ABSTRACT
An analysis of scar tissue is necessary to understand the pathological tissue conditions during or after the wound healing process. Hematoxylin and eosin (HE) staining has conventionally been applied to understand the morphology of scar tissue. However, the scar lesions cannot be analyzed from a whole slide image. The current study aimed to develop a method for the rapid and automatic characterization of scar lesions in HE-stained scar tissues using a supervised and unsupervised learning algorithm. The supervised learning used a Mask region-based convolutional neural network (RCNN) to train a pattern from a data representation using MMDetection tools. The K-means algorithm characterized the HE-stained tissue and extracted the main features, such as the collagen density and directional variance of the collagen. The Mask RCNN model effectively predicted scar images using various backbone networks (e.g., ResNet50, ResNet101, ResNeSt50, and ResNeSt101) with high accuracy. The K-means clustering method successfully characterized the HE-stained tissue by separating the main features in terms of the collagen fiber and dermal mature components, namely, the glands, hair follicles, and nuclei. A quantitative analysis of the scar tissue in terms of the collagen density and directional variance of the collagen confirmed 50% differences between the normal and scar tissues. The proposed methods were utilized to characterize the pathological features of scar tissue for an objective histological analysis. The trained model is time-efficient when used for detection in place of a manual analysis. Machine learning-assisted analysis is expected to aid in understanding scar conditions, and to help establish an optimal treatment plan.
ABSTRACT
Gastric cancer is a frequently occurring cancer and is the leading cause of cancer-related deaths. Recent studies have shown that aberrant glycosylation of serum haptoglobin is closely related to gastric cancer and has enormous potential for use in diagnosis. However, there is no platform with high reliability and high reproducibility to comprehensively analyze haptoglobin glycosylation covering microheterogeneity to macroheterogeneity for clinical applications. In this study, we developed a middle-up-down glycoproteome platform for fast and accurate monitoring of haptoglobin glycosylation. This platform utilizes an online purification of LC for sample desalting, and an in silico haptoglobin glycopeptide library constructed by combining peptides and N-glycans to readily identify glycopeptides. In addition, site-specific glycosylation with glycan heterogeneity can be obtained through only a single MS analysis. Haptoglobin glycosylation in clinical samples consisting of healthy controls (n = 47) and gastric cancer patients (n = 43) was extensively investigated using three groups of tryptic glycopeptides: GP1 (including Asn184), GP2 (including Asn207 and Asn211), and GP3 (including Asn241). A total of 23 individual glycopeptides were determined as potential biomarkers (p < 0.00001). In addition, to improve diagnostic efficacy, we derived representative group biomarkers with high AUC values (0.929 to 0.977) through logistic regression analysis for each GP group. It has been found that glycosylation of haptoglobin is highly associated with gastric cancer, especially the glycosite Asn241. Our assay not only allows to quickly and easily obtain information on glycosylation heterogeneity of a target glycoprotein but also makes it an efficient tool for biomarker discovery and clinical diagnosis.
ABSTRACT
Muscle synergy describes reduced set of functional muscle co-activation patterns. We aimed to identify muscle synergies of turning compared with straight walking. Twelve healthy adults (men: 7, women: 5) performed straight walking (SW), left turning (LT), and right turning (RT) at self-selected speeds. By using non-negative matrix factorization (NMF), we extracted muscle synergies from sixteen electromyography (EMG) signals on the right side and assigned similar muscle synergies among SW, LT, and RT into the same cluster by combining k-means clustering and intraclass correlation coefficient (ICC) analysis. We obtained task-specific clusters of muscle synergies extracted from SW, LT, or RT condition and identified the clusters that share synergies among the conditions. The central nervous system produces specific synergies involving turning behaviors and fundamental synergies for walking.
Subject(s)
Biomechanical Phenomena/physiology , Muscle, Skeletal/physiology , Walking/physiology , Adult , Electromyography , Female , Humans , MaleABSTRACT
PURPOSE: To examine the effectiveness and adherence to a self-determination theory (SDT)-based self-myofascial release (SMR) program in older adults with myofascial trigger points (MTrPs), and to investigate the factors that influence participant behavioral change while conducting the program in a home setting. METHODS: An explanatory mixed-method design was used to evaluate a 12-week SDT-based SMR program, including a 4-week group-based education and practice (EP) phase and an 8-week home-based self-management (SM) phase. Pain intensity on palpation and sensitivity to pain were assessed at baseline and the post EP and post SM phase. Focus group interviews were conducted at the post SM phase. FINDINGS: Fifteen participants completed the study. Pain intensity and sensitivity to pain significantly improved at the post SM phase compared with the baseline. Adherence increased during the SM phase compared with that during the EP phase. Four main themes emerged as factors that influenced participant behavioral change: 1) "awareness of the effectiveness"; 2) "a sense of duty to perform the exercise"; 3) "obedience to expert instruction"; and 4) "lack of friendship." CONCLUSIONS: These results support the effectiveness of an SDT-based SMR program for the treatment of MTrPs and in motivating older adults to participate in the program.
