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Remote photoplethysmography (rPPG) enables non-contact vital sign measurements using standard smart device cameras, opening up the potential of scalable health applications on consumer smart devices. However, rPPG signal quality is highly sensitive to camera sensor characteristics and image processing pipelines, which can vary between devices. This variation limits reproducibility and generalisation of rPPG-based algorithms beyond specific hardware platforms. This work presents a reproducible test environment for the validation of the camera sensor response in the context of rPPG signals. A microcontroller-driven illumination system and mechanically constrained setup are used to generate controlled, repeatable optical signals. Two characterisation tests are introduced: a time domain morphology analysis and a frequency domain attenuation analysis. Pulse timing consistency, pulse waveform morphology and normalised frequency responses are compared to assess sensor similarity. This method is applied to selected consumer devices and demonstrates consistent camera response patterns under the controlled test conditions. By explicitly addressing validation of the camera sensor and image processing pipeline, this work supports the development of more robust and transferable rPPG-based vital sign applications across a wider range of consumer devices.

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.

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.