MétaCan
Menu
Back to cohort

[Study on polymorphisms of genes with susceptibility to drug induced liver injury in a cohort receiving anti-tuberculosis treatment].

2016· article· en· W2517821145 on OpenAlexaff
Ru Chen, Jing Wang, Shaowen Tang, Xiaozhen Lyu, Yuan Zhang, Shanshan Wu, Yinyin Xia, Siyan Zhan

Bibliographic record

VenuePubMed · 2016
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicDrug-Induced Hepatotoxicity and Protection
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSLCO1B1Single-nucleotide polymorphismTuberculosisMedicineLiver injuryLogistic regressionInternal medicineBiologyGeneGenotypeGeneticsPathology

Abstract

fetched live from OpenAlex

OBJECTIVE: To investigate the association between the polymorphisms of genes involving in drug metabolism and transport as well as immunological reaction and the risk of anti-tuberculosis drug-induced liver injury(ATLI)in Chinese. METHODS: This 1∶4 matched case-control study was conducted by using the data from a cohort study of Anti-tuberculosis Drugs Induced Adverse Reactions in National Tuberculosis Prevention and Control Progtam of China. Genes involving in three phase of drug metabolism and transport as well as related immunological reaction were chosen and single nucleotide polymorphisms(SNPs)were genotyped by TaqMan allele discrimination technology. Lasso regression and multivariate conditional logistic regression analysis were used to select susceptible genes. RESULTS: A total of 33 genes with 75 SNPs were tested. The combined results of Lasso and regression logistic regression analysis showed that genetic polymorphism of SLCO1B1 rs4149014, HSPA1L rs2227956, STAT3 rs1053023 and IL-6 rs2066992 were significantly associated with the risk of ATLI(P<0.05). CONCLUSION: SLCO1B1, HSPA1L, STAT3 and IL-6 might be the susceptibility genes of drug induced liver injury in patients receiving anti-tuberculosis treatment.

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 score0.258
Threshold uncertainty score0.801

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.115
GPT teacher head0.362
Teacher spread0.247 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

Same venuePubMedSame topicDrug-Induced Hepatotoxicity and ProtectionFrench-language works237,207