Editorial: Toll-Like Receptors Throughout Life: From Controlling Physiological Processes to Determinants of Disease
Bibliographic record
Abstract
From Controlling Physiological Processes to Determinants of DiseaseSince their discovery, Toll-like receptors (TLRs) have been deeply investigated in different areas of the biomedical research.TLRs are usually associated to their ability to trigger a potent immune response against invading pathogens (Kawasaki and Kawai, 2014;Vijay, 2018) and the study by Valdés-López et al. is another proof in this scenario.The Authors described the involvement of the TLR4 signaling as important factor in inducing an antiviral response, mediated by macrophages, against Chikungunya Virus.However, the importance of TLRs is not restricted to the detection of potential harmful microorganisms.It should be recalled that all the mucosal surfaces of human body are virtually colonized by bacteria, the so-called microbiota, and TLRs continuously interact with these commensal microbes, in a bidirectional crosstalk aimed at preserving homeostasis (Semin et al., 2021), as reported in the review by Le Noci et al.By sensing pathogens and commensals, TLRs provide a double beneficial effect either protecting the host and, at the same time, maintaining a good health status.However, the activation of TLRs not always produces a positive outcome and the initiation of a inflammatory process, mediated by TLR signaling pathway, can be associated to the pathogenesis of different diseases, including cancer (Cook et al., 2004).For instance, Zheng et al. investigated the role of TLR9 during the transition from acute kidney injury (AKI) to chronic kidney disease (CKD), demonstrating that TLR9 expressed by macrophages has a critical role in this process.Moreover, it has been observed that bacteria present in the tumor microenvironment trigger TLRs expressed by tumor-infiltrating immune cells but, instead of generating an anti-tumor immune response, promote immunosuppression and sustain cancer cell growth.The impact of TLRs on cancer development and progression has been also evaluated considering the prognostic value of these receptors, as shown in the study by Lu et al.The Authors observed a positive correlation between TLR4 expression and the overall survival of bladder cancer patients.Moreover, low TLR4 expression was also associated to a better response to chemotherapy.Since TLRs can be included in the etiology of several pathological conditions, they can consequently represent potential good targets for novel therapeutic options (El-Zayat et al., 2019).For example, decreasing TLR expression by quercetin treatment was able to reduce the mortality of broilers by impacting on intestinal microbiota composition Ying et al.Exploiting their immune-activation ability, TLRs have been also investigated as immunotherapeutic drugs, alone or in combination with other therapeutic regimens, as comprehensively described by Farooq et al.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.022 | 0.017 |
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".