alone, and a checkpoint inhibitor (CPI)‐based regimens before ASCT. Study endpoints included event‐free survival (EFS), progression‐free survival (PFS), and overall survival (OS). All endpoints are defined from relapse. Of 936 patients, 728 received conventional chemotherapy, 73 received BV + chemotherapy, 70 received BV alone, and 65 received CPI‐based regimens prior to ASCT. When adjusted for time to relapse, pre‐ASCT response and use of BV maintenance, patients receiving CPI‐based regimens had superior 2‐year EFS compared to conventional chemotherapy, BV + chemotherapy, and BV alone (79.7, 49.6, 62.3, and 36.9%, respectively, p < .0001). Among 649 patients transplanted after 1 line of salvage therapy, CPI‐based regimens were associated with superior 2‐year PFS compared to conventional chemotherapy (98% vs. 68.8%, hazard ratio: 0.1, 95% confidence interval: 0.03–0.5, p < .0001). OS did not differ by pre‐ASCT salvage regimen. In this large multicenter retrospective study, CPI‐based regimens improved EFS and PFS compared to other salvage regimens independent of pre‐ASCT response. These data support earlier sequencing of CPI‐based regimens in R/R cHL in the pre‐ASCT setting.

Background and Objectives: Brain Tumor Fusion-based Segments and Classification-Non-enhancing tumor (BTFSC-Net) is a hybrid system for classifying brain tumors that combine medical image fusion, segmentation, feature extraction, and classification procedures. Materials and Methods: to reduce noise from medical images, the hybrid probabilistic wiener filter (HPWF) is first applied as a preprocessing step. Then, to combine robust edge analysis (REA) properties in magnetic resonance imaging (MRI) and computed tomography (CT) medical images, a fusion network based on deep learning convolutional neural networks (DLCNN) is developed. Here, the brain images’ slopes and borders are detected using REA. To separate the sick region from the color image, adaptive fuzzy c-means integrated k-means (HFCMIK) clustering is then implemented. To extract hybrid features from the fused image, low-level features based on the redundant discrete wavelet transform (RDWT), empirical color features, and texture characteristics based on the gray-level cooccurrence matrix (GLCM) are also used. Finally, to distinguish between benign and malignant tumors, a deep learning probabilistic neural network (DLPNN) is deployed. Results: according to the findings, the suggested BTFSC-Net model performed better than more traditional preprocessing, fusion, segmentation, and classification techniques. Additionally, 99.21% segmentation accuracy and 99.46% classification accuracy were reached using the proposed BTFSC-Net model. Conclusions: earlier approaches have not performed as well as our presented method for image fusion, segmentation, feature extraction, classification operations, and brain tumor classification. These results illustrate that the designed approach performed more effectively in terms of enhanced quantitative evaluation with better accuracy as well as visual performance.

As cellular therapies gradually become the mainstay of treatment for several nonmalignant diseases, there appears to be varied accessibility to these therapies globally. Despite considerable burden of nonmalignant conditions, such as sickle cell disease, thalassemia, and aplastic anemia in populations of low–middle-income countries, the utilization of cellular therapies remain sparse because of lack of resources. Globally, the frequency of hematopoietic stem cell transplant (HSCT) has increased disproportionately in countries with higher gross national income (GNI) per capita, governmental healthcare expenditures, and a high human development index. This leads to a large subset of international patients seeking care in the United States. This review summarizes the unique set of challenges that often arise when offering sophisticated therapies such as HSCT to international patients constituting of cross-cultural, logistical, financial, and medical challenges and the opportunities that are available to bridge the gap.

Background: Wearable device technology has recently been involved in the healthcare industry substantially. India is the world’s third largest market for wearable devices and is projected to expand at a compound annual growth rate of ~26.33%. However, there is a paucity of literature analyzing the factors determining the acceptance of wearable healthcare device technology among low-middle-income countries. Methods: This cross-sectional, web-based survey aims to analyze the perceptions affecting the adoption and usage of wearable devices among the Indian population aged 16 years and above. Results: A total of 495 responses were obtained. In all, 50.3% were aged between 25–50 years and 51.3% belonged to the lower-income group. While 62.2% of the participants reported using wearable devices for managing their health, 29.3% were using them daily. technology and task fitness (TTF) showed a significant positive correlation with connectivity (r = 0.716), health care (r = 0.780), communication (r = 0.637), infotainment (r = 0.598), perceived usefulness (PU) (r = 0.792), and perceived ease of use (PEOU) (r = 0.800). Behavioral intention (BI) to use wearable devices positively correlated with PEOU (r = 0.644) and PU (r = 0.711). All factors affecting the use of wearable devices studied had higher mean scores among participants who were already using wearable devices. Male respondents had significantly higher mean scores for BI (p = 0.034) and PEOU (p = 0.009). Respondents older than 25 years of age had higher mean scores for BI (p = 0.027) and Infotainment (p = 0.032). Conclusions: This study found a significant correlation with the adoption and acceptance of wearable devices for healthcare management in the Indian context.