Corneal nerve loss in patients with TIA and acute ischemic stroke in relation to circulating markers of inflammation and vascular integrity
Bibliographic record
Abstract
Vascular and inflammatory mechanisms are implicated in the development of cerebrovascular disease and corneal nerve loss occurs in patients with transient ischemic attack (TIA) and acute ischemic stroke (AIS). We have assessed whether serum markers of inflammation and vascular integrity are associated with the severity of corneal nerve loss in patients with TIA and AIS. Corneal confocal microscopy (CCM) was performed to quantify corneal nerve fiber density (CNFD), corneal nerve branch density (CNBD) and corneal nerve fiber length (CNFL) in 105 patients with TIA (n = 24) or AIS (n = 81) and age matched control subjects (n = 56). Circulating levels of IL-6, MMP-2, MMP-9, E-Selectin, P-Selectin and VEGF were quantified in patients within 48 h of presentation with a TIA or AIS. CNFL (P = 0.000, P = 0.000), CNFD (P = 0.122, P = 0.000) and CNBD (P = 0.002, P = 0.000) were reduced in patients with TIA and AIS compared to controls, respectively with no difference between patients with AIS and TIA. The NIHSS Score (P = 0.000), IL-6 (P = 0.011) and E-Selectin (P = 0.032) were higher in patients with AIS compared to TIA with no difference in MMP-2 (P = 0.636), MMP-9 (P = 0.098), P-Selectin (P = 0.395) and VEGF (P = 0.831). CNFL (r = 0.218, P = 0.026) and CNFD (r = 0.230, P = 0.019) correlated with IL-6 and multiple regression analysis showed a positive association of CNFL and CNFD with IL-6 (P = 0.041, P = 0.043). Patients with TIA and AIS have evidence of corneal nerve loss and elevated IL6 and E-selectin levels. Larger longitudinal studies are required to determine the association between inflammatory and vascular markers and corneal nerve fiber loss in patients with cerebrovascular disease.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".