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Record W2795774894 · doi:10.14440/bladder.2018.582

Non-invasive voiding assessment in conscious mice

2018· article· en· W2795774894 on OpenAlexaff
Martin Sidler, Karen Aitken, Sarah Forward, I. Alex Vitkin, Darius Bägli

Bibliographic record

VenueBladder · 2018
Typearticle
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsCystometryMedicineBladder outlet obstructionUrinationUrologyP70-S6 Kinase 1Urinary bladderUrinary systemInternal medicinePI3K/AKT/mTOR pathwayBiologySignal transductionProstate

Abstract

fetched live from OpenAlex

OBJECTIVE: To review available options of assessing murine bladder function and to evaluate a non-invasive technique suitable for long-term recording. METHODS: We reviewed previously described methods to record rodent bladder function. We used modified metabolic cages to capture novel recording tracings of mouse micturition. We evaluated our method in a pilot study with female mice undergoing partial bladder outlet obstruction or sham operation, respectively; half of the partial obstruction and sham group received treatment with an S6K-inhibitor, targeting the mTOR pathway, which is known to be implicated in bladder response to obstruction. RESULTS: Our non-invasive method using continuous urine weight recording reliably detected changes in murine bladder function resulting from partial bladder outlet obstruction or treatment with S6K-inhibitor. We found obstruction as well as treatment with S6K-inhibitor to correlate with a hyperactive voiding pattern. CONCLUSIONS: While invasive methods to assess murine bladder function largely disturb bladder histology and intrinsically render post-cystometry gene expression analysis of questionable value, continuous urine weight recording is a reliable, inexpensive, and critically non-invasive method to assess murine bladder function, suitable for a long-term application.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.506
Threshold uncertainty score0.333

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 head0.014
GPT teacher head0.303
Teacher spread0.288 · 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
Published2018
Admission routes1
Has abstractyes

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