Quantitative Lung Ultrasound and Pediatric Critical Care: Any Excuse Not to Use It?
Bibliographic record
Abstract
death, with a survival rate 35% lower than the TLR9 low expression subset after 2 years.TLR9 has also been examined in systemic sclerosis (SSc) and SSc associated with interstitial lung disease (SSc-ILD) (8, 9).Increased TLR9 expression and TLR9-regulated signature genes such as CXCL10, CCL4, and TNF in SSc skin biopsies were reported (9).The use of TLR9 inhibitors to develop targeted and effective treatments for SSc and SSc-related complications is necessary. ConclusionThis study provides an improved understanding of the role of TLR9 in IPF disease.This also necessitates additional research on the potential impact of TLR9 antagonism on other fibrotic conditions such as SSc-ILD.A fascinating publication linked TLR9 and SSc-ILD, whereby TLR9 activation increases FN-EDA (fibronectin-extra domain A) accumulation in SSc-ILD fibroblasts via reduced FN-EDA ubiquitination.The authors demonstrated that TLR9 ligand ODN2006 reduces ubiquitinated FN-EDA destined for lysosomal degradation, an effect that was abrogated with TLR9 knockdown or inhibition (10).Therefore, enhancing intracellular degradation of ECM components through TLR9 inhibition or enhanced ECM turnover could be a novel strategy to attenuate pathogenic ECM accumulation.That could be a potential way to prevent pulmonary fibrosis, which requires further investigation.Because of progressive ECM deposition, it is essential to determine if direct TLR9 inhibition can potentially slow or reverse this phenomenon in the context of IPF.It will be interesting to determine the interplay between TLR9 and TGF-b signaling, the interaction among different cell types and their contributions, and the therapeutic potential of direct TLR9 inhibition relative to other U.S. Food and Drug Administration-approved therapeutic prospects.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.034 | 0.013 |
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".