Potential of monoclonal antibodies against neonatal sepsis: lessons from age-specific in vitro models
Bibliographic record
Abstract
Populair wetenschappelijke samenvatting Engels Infants, especially those born prematurely, are highly vulnerable to sepsis, a life-threatening condition that remains one of the leading causes of newborn mortality worldwide. Over the past decades, limited progress has been made in developing preventive strategies against severe bacterial infections in this population. At the same time, rising antibiotic resistance calls for new ways to prevent and treat these infections safely and effectively. This thesis explores how newborns can be better protected against bacterial infections by studying the interplay between monoclonal antibodies and the neonatal immune system. We focus on three key components of antibacterial defense: antibodies, complement, and neutrophils - all of which are known to be deficient in preterm infants. Using blood from umbilical cords, we developed laboratory models that more accurately reflect the immune system of newborns than conventional cell-line models. These age-appropriate models allowed us to test the potential of new antibody-based therapies. We engineered antibodies to enhance their complement-activating potential and found that these modified antibodies triggered stronger immune responses in neonatal models compared to conventional therapies, such as pooled immunoglobulins from healthy donors (IVIG). Furthermore, we show that differences in complement profiles in neonates contribute to their increased susceptibility to Gram-negative infections. Together, the results of this thesis highlight the importance of considering the immunological characteristics of newborns when developing new antibody-based therapies. Antibodies that effectively activate the complement system may offer a promising strategy to protect newborns from severe bacterial infections.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".