Role of Toll-Like Receptors in Immune Responses to Chlamydial Infections
Bibliographic record
Abstract
Chlamydiae are important human pathogens which are leading causative agents for a variety of disease conditions including ocular, respiratory and sexually transmitted diseases, thus causing significant morbidity worldwide. Many of the human diseases caused by Chlamydia species are considered to be immunopathologically mediated. Toll like receptors (TLRs) have emerged as one of the major components of the immune system. Recognition of pathogen associated molecular patterns (PAMPs) by TLRs results in the activation of signaling events that induce the expression of effector molecules such as cytokines and chemokines which control the activation of adaptive immune responses. The precise immune mechanisms involved in resistance or pathogenesis to chlamydial infection, especially in the TLR signaling and downstream events during the innate phase of infection initiating the adaptive immune responses remains largely unknown. This review focuses on elaborating the current knowledge on the role of TLRs in immune responses to chlamydial infection. Although chlamydial components like lipopolysaccharide (LPS) and chlamydial heat shock protein 60 (cHSP60) are recognized by TLR4, the intact organisms stimulates the innate immune cells through TLR2, which also plays an important role as a PRR for Chlamydia. While the individual role of different TLRs such as TLR2, TLR4 and TLR9 in chlamydial infection is becoming delineated, studies have demonstrated the essential role of the TLR adapter molecule MyD88 in the generation of immune responses to Chlamydia infection. Given that there is no effective vaccine available for Chlamydia till date, a better understanding of the immunological and molecular mechanisms mediated by TLRs will greatly aid in possibly exploiting these molecules as immunotherapeutic targets.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".