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Record W2804175295 · doi:10.7759/cureus.2652

A Review of the Mechanism of Antagonism of N-methyl-D-aspartate Receptor by Ketamine in Treatment-resistant Depression

2018· review· en· W2804175295 on OpenAlexaff
Yasar Sattar, John A. Wilson, Ali Khan, Mahwish Adnan, Daniel Azzopardi Larios, Shristi Shrestha, Quazi Abidur Rahman, Zeeshan Mansuri, Ali Hassan, Nirav Patel, Nargis Tariq, Sharaad Latchana, Stefany C Lopez Pantoja, Sadiasept Vargas, Naveed A Shaikh, Daaman Mittal, Fatima Rumesa

Bibliographic record

VenueCureus · 2018
Typereview
Languageen
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsCentre for Addiction and Mental Health
Fundersnot available
KeywordsNMDA receptorSynaptogenesisKetamineLong-term potentiationGlutamate receptorNeurotrophic factorsNeuroscienceAntidepressantDepression (economics)NorepinephrineBrain-derived neurotrophic factorMechanism (biology)Treatment-resistant depressionDopaminePsychologyPharmacologyHippocampusReceptorMedicineInternal medicine

Abstract

fetched live from OpenAlex

The biochemical processes involved in depression go beyond serotonin, norepinephrine, and dopamine. The N-methyl-D-aspartate (NMDA) receptor has a major role in the neurophysiology of depression. Ketamine, one of the prototypical NMDA antagonists, works rapidly in controlling depressive symptoms, including acutely suicidal behavior, by just a single injection. Ketamine may rapidly increase the glutamate levels and lead to structural neuronal changes. Increased neuronal dendritic growth may contribute to synaptogenesis and an increase in brain-derived neurotrophic factor (BDNF). Activation of the mechanistic target of rapamycin (mTOR), as well as increased levels of BDNF, may increase long-term potentiation and result in an improvement in the symptoms of depression. The mechanisms of ketamine's proposed effect as an off-label treatment for resistant depression are outlined in this paper.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.042
GPT teacher head0.351
Teacher spread0.308 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations50
Published2018
Admission routes1
Has abstractyes

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Same venueCureusSame topicTreatment of Major DepressionFrench-language works237,207