Application of biosensors based on nanomaterials in cancer cell detection
Bibliographic record
Abstract
Abstract Cancer, killing millions of people every year, is the most serious disease in the world. The survival of cancer patients is closely related to the diagnosis of cancer cells. Therefore, in the early stage of cancer, the detection of cancer biomarkers from cellular level is of great significance to improve the survival rate of cancer patients. Nanomaterials-based biosensors play the increasingly important role in the treatment of cancer, owing to their ultra-high sensitivity and high selectivity. Nowadays, it is widely used in the detection of cancer cells to provide reference for cancer treatment. Herein, the present minireview summarizes application of nanomaterials-based biosensors for the detection of cancer cells, such as colorimetric biosensors, fluorescence biosensors, surface-enhanced Raman scattering (SERS) biosensors, electrochemical biosensors and other types of biosensors. We further introduce the construction principle of these biosensing methods, and compare the advantages of these biosensors in detecting cancer cells.
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 | 0.000 | 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".