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Record W2114022102 · doi:10.1002/nau.20964

Exercise improves bladder function in diabetic mice

2010· article· en· W2114022102 on OpenAlexafffund
Manasi Vadhavkar, Saeid Golbidi, Jason Sea, Michelle Longpre, Lynn Stothers, Ismail Laher

Bibliographic record

VenueNeurourology and Urodynamics · 2010
Typearticle
Languageen
FieldMedicine
TopicUrinary Bladder and Prostate Research
Canadian institutionsUniversity of British Columbia
FundersHeart and Stroke Foundation of Canada
KeywordsMedicineResidual volumeEndocrinologyInternal medicineUrologyOveractive bladder

Abstract

fetched live from OpenAlex

AIMS: We determined the effect of exercise on bladder dysfunction and voiding frequency in db/db mice. MATERIALS AND METHODS: Diabetic db/db female mice (BKS.Cg-Dock7m +/+ Leprdb/J strain) and their age-matched wild-type controls (WT) were equally divided into sedentary and exercise groups. Mice were exercised for 1 hr everyday for 8 weeks (speed of 5.2 m/min). We performed a voiding pattern test, cystometric analysis and reactivity of isolated bladder strips in WT and db/db mice, both sedentary and exercised. RESULTS: Diabetes increased the frequency of voiding, bladder capacity, and residual volume. Exercise decreased voiding frequency in db/db mice; voiding frequency was 5.8 ± 0.5 (db/db exercise) versus 10.8 ± 1.1 (db/db control, P < 0.001). In cystometric analysis, the bladder capacity of db/db sedentary mice was 0.27 ± 0.05 ml and was 0.14 ± 0.02 ml in the db/db exercise group (P < 0.05), whereas the residual volume was 0.2 ± 0.03 ml in db/db sedentary mice and 0.06 ± 0.02 ml in db/db Ex mice. Isolated strips of bladder muscle from sedentary db/db mice were more responsive to carbachol than strips from db/db exercise mice. Exercise did not improve the urodynamic properties of WT mice, both sedentary and exercised. CONCLUSIONS: Exercise improves bladder function in diabetic mice by reducing voiding frequency and improving urodynamic parameters.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.575
Threshold uncertainty score0.429

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 integrity0.0000.001
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 head0.006
GPT teacher head0.249
Teacher spread0.243 · 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 designObservational
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

Citations15
Published2010
Admission routes2
Has abstractyes

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