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Record W3212735015 · doi:10.26685/urncst.283

ALSTaR Cells - Novel Stem-cell-based Therapy for Amyotrophic Lateral Sclerosis: A Research Protocol

2021· article· en· W3212735015 on OpenAlexaff
Niharikaa Aiyar, Maryam Dadabhoy, Nitya Gulati

Bibliographic record

VenueUndergraduate Research in Natural and Clinical Science and Technology (URNCST) Journal · 2021
Typearticle
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsAmyotrophic lateral sclerosisNeuroscienceAutophagyStem cellBiologyRegenerative medicineNeural stem cellTransplantationCell therapyCell biologyMedicinePathologyDiseaseApoptosisInternal medicine

Abstract

fetched live from OpenAlex

Introduction: Amyotrophic Lateral Sclerosis (ALS) is a progressive neurodegenerative disease that results in the loss of motor neurons leading to limb paralysis and eventual death. Stem cell transplantation can be used to replenish the atrophied motor neurons and slow the progression of the disease, while the use of biomaterials, genetic engineering, and nanoparticles can reduce the hostility of the microenvironment. Thus, we propose a novel combinatorial therapeutic approach termed Amyotrophic Lateral Sclerosis Therapeutic and Regenerative (ALSTaR) cell treatment. Methods: This strategy will employ miRNA-124 and chitosan polyplex biomaterial to enhance engraftment of neural progenitor cells (NPCs), which will be engineered to overexpress an autophagy-regulatory gene, TFEB, and secrete an autophagy-inducing drug, trehalose. We will bioengineer an organoid model of the ventral column of the spinal cord which will be used for extensive in vitro characterization of ALSTaR cells through single-slice electrophysiology and immunocytochemistry. Using the SOD1G93A mutant mouse model, weekly behavioural assessments of motor recovery will be conducted until the humane endpoint; thereafter extensive immunohistochemical and protein quantification analyses will be performed to determine treatment efficacy in vivo. Results: ALSTaR cell treatment should result in motor functional recovery and lifespan elongation through several cellular and molecular mechanisms, including decreased neuroinflammation, upregulation of autophagy, degradation of protein aggregates, enhancement of transplanted cell integration, and regeneration of axons. Discussion: In vitro characterization of ALSTaR cells in bioengineered spinal cord organoids will reveal stable electrophysiological recordings of motor neurons, higher levels of neuronal differentiation markers, and lower levels of inflammation markers in the ALSTaR group compared to the control group or the groups treated with the biomaterial or the NPCs alone, suggesting neuronal recovery. Behavioural assessments in the ALS mouse model will reveal increased motor coordination, neuromuscular strength, and motor activity in the ALSTaR group compared to other groups, suggesting motor functional recovery. In vivo characterization of ALSTaR cells in the SOD1G93A mice will suggest enhanced stem cell integration and recovery of cellular and molecular processes. Conclusion: With currently no effective treatment for ALS, this novel combinatorial treatment strategy could improve the health-related quality of life of patients suffering from this debilitating 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)0.000
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: Protocol · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.007

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 head0.181
GPT teacher head0.466
Teacher spread0.285 · 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
GenreProtocol

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
Published2021
Admission routes1
Has abstractyes

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