Photobiomodulation (PBM) therapy on the brain employs red to near-infrared (NIR) light to treat various neurological and psychological disorders. The mechanism involves the activation of cytochrome c oxidase in the mitochondrial respiratory chain, thereby enhancing ATP synthesis. Additionally, light absorption by ion channels triggers the release of calcium ions, instigating the activation of transcription factors and subsequent gene expression. This cascade of events not only augments neuronal metabolic capacity but also orchestrates anti-oxidant, anti-inflammatory, and anti-apoptotic responses, fostering neurogenesis and synaptogenesis. It shows promise for treating conditions like dementia, stroke, brain trauma, Parkinson’s disease, and depression, even enhancing cognitive functions in healthy individuals and eliciting growing interest within the medical community. However, delivering sufficient light to the brain through transcranial approaches poses a significant challenge due to its limited penetration into tissue, prompting an exploration of alternative delivery methods such as intracranial and intranasal approaches. This comprehensive review aims to explore the mechanisms through which PBM exerts its effects on the brain and provide a summary of notable preclinical investigations and clinical trials conducted on various brain disorders, highlighting PBM’s potential as a therapeutic modality capable of effectively impeding disease progression within the organism—a task often elusive with conventional pharmacological interventions.

Photobiomodulation therapy (PBMT) effects an important role in neural regeneration and function enhancement, such as expression of nerve growth factor and nerve regeneration, in neuronal tissues, and inhibition of cell death by amyloid beta in neurons is inhibited by PBMT. However, there no studies evaluated the effects of PBMT on oxidative stress in the hippocampus. The aim of this study is to evaluate the effects of PBMT on oxidative stress in the hippocampus. This study assessed the anti-oxidative effect, the expression of BDNF and antioxidant enzymes, as well as the activation of cAMP response element binding (CREB) and extracellular signal-regulated kinase (ERK) signal transduction pathways assess using a hippocampal cell line (HT-22) and mouse organotypic hippocampal tissues by PBMT (LED, 660 nm, 20 mW/cm²). PBMT inhibited HT-22 cell death by oxidative stress and increased BDNF expression via ERK and CREB signaling pathway activation. In addition, PBMT increased BDNF expression in hippocampal organotypic slices and the levels of phosphorylated ERK and CREB, which were reduced by oxidative stress, as well as the expression of the antioxidant enzyme superoxide dismutase. These data demonstrate that PBMT inhibits hippocampal damage induced by oxidative stress and increases the expression of BDNF, which can be used as an alternative to treat a variety of related disorders that lead to nerve damage. Activation and redox homeostasis in neuronal cells may be a notable mechanism of the 660-nm PBMT-mediated photobioreactivity.

This paper presents a relaxation labeling process with the newly defined compatibility measure for solving a general nonrigid point matching problem. In the literature, there exists a point matching method using relaxation labeling; however, the compatibility coefficient takes a binary value of zero or one depending on whether a point and a neighbor have corresponding points. Our approach generalizes this relaxation labeling method. The compatibility coefficient takes n-discrete values which measure the correlation between point pairs. In order to improve the speed of the algorithm, we use a diagram of log distance and polar angle bins to compute the correlation. The extensive experiments show that the proposed topology preserving relaxation algorithm significantly improves the matching performance compared to other state-of-the-art point matching algorithms.

Hip joint ultrasonographic (US) imaging is the golden standard for developmental dysplasia of the hip (DDH) screening. However, the effectiveness of this technique is subject to interoperator and intraobserver variability. Thus, a multi-detection deep learning artificial intelligence (AI)-based computer-aided diagnosis (CAD) system was developed and evaluated. The deep learning model used a two-stage training process to segment the four key anatomical structures and extract their respective key points. In addition, the check angle of the ilium body balancing level was set to evaluate the system’s cognitive ability. Hence, only images with visible key anatomical points and a check angle within ±5° were used in the analysis. Of the original 921 images, 320 (34.7%) were deemed appropriate for screening by both the system and human observer. Moderate agreement (80.9%) was seen in the check angles of the appropriate group (Cohen’s κ = 0.525). Similarly, there was excellent agreement in the intraclass correlation coefficient (ICC) value between the measurers of the alpha angle (ICC = 0.764) and a good agreement in beta angle (ICC = 0.743). The developed system performed similarly to experienced medical experts; thus, it could further aid the effectiveness and speed of DDH diagnosis.

This paper presents a review of Tactile Sensing Systems, with an emphasis on their application to the non-invasive characterization of tumors. Emulating the perceptual mechanism of the human skin, the Tactile Sensing Systems characterize tumors using touch sensors by quantifying the mechanical properties such as size and elasticity. The authors survey several tactile transduction methods: capacitive, piezoresistive, piezoelectric, magnetic, and optical. The advantages and disadvantages of different tactile sensors are discussed. The complex human sense of touch is emulated using tactile sensor data, novel data processing algorithms, and near real-time interpretation in a human-readable format. Tactile Sensing Systems utilize tactile sensors and other subsystems to come up with accurate mechanical properties of the touched objects. We review Elasticity Determination Systems, which are a special case of Tactile Sensing Systems. These systems are based on capacitive sensors, piezoelectric sensors, elastography, and optical tactile sensors. Then the optical Tactile Sensing System is discussed in detail; architecture, sensing principle, and algorithms to compute a risk score. Moreover, a survey of multimodal Tactile Sensing Systems, which broaden the capabilities of existing tactile sensing systems, is presented. The paper concludes with discussions and future research directions.