C-C chemokine receptor type 5 links COVID-19, Rheumatoid arthritis, and Hydroxychloroquine: In silico analysis 
Bibliographic record
Abstract
Abstract Patients with rheumatoid arthritis (RA) represent one of the fragile patient groups that might be susceptible to the critical form of the coronavirus disease -19 (COVID-19) . On the other side, RA patients have been found not to have an increased risk of COVID-19 infection. Moreover, some of the Disease-Modifying Anti-Rheumatic Drugs (DMARDS) commonly used to treat rheumatic diseases like Hydroxychloroquine (HCQ) were proposed as a potential therapy for COVID-19 with a lack of full understanding of their molecular mechanisms. This highlights the need for the discovery of common pathways that may link both diseases at the molecular side. In this research, we used the in silico approach to investigate the transcriptomic profile of RA synovium to identify shared molecular pathways with that of severe acute respiratory syndrome-corona virus-2 (SARS-COV-2) infected lung tissue. Our results showed upregulation of chemotactic factors, including CCL4, CCL8, and CCL11, that all shared CCR5 as their receptor, as a common derangement observed in both diseases; RA and COVID-19. Moreover, our results also highlighted a possible mechanism through which HCQ, which can be used as a monotherapy in mild RA or as one of the triple-DMARDs therapy (tDMARDs; methotrexate, sulphasalazine, and HCQ), might interfere with the COVID-19 infection. This might be achieved through the ability of HCQ to upregulate specific immune cell populations like activated natural killer (NK) cells, which were found to be significantly reduced in COVID-19 infection. In addition to its ability to block CCR5 rich immune cell recruitment that also was upregulated in the SARS-COV-2 infected lungs. This might explain some of the reports that showed beneficial effects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".