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Record W4399248686 · doi:10.1186/s13287-024-03771-8

Bridging the gap between in vitro and in vivo models: a way forward to clinical translation of mitochondrial transplantation in acute disease states

2024· review· en· W4399248686 on OpenAlexaff
David F. Bodenstein, Gabriel Siebiger, Yimu Zhao, Aaron J. Clasky, Avinash Naraiah Mukkala, Erika L. Beroncal, Lauren Banh, Lili Aslostovar, Sonya Brijbassi, Sarah Hogan, James D. McCully, Mohadeseh Mehrabian, Thomas Petersen, Lisa A. Robinson, Melanie Walker, Constantine Zachos, Sowmya Viswanathan, Frank Gu, Ori D. Rotstein, Marcelo Cypel, Milica Radisic, Ana C. Andreazza

Bibliographic record

VenueStem Cell Research & Therapy · 2024
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMitochondrial Function and Pathology
Canadian institutionsMitel (Canada)Structural Genomics ConsortiumHospital for Sick ChildrenOccupational Cancer Research CentreCanada Research ChairsUniversity Health NetworkUniversity of New BrunswickUniversity of TorontoToronto General HospitalOntario Genomics
Fundersnot available
KeywordsMitochondrionTransplantationClinical trialTranslation (biology)Bridging (networking)MedicineStem cellImmune systemDiseaseBioinformaticsBiologyImmunologyCell biologyPathologyComputer scienceInternal medicineBiochemistry

Abstract

fetched live from OpenAlex

Mitochondrial transplantation and transfer are being explored as therapeutic options in acute and chronic diseases to restore cellular function in injured tissues. To limit potential immune responses and rejection of donor mitochondria, current clinical applications have focused on delivery of autologous mitochondria. We recently convened a Mitochondrial Transplant Convergent Working Group (CWG), to explore three key issues that limit clinical translation: (1) storage of mitochondria, (2) biomaterials to enhance mitochondrial uptake, and (3) dynamic models to mimic the complex recipient tissue environment. In this review, we present a summary of CWG conclusions related to these three issues and provide an overview of pre-clinical studies aimed at building a more robust toolkit for translational trials.

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)0.004
metaresearch head score (Gemma)0.003
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 score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0020.004
Open science0.0010.001
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0040.003

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 head0.155
GPT teacher head0.426
Teacher spread0.271 · 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

Citations32
Published2024
Admission routes1
Has abstractyes

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