An evaluation of the tumour endothelial marker <scp>CLEC14A</scp> as a therapeutic target in solid tumours
Classification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Earlier studies identified the transmembrane cell surface C-type lectin CLEC14A as a putative tumour endothelial marker. For CLEC14A to progress as a vascular target in solid tumours an in-depth analysis of CLEC14A expression in human healthy and tumour tissue is needed. It is here shown that an analysis of 5332 RNA expression profiles in the public domain confirmed high expression of CLEC14A in tumour compared to healthy human tissue. It is further shown by immunohistochemistry that CLEC14A protein is absent, or expressed at a very low level, in healthy human and primate tissue. In contrast, CLEC14A is expressed on the vasculature of a range of human solid tumours, with particularly high expression in more than half of renal cell carcinomas. Elevated levels of CLEC14A transcripts were identified in some non-cancer pathologies; such comorbidities may need to be excluded from trials of therapies targeting this marker. It is further shown that, as CLEC14A expression can be induced by the absence of shear stress, it is imperative that freshly collected as opposed to aged or post-mortem tissue be analysed. We conclude that CLEC14A is a promising target to enable development of novel anti-cancer therapies for solid tumours.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it