JoVE Video Dataset
Bibliographic record
Abstract
Group B Streptococcus (GBS) is one of the most common bacteria isolated during human pregnancy. It is a leading cause of placental infection/inflammation, termed chorioamnionitis. Chorioamnionitis exposes the developing fetus to a high risk of organ injuries, perinatal morbidity, and mortality, as well as life-long neurobehavioral impairments and other non-neurological developmental issues. The two most frequent subtypes of GBS isolates from maternal and fetal tissues are serotypes Ia (13%-23%) and III (25%-53%). Our lab has developed and characterized a rat model of GBS-induced chorioamnionitis to study subsequent impacts on the central nervous system of the developing fetus and to understand underlying mechanistic aspects. This article presents the design as well as uses of the preclinical rat model, which closely reproduces the hallmark of GBS-induced chorioamnionitis in humans. This article aims to help scientists reproduce the experimental design as well as to provide support through examples of troubleshooting. The present model may also contribute to potential discoveries through uncovering causes, mechanisms, and novel therapeutic avenues, which remain unsettled in many developmental impairments arising from chorioamnionitis. Furthermore, the use of this model may be extended to the studies of perinatal non-neurological common and severe morbidities affecting, for instance, the retina, bowel, lung, and kidney. The main interest of this research is in the field of GBS-induced fetal neurodevelopmental impairments such as cerebral palsy (CP), attention deficit hyperactivity disorder (ADHD), and autism spectrum disorder (ASD). The rationale supporting this model is presented in this article, followed by procedures and results.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.526 | 0.377 |
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".