Bibliographic record
Abstract
Bone is a highly hospitable environment for colonization and growth of metastatic tumors, and some of the most common human malignancies, notably breast cancer and prostate cancer, have a strong propensity to produce skeletal metastases (1). Tumor cells, in turn, can produce a spectrum of skeletal manifestations which spans diffuse osteopenia, focal osteolysis, focal osteogenesis, and osteomalacia (2). The most common skeletal manifestation of malignancy is focal osteolysis in association with metastases. In order for tumor cells to grow and invade mineralized bone, osteolysis must occur. Osteoclasts appear uniquely adapted to produce the microenvironment and the biochemical milieu that are needed to resorb bone. Although previous reports have indicated that some tumor cells appear capable of assuming an osteoclast phenotype and directly resorbing bone (3), the bulk of the evidence suggests that most tumor cells act indirectly by co-opting the physiologic mechanisms that normally favor bone resorption. Thus, they release agents such as hormones, eicosanoids, growth factors, and cytokines into the bone microenvironment, which act on osteoblastic stromal cells to enhance the production of osteoclast activating factors. Most notable of these is the cell membrane–associated protein termed receptor activator of NF-κB ligand (RANKL), which is a member of the TNF family of cytokines. RANKL can then bind to its cognate receptor (RANK) on osteoclast precursors and, in the presence of M-CSF, enhance the differentiation and fusion of these cells to produce functioning multinucleated osteoclasts (4) (Figure (Figure1).1). Concomitantly, production of a soluble decoy receptor for RANKL, termed osteoprotegerin (OPG) (5), may be downregulated (6), thus eliminating one means by which the ensuing osteolysis could be repressed. Figure 1 Schematic representation of tumor-cell induced osteolysis. A tumor cell may release soluble mediators such as hormones (e.g., PTHrP), eicosanoids, cytokines (e.g., IL-6), or growth factors (e.g., TGF-α) that act on an osteoblastic stromal cell. ... Mineralized bone matrix is a rich source of stored growth factors such as TGF-β. Such growth factors, once released from degraded bone matrix, may further accelerate growth of the tumor, which can now expand within the lysed area. Such growth factors also appear capable of further increasing the release from tumor cells of osteolytic mediators, such as parathyroid hormone–related peptide (PTHrP) (7, 8). A cycle may therefore be initiated that consists of release of osteolytic mediators by tumor cells, bone degradation, release of growth factors from degraded bone, enhanced tumor cell growth, and further release of osteolytic mediators (9).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
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".