Bibliographic record
Abstract
The self-assembly properties of DNA have been exploited to generate 2D and 3D structures on surfaces and in solution. DNA now can be modified by metal centers at individual bases or at either termini. The ability to label DNA sites specifically with metal centers allows us to influence the electronic properties of the assembly. Potential applications of these modified DNA constructs in the design of nanoelectronic or bioelectronic circuitry may be within reach. Especially, the most recent developments in the area of direct basepair metallation, such as Cu-DNA and M-DNA, allow superb control over the electronics of the DNA construct and reduce the synthetic efforts. As shown in this review, a large driving force for research in the area of metal-DNA conjugates stems from sensor applications, for example in the detection of genetic defects, genomic fingerprinting for identification purposes, or applications in personalized medicine. Electrochemical detection methods offer a superior sensitivity to currently available optical gene chip technology. Gene chip technology has to rely on attaching a fluorescence tag to one of the DNA strands after PCR amplification. Recent advances in the area of electrochemical DNA detection described in this review should allow the development of biosensors that do not require labeling of the target DNA. One of the holy grails of E-biosensors is the detection of single nucleotide mismatches time- and cost-effectively without the need for a lengthy PCR amplification, or in heterozygote mixtures or under non-ideal hybridization conditions. These goals are yet unrealized. Especially difficult will be the detection of DNA under “real-to-life” conditions, such as high salt concentrations or in the presence of large quantities of impurities. However, the possibility of single molecule detection using ultra-fast electrochemical techniques on ultramicroelectrodes may offer solutions to this problem.
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 | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".