Bibliographic record
Abstract
The main problems with cancers are: (1) they seem to have unlimited division potential; and (2) they display intra-tumor heterogeneity. We discuss three different hypotheses that attempt to explain the origin and continuous growth of tumors: (a) the clonal evolution (CE) hypothesis, (b) the cancer stem cell (CSC) hypothesis, and (c) the neosis paradigm of carcinogenesis (NPC). The CE hypothesis assumes that any cell with defective DNA can give rise to cancer cell by mitosis and when this abnormal cell proliferates, it accumulates random additional mutations and the dominant clone will be selected against others. According to the CSC hypothesis, only tissue-specific stem cells or adult stem cells (ASCs) or the progenitor cells with mutation (the CSCs) can initiate the tumor growth. It was assumed that like stem cells, the CSCs have unlimited division potential and that by asymmetric mitosis, CSCs give rise to one CSC and the other cell undergoes transit amplification and defective differentiation to form tumor tissue contributing to tumor cell heterogeneity. Like normal differentiated cells, tumor cell do not have the potential to divide and therefore do not have the capacity to form tumors. From the beginning, the CSC hypothesis was plagued by the possible isolation method-induced artifacts, which have turned out to be true according recent reports. Both the CE and CSC hypotheses do not explain the mode of origin of heterogeneity and of the evolution of malignancy. NPC hypothesis is based on video documentation and the fact that senescence is a tumor suppressor mechanism. The cells defective in the senescent checkpoint pathway escape senescence induced mitotic catastrophe via neosis by suppressing spindle checkpoint and executing karyokinesis by nuclear budding to yield Tumor Initiating Raju Cells (TIRCs), with transient progenitor stem cell-like properties. The neosis mother cell dies after yielding several Raju cells. Video documentation proves that cancer cells are mortal and when they reach senescence, they escape death due to transformation via neosis, thus producing the next generation of Tumor Repopulating Raju Cells (TRRCs). Exposure of cancer cells to genotoxins induce premature senescence and senescent cells escape cell death via neosis by yielding resistant offspring. Neosis executes epigenetic genome modulation via Epithelial- Mesenchymal Transition (EMT), and the tumor cells go through several EMTs and the reverse process of Mesenchymal-Epithelial Transitions during growth. This explains the mode of introduction of heterogeneity in the tumor cell population, which is subject to natural selection. The NPC incorporates the transient stem cell properties in Raju cells, which, at the end of their extended symmetric, MLS, will repeat the cycle of polyploidization followed by EMT executed by neosis yielding diploid (near diploid or aneuploid) cell population of TRRCs, again and again. The scientific community is neglecting this mode of cell division to the peril of humanity.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".