A step to diagnosis of sleep apnea with next generation sequencing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The introduction of next generation sequencing (NGS) has led to an exponential increase of elucidated genetic causes in both extremely rare diseases and common but heterogeneous disorders. It can be applied to the whole or to selected parts of the genome (genome or exome sequencing, gene panels). NGS is applied in both research and clinical settings, and there is a rapid transition of research findings to diagnostic applications. These developments may greatly help to minimize the "diagnostic odyssey" for patients as whole-genome analysis can be performed in a few days at reasonable costs compared with gene-by-gene analysis based on Sanger sequencing following diverse clinical tests. Despite the enthusiasm about NGS, one has to keep in mind its limitations, such as a coverage and accuracy of < 100%, resulting in missing variants and false positive findings. Therefore, there is an urgent need to define standards for NGS with respect to run quality and variant interpretation, as well as mechanisms of quality control. Aberrant respiratory control mechanisms have been implicated in dentofacial deformities, such as long face syndrome or adenoid facies. Obstructive sleep apnea (OSA) is a potentially life-threatening condition in which the patient suffers periodic cessation of breathing during sleep and is the most important etiological factor in the long face syndrome. The symptoms include loud snoring, irregular breathing patterns and restless movements during sleep which impairs the quality of life. The aim of this study is to determine the genetic association of obstructive sleep apnea associated genes (ACE, TNF-α, IL-6, 5-HTR2A, 5-HTR2C, 5-HTT, LEPR, PPAR-γ, ADRB, and APOE) with specific primers in polymerized chain reaction through an extensive genome search in Odisha population.
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.
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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it