Mihaly, L. | Search bioRxiv

bioRxiv2026

Network reorganization distinguishes vulnerability and resilience to observational fear

Individuals vary widely in their responses to stress and threat, with some developing persistent fear after adverse experiences while others remain resilient. Such variability also extends to social contexts, where individuals can acquire information about danger by observing others in distress through observational fear learning. The neural mechanisms underlying individual differences in responses to socially conveyed threat remain poorly understood. Here, we examined how variability in OFL relates to large-scale brain network organization. Rats observed conspecifics receiving tone-shock pairings and were later tested for fear responses to the conditioned stimulus. Behavioral analysis revealed two phenotypes: observational-susceptible rats displaying robust freezing and observational-resilient rats showing freezing levels comparable to controls. Despite these differences, both groups exhibited elevated corticosterone responses, indicating that socially conveyed threat was detected across animals. Brain-wide c-Fos mapping across 84 regions combined with graph-theoretical analysis revealed distinct network architectures associated with each phenotype. These findings suggest that susceptibility and resilience to socially acquired fear emerge from differences in distributed brain network organization.

neuroscience↗

bioRxiv2026

Model Ensembling and Machine Learning Approaches to Predict the First Dose of Amoxicillin in Intensive Care

A priori model informed precision dosing (MIPD) recommends an appropriate first dose based solely on the patients covariates enabling faster target attainment without required concentration measurements. Population pharmacokinetic model ensembling and machine learning (ML) approaches were developed and evaluated to predict a first dose of amoxicillin in intensive care. Following a bibliographic review, a virtual patient population was simulated based on cohorts from four published adult amoxicillin PopPK models. Model-development cohorts were reproduced, and steady-state trough concentrations were simulated using cohort-specific dosing regimens. As reference methods, weighted model ensembling (WME) and classification tree (CT)-informed ensembling were implemented. Two novel ensembling strategies were developed: regression tree (RT)-informed ensembling, using RT to predict the log individual prediction/observation ratio, and factor analysis of mixed data (FAMD), assigning model weights based on patient similarity to original model cohorts. In parallel, four ML algorithms (support vector machine, k-nearest neighbors, random forest, and XGBoost) were trained to predict the dose achieving target concentrations based on covariates and dosing scheme. All approaches were compared with single-model PopPK dosing, standard dosing, and a nomogram, and externally validated using clinical data. Most MIPD methods outperformed standard dosing. On simulated data, ensembling (30-42 % correct predictions) and ML (36-39 %) exceeded single-model approaches (14-32 %). RT-informed and FAMD ensembling improved performance by 6-10 % over uninformed ensembling on clinical data. In clinical patients receiving continuous infusion, ensembling further improved performance, with FAMD achieving 49 % correct predictions. ML-based ensembling eliminates the need for model selection and increase target attainment.

pharmacology and toxicology↗