Intermuscular and intramuscular adipose tissues: Bad vs. good adipose tissues
Bibliographic record
Abstract
Human studies of the influence of aging and other factors on intermuscular fat (INTMF) were reviewed. Intermuscular fat increased with weight loss, weight gain, or with no weight change with age in humans. An increase in INTMF represents a similar threat to type 2 diabetes and insulin resistance as does visceral adipose tissue (VAT). Studies of INTMF in animals covered topics such as quantitative deposition and genetic relationships with other fat depots. The relationship between leanness and higher proportions of INTMF fat in pigs was not observed in human studies and was not corroborated by other pig studies. In humans, changes in muscle mass, strength and quality are associated with INTMF accretion with aging. Gene expression profiling and intrinsic methylation differences in pigs demonstrated that INTMF and VAT are primarily associated with inflammatory and immune processes. It seems that in the pig and humans, INTMF and VAT share a similar pattern of distribution and a similar association of components dictating insulin sensitivity. Studies on intramuscular (IM) adipocyte development in meat animals were reviewed. Gene expression analysis and genetic analysis have identified candidate genes involved in IM adipocyte development. Intramuscular (IM) adipocyte development in human muscle is only seen during aging and some pathological circumstance. Several genetic links between human and meat animal adipogenesis have been identified. In pigs, the Lipin1 and Lipin 2 gene have strong genetic effects on IM accumulation. Lipin1 deficiency results in immature adipocyte development in human lipodystrophy. In humans, overexpression of Perilipin 2 (PLIN2) facilitates intramyocellular lipid accretion whereas in pigs PLIN2 gene expression is associated with IM deposition. Lipins and perilipins may influence intramuscular lipid regardless of species.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".