Use of next generation sequencing technologies in research and beyond: are participants with mental health disorders fully protected?
Bibliographic record
Abstract
BACKGROUND: Next Generation Sequencing (NGS) is expected to help find the elusive, causative genetic defects associated with Bipolar Disorder (BD). This article identifies the importance of NGS and further analyses the social and ethical implications of this approach when used in research projects studying BD, as well as other psychiatric ailments, with a view to ensuring the protection of research participants. METHODS: We performed a systematic review of studies through PubMed, followed by a manual search through the titles and abstracts of original articles, including the reviews, commentaries and letters published in the last five years and dealing with the ethical and social issues raised by NGS technologies and genomics studies of mental disorders, especially BD. A total of 217 studies contributed to identify the themes discussed herein. RESULTS: The amount of information generated by NGS renders individuals suffering from BD particularly vulnerable, and increases the need for educational support throughout the consent process, and, subsequently, of genetic counselling, when communicating individual research results and incidental findings to them. Our results highlight the importance and difficulty of respecting participants' autonomy while avoiding any therapeutic misconception. We also analysed the need for specific regulations on the use and communication of incidental findings, as well as the increasing influence of NGS in health care. CONCLUSIONS: Shared efforts on the part of researchers and their institutions, Research Ethics Boards as well as participants' representatives are needed to delineate a tailored consent process so as to better protect research participants. However, health care professionals involved in BD care and treatment need to first determine the scientific validity and clinical utility of NGS-generated findings, and thereafter their prevention and treatment significance.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".