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Record W4287832766 · doi:10.3791/64237

Quantification of Immunostained Caspase-9 in Retinal Tissue

2022· article· en· W4287832766 on OpenAlexaff
Crystal K. Colón Ortiz, Anna M. Potenski, Kendra V. Johnson, Claire W. Chen, Scott J. Snipas, Ying Y. Jean, Maria I. Avrutsky, Carol M. Troy

Bibliographic record

VenueJournal of Visualized Experiments · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell death mechanisms and regulation
Canadian institutionsColumbia College
FundersNational Institute of Neurological Disorders and StrokeNational Eye Institute
KeywordsCaspaseBiologyProteasesProgrammed cell deathApoptosisCell biologyNeuroscienceIntrinsic apoptosisCaspase 2CellComputational biologyGenetics

Abstract

fetched live from OpenAlex

The family of caspases is known to mediate many cellular pathways beyond cell death, including cell differentiation, axonal pathfinding, and proliferation. Since the identification of the family of cell death proteases, there has been a search for tools to identify and expand the function of specific family members in development, health, and disease states. However, many of the currently commercially available caspase tools that are widely used are not specific for the targeted caspase. In this report, we delineate the approach we have used to identify, validate, and target caspase-9 in the nervous system using a novel inhibitor and genetic approaches with immunohistochemical read-outs. Specifically, we used the retinal neuronal tissue as a model to identify and validate the presence and function of caspases. This approach enables the interrogation of cell-type specific apoptotic and non-apoptotic caspase-9 functions and can be applied to other complex tissues and caspases of interest. Understanding the functions of caspases can help to expand current knowledge in cell biology, and can also be advantageous to identify potential therapeutic targets due to their involvement in 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 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 score0.011
Threshold uncertainty score0.256

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 head0.020
GPT teacher head0.393
Teacher spread0.373 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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