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Record W93288809

Therapeutic potentials of pentoxifylline for treatment of cardiovascular diseases.

2004· article· en· W93288809 on OpenAlexaff
Ming Zhang, Yan‐Jun Xu, Shushma A Mengi, Amarjit S. Arneja, Naranjan S. Dhalla

Bibliographic record

VenuePubMed · 2004
Typearticle
Languageen
FieldImmunology and Microbiology
TopicBiomarkers in Disease Mechanisms
Canadian institutionsSt. Boniface Hospital
Fundersnot available
KeywordsPentoxifyllineMedicineMicrocirculationPharmacologyPhosphodiesterase inhibitorDegranulationTumor necrosis factor alphaPhosphodiesteraseVasodilationVascular permeabilityInflammationImmunologyIschemiaInternal medicineBiochemistryEnzymeChemistry
DOInot available

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.871
Threshold uncertainty score0.408

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.223
Teacher spread0.201 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations69
Published2004
Admission routes1
Has abstractyes

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