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Record W2888590062 · doi:10.15562/gnc.64

Overexpression of Chemokine Receptors on Neural Stem Cells Pretreated with Valproic acid: Towards Improved Homing

2018· article· en· W2888590062 on OpenAlexvenueno aff
Fahime Karimi, Mohammad Reza Hashemzadeh, Mohammad Amin Edalatmanesh, Hojjat Naderi‐Meshkin

Bibliographic record

VenueJournal of Genes and Cells · 2018
Typearticle
Languageen
FieldNeuroscience
TopicNeurogenesis and neuroplasticity mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsHoming (biology)Neural stem cellAmyotrophic lateral sclerosisNestinMultiple sclerosisSOX2Stem cellChemokine receptorCCR1NeuroscienceBiologyChemokineMedicineImmunologyCell biologyImmune systemPathologyDiseaseEmbryonic stem cell

Abstract

fetched live from OpenAlex

Neural stem cells (NSCs) have considerable capacity for self-renewing and also ability for generating neurons in the mammalian brain. However, one of the big challenges is the migration and targeted homing of transplanted NSCs into the injured site to treat neurodegenerative diseases including Alzheimer´s disease (AD), Parkinson’s disease (PD), multiple sclerosis (MS), amyotrophic lateral sclerosis (ALS), brain ischemia (BI) and spinal cord injury (SCI). To improve homing capacity, pretreatment of NSCs with Valproic acid (VPA), which is supposed to cause diverse effects on migration ability of NSCs, is a strategy. More recently, hind brain and olfactory bulbs have been introduced as a good source of NSCs. So, NSCs were isolated from these two sources of postnatal day 1 (PND1) rats. These isolated cells were characterized by expressing neuronal markers such as Nestin and Sox2. The expression of four selected chemokine receptors (CXCR4, CXCR6, CCR1 and CCR7), which are important effectors in homing of stem cells, was investigated. It is concluded that VPA treatment enhances NSCs migration and homing showing its potential to be applied for cell-based therapies.

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 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 score0.006
Threshold uncertainty score0.549

Codex and Gemma teacher scores by category

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 communication0.0000.000
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 head0.022
GPT teacher head0.231
Teacher spread0.209 · 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 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
Published2018
Admission routes1
Has abstractyes

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