Report and Abstracts of the 17th Meeting of IIM, the Interuniversity Institute of Myology:Virtual meeting, October 16-18, 2020
Bibliographic record
Abstract
In 2020, due to the COVID-19 pandemic, the annual meeting of the Interuniversity Institute of Myology (IIM), took place on a virtual platform. Attendees were scientists and clinicians, as well as pharmaceutical companies and patient organization representatives from Italy, several European countries, Canada and USA. Four internationally renowned Keynote speakers presented recent advances on muscle stem cells regulation, skeletal muscle regeneration, quantitative biology approaches, and metabolic regulation of muscle homeostasis. Novel, unpublished data by young trainees were presented as oral communications or posters, in five scientific sessions and two poster sessions. On October 15, 2020, selected young trainees participated to the High Training Course on "Advanced Myology", organized together with the University of Perugia, Italy. The course, on a virtual platform, showcased lectures on muscle development and regulation of muscle gene expression by international speakers, and roundtables discussions on "Single cell analysis of skeletal muscle" and "Skeletal muscle stem cell in healthy muscle and disease". The Young IIM Committee, composed by young trainee winners of awards in the past IIM Meeting editions, was directly involved in the selection of keynote speakers, the organization of scientific sessions and roundtables discussions tailored to the interests of their peers. A broad audience of Italian, European and North American participants contributed to the different initiatives. The meeting was characterized by a friendly and inclusive atmosphere, facilitating lively and stimulating discussions on emerging areas of muscle research. The meeting stimulated scientific cross-fertilization fostering novel ideas and scientific collaborations aimed at better understanding muscle normal physiology and the mechanisms underlaying muscle diseases, with the ultimate goal of developing better therapeutic strategies. The meeting was a success, and the number of meeting attendees was the highest of all IIM Meeting editions. Despite the current difficulties imposed by the COVID-19 pandemic, we are confident that the IIM community will continue to grow and deliver significant contributions to the understanding of muscle development and function, the pathogenesis of muscular diseases and the development of novel therapeutic approaches. Here, abstracts of the meeting illustrate the new results on basic, translational, and clinical research, confirming that our field is strong and healthy.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".