Meeting report: the inaugural muscle biology and cachexia conference at the University of Houston, May 18-20, 2025, Houston, Texas, USA
Bibliographic record
Abstract
In May 2025, the University of Houston (UH) hosted the inaugural Muscle Biology and Cachexia conference, organized by Drs. Ashok Kumar and Radbod Darabi. The conference attracted nearly 300 participants, including established scientists, early-career researchers, and students from across the United States, Canada, Italy, Singapore, and Turkey. Research was presented through a combination of oral presentations and poster sessions. The conference was driven by the increasing interest in skeletal and cardiac muscle biology and cancer cachexia among institutions at the Texas Medical Center and surrounding universities. It served as a platform to promote knowledge exchange and foster collaboration within this growing scientific community. The conference was supported by the UH College of Pharmacy (UHCOP), Division of Research (DOR), Drug Discovery Institute (DDI), and the Department of Pharmacological and Pharmaceutical Sciences (PPS). In conjunction with the conference, UH announced the formation of the Institute of Muscle Biology and Cachexia (IMBC). The IMBC aims to strengthen collaborative research efforts and enhance understanding of the molecular and signaling pathways that regulate muscle physiology and 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.220 | 0.080 |
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".