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Lights, Volume 2, Issue 1 (March 2026) – 2 articles

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15 pages, 519 KB  
Review
Photobiomodulation Applications in Clinical Veterinary Surgery: Current Status and Future Perspectives
by Mario García-González, Francisco Vidal-Negreira and Antonio González-Cantalapiedra
Lights 2026, 2(1), 2; https://doi.org/10.3390/lights2010002 - 3 Feb 2026
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Abstract
Photobiomodulation (PBM) has emerged as a noninvasive therapeutic tool with promising clinical applications in veterinary clinical surgery. Its mechanism of action is based on the stimulation of cellular processes through low-intensity light, promoting adenosine triphosphate production, inflammatory modulation, and tissue regeneration. This narrative [...] Read more.
Photobiomodulation (PBM) has emerged as a noninvasive therapeutic tool with promising clinical applications in veterinary clinical surgery. Its mechanism of action is based on the stimulation of cellular processes through low-intensity light, promoting adenosine triphosphate production, inflammatory modulation, and tissue regeneration. This narrative review examines the current state of knowledge on the use of PBM in veterinary surgical contexts, with an emphasis on its clinical application in wound healing, postoperative pain control, and functional recovery. The physiological foundations of the technique, the main technical parameters that determine its effectiveness (wavelength, dose, frequency, and mode of application), and the available clinical evidence from different specialties such as soft tissue surgery, orthopedics, dentistry, and neurosurgery are analyzed. Current limitations, such as the lack of standardized protocols and their limited inclusion in clinical guidelines, are also addressed, as are future opportunities related to treatment personalization, the development of specific veterinary devices, and integration with emerging technologies. PBM represents a safe and effective adjuvant therapeutic strategy with the potential to become an integral part of veterinary postoperative management. Full article
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22 pages, 12979 KB  
Article
DeepFluoNet: A Novel Deep Learning Framework for Enhanced Analysis of Fluorescence Microscopy Data
by Fatema A. Albalooshi, M. R. Qader, Mazen Ali and Yasser Ismail
Lights 2026, 2(1), 1; https://doi.org/10.3390/lights2010001 - 4 Jan 2026
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Abstract
Fluorescence microscopy is a cornerstone technique in biological research, offering unparalleled insights into cellular and subcellular structures. However, inherent limitations such as photobleaching, phototoxicity, and low signal-to-noise ratios (SNR) often hinder its full potential. This paper introduces DeepFluoNet, a novel deep learning framework [...] Read more.
Fluorescence microscopy is a cornerstone technique in biological research, offering unparalleled insights into cellular and subcellular structures. However, inherent limitations such as photobleaching, phototoxicity, and low signal-to-noise ratios (SNR) often hinder its full potential. This paper introduces DeepFluoNet, a novel deep learning framework designed to significantly enhance the analysis of fluorescence microscopy data. DeepFluoNet leverages a sophisticated convolutional neural network architecture, meticulously optimized for denoising, segmentation, and classification tasks in fluorescence images. DeepFluoNet achieved a 98.5% accuracy in cell nucleus classification, a 95.2% F1-score in mitochondrial segmentation, and a 25% improvement in SNR for low-light images, surpassing state-of-the-art methods by an average of 7.3% in overall performance metrics. Furthermore, the inference time of DeepFluoNet is optimized to be 0.05 s per image, making it suitable for high-throughput analysis. This research bridges critical gaps in existing methodologies by providing a robust, efficient, and highly accurate solution for fluorescence microscopy data analysis, paving the way for more precise biological discoveries. Full article
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