Hyaluronan Associated Inflammation and Microenvironment Remodelling Influences Breast Cancer Progression
Bibliographic record
Abstract
The breast is an organ composed predominantly of glandular, fatty, and fibrous tissues. Glandular tissue is composed of ducts lined by luminal epithelial cells that secrete milk, and is surrounded by a layer of myoepithelial cells that contract to release milk. Myoepithelial cells produce proteases, growth factors and growth factor receptors that contribute to remodelling during breast tissue expansion. Each duct is enclosed by a laminin-rich basement membrane and embedded in extracellular matrix (ECM). Mammary gland ECM and is a mixture of fibrillar proteins such as collagens, laminins, fibronectin, and polysaccharides such as heparin sulphate, chondroitin sulphate and hyaluronan (HA). These collectively provide the mechanical and structural support required for maintaining mammary tissue architecture and for storage of the soluble regulatory molecules needed for tissue homeostasis, plasticity, and remodelling. ECM promotes both the differentiated, homeostatic integrity of mammary tissue and is also a key determinant in branching morphogenesis, response-to-injury and pathological processes such as neoplastic disease. The importance of the ECM in determining homeostatic vs. tumourigenic events was originally demonstrated three decades ago by Beatrice Mintz, who showed that marked embryonic carcinoma cells injected into blastocysts do not give rise to tumours but instead contribute to normal tissue architecture. The same cells injected into adult mice develop into tumours Components of the microenvironment that support tumour progression have since been identified. For example, chick embryos infected with Rous Sarcoma virus express the oncogene v-src in every cell but tumours develop only at sites of wounding due to the accumulation of TGF-1 (Weigelt and Bissell, 2008).
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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".