Bibliographic record
Abstract
The clinical use of chemotherapeutic agents against malignant tumors is successful in many cases but suffers from major drawbacks. One drawback is lack of selectivity, which leads to severe side effects and limited efficacy; and another is the emergence/selection of drug-resistance. To limit non-specific toxicity and to improve the efficiency of cancer therapy, "tumor markers", which are proteins generally overexpressed on the surface of tumor cells, can be selectively targeted. Growth factor receptors are one of the most extensively studied tumor markers. The implication of growth factor receptors in the pathogenesis and evolution of cancer has clearly been established and therefore, provides a rationale for therapeutic intervention. The targeting of cytotoxic substances to tumor markers with "magic bullets" is an old idea that raised high expectations but also disappointment. Over the past decade, newly gained understanding of mechanisms for targeted therapy have brought new hopes. Pharmacological agents that selectively target and block the action of growth factors and their receptors have been attempted, such as monoclonal antibodies (mAbs) (whole molecule or fragments), bispecific antibodies, mAbs conjugated to drugs, toxins or radioisotopes, small peptidic and peptidomimetic molecules in free form or conjugated to drugs, anti-sense oligonucleotides, immunoliposomes-encapsulated drugs, and small molecule inhibitors. This review will focus on current developments of selective targeting and bypassing drug resistance in the management of growth factor receptor-overexpressing tumors.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".