THE INTERACTION BETWEEN ADVANCED TECHNOLOGIES AND THE BIOMEDICAL IN THE TREATMENT OF GENETIC DISEASES: A BIBLIOGRAPHIC REVIEW
Bibliographic record
Abstract
Genetic manipulation is a promising approach to treating diseases caused by genetic defects. Gene therapy has experienced notable advances, allowing the replacement or manipulation of dysfunctional genes; Genome editing techniques such as CRISPR-Cas9 and CRISPR-Cas12a have been developed and represent long-lasting approaches to treating genetic diseases. The role of the biomedical scientist, with his interdisciplinary training in molecular biology and genetics, is essential in this context. The advancement of molecular and biotechnology technologies promotes new perspectives in the diagnosis, treatment, and prevention of diseases, revolutionizing medicine and improving the health of the population. OBJECTIVE: This study aims to list new genetic technologies in the context of disease treatment, with the aim of supplying pertinent data for future research in genetic manipulation. Additionally, we look to highlight the role of biomedicine in the current technological and scientific panorama. METHODOLOGY: Literary bibliographic surveys selected through specific platforms were used to analyze article data, using specific criteria; In the end, 9 articles were selected. CONCLUSIONS: The researched literature showed that technological innovations in gene therapy present revolutionary perspectives for medicine, by offering promising treatment possibilities for diseases of genetic and bought origin. In this context, biomedical scientists play a significant role, conducting research, implementing therapies, and providing guidance, with the aim of ensuring the safety and effectiveness of these therapeutic approaches.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.028 | 0.036 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".