CHARACTERIZATION OF MUSCLE METABOLIC DYSFUNCTION IN CYSTIC FIBROSIS USING 31P-MRS
Bibliographic record
Abstract
Objectives: The objective of this research-in-progress is to identify and characterize the underlying defect (s) in muscle metabolism in individuals with cystic fibrosis (CF) using 31phosphorus magnetic resonance spectroscopy (31P-MRS). Previous work has demonstrated a decrease in peak anaerobic power in female athletes with CF despite normal lung function and good nutritional status (Selvadurai et al 2003; Am J Respir Crit Care Med 168:1476). Methods: We are recruiting and testing 80 participants (40 females, 40 males). Half of the subjects are individuals with cystic fibrosis who are being matched with healthy controls for gender, age, and activity level (HAES). Using specific ergometric protocols to assess aerobic and anareobic performance, we are applying non-invasive 31P-MRS technology to quantify abnormalities in aerobic and/or anaerobic metabolism and their relative impact on muscle energy production and substrate recovery. Aerobic and anaerobic metabolic parameters are being derived from graphical analysis. We are correlating these abnormalities with the metabolic profile and motor performance obtained from the forearm ischemic lactate test and from standardized cycle ergometry (maximal incremen tal and isokinetic cycling). Results: Data collection is underway and parameter estimates are being collected for inorganic phosphorus, phosphocreatine, and adenosine triphosphate peaks, as well as pH, Mg2+, and ATP-ase activity during rest, aerobic exercise, anaerobic exercise, and recovery for individuals with cystic fibrosis and healthy controls, for both male and female participants. Results will be analyzed using a 2-way ANOVA (factors: gender, group). Our preliminary results suggest important differences between healthy controls and CF patients in resting phosphocreatine levels, which are reduced in CF, and in end-exercise pH, which is less acidotic in CF patients. Conclusion: Elucidation of the mechanism (s) by which CF adversely affects muscle metabolism, will increase our understanding of the fundamental nature of the muscle dysfunction in CF and facilitate the development of energy-system specific ergometric treatment protocols. Furthermore, this 31P-MRS technique may provide a useful prognostic tool and a means to assess the efficacy of therapeutic interventions in CF.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".