An Automatic Method for Sublingual Image Segmentation and Color Analysis.

Automatic segmentation of sublingual images and color quantification of sublingual vein are of great significance to disease diagnosis in traditional Chinese medicine. With the development of computer vision, automatic sublingual image processing provides a noninvasive way to observe patients' tongue and is convenient for both doctors and patients. However, current sublingual image segmentation methods are not accurate enough. Besides, the differences in subjective judgments by different doctors bring more difficulties in color analysis of sublingual veins. In this paper, we propose a method of sublingual image segmentation based on a modified UNet++ network to improve the segmentation accuracy, a color classification approach based on triplet network, and a color quantization method of sublingual vein based on linear discriminant analysis to provide intuitive one-dimensional results. Our methods achieve 88.2% mean intersection over union (mIoU) and 94.1% pixel accuracy on tongue dorsum segmentation, and achieves 69.8% mIoU and 82.7% pixel accuracy on sublingual vein segmentation. Compared with the state-of-art methods, the segmentation mIoUs are improved by 5.8% and 5.3% respectively. Our sublingual vein color classification method has the highest overall accuracy of 81.2% and the highest recall for the minority class of 77.5%, and the accuracy of color quantization is 90.5%.Clinical Relevance- The methods provide accurate and quantified information of the sublingual image, which can assist doctors in diagnosis.

Neuroprotection and Mechanism of Gas-miR36-5p from Gastrodia elata in an Alzheimer's Disease Model by Regulating Glycogen Synthase Kinase-3ß.

Alzheimer's disease (AD) is currently the most common neurodegenerative disease. Glycogen synthase kinase 3ß (GSK-3ß) is a pivotal factor in AD pathogenesis. Recent research has demonstrated that plant miRNAs exert cross-kingdom regulation on the target genes in animals. Gastrodia elata (G. elata) is a valuable traditional Chinese medicine that has significant pharmacological activity against diseases of the central nervous system (CNS). Our previous studies have indicated that G. elata-specific miRNA plays a cross-kingdom regulatory role for the NF-κB signaling pathway in mice. In this study, further bioinformatics analysis suggested that Gas-miR36-5p targets GSK-3ß. Through western blot, RT-qPCR, and assessments of T-AOC, SOD, and MDA levels, Gas-miR36-5p demonstrated its neuroprotective effects in an AD cell model. Furthermore, Gas-miR36-5p was detected in the murine brain tissues. The results of the Morris water maze test and western blot analysis provided positive evidence for reversing the learning deficits and hyperphosphorylation of Tau in AD mice, elucidating significant neuroprotective effects in an AD model following G. elata RNA administration. Our research emphasizes Gas-miR36-5p as a novel G. elata-specific miRNA with neuroprotective properties in Alzheimer's disease by targeting GSK-3ß. Consequently, our findings provide valuable insights into the cross-kingdom regulatory mechanisms underlying G. elata-specific miRNA, presenting a novel perspective for the treatment of Alzheimer's disease.

Gastrodia elata specific miRNA attenuates neuroinflammation via modulating NF-κB signaling pathway.

AIMS: Based on our previous research on the specific miRNAs identified from Gastrodia elata, we selected Gas-miR2-3p to investigate its effects on neuroinflammation via in vitro and in vivo experiments. RESULTS: RT-qPCR analysis indicated that G. elata specific Gas-miR2-3p was detected in all murine tissues post-oral administration, suggesting their potential as orally bioavailable miRNA. The analysis of RT-qPCR, Western blotting and ELISA assays consistently demonstrate that the expression of inflammatory factors as TNF-α, IL-6, IL-1ß was decreased and the expression levels of p-p65 and p-IκBα were downregulated after the action of Gas-miR2-3p in both cell and animal experiments. CONCLUSION: Gas-miR2-3p can attenuate neuroinflammation by regulating the inflammation factors and suppressing the activation of the NF-κB signaling pathway. Our findings indicate that G. elata miRNAs, as novel active components, perform a modulatory role in the NF-κB signaling pathway associated with neuroinflammation in a cross-species way.

A Portable Pressure and Force Line Trajectory Measuring System for Unicondylar Knee Arthroplasty.

The pressure and force line trajectory of the distal femoral prosthesis relative to the prosthesis gasket are key factors in judging the appropriate position of implants in unicondylar knee arthroplasty (UKA) surgeries, which is critical to the success of UKA surgeries. In this paper, we propose a portable pressure and force line trajectory measurement system, which includes a pressure sensors array, an analog-to-digital converter (ADC), and a microcontroller unit (MCU). Data from twenty sensors is obtained through time-sharing scanning and transmitted to the host computer through the USB interface. We put forward an algorithm to calculate pressure value and fit the force line trajectory for better accuracy. Both the pressure value and force line trajectory are calculated and displayed on the screen of the host computer by developed software in real-time. The experiments results show that the root mean square error of fitting force line trajectory in the experiments is ±0.342mm, which has 63% reduction compared with that in the previous work, and the average pressure value measurement error is 10.03%. Besides, the pressure sensors array, the ADC and the MCU in the system are integrated in a portable handle, which is easier for clinic trial.Clinical Relevance-This system can assist doctors to improve the success rate of unicondylar knee arthroplasty and reduce patient's pain.

An Automatic Petechia Dots Detection Method on Tongue.

Tongue diagnosis with features like tongue coating, petechia, color, size and so on is of great effectiveness and convenience in traditional Chinese medicine. With the development of image processing techniques, automatic image processing can reduce hospital inspection for patients. However, there are ubiquitous problems of inadequate accuracy in petechia dots detection with previous methods. In this paper, we propose a method of petechia dots detection on tongue based on SimpleBlobDetector function in OpenCV library and support vector machines model, which improves the detective accuracy. We test 128 clinic tongue images and select 9 of the images with plentiful petechia dots for further experiments. Our method achieves mean value of false alarm rate 4.6% and missing alarm rate 11.8%, which have 19.4% and 8.2% reduction respectively compared to previous work.Clinical Relevance-The method can provide detailed information of tongue, which assists doctors to investigate curative effect.