Cellular and molecular mechanisms of action of transcranial direct current stimulation: evidence from in vitro and in vivo models.

Transcranial direct current stimulation is a noninvasive technique that has been experimentally tested for a number of psychiatric and neurological conditions. Preliminary observations suggest that this approach can indeed influence a number of cellular and molecular pathways that may be disease relevant. However, the mechanisms of action underlying its beneficial effects are largely unknown and need to be better understood to allow this therapy to be used optimally. In this review, we summarize the physiological responses observed in vitro and in vivo, with a particular emphasis on cellular and molecular cascades associated with inflammation, angiogenesis, neurogenesis, and neuroplasticity recruited by direct current stimulation, a topic that has been largely neglected in the literature. A better understanding of the neural responses to transcranial direct current stimulation is critical if this therapy is to be used in large-scale clinical trials with a view of being routinely offered to patients suffering from various conditions affecting the central nervous system.

The morphological and molecular changes of brain cells exposed to direct current electric field stimulation.

BACKGROUND: The application of low-intensity direct current electric fields has been experimentally used in the clinic to treat a number of brain disorders, predominantly using transcranial direct current stimulation approaches. However, the cellular and molecular changes induced by such treatment remain largely unknown. METHODS: Here, we tested various intensities of direct current electric fields (0, 25, 50, and 100V/m) in a well-controlled in vitro environment in order to investigate the responses of neurons, microglia, and astrocytes to this type of stimulation. This included morphological assessments of the cells, viability, as well as shape and fiber outgrowth relative to the orientation of the direct current electric field. We also undertook enzyme-linked immunosorbent assays and western immunoblotting to identify which molecular pathways were affected by direct current electric fields. RESULTS: In response to direct current electric field, neurons developed an elongated cell body shape with neurite outgrowth that was associated with a significant increase in growth associated protein-43. Fetal midbrain dopaminergic explants grown in a collagen gel matrix also showed a reorientation of their neurites towards the cathode. BV2 microglial cells adopted distinct morphological changes with an increase in cyclooxygenase-2 expression, but these were dependent on whether they had already been activated with lipopolysaccharide. Finally, astrocytes displayed elongated cell bodies with cellular filopodia that were oriented perpendicularly to the direct current electric field. CONCLUSION: We show that cells of the central nervous system can respond to direct current electric fields both in terms of their morphological shape and molecular expression of certain proteins, and this in turn can help us to begin understand the mechanisms underlying the clinical benefits of direct current electric field.

BERNN: Enhancing classification of Liquid Chromatography Mass Spectrometry data with batch effect removal neural networks.

Liquid Chromatography Mass Spectrometry (LC-MS) is a powerful method for profiling complex biological samples. However, batch effects typically arise from differences in sample processing protocols, experimental conditions, and data acquisition techniques, significantly impacting the interpretability of results. Correcting batch effects is crucial for the reproducibility of omics research, but current methods are not optimal for the removal of batch effects without compressing the genuine biological variation under study. We propose a suite of Batch Effect Removal Neural Networks (BERNN) to remove batch effects in large LC-MS experiments, with the goal of maximizing sample classification performance between conditions. More importantly, these models must efficiently generalize in batches not seen during training. A comparison of batch effect correction methods across five diverse datasets demonstrated that BERNN models consistently showed the strongest sample classification performance. However, the model producing the greatest classification improvements did not always perform best in terms of batch effect removal. Finally, we show that the overcorrection of batch effects resulted in the loss of some essential biological variability. These findings highlight the importance of balancing batch effect removal while preserving valuable biological diversity in large-scale LC-MS experiments.