Vitamin D: Immuno-modulation and tuberculosis treatment
Bibliographic record
Abstract
Tuberculosis (TB) is a major global health problem and often coincides with vitamin D deficiency. High doses of vitamin D were widely used to treat TB during the pre-antibiotic era. Vitamin D exerts its action through vitamin D receptor (VDR), and VDR gene polymorphisms are associated with susceptibility or resistance to tuberculosis as well as sputum smear and culture conversion during anti-TB treatment. In-vitro studies have revealed that 1,25-dihydroxyvitamin D3 enhances innate immunity by increased expression of various antimicrobial peptides, including cathelicidin, and induction of autophagy of the infected cells thus restricts the intracellular growth of Mycobacterium tuberculosis in macrophages. On the other hand, vitamin D has been shown to suppress the pro-inflammatory cytokine response and enhance the anti-inflammatory response. Supplementation with vitamin D in concert with treatment for TB may be beneficial with respect to minimizing the excessive tissue damage that occurs during the active stage of tuberculosis disease. Several clinical trials have evaluated vitamin D supplementation as an adjunct therapy in the treatment for tuberculosis. However, results are conflicting, owing to variations in dose regimens and outcomes. Further investigations are needed to find the optimal concentration of vitamin D for supplementation with standard anti-TB drugs to optimize treatment, which could help to effectively manage both drug-sensitive and drug-resistant tuberculosis.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".