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Record W4220719430 · doi:10.3389/fonc.2022.808475

Interplay Between Non-Coding RNAs and Programmed Cell Death Proteins

2022· review· en· W4220719430 on OpenAlexaff
Soudeh Ghafouri‐Fard, Bashdar Mahmud Hussen, Mahdi Mohaqiq, Hamed Shoorei, Aria Baniahmad, Mohammad Taheri, Elena Jamali

Bibliographic record

VenueFrontiers in Oncology · 2022
Typereview
Languageen
FieldMedicine
TopicAutophagy in Disease and Therapy
Canadian institutionsOttawa HospitalCentennial CollegeUniversity of Ottawa
Fundersnot available
KeywordsMALAT1microRNAMEG3BiologyDiseaseContext (archaeology)Programmed cell deathComputational biologyRNALong non-coding RNABioinformaticsApoptosisCancer researchGeneticsMedicineGeneInternal medicine

Abstract

fetched live from OpenAlex

Programmed cell death (PDCD) family of proteins includes at least 12 members, function of seven of them being more investigated. These members are PDCD1, PDCD2, PDCD4, PDCD5, PDCD6, PDCD7 and PDCD10. Consistent with the important roles of these proteins in the regulation of apoptosis, dysregulation of PDCDs is associated with diverse disorders ranging from intervertebral disc degeneration, amyotrophic lateral sclerosis, immune thrombocytopenia, type 1 diabetes, congenital hypothyroidism, Alzheimer's disease to different types of cancers. More recently, the interaction between non-coding RNAs and different members of PDCD family is being discovered. In the current study, we described the functional interactions between PDCDs and two classes of non-coding RNAs, namely microRNAs (miRNAs) and long non-coding RNAs (lncRNAs). miR-21 and miR-183 are two miRNAs whose interactions with PDCDs have been assessed in different contexts. The lncRNAs interaction with PDCDs is mainly assessed in the context of neoplasia indicating the role of MALAT1, MEG3, SNHG14 and LINC00473 in this process.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.990
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.047
GPT teacher head0.390
Teacher spread0.343 · 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.

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

Citations15
Published2022
Admission routes1
Has abstractyes

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