Bibliographic record
Abstract
New frontline drugs and therapies are urgently required to protect the body from primary and secondary injuries. We review more than 10 years of work on adenosine, lidocaine, and magnesium (ALM) and its possible significance to civilian and military medicine. Adenosine is an endogenous nucleoside involved in nucleotide production, adenosine triphosphate turnover, and restoration of supply and demand imbalances. Lidocaine is a local anesthetic and Class 1B antiarrhythmic, and magnesium is essential for ionic regulation and cellular bioenergetics. Individually, each plays important roles in metabolism, immunomodulation, inflammation, and coagulation. The original idea to combine all three was as a "polarizing" cardioplegia, an idea borrowed from natural hibernators. Two recent prospective, randomized human trials have demonstrated its safety and superiority in myocardial protection over high-potassium "depolarizing" solutions. The next idea came from witnessing how the human heart spontaneously reanimated after complex operations with little inotropic support. At high doses, ALM arrests the heart, and at lower doses, it resuscitates the heart. In rat and pig models, we have shown that ALM intravenous bolus and infusion "drip" protects against acute regional myocardial ischemia, lethal arrhythmias, cardiac arrest, compressible and noncompressible blood loss and shock, endotoxemia, and sepsis. Individually, adenosine, lidocaine, or magnesium fails to protect. Protection is afforded in part by reducing inflammation, correcting coagulopathy, and lowering energy demand. We propose a unifying hypothesis involving improved central, cardiovascular and endothelium coupling to maintain sufficient tissue oxygenation and reduce primary and secondary "hit" complications. As with any new drug innovation, translation into humans is challenging.
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 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) | 0.001 | 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".