Our Foundation

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DefenSight’s analytical engine is the culmination of over a decade of peer-reviewed research in host-pathogen kinetics and high-content imaging (HCI). This extensive body of work provides the mathematical rigor and biological validation required for high-consequence CBRN-B decision support.

The Scientific Backbone 

Led by our founder, Dr. Sonja Frölich, our foundational research across parasitic, bacterial, and viral models has established the industry-standard for quantitative phenotyping. These studies provide the validated datasets and assay discipline that allow DefenSight to deliver reproducible, interpretable imaging analytics in BSL-2/3 environments.

Methodological Provenance

The following publications detail the methodological evolution of our platform. They serve as the forensic evidence for our feature-extraction logic and algorithmic transparency.

For TRL-specific performance data and Operational Validation Packs, please contact our technical team for a secure briefing.

1. Pathogen-Host Metabolic Remodelling Shigella flexneri remodeling and consumption of host lipids during infection (2023). Journal of Bacteriology. doi: https://doi.org/10.1128/jb.00320-23

• Operational Application: Validated the simultaneous detection of pathogen replication kinetics and host-cell metabolic stress. This allows for the characterization of threat severity before traditional clinical symptoms appear.

2. Mechanism of Viral Hijacking & Countermeasure Targets Genome-wide CRISPR screen identifies RACK1 as a critical host factor for flavivirus replication (2021). Journal of Virology. doi: https://doi.org/10.1128/jvi.00596-21

• Operational Application: Advanced quantitative analysis of viral replication cycles. Demonstrates the platform’s ability to identify specific host-factors targeted by pathogens, critical for Rapid Countermeasure Development (RCD).

3. ML-Driven High-Content Screening (HCS) Development of automated microscopy-assisted high-content multiparametric assays. (2020). Cytometry Part A. doi: https://doi.org/10.1002/cyto.a.23988

• Operational Application: High-throughput, Machine Learning-based analysis of cellular injury and genomic damage. This is the core engine for evaluating the efficacy of medical countermeasures against mass-exposure scenarios.

4.  High-Resolution Malaria Invasion Dynamics PfCERLI1 is a conserved rhoptry-associated protein essential for Plasmodium falciparum merozoite invasion of erythrocytes. (2020) Nature Communications. doi: https://doi.org/10.1038/s41467-020-15127-w

• Operational Application: Multi-parametric quantitative mapping of Pathogen Invasion Trajectories. Proves the sub-200nm precision required to distinguish between successful infection and neutralised pathogen particles.

5. Zoonotic Threat Transmission Mapping Use of fluorescent nanoparticles to investigate nutrient acquisition by developing Eimeria maxima macrogametocytes. (2016). Scientific Reports.doi: https://doi.org/10.1038/srep29030

• Operational Application: Analysis of pathogen transmission and phenotypic shifts. Critical for modeling the transition of zoonotic threats into human populations.

For a comprehensive record of the peer-reviewed datasets underpinning the DefenSight engine, access the full repository via Dr Frölich’s Google Scholar profile.

Link: https://scholar.google.com/citations?user=ACJhd5oAAAAJ