Automated Spectrophotometric Analysis of Mitochondrial Respiratory Chain Complex Enzyme Activities in Cultured Skin Fibroblasts
Bibliographic record
Abstract
BACKGROUND: Mitochondrial respiratory chain complex (RCC) disorders may occur as commonly as 1 in 8500 individuals. Because of the great variability of phenotypic presentations, measurement of individual RCC enzyme activities is a crucial diagnostic process. Current assay methods are time-consuming and labor-intensive and thus constitute a major impediment to clinical practice. A method with a faster turnaround time would therefore be beneficial. METHOD: We developed an automated spectrophotometric method for measuring the respiratory chain enzyme activities of complex I, complex II + III, and complex IV with the Hitachi 912, an automated spectrophotometer. Mitochondrial citrate synthase was also determined for normalization of the RCC activities. RESULTS: A blinded method comparison with samples from an external testing center yielded a 91% concordance of interpretations. Mean intraassay imprecision (as CV; n = 20) in a single batch analysis of each RCC was 5.9%. Interassay imprecision, evaluated on 2 samples harvested and analyzed 3 times each, gave mean CVs of 10%-18%. CONCLUSIONS: With this automated method, a panel of RCC enzyme activities can be determined in <2 h. In addition, an immunoblot assay using monoclonal antibodies against specific subunits of RCC enzyme complexes can be informative in cases of borderline enzyme activity. Our results suggest that in vitro diagnosis of RCC enzyme deficiencies in skin fibroblasts is an effective alternative to invasive muscle biopsy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".