Subacute thyroiditis following COVID-19: A systematic review.

Background: Subacute thyroiditis (SAT) is a self-limiting thyroid inflammatory disease occurring specifically after upper respiratory tract infections. Since COVID-19 is a respiratory disease leading to multi-organ involvements, we aimed to systematically review the literature regarding SAT secondary to COVID-19. Methods: We searched Scopus, PubMed/MEDLINE, Cochrane, Web of Science, ProQuest, and LitCovid databases using the terms "subacute thyroiditis" and "COVID-19" and their synonyms from inception to November 3, 2022. We included the original articles of the patients with SAT secondary to COVID-19. Studies reporting SAT secondary to COVID-19 vaccination or SAT symptoms' manifestation before the COVID-19 infection were not included. Results: Totally, 820 articles were retained. Having removed the duplicates, 250 articles remained, out of which 43 articles (40 case reports and three case series) with a total of 100 patients, were eventually selected. The patients aged 18-85 years (Mean: 42.70, SD: 11.85) and 68 (68%) were women. The time from the onset of COVID-19 to the onset of SAT symptoms varied from zero to 168 days (Mean: 28.31, SD: 36.92). The most common symptoms of SAT were neck pain in 69 patients (69%), fever in 54 (54%), fatigue and weakness in 34 (34%), and persistent palpitations in 31 (31%). The most common ultrasonographic findings were hypoechoic regions in 73 (79%), enlarged thyroid in 46 (50%), and changes in thyroid vascularity in 14 (15%). Thirty-one patients (31%) were hospitalized, and 68 (68%) were treated as outpatients. Corticosteroids were the preferred treatment in both the inpatient and outpatient settings (25 inpatients (81%) and 44 outpatients (65%)). Other preferred treatments were nonsteroidal anti-inflammatory drugs (nine inpatients (29%) and 17 outpatients (25%)) and beta-blockers (four inpatients (13%) and seven outpatients (10%)). After a mean duration of 61.59 days (SD: 67.07), 21 patients (23%) developed hypothyroidism and thus, levothyroxine-based treatment was used in six of these patients and the rest of these patients did not receive levothyroxine. Conclusion: SAT secondary to COVID-19 seems to manifest almost similarly to the conventional SAT. However, except for the case reports and case series, lack of studies has limited the quality of the data at hand.

A multimodal deep learning model to infer cell-type-specific functional gene networks.

BACKGROUND: Functional gene networks (FGNs) capture functional relationships among genes that vary across tissues and cell types. Construction of cell-type-specific FGNs enables the understanding of cell-type-specific functional gene relationships and insights into genetic mechanisms of human diseases in disease-relevant cell types. However, most existing FGNs were developed without consideration of specific cell types within tissues. RESULTS: In this study, we created a multimodal deep learning model (MDLCN) to predict cell-type-specific FGNs in the human brain by integrating single-nuclei gene expression data with global protein interaction networks. We systematically evaluated the prediction performance of the MDLCN and showed its superior performance compared to two baseline models (boosting tree and convolutional neural network). Based on the predicted cell-type-specific FGNs, we observed that cell-type marker genes had a higher level of hubness than non-marker genes in their corresponding cell type. Furthermore, we showed that risk genes underlying autism and Alzheimer's disease were more strongly connected in disease-relevant cell types, supporting the cellular context of predicted cell-type-specific FGNs. CONCLUSIONS: Our study proposes a powerful deep learning approach (MDLCN) to predict FGNs underlying a diverse set of cell types in human brain. The MDLCN model enhances prediction accuracy of cell-type-specific FGNs compared to single modality convolutional neural network (CNN) and boosting tree models, as shown by higher areas under both receiver operating characteristic (ROC) and precision-recall curves for different levels of independent test datasets. The predicted FGNs also show evidence for the cellular context and distinct topological features (i.e. higher hubness and topological score) of cell-type marker genes. Moreover, we observed stronger modularity among disease-associated risk genes in FGNs of disease-relevant cell types. For example, the strength of connectivity among autism risk genes was stronger in neurons, but risk genes underlying Alzheimer's disease were more connected in microglia.

A Machine Learning Approach to Predicting Autism Risk Genes: Validation of Known Genes and Discovery of New Candidates.

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with a strong genetic basis. The role of de novo mutations in ASD has been well established, but the set of genes implicated to date is still far from complete. The current study employs a machine learning-based approach to predict ASD risk genes using features from spatiotemporal gene expression patterns in human brain, gene-level constraint metrics, and other gene variation features. The genes identified through our prediction model were enriched for independent sets of ASD risk genes, and tended to be down-expressed in ASD brains, especially in frontal and parietal cortex. The highest-ranked genes not only included those with strong prior evidence for involvement in ASD (for example, NBEA, HERC1, and TCF20), but also indicated potentially novel candidates, such as, MYCBP2 and CAND1, which are involved in protein ubiquitination. We also showed that our method outperformed state-of-the-art scoring systems for ranking curated ASD candidate genes. Gene ontology enrichment analysis of our predicted risk genes revealed biological processes clearly relevant to ASD, including neuronal signaling, neurogenesis, and chromatin remodeling, but also highlighted other potential mechanisms that might underlie ASD, such as regulation of RNA alternative splicing and ubiquitination pathway related to protein degradation. Our study demonstrates that human brain spatiotemporal gene expression patterns and gene-level constraint metrics can help predict ASD risk genes. Our gene ranking system provides a useful resource for prioritizing ASD candidate genes.