Sarmadi, S. | Search bioRxiv

bioRxiv2025

Preventing Microglial Reactivity Protects from Acute and Progressive Neuronal Dysfunction, Motor Impairments and Sedation following Alcohol Abuse

Alcohol abuse is the primary risk factor for alcohol use disorder (AUD), a leading cause of preventable morbidity and mortality, characterized by systemic inflammation, multi-organ damage, and neurological impairments. While direct effects of alcohol on brain function are well-established, the role of microglia in acute and chronic neurological dysfunction in AUD remains unclear. Using longitudinal in vivo imaging in mice during acute and repeated alcohol abuse, we found that microglia exhibit dynamic morphological responses that precede but parallel ethanol-induced sedation. Ethanol also induced microglia-dependent synapse elimination and reduced neuronal activity and density. Genetic disruption of microglial MyD88 reversed these ethanol-associated changes in microglial reactivity, neuronal structure, and function, while protecting against alcohol-induced intoxication and motor impairments. These findings identify microglia as cellular drivers of acute and chronic brain dysfunction following alcohol abuse, and highlight MyD88 as a critical therapeutic target for the detrimental neurological consequences of AUD.

neuroscience↗

bioRxiv2021

Stochastic Neural Networks for Automatic Cell Tracking in Microscopy Image Sequences of Bacterial Colonies

We describe an automated analysis method to quantify the detailed growth dynamics of a population of bacilliform bacteria. We propose an innovative approach to frame-sequence tracking of deformable-cell motion by the automated minimization of a new, specific cost functional. This minimization is implemented by dedicated Boltzmann machines (stochastic recurrent neural networks). Automated detection of cell divisions is handled similarly by successive minimizations of two cost functions, alternating the identification of children pairs and parent identification. We validate this automatic cell tracking algorithm using recordings of simulated cell colonies that closely mimic the growth dynamics of E. coli in microfluidic traps. On a batch of 1100 image frames, cell registration accuracies per frame ranged from 94.5% to 100%, with a high average. Our initial tests using experimental image sequences of E. coli colonies also yield convincing results, with a registration accuracy ranging from 90% to 100%.

bioinformatics↗