Recent Progress in Electrochemical Detection of Human Papillomavirus (HPV) via Graphene‐Based Nanosensors
Bibliographic record
Abstract
In the present study, applications of advanced nanomaterials such as graphene oxide (GO), reduced graphene oxide (rGO), and graphene quantum dots (GQDs) as leading and potential candidates toward treating the human papillomaviruses (HPVs) were investigated. In this matter, a comprehensive summary of the formation of GO, rGO, and GQDs will be reported in detail. Continuous efforts have been exerted to develop high‐performance biosensors and electrochemical detection systems toward accurate detection of HPV using novel routes. This review paper showed that HPVs appeared in different types and species. These HPVs have many complications on humans, and thus, there are different ways that a person could be exposed to them. Meanwhile, several routes of transmitting the HPVs to human cells are discussed too. Indeed, sexually transmitted diseases could be easily infected by HPVs, although the human immune system can also be boosted to treat this dangerous virus effectively. Some of the HPVs such as HPV‐16 and HPV‐18 are so dangerous, and HPV DNA could be detected in several vertical cancers. Herein, we reported and summarized some recent progress in electrochemical detection of HPVs using graphene‐based nanosensors.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".