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Record W4360610618 · doi:10.3389/fmicb.2023.1133773

Mitochondrial nanomotion measured by optical microscopy

2023· article· en· W4360610618 on OpenAlexaff
Priyanka Parmar, María Inés Villalba, Alexandre Seiji Horii Huber, Aleksandar Kalauzi, Dragana Bartolić, Ksenija Radotić, Ronnie Willaert, Derrick F. MacFabe, Sandor Kasas

Bibliographic record

VenueFrontiers in Microbiology · 2023
Typearticle
Languageen
FieldNeuroscience
TopicPhotoreceptor and optogenetics research
Canadian institutionsAutism Canada
FundersFonds Wetenschappelijk OnderzoekBelgian Federal Science Policy OfficeSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungEuropean Space AgencyAutism Research Institute
KeywordsOrganelleYeastMitochondrionMicroscopyJurkat cellsBiologyMotilityCell biologyBiophysicsBiochemistryChemistryGeneticsPhysicsOptics

Abstract

fetched live from OpenAlex

Nanometric scale size oscillations seem to be a fundamental feature of all living organisms on Earth. Their detection usually requires complex and very sensitive devices. However, some recent studies demonstrated that very simple optical microscopes and dedicated image processing software can also fulfill this task. This novel technique, termed as optical nanomotion detection (ONMD), was recently successfully used on yeast cells to conduct rapid antifungal sensitivity tests. In this study, we demonstrate that the ONMD method can monitor motile sub-cellular organelles, such as mitochondria. Here, mitochondrial isolates (from HEK 293 T and Jurkat cells) undergo predictable motility when viewed by ONMD and triggered by mitochondrial toxins, citric acid intermediates, and dietary and bacterial fermentation products (short-chain fatty acids) at various doses and durations. The technique has superior advantages compared to classical methods since it is rapid, possesses a single organelle sensitivity, and is label- and attachment-free.

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 score0.124
Threshold uncertainty score0.608

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.025
GPT teacher head0.295
Teacher spread0.270 · 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

Citations9
Published2023
Admission routes1
Has abstractyes

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