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Record W7133020561

Investigating the Mechanisms of Perinatal Neuron Degeneration, Survival, and their Differentiation from Neural Precursor Cells of the V-SVZ

2023· dissertation· W7133020561 on OpenAlexfundno aff
Adelaida Kolaj

Bibliographic record

VenueTSpace · 2023
Typedissertation
Language
FieldNeuroscience
TopicNerve injury and regeneration
Canadian institutionsnot available
FundersHospital for Sick ChildrenUniversity of Toronto
KeywordsProgenitor cellMechanism (biology)Neuroepithelial cellNeuronProgenitorEmbryonic stem cellNervous systemHomeostasisCentral nervous systemNeural stem cell
DOInot available

Abstract

fetched live from OpenAlex

For the formation and maintenance of functional neuronal networks, the generation of neurons from their progenitors developmentally as well as their continued survival once the circuitry has been established are both crucial for nervous system homeostasis and plasticity, with disruptions resulting in neuropathologies. For the integrity of this process, first, cellular mechanisms need to temporally orchestrate the correct neuronal fate delineation from multipotent precursors and second, transduced pro-life signals need to suppress degeneration-promoting transcriptional programs to ensure continued survival. In this thesis, I describe a novel 4E-T-based translational regulation mechanism that “fine-tunes” the timing of neurogenesis, coordinating the correct emergence ofinterneurons from transcriptionally pre-fated early postnatal precursors (Chapter 5), in addition to characterizing a novel neuroprotectant promoting survival by suppressing the degeneration of peripheral neurons and potently protecting axonal mitochondria (Chapter 4). Postnatal precursors which possess multi-lineage potency and the ability to renew long-term, are a continuous source of neuronal progeny for tissue formation and repair, and must balance their maintenance through self-renewal, expansion of their pools through proliferation, and the need to generate progeny through differentiation. In Chapter 5 of this thesis, I demonstrate that postnatal precursors are “pre-fated”, expressing neuronal differentiation instructing specifier transcripts, and that a 4E-T-based regulatory mechanism involving translational repression modulates protein synthesis levels of these mRNA targets, controlling timely neurogenesis. Established peripheral neurons and their axonal projections need to then be preserved for the lifetime of the organism to ensure proper circuit function. One goal of the work presented in this thesis is to identify safe-in-man drugs that can block or delay axon degeneration, and to use these drugs to characterize new degeneration pathways. To this end, the work described in Chapter 4 describes and identifies the multikinase inhibitor Foretinib as being protective in sympathetic, sensory and motor neurons across multiple degeneration paradigms. Thus, my thesis traces the trajectory of neurons in two main aspects of neurobiology: from onset of fate commitment and appropriate neuronal lineage acquisition, to maintenance and protection in maturity once neurons integrate into circuits, highlighting the consequences to the organism in the case of aberration in either 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 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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.035
GPT teacher head0.286
Teacher spread0.251 · 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 designBench or experimental
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

Explore more

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