Bibliographic record
Abstract
Working with Ken Bloch, MD, he cloned the gene responsible for the production of nitric oxide in endothelial cells.Since then, he has taken it from the gene to transgenic animal models-small animals, then large-to clinical trials, and then to the coronary care unit."At the time, nitric oxide was one of the most fascinating molecules that was discussed, and we sensed that it was important for the heart and for the circulation, but the gene that was responsible for the production of nitric oxide in the heart was not known," he recalls."Over the past 10 to 15 years we've been working with lots of genes involved in nitric oxide biology and how it applies to cardiac disease." "The Start of My Real Interest in Learning About What It Is That Makes People Sick, ot Just Physiology and Symptoms, but Mechanisms of Disease"Professor Janssens' decision to pursue medicine at the University of Leuven, where he received his MD in 1984, is attributable to a high school role model who had chosen it for a career path.The scientific interest came during his first year as an intern when he was supervised by Marc Decramer, MD, PhD, now chief of the hospital's pulmonary division, but at the time a young staff member who had spent a couple of years doing research training in Montreal, Canada."He was the one who inspired me to learn the science," says Professor Janssens."All of the other people I had worked with were MDs mainly focusing on clinical medicine with limited scientific interest."At that time in Europe, medical students studied physiology, with a bit of pathology and biochemistry thrown in, but little emphasis was placed on the mechanisms of disease.Molecular biology was still a new subject, and it first became established in the research laboratories in the United States.Professor Janssens says, "He [Decramer] got exposed to basic science, and that was the start of my real interest in learning about what it is that makes people sick, not just physiology and symptoms, but mechanisms of disease.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.521 | 0.322 |
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".