Analysis of the impact of SARS-CoV-2 infection on immune function and metabolic changes in college students
Bibliographic record
Abstract
Background: The long-term immune and metabolic effects of COVID-19 in vaccinated populations remain incompletely characterized. This study aimed to analyze dynamic changes in lymphocyte subpopulations (T, B, and Natural Killer [NK] cells [TBNK]) and key metabolic indicators among college students post–Omicron infection with prior vaccination. Methods: A prospective observational cohort of 71 male students infected with the Omicron variant of COVID-19 (Beijing, China; March–April 2022) and 18 uninfected controls was followed for 2 years. TBNK subsets and metabolic parameters (uric acid, lipid profiles, β2-microglobulin) were analyzed at 3, 6, 12, and 24 months post-infection. Results: Immunologically, total lymphocytes were elevated at 3 months when compared with controls ( P = 0.0063). Total T cells declined at 6 and 12 months but rebounded by 24 months ( P < 0.0001). NK cells increased until 12 months, then declined ( P < 0.0001). B cells decreased persistently ( P < 0.05). Metabolically, uric acid and lipid parameters (total cholesterol, LDL-C, lipoprotein [a]) showed significant fluctuations, with notable increases at 1 year post-infection ( P < 0.05). β2-microglobulin levels decreased significantly over time ( P < 0.0001). Conclusion: Omicron infection induces immune and metabolic disturbances lasting at least 1 year, with gradual but incomplete recovery by 2 years. The interplay between immune dysregulation and metabolic alterations may contribute to the long-term health effects of COVID-19. Monitoring both lymphocyte and metabolic dynamics may guide the long-term management of post-COVID-19 sequelae.
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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 | 0.000 | 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) | 0.001 | 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".