Impact of COVID-19 vaccine doses and viral waves on inflammatory and immunological responses to COVID-19 infections in India
Bibliographic record
Abstract
Abstract Background Investigation of the effect of SARS-CoV-2 variants and COVID-19 vaccination on inflammatory and immune response to SARS-CoV-2 infection is limited in South Asia. Objectives We aimed to examine the impact of COVID-19 vaccination and waves of COVID- 19 on inflammatory and immunological biomarkers among COVID-19 patients in India. Methods This cross-sectional analysis used baseline data from a randomized controlled trial of vitamin D and zinc during COVID-19 infection in India (N=181). Blood samples and data regarding vaccination doses were collected. The second (Delta) or third (Omicron) wave was determined by date of enrolment. Mixed effects linear regression with robust standard errors was used to examine associations between COVID-19 vaccination dose or wave at enrolment and C-Reactive Protein (CRP), ferritin, lactate dehydrogenase (LDH), D-dimer, interleukin-6 (IL-6), angiopoietin-2 (Ang-2), soluble triggering receptor expressed on myeloid cells-1 (sTREM-1), immunoglobulin G (IgG) and immunoglobulin M (IgM). Results Compared to no vaccination, full vaccination was associated with lower LDH ( P< 0.001), D-dimer ( P= 0.521) and Ang-2 ( P= 0.046), and higher IgG levels ( P< 0.001). Partial vaccination was associated with lower IL-6 ( P= 0.040) and higher IgG ( P< 0.001). Enrolment during the third wave was associated with lower IL-6 ( P< 0.001), CRP ( P= 0.056), IgM ( P= 0.013), and IgG ( P< 0.001), but higher D-dimer levels ( P< 0.001). Conclusions COVID-19 vaccination status and SARS-CoV-2 variant influence the inflammatory and immunologic response during SARS-CoV-2 infection, contributing to the severity of clinical presentation.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".