TNFα compromises the inner ear microcirculation in a sphingosine kinase 1/sphingosine‐1‐phosphate dependent manner ‐ a novel mechanism for sudden hearing loss (SHL)
Bibliographic record
Abstract
This study establishes a link between inflammation and inner ear vascular dysfunction. Recovery of auditory function in of SHL patients treated with a TNFα inhibitor were consistent with a vascular origin. We investigated the inner ear microcirculation using (1) an in vitro model of the spiral modiolar artery (SMA), the end artery feeding the inner ear, (2) intra vital microscopy of stria vascularis (SV) perfusion, and (3) in vitro measurement of cochlear lateral wall capillary (LWC) constriction. We demonstrate that in all parts of the cochlear microcirculation (SMA, SV and LWC), TNFα induces a proconstrictive state via activation of sphingosine‐1‐phosphate (S1P). Analysis of the molecular signalling pathway identified the phosphorylation of sphingosine kinase 1 (the S1P generating enzyme activated by TNFα) as a new therapeutic target for SHL. We conclude that any pathology linked to the release of TNFα has the potential to reduce cochlear blood flow and cause SHL. The present study integrates SHL into the family of cardiovascular pathologies, with immediate implications related to risk stratification, diagnosis and treatment. CIHR‐MOP‐84402, CFI‐11810, ORF‐11810, CSN, KRICRF‐TUM‐8758155, HSFO‐NI, HSFO‐CI, SFUT, Boehringer Ingelheim, DAA, NIH‐R01‐DC04280.
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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) | 0.001 | 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".