Redefining Malignant Hyperthermia: Multi-Omics Insights into a Complex Anesthetic Disorder
Bibliographic record
Abstract
BACKGROUND: Malignant hyperthermia (MH) is a rare but serious pharmacogenetic disorder triggered by specific anesthetic agents, leading to a rapid and often fatal hypermetabolic response. While its genetic roots - primarily involving RYR1 and CACNA1S mutations - are well documented, many susceptible individuals remain undiagnosed until they are exposed to the triggering anesthetic. Despite dantrolene being an instrumental drug in combatting MH mortality, global access remains inconsistent, and morbidity rates remains high. Current diagnostic tools are invasive and limited to specialized centers, and routine screening is rarely feasible. SUMMARY: This review explores how recent advances in multi-omics - genomics, proteomics, metabolomics, transcriptomics, and radiomics - are reshaping our understanding of MH pathophysiology. From chronic calcium dysregulation and mitochondrial dysfunction to shifts in energy metabolism and subtle muscle changes, a complex picture is emerging. Integrative analyses reveal promising biomarkers for early detection, while CRISPR-based gene editing and machine learning offer potential pathways for future targeted interventions. Noninvasive imaging, blood-based metabolic profiling, and genomic risk prediction may soon offer safer, more effective screening tools for anesthesia planning. KEY MESSAGES: Ultimately, a shift from reactive crisis management to proactive risk identification could redefine how we approach MH - potentially improving patient outcomes and saving lives.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".