Natural killer (NK) and HLA-C in in hepatitis C virus infected patient in Egypt during infection with Covid 19
Bibliographic record
Abstract
Abstract Hepatitis C virus (HCV) infection develops into chronic hepatitis in over two-thirds of acute infections. While current treatments with Sovaldi and ribavirin achieve HCV eradication in >95% of cases, no vaccine is available and re-infection can readily occur. Natural killer (NK) cells represent a key cellular component of the innate immune system, participating in early defence against infectious diseases, viruses, and cancers. When acute infection becomes chronic, however, NK cell function is altered. the NK immune response is a double-edged sword that is a significant component of the innate immune antiviral response, but persistent activation can drive tissue damage during chronic infection. Natural killer (NK) and lymphocytes with NK receptors (NKRs) is thought to play a key role in determining whether host immune responses to hepatitis C virus (HCV) infection result in viral clearance or disease progression. The aim of the present work was to Study of some immunological cells (cd 16, cd 56), genetic factors (HLA-C) in hepatitis C virus infected patients. A total number of 60 patients were included in this study. Their age ranged from 38-62 years old. The included patients were classified into 3 groups: group IA and IB (n=40) including cases with positive HCV antibodies (HCV-Ab), but negative real-time PCR for HCV after treatment with Sovaldi and Ribavirin, and they were considered as Responders, group IA Infected with covid 19, while group IB not Infected with covid 19. While group II (n=20) including patients with HCV infection ( HCV-Ab positive and real-time PCR for HCV is positive ) after treatment with Sovaldiand Ribavirin ( Non responders ) Considered as Non Responders. And group III ( n=60 ) healthy persons with matched age, sex and environmental status also included as controls.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".