Unexpected Actors in Inflammatory Bowel Disease Revealed by Machine Learning from Whole-Blood Transcriptomic Data.

Although big data from transcriptomic analyses have helped transform our understanding of inflammatory bowel disease (IBD), they remain underexploited. We hypothesized that the application of machine learning using lasso regression to transcriptomic data from IBD patients and controls can help identify previously overlooked genes. Transcriptomic data provided by Ostrowski et al. (ENA PRJEB28822) were subjected to a two-stage process of feature selection to discriminate between IBD and controls. First, a principal component analysis was used for dimensionality reduction. Second, the least absolute shrinkage and selection operator (lasso) regression was employed to identify genes potentially involved in the pathobiology of IBD. The study included data from 294 participants: 100 with ulcerative colitis (48 adults and 52 children), 99 with Crohn's disease (45 adults and 54 children), and 95 controls (46 adults and 49 children). IBD patients presented a wide range of disease severity. Lasso regression preceded by principal component analysis successfully selected interesting features in the IBD transcriptomic data and yielded 12 models. The models achieved high discriminatory value (range of the area under the receiver operating characteristic curve 0.61-0.95) and identified over 100 genes as potentially associated with IBD. PURA, GALNT14, and FCGR1A were the most consistently selected, highlighting the role of the cell cycle, glycosylation, and immunoglobulin binding. Several known IBD-related genes were among the results. The results included genes involved in the TGF-beta pathway, expressed in NK cells, and they were enriched in ontology terms related to immunity. Future IBD research should emphasize the TGF-beta pathway, immunoglobulins, NK cells, and the role of glycosylation.

Case Report: Brentuximab Vedotin Associated Acute Pancreatitis in a Pediatric Hodgkin Lymphoma Patient: Case Report and Literature Review.

Brentuximab vedotin is a conjugate drug used mainly in Hodgkin lymphoma, systemic and primary cutaneous anaplastic large cell lymphomas, and CD30-expressing peripheral T-cell lymphoma. We report a unique case of acute pancreatitis associated with brentuximab vedotin in a 17-year-old male patient suffering from classical Hodgkin lymphoma. Diagnosed in 2020, the patient was classified to an intermediate therapeutic group and disease's grade was IIIAE. The patient was treated with brentuximab vedotin and bendamustine in the third line. Two weeks after the drug administration, the patient developed acute epigastric pain. Laboratory and radiological findings confirmed the clinical suspicion of acute pancreatitis that was managed with opioid pain medications, meropenem, parenteral nutrition, ondansetron and omeprazole. This is the first case report of brentuximab vedotin-associated acute pancreatitis in the pediatric patient reported in the literature to the best of our knowledge.

Differential correlation network analysis of rectal transcriptomes reveals cystic fibrosis-related disturbance.

Background: Intestinal pathology in cystic fibrosis (CF) remains mechanistically underexplored. Aim: We hypothesized that differential correlation network analysis of expression data would reveal hub genes of CF-related disturbance in the large bowel. Materials & methods: Transcriptomes of 29 rectal tissue samples were accessed at ArrayExpress (E-GEOD-15568 by Stanke et al.). Results: We identified 279 transcript pairs differentially correlating in CF and controls, including: ESRRA and RPL18 (rCF = 0.55; rcontrols = -0.68; padj = 1.60 × 10-100), SRP14 and FAU (rCF = -0.69; rcontrols = 0.48; padj = 2.99 × 10-90), SRP14 and GDI2 (rCF = -0.34; rcontrols = 0.60; padj = 1.05 × 10-78). The genes related to membrane protein targeting (q = 8.34 × 10-14) and one cluster was clearly linked to male infertility. Conclusion:FAU, SRP14 and GDI2 may be involved in a compensatory protein trafficking mechanism in CF rectum, highlighting their potential therapeutic value.

Whose Tweets on COVID-19 Gain the Most Attention: Celebrities, Political, or Scientific Authorities?

Twitter has considerable capacity for health education and proves to be an efficient and accessible communication tool in the coronavirus disease-19 (COVID-19) pandemic. Although many stakeholders saturate Twitter with COVID-19-related information, it remains unknown who disseminates information most efficaciously. COVID-19-related tweets were obtained from Twitter accounts of health agencies, governmental authorities, universities, scientific journals, medical associations, and celebrities. Posts' impact was measured with the nominal and relative (%followers) number of likes and retweets. A sentiment analysis was conducted.We have identified 17,331 COVID-19-related tweets posted by 338 accounts in >4 months since the virus began to spread. The largest number of likes was received by tweets of celebrities (median nominal, relative likes; 14,918, 0.036 percent), politicians (259, 0.174 percent), and health agencies (231, 0.007 percent). Most retweeted messages were also posted by celebrities (2,366, 0.005 percent), health agencies (130, 0.004 percent), and politicians (55, 0.041 percent). Retweets and likes peaked in March 2020, and the overall sentiment of the tweets was growing steadily. Whereas celebrities and politicians posted positive messages, the scientific and health authorities often employed a negative vocabulary. The posts with positive sentiment gained more likes and relative likes than nonpositive. During the pandemic, the tweets of celebrities and politicians related to COVID-19 outperform those coming from health and scientific institutions. Active engagement of Twitter influencers may help key messages go viral.