Biobanking Framework: “One Size Fits All”
Bibliographic record
Abstract
Abstract Background Biobanking has been identified as a key area for development in order to accelerate the discovery and development of new drugs. biobanks include not only a collection of specimens but associated -omics data, thus the need for databases that inventory samples, associated clinical and omics data. As access to human biospecimens is becoming less of a barrier to translational studies, it is becoming clear that annotation of human samples and complex databases is our next hurdle. Purpose In this paper, we elaborate on the steps and processes that were considered in order to establish the Research Institute of the McGill University Health Center Liver Disease Biobank (RIMUHC-LDB) and highlight the success of our translational projects that sustain this biobank. Results The workflow model is based on a two-tier approach: a “mother” protocol that requires participant’ signed consent form and a “companion” protocol which allows the use of biospecimens and data for research. The “companion” protocol is based on a review of the protocol by the biospecimen access committee (BAC) and approval followed by an expedited review by the research ethics board. Our workflow is open, in addition, to include different prearranged requirements for collection of biospecimen and data from different project. Following strict standard operating procedures and ensuring that biospecimens are processed in a short amount of time after procurement, we are able to provide high quality biospecimen and data. Also, integrated in our biospecimen procurement process is our Quality Assurance Program (QAP). Every 4 months two samples are randomly selected and screened. We regularly isolate RNA from these tissue samples, labeled Quality Assurance/ Quality Control (QA/QC), and assess their RNA integrity number (RIN). Conclusions The biobank has enabled national and international access of biospecimen and data for genomic, proteomic and phenotypic research in addition to provide the biobank financial sustainability. Understanding the complexity of disease has and will always remain a challenge. As disease burden has shifted from acute conditions to chronic conditions, primarily seen in community and primary care (PC) rather than tertiary care centers, new approaches for forging relationships with local and regional community partners will become increasingly critical. A personalised PC Biobank along with disease-specific biobanks and industry biobanks (clinical trials) will ensure that the best personalised care is delivered to participants.
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 | 0.163 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.003 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 0.033 |
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; both teacher heads agree on what is shown here.
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".