Understanding altered toll-like receptor function in the elderly (P4251)
Bibliographic record
Abstract
Abstract As the number of elderly individuals increases, infectious diseases, such as influenza, pneumonia and tuberculosis have become a significant public health concern. Recent interest in pathogen recognition via toll-like receptors (TLR) and the ability of TLR agonists to generate enhanced vaccine immunogenicity has led to questions regarding altered immune function in the elderly. In contrast to young mice, recent studies in our lab have shown that in old mice, Mycobacterium tuberculosis infected pulmonary macrophages can utilize a TLR2 independent pathway to generate cytokine secretion. This new development of specific altered TLR function in the elderly requires more analysis before vaccines can be generated to increase protective immunity. In this study, we will elucidate the differences in cytokine profile in the presence and absence of TLR2 by siRNA knockdown in old mice. This will allow us to determine changes that impair protection against infectious disease in old age. We will also examine pattern recognition receptors that compensate for cytokine production in the absence of TLR2. Understanding the compensatory mechanism could give us insight into targeting alternate receptors as adjuvants in vaccines development. Investigation into these age-related alterations in TLR function is of utmost importance in order to boost vaccine’s potential to generate strong immunity in the elderly.
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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 | 0.000 |
| 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 | 0.001 |
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".