Region-specific brain organoids – a novel model to study Zika virus-induced neurodevelopmental disease and therapeutic testing
Bibliographic record
Abstract
Abstract: Therapeutic development and drug discovery processes are heavily challenged by delays in the preclinical experimental stages before being offered for Phase 1 clinical trials. Virtual Embryo-like approaches, stem cell technologies, and cutting-edge computational models offer promising future tools (Shafique, 2018) [Fig. 3]. ZV infection results in pathological/morphological changes in the developing brain (Shafique, 2018a) resulting in the birth defect of microcephaly with immense psychosocial impact (Shafique, 2017a). The brain organoids have multiple applications including investigating developmental biology modeling neurodevelopmental defects such as microcephaly (Lancaster et al., 2013) [Fig 1,2]. More specialized region-specific organoids of brain regions such as the forebrain organoid platform to model Zika virus (ZIKV) exposure have been developed (Qian et al., 2017) [Fig. 4,5]. This forebrain-focused organoid model is a renewable, easily available, and economically novel tool to evaluate and screen for therapeutic drugs, such as antiviral compounds, and determine their efficacy and safety for the phase 1 clinical trials. This poster is an overview encompassing the currently available options for preclinical developmental toxicity testing with a focus on the potential applications of 3D organoids in context with investigating Zika virus-related domains. These sustainable, accessible, and renewable tools could help the researchers to replace the use of animal models for the purposes of toxicity testing during the drug development process. References: Shafique, S. (2018). Preclinical developmental toxicity testing and advanced in-vitro stem cell-based systems. Timely Topics Clinical Vaccine Research, 2(1), 2–4. Shafique, S. (2018a). Zika-Induced Microcephaly and Neurodevelopment. Res J Congenit Disease, 1(1), 1– 3. http://www.imedpub.com/ Shafique, S. (2017a). Pregnancy with Zika Virus-Psychosocial Impacts and High Risk for Post-Traumatic Stress Disorder (PTSD). Research Journal of Congenital Diseases, 1(1), 1–2.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".