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Record W2744420379

THE ROLE OF SEROTONINERGIC SYSTEM IN SKIN HEALING

2017· article· en· W2744420379 on OpenAlexaff
Ahmed Shah, Saeid Amini‐Nik

Bibliographic record

VenueInternational Journal of Drug Research and Technology · 2017
Typearticle
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsSunnybrook Health Science CentreUniversity of Toronto
Fundersnot available
KeywordsSerotonergicWound healingMedicineSerotoninInflammationContext (archaeology)NeuroscienceReceptorSurgeryImmunologyBiologyInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

Wounds are a disruption to the continuity of cells that is repaired through well-coordinated steps including inflammation, proliferation and extra-cellular matrix (ECM) remodeling. Often these processes are dysregulated, resulting in either impaired wound healing seen in chronic diabetic wounds, or excessive healing seen in hypertrophic scarring. The serotoninergic system is historically known for its action in the central and peripheral nervous systems, but its role in wound healing is recently coming to light. Serotonin (5HT) has an important role in the promotion of wound healing, particularly in the inflammatory and proliferative stages. In this review, we discuss the role of serotonergic agents and serotonin receptor antagonists in wound healing. Moreover, we discuss the potential mechanisms of actions, and the advantages and limitations of these drugs in the treatment of acute wounds, chronic wounds, and hypertrophic scarring. Since the effects of the serotoninergic pathway in the context of wound healing is largely unexplored, we also discuss where future research in the field is warranted.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.416
Threshold uncertainty score0.160

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.027
GPT teacher head0.404
Teacher spread0.376 · 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 designObservational
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

Citations8
Published2017
Admission routes1
Has abstractyes

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