Recent Progress in Biosensors for Diagnosis of Cancer Cells, Tissues and Tumors Biomarkers
Bibliographic record
Abstract
Raman spectroscopy is an important method for identifying molecules, which is widely used in determining the chemical and structural characteristics of various substances. Many materials have a special Raman spectrum, so that this phenomenon has turned the Raman device into an efficient tool for studying the structural and chemical properties of molecules. Since it is possible to obtain detailed information about the chemical and structural characteristics of biological compounds from Raman spectroscopy, the use of this method is rapidly expanding in the field of life sciences, especially in biological and medical studies. There is no need for special, time-consuming and expensive preparations in the study of materials with the help of a Raman device. In the protein Raman spectrum, distinct bands arise from the vibrational states of the peptide backbone and amino acid side chains. Therefore, based on the position and intensity of the protein’s Raman spectrum, it is possible to obtain valuable information about its second, third, and fourth structures. Also, the Raman spectrum of the protein contains information about the orientation and surrounding environment of the amino acid side chains. The correct formation of the disulfide bond in the protein structure can also be studied with the help of the Raman device. In general, the Raman spectrum of proteins contains multiple discrete bands that represent the vibrational states of the molecule and is used as a selective fingerprint to accurately determine the three–dimensional structure of proteins, intramolecular dynamics, and intermolecular interactions (Graphical Abstract).
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".