Maternal Immune Activation with the Viral Mimetic Poly:IC in Pregnant Rats
Bibliographic record
Abstract
Maternal immune activation (MIA) is increasingly well appreciated as an environmental risk factor for some psychiatric disorders. Administration of proinflammatory compounds such as the synthetic double-stranded RNA molecule polyinosinic-polycytidylic acid (polyI:C) to pregnant rodents results in the release of proinflammatory cytokines in the maternal circulation. Various behavioural and brain changes are produced in the offspring that are associated with psychiatric disorders such as autism and schizophrenia. This protocol describes the steps necessary for inducing MIA in pregnant rat dams, which will allow for investigations into the mechanisms in the dam and offspring that mediate the long-term effects of exposure to inflammation while in utero. Increasing our understanding of these mechanisms may provide new insights for the diagnosis, treatment, and prevention of psychiatric disorders. This protocol has been developed and improved over the years by various researchers in Dr. Howland’s laboratory at the University of Saskatchewan., [摘要]孕产妇免疫激活（MIA）作为某些精神疾病的环境危险因素，越来越受到人们的赞赏。给予促炎化合物，例如合成的双链RNA分子多肌苷酸-聚胞苷酸（polyI：C ）给怀孕的啮齿动物导致母体循环中促炎细胞因子的释放。在后代中会产生各种行为和大脑变化，这些变化与诸如自闭症和精神分裂症等精神疾病有关。该协议描述了在妊娠大鼠大坝中诱导MIA的必要步骤，这将有助于调查大坝和后代在子宫内介导暴露于炎症的长期影响的机制。我们对这些机制的了解可能会为精神疾病的诊断，治疗和预防提供新的见解。多年来，萨斯喀彻温大学Howland博士的实验室中的许多研究人员已经开发并改进了该协议。[背景]流行病学研究为子孙后代产前感染与某些神经精神疾病（例如精神分裂症和自闭症）的发生风险增加之间的关联提供了大量证据（Brown和Meyer，2018年）。确实，妊娠炎症可能会改变胎儿的正常发育，从而增加出现精神病理学的风险。准确识别这些作用的潜在机制可能会建议早期干预。结果，许多研究小组在啮齿动物和非人类灵长类动物中开发了MIA模型，为检查复杂的精神疾病的复杂病因提供了丰硕的机会。研究人员已使用这些模型评估了后代MIA的行为，药理和病理生理结果（有关该文献的评论，请参见：Piontkewitz等人，2012; Reisinger等人，2015; Estes和McAllister，2016; Careaga等人）等人，2017;布朗和迈耶，2018;伯格多特和杜纳夫斯基，2019;古穆索卢和史蒂文斯，2019;肯特纳等人，2019;迈耶，2019 ）。我们下面描述的协议改编自Lins等。（2018年和2019年）进行了一些更改，包括我们对大鼠内部定时怀孕繁殖的协议。使用病毒模拟物代替病毒感染是有利的，因为可以更好地控制效果的持续时间并减少生物安全性。一些研究表明，用流感病毒接种妊娠小鼠后，对后代具有类似的作用，从而证明了polyI：C模型的有效性（Shi等，2003）。
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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 | 0.000 | 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) | 0.001 | 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".