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Record W4318065960 · doi:10.21474/ijar01/15992

EFFECT OFVARIATIONSIN ABCC2, CYP2C9, CYP2C19 & SCN2A GENESON TREATMENT RESPONSETO ANTICONVULSANTS- A SYSTEMATIC REVIEWAND META-ANALYSIS OF GENETIC ASSOCIATION STUDIES

2023· article· en· W4318065960 on OpenAlexaboutno aff
Mamillapally Loukya, Defria Zeneth B., Samuel Gideon George P.

Bibliographic record

VenueInternational Journal of Advanced Research · 2023
Typearticle
Languageen
FieldMedicine
TopicDrug Transport and Resistance Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsMeta-analysisCYP2C19Publication biasMedicineFunnel plotInternal medicineGeneticsBiologyGeneGenotype

Abstract

fetched live from OpenAlex

Objective: This study was aimed to determine the effect of genetic polymorphisms (non-synonymous, missense, and copy number variations) in ABCC2, CYP2C9, CYP2C19&SCN2A genes on treatment response to anticonvulsants. Methods: The search was carried out in PubMed, Scopus, Cochrane Central Register of Controlled Trials, Embase, LILACS, Google Scholar, MEDLINE, ScienceDirect, Web of Science, and the DOAJ database. Hardy Weinberg Equilibrium (HWE), New-Castle Ottawa scale value, Cochrane Review Manager 5.0 (&R 4.0.3,) and Rayyan QCRI are used for assessing data synthesis, risk of bias, heterogeneity assessment using I[2]statistics and calculating Inter-rater agreement respectively. Publication bias assessment was performed using Eggers test and the Funnel plot. For statistical analysis, random effects modeling was used to explain the association between genetic variations in ABCC2, CYP2C9, CYP2C19 & SCN2A genes related to drug resistance or treatment failure. Results: This meta-analysis includes a total of 29 studies. We found a greater risk of AED resistance in ABCC2rs2273697 genetic variations (OR=1.51 [ 0.93-2.47], p value=0.03 at 95% CI), ABCC2 rs3740066 genetic variation has a greater possibility of AED resistance was seen in pooled population (OR= 0.85 [0.12-5.85], p-value<0.01 at 95% CI), risk of drug resistance was increased by ABCC2 rs717620 polymorphism. (OR =2.13, [1.02-4.44], p-value<0.01 at 95% CI), CYP2C9 rs1799853 polymorphism had a significant increase in AED resistance (OR =1.27, [0.49-3.32] p-value<0.01 at 95% CI), CYP2C9 rs1057910 polymorphism. (OR= 0.74, [0.32-1.70] p-value 0.01 at 95% CI), CYP2C19 rs4244285 polymorphism. (OR= 0.68, [0.29-1.62], p value=0.02 at 95% CI), SCN2A rs2304016 polymorphism. (OR= 1.20, [0.48-3.05], p value<0.01 at 95% CI), SCN2Ars17183814 polymorphism. (OR =1.51, [1.12-2.03], p value=0.30 at 95% CI). Conclusions:Gene polymorphisms play a key role in epilepsy development and therapeutic efficacy, and could have greater impact treatment outcomes.

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)0.014
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.026
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0160.031
Bibliometrics0.0070.008
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.222
GPT teacher head0.518
Teacher spread0.296 · 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 designMeta-analysis
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

Citations0
Published2023
Admission routes1
Has abstractyes

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