Role of Toll-Like Receptors in Tuberculosis Infection
Bibliographic record
Abstract
BACKGROUND: . Investigation of Toll-like receptors (TLRs) has revealed new information regarding the immunopathogenesis of this disease. Toll-like receptors can recognize various ligands with a lipoprotein structure in the bacilli. Toll-like receptor 2 and TLR-4 have been identified in association with tuberculosis infection. OBJECTIVES: The aim of our study was to investigate the relationship between TLR polymorphism and infection progress. METHODS: Twenty-nine patients with a radiologically, microbiologically, and clinically proven active tuberculosis diagnosis were included in this 25-month study. Toll-like receptor 2 and TLR-4 polymorphisms and allele distributions were compared between these 29 patients and 100 healthy control subjects. Peripheral blood samples were taken from all patients. Genotyping of TLR-2, TLR-4, and macrophage migration inhibitory factor was performed. The extraction step was completed with a Qiagen mini blood purification system kit (Qiagen, Ontario, Canada) using a peripheral blood sample. The genotyping was performed using polymerase chain reaction-restriction fragment length polymorphism. RESULTS: In total, 19 of the 29 patients with tuberculosis infection had a TLR-2 polymorphism, and 20 of the 100 healthy subjects had a TLR-2 polymorphism (P < 0.001). The TLR-4 polymorphism and interferon-γ allele distributions were not statistically correlated. CONCLUSIONS: Toll-like receptor 2 polymorphism is a risk factor for tuberculosis infection. The limiting factor in this study was the lack of investigation of the interferon-γ and tumor necrosis factor-α levels, which are important in the development of infection. Detection of lower levels of these cytokines in bronchoalveolar lavage specimens, especially among patients with TLR-2 defects, will provide new data that may support the results of this study.
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) | 0.000 | 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) | 0.002 | 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".