[This corrects the article DOI: 10.1007/s13534-023-00295-7.].

Fibroblast Growth Factor plays a crucial role in neurological health, contributing to neuron protection, injury recovery, and angiogenesis. It is also significantly involved in the onset and progression of neurodegenerative disorders such as Huntington's, Alzheimer's, Parkinson's disease, and stroke, making FGF a vital target for therapeutic interventions. Despite its importance, no computational tool has been developed to predict FGF proteins. In this study, we present the first novel deep learning-based computational approach designed for the prediction of FGF proteins. We constructed two novel, high-quality datasets curated from the UniProt database for training and evaluation. Sequences were transformed into numerical representations using three complementary feature encoding methods including Dipeptide Composition, Dipeptide Deviation from Expected Mean, and Grouped Amino Acid Composition. These features capture both local and global sequence information. Multiple deep learning models were explored, including Convolutional Neural Network, Bidirectional Long Short-Term Memory, Generative Adversarial Network, and Gated Recurrent Unit. Among these, our proposed Convolutional Neural Network-based model outperformed all others, achieving an accuracy of 83.50%, sensitivity of 84.30%, specificity of 82.67%, F1 score of 83.42%, and a Matthews Correlation Coefficient of 0.671. The proposed approach has the potential to advance therapeutic discovery by enabling accurate identification of Fibroblast Growth Factor and improving our understanding of their role in neurological health and disease.

Abstract This study investigates the feasibility of transcutaneous interferential spinal cord stimulation (tISCS), a novel non-invasive neuromodulation method, using temporal interference to enhance focality and comfort in spinal cord stimulation. The central research question is whether tISCS can achieve targeted activation of spinal cord circuits while reducing unwanted stimulation of skin and muscle tissues, which are common limitations of conventional transcutaneous spinal cord stimulation (tSCS). A finite element model of the lower thorax was developed to simulate electric field distributions for various skin electrode montages. To address the computational bottleneck associated with high-resolution modeling and montage optimization, we implemented a leadfield-based Pareto optimization strategy to identify the electrode configuration that maximizes the electric field in the spinal cord and minimizes it in off-target tissues. tISCS montages were compared with tSCS montages in terms of focality and stimulation efficiency. Optimized tISCS configurations significantly reduced electric field intensity in the skin by over 20-fold compared to tSCS. The ratio of spinal cord to skin electric fields increased by at least tenfold, indicating enhanced focality. The injection current efficiency in tISCS can be leveraged to increase spinal cord electric fields by at least fivefold while keeping skin exposure below the levels observed with tSCS. tISCS enables deeper and more selective spinal cord stimulation compared to tSCS, with substantially reduced off-target effects. In conclusion, this is the first computational demonstration of tISCS feasibility, and leadfield-guided Pareto optimization enables efficient montage selection, providing a foundation for future experimental applications.