Cytocapsular tube network-tumor system is an integrated physical target for the highly effective and efficient pharmacotherapy of solid cancers
Bibliographic record
Abstract
Abstract Cancer is a leading cause of human lethality and cancer drug pan-resistant tumor metastasis (cdp-rtm) is a major source of cancer death. The integrated cytocapsular tube (CCT) networktumor system (CNTS) is essential for cancer evolution procedures including cancer cell proliferation, migration and dissemination in CCT networks, new tumor growth in CCT terminals, conventional cancer drug pan-resistance, and tumor relapse. The preclinical screening experimentations with CCTs and networks are necessary prerequisites for the discovery and development of cancer drug candidates for efficient clinical trials, and effective and precise clinical cancer pharmacotherapy. However, it is unknown whether the popularly employed conventional mouse experimentations for preclinical therapeutic development generate CCTs and networks. Here, we comprehensively investigated the cancer cell line derived xenografts (CDX) of 8 kinds of cancer cell lines, and patient cancer cells derived-xenografts (PDX) with 16 kinds of clinical primary and metastatic malignant tumor cells in multiple cancer stages by immunohistochemistry staining assays with CCT marker protein antibodies of anti-CM-01 antibodies. We found that there are no CCTs or CCT networks in all these examined 16 CDX and 22 PDX transplanted tumors. Our data evidenced that the conventional transplantation tumors for preclinical therapeutic development do not engender CCTs or CNTS, which is consistent with and provides an explanation of the poor clinical outcomes of the marketed cancer drugs. This study demonstrated that CCT network-tumor system (CNTS) is an integrated physical target for pharmacotherapy, and that preclinical experimentations engendering CNTS (such as CCT xenograft, CCTX) should be employed for the discovery and development of highly effective and efficient cancer drugs aimed for cure of solid cancers.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".