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Record W3038920640 · doi:10.3390/biomedicines8070188

The Nuclear Lamina: Protein Accumulation and Disease

2020· review· en· W3038920640 on OpenAlexafffund
Carla Almendáriz-Palacios, Zoe E. Gillespie, Matthew Janzen, Valeria Martinez, Joanna M. Bridger, Troy A. A. Harkness, Darrell D. Mousseau, Christopher H. Eskiw

Bibliographic record

VenueBiomedicines · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNuclear Structure and Function
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaSaskatchewan Health Research FoundationAlzheimer SocietyCanadian Institutes of Health ResearchUniversity of TorontoUniversity of Saskatchewan
KeywordsProteostasisNuclear laminaCell biologyBiologyEpigeneticsLaminaNuclear proteinGeneticsGeneTranscription factor

Abstract

fetched live from OpenAlex

Cellular health is reliant on proteostasis-the maintenance of protein levels regulated through multiple pathways modulating protein synthesis, degradation and clearance. Loss of proteostasis results in serious disease and is associated with aging. One proteinaceous structure underlying the nuclear envelope-the nuclear lamina-coordinates essential processes including DNA repair, genome organization and epigenetic and transcriptional regulation. Loss of proteostasis within the nuclear lamina results in the accumulation of proteins, disrupting these essential functions, either via direct interactions of protein aggregates within the lamina or by altering systems that maintain lamina structure. Here we discuss the links between proteostasis and disease of the nuclear lamina, as well as how manipulating specific proteostatic pathways involved in protein clearance could improve cellular health and prevent/reverse disease.

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)0.001
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 score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.004

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 head0.026
GPT teacher head0.306
Teacher spread0.280 · 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

Citations19
Published2020
Admission routes2
Has abstractyes

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