Therapeutic potentials of pentoxifylline for treatment of cardiovascular diseases.
Bibliographic record
Abstract
BACKGROUND: Cardiovascular diseases are life-threatening conditions and, thus, have received a great deal of attention over the years. Several mechanisms, including hemorheology changes and inflammatory effects, are considered to be involved in the pathogenesis of these diseases. Because cardiovascular dysfunction is also known to worsen hemorheology changes and influence vital symptoms, it has become critical to formulate effective therapeutic strategies to combat the deleterious effects of cardiovascular diseases. Although a wide variety of drugs have been developed for the treatment of cardiovascular diseases, the effectiveness of any agent for therapy of a given disease cannot be indicated with certainty. OBJECTIVES AND OBSERVATIONS: Pentoxifylline (PTXF), a phosphodiesterase inhibitor, has been investigated for close to two decades because of its primary pharmacological actions on hemorheology and other anti-inflammatory effects. Several studies have been conducted to investigate the effects and mechanisms of PTXF in ischemic injury, peripheral vascular disease and heart failure. The present article is intended to emphasize the therapeutic potentials of PTXF in different types of cardiovascular diseases, focusing on the mechanisms of its pharmacological actions.
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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".