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Can Muscle Sarcoplasmic Reticulum Ca2+-ATPase Defects Be Implicated In Exercise Intolerance In Chronic Heart Failure?

2005· article· en· W1998337830 on OpenAlexaffabout
Howard J. Green, David Lounsbury, James W. E. Rush, J. Ouyang

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2005
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Effects of Exercise
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSERCAVentricleInternal medicineHeart failureEndocrinologyCardiologyEndoplasmic reticulumMedicineSkeletal muscleChemistryATPaseExercise intoleranceEnzymeBiochemistry

Abstract

fetched live from OpenAlex

PURPOSE The purpose of this study was to determine the effects of experimental chronic heart failure (CHF) on maximal sarcoplasmic reticulum Ca2+-ATPase activity (Vmax) and Hill co-efficient (nH) in soleus (SOL), red gastrocnemius (RG) and white gastrocnemius (WG) and left ventricle (LV). METHODS Experimental CHF following ligation of the left main coronary artery in adult rats was confirmed by elevations (p < 0.05) in left-ventricle to body wt ratios (0.168±0.04 vs 0.186±0.07 g/g.100) in CHF compared to controls (CON). RESULTS Comparisons between CHF (n = 8) and CON (n = 8) indicated reduced (p < 0.05) Vmax (μmol.g protein−1·min−1) in SOL (197±22 vs 152±20), RG (399±8.2 vs 314±25) and WG (584±47 vs 449±59) in CHF. The nH was not different between CHF and CON for any of the muscles examined. The lower Vmax was accompanied by increases (p < 0.05) in SERCA 2 (% standard) in RG (38.7±10 vs 56.1±7.6) and WG (19.4±3.0 vs 27.0±2.2) only. No changes were observed in SERCA 1, as measured by Western blotting techniques. Reductions in Vmax were observed for LV (207±22 vs 162±14) in the absence of change in SERCA 2. CONCLUSION These results suggest that altered protein levels cannot explain the reduced Vmax observed in CHF. The results also suggest that defects in Ca2+-cycling may be involved in weakness and fatigue in CHF. Supported by Heart and Stroke Foundation (Ontario)

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.145
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0030.007
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.008
GPT teacher head0.264
Teacher spread0.256 · 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.

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

Citations0
Published2005
Admission routes2
Has abstractyes

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