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Record W4307560932 · doi:10.1186/s12575-022-00178-8

Shrimp miR-965 transfers tumoricidal mitochondria

2022· article· en· W4307560932 on OpenAlexaff
Hyueyun Kim, Ji Ha Choi, Chang Mo Moon, Jihee Lee Kang, Minna Woo, Minsuk Kim

Bibliographic record

VenueBiological Procedures Online · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMitochondrial Function and Pathology
Canadian institutionsToronto General HospitalUniversity of TorontoUniversity Health Network
FundersMinistry of Science and ICT, South KoreaNational Research Foundation of KoreaNational Research Foundation
KeywordsMitochondrionApoptosisCell biologyProgrammed cell deathChemistryBiologyCancer researchBiochemistry

Abstract

fetched live from OpenAlex

BACKGROUND: Micro RNA of Marsupenaeus japonicas has been known to promote apoptosis of tumor cells. However, the detailed mechanisms are not well understood. RESULTS: Using tomographic microscope, which can detect the internal structure of cells, we observed breast tumor cells following treatment of the miRNA. Intriguingly, we found that mitochondria migrate to an adjacent tumor cells through a tunneling nanotube. To recapitulate this process, we engineered a microfluidic device through which mitochondria were transferred. We show that this mitochondrial transfer process released endonuclease G (Endo G) into tumor cells, which we referred to herein as unsealed mitochondria. Importantly, Endo G depleted mitochondria alone did not have tumoricidal effects. Moreover, unsealed mitochondria had synergistic apoptotic effects with subtoxic dose of doxorubicin thereby mitigating cardiotoxicity. CONCLUSIONS: Together, we show that the mitochondrial transfer through microfluidics can provide potential novel strategies towards tumor cell death.

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.024
GPT teacher head0.265
Teacher spread0.242 · 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
Published2022
Admission routes1
Has abstractyes

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