Background Many of the studies on COVID‐19 severity and its associated symptoms focus on hospitalized patients. The aim of this study was to investigate the relationship between acute GI symptoms and COVID‐19 severity in a clustering‐based approach and to determine the risks and epidemiological features of post‐COVID‐19 Disorders of Gut–Brain Interaction (DGBI) by including both hospitalized and ambulatory patients. Methods The study utilized a two‐phase Internet‐based survey on: (1) COVID‐19 patients’ demographics, comorbidities, symptoms, complications, and hospitalizations and (2) post‐COVID‐19 DGBI diagnosed according to Rome IV criteria in association with anxiety (GAD‐7) and depression (PHQ‐9). Statistical analyses included univariate and multivariate tests. Results Five distinct clusters of symptomatic subjects were identified based on the presence of GI symptoms, loss of smell, and chest pain, among 1114 participants who tested positive for SARS‐CoV‐2. GI symptoms were found to be independent risk factors for severe COVID‐19; however, they did not always coincide with other severity‐related factors such as age >65 years, diabetes mellitus, and Vitamin D deficiency. Of the 164 subjects with a positive test who participated in Phase‐2, 108 (66%) fulfilled the criteria for at least one DGBI. The majority ( n = 81; 75%) were new‐onset DGBI post‐COVID‐19. Overall, 86% of subjects with one or more post‐COVID‐19 DGBI had at least one GI symptom during the acute phase of COVID‐19, while 14% did not. Depression (65%), but not anxiety (48%), was significantly more common in those with post‐COVID‐19 DGBI. Conclusion GI symptoms are associated with a severe COVID‐19 among survivors. Long‐haulers may develop post‐COVID‐19 DGBI. Psychiatric disorders are common in post‐COVID‐19 DGBI.
Cell identification within the H&E slides is an essential prerequisite that can pave the way towards further pathology analyses including tissue classification, cancer grading, and phenotype prediction. However, performing such a task using deep learning techniques requires a large cell-level annotated dataset. Although previous studies have investigated the performance of contrastive self-supervised methods in tissue classification, the utility of this class of algorithms in cell identification and clustering is still unknown. In this work, we investigated the utility of Self-Supervised Learning (SSL) in cell clustering by proposing the Contrastive Cell Representation Learning (CCRL) model. Through comprehensive comparisons, we show that this model can outperform all currently available cell clustering models by a large margin across two datasets from different tissue types. More interestingly, the results show that our proposed model worked well with a few number of cell categories while the utility of SSL models has been mainly shown in the context of natural image datasets with large numbers of classes (e.g., Im-ageNet). The unsupervised representation learning approach proposed in this research eliminates the time-consuming step of data annotation in cell classification tasks, which enables us to train our model on a much larger dataset compared to previous methods. Therefore, considering the promising outcome, this approach can open a new avenue to automatic cell representation learning.
In clinical practice, many diagnosis tasks rely on the identification of cells in histopathology images. While supervised machine learning techniques require labels, providing manual cell annotations is time-consuming due to the large number of cells. In this paper, we propose a self-supervised framework (VOLTA) for cell representation learning in histopathology images using a novel technique that accounts for the cell's mutual relationship with its environment for improved cell representations. We subjected our model to extensive experiments on the data collected from multiple institutions around the world comprising of over 700,000 cells, four cancer types, and cell types ranging from three to six categories for each dataset. The results show that our model outperforms the state-of-the-art models in cell representation learning. To showcase the
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.