Jordan M. Sorokin

The paper assesses AAV serotype transduction efficiency in human cortical organoids, showing cell-specific tropism and donor variability.

This study creates a C4A overexpressing neuroimmune cortical organoid model that exhibits schizophrenia endophenotypes like inflammation and synaptic pruning.

This paper uses machine learning models to predict epileptic seizures in mice, achieving 98% accuracy with logistic regression and convolutional neural networks.

This paper develops an automated system to scale patient-derived brain organoids, revealing consistent donor and batch effects and demonstrating reproducible disease phenotypes in tuberous sclerosis models.

The paper identifies pre-seizure thalamic activity patterns in absence epilepsy that vary across seizures and may improve seizure prediction.