A systematic review of immune-based interventions for perinatal neuroprotection: closing the gap between animal studies and human trials
Bibliographic record
Abstract
BACKGROUND: Perinatal infection/inflammation is associated with a high risk for neurological injury and neurodevelopmental impairment after birth. Despite a growing preclinical evidence base, anti-inflammatory interventions have not been established in clinical practice, partly because of the range of potential targets. We therefore systematically reviewed preclinical studies of immunomodulation to improve neurological outcomes in the perinatal brain and assessed their therapeutic potential. METHODS: We reviewed relevant studies published from January 2012 to July 2023 using PubMed, Medline (OvidSP) and EMBASE databases. Studies were assessed for risk of bias using the SYRCLE risk of bias assessment tool (PROSPERO; registration number CRD42023395690). RESULTS: Forty preclinical publications using 12 models of perinatal neuroinflammation were identified and divided into 59 individual studies. Twenty-seven anti-inflammatory agents in 19 categories were investigated. Forty-five (76%) of 59 studies reported neuroprotection, from all 19 categories of therapeutics. Notably, 10/10 (100%) studies investigating anti-interleukin (IL)-1 therapies reported improved outcome, whereas half of the studies using corticosteroids (5/10; 50%) reported no improvement or worse outcomes with treatment. Most studies (49/59, 83%) did not control core body temperature (a known potential confounder), and 25 of 59 studies (42%) did not report the sex of subjects. Many studies did not clearly state whether they controlled for potential study bias. CONCLUSION: Anti-inflammatory therapies are promising candidates for treatment or even prevention of perinatal brain injury. Our analysis highlights key knowledge gaps and opportunities to improve preclinical study design that must be addressed to support clinical translation.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| 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 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".