CHAO LIU^*

BIOCELL, Vol.48, No.2, pp. 339-351, 2024, DOI:10.32604/biocell.2023.047044

Abstract Introduction: Osteoarthritis (OA) is still an important health problem, and understanding its pathological mechanisms is essential for its diagnosis and treatment. There is evidence that autophagy may play a role in OA progression, but the exact mechanism remains unclear. Methods: In this study, we adopted a multi-prong approach to systematically identify the key autophagy-related genes (ARGs) associated with OA. Through weighted gene co-expression network analysis, we initially identified significant gene modules associated with OA. Subsequent differential gene analysis performed on normal and OA specimens. Further analysis later using the MCC algorithm highlighted hub ARGs. These genes were then incorporated into… More >

MINGCAI ZHANG, TANNER CAMPBELL, SPENCER FALCON, JINXI WANG^*

BIOCELL, Vol.47, No.10, pp. 2125-2132, 2023, DOI:10.32604/biocell.2023.030161

Abstract Osteoarthritis (OA), the most common form of joint disease, is characterized clinically by joint pain, stiffness, and deformity. OA is now considered a whole joint disease; however, the breakdown of the articular cartilage remains the major hallmark of the disease. Current treatments targeting OA symptoms have a limited impact on impeding or reversing the OA progression. Understanding the molecular and cellular mechanisms underlying OA development is a critical barrier to progress in OA therapy. Recent studies by the current authors’ group and others have revealed that the nuclear factor of activated T cell 1 (NFAT1), a member of the NFAT… More > Graphic Abstract

Abdul Haseeb¹, Muhammad Attique Khan^1,*, Faheem Shehzad¹, Majed Alhaisoni², Junaid Ali Khan¹, Taerang Kim³, Jae-Hyuk Cha³

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2397-2415, 2023, DOI:10.32604/csse.2023.040529

Abstract X-Ray knee imaging is widely used to detect knee osteoarthritis due to ease of availability and lesser cost. However, the manual categorization of knee joint disorders is time-consuming, requires an expert person, and is costly. This article proposes a new approach to classifying knee osteoarthritis using deep learning and a whale optimization algorithm. Two pre-trained deep learning models (Efficientnet-b0 and Densenet201) have been employed for the training and feature extraction. Deep transfer learning with fixed hyperparameter values has been employed to train both selected models on the knee X-Ray images. In the next step, fusion is performed using a canonical… More >

Abdul Qadir¹, Rabbia Mahum¹, Suliman Aladhadh^2,*

Computer Systems Science and Engineering, Vol.47, No.2, pp. 1365-1384, 2023, DOI:10.32604/csse.2023.037033

Abstract A considerable portion of the population now experiences osteoarthritis of the knee, spine, and hip due to lifestyle changes. Therefore, early treatment, recognition and prevention are essential to reduce damage; nevertheless, this time-consuming activity necessitates a variety of tests and in-depth analysis by physicians. To overcome the existing challenges in the early detection of Knee Osteoarthritis (KOA), an effective automated technique, prompt recognition, and correct categorization are required. This work suggests a method based on an improved deep learning algorithm that makes use of data from the knee images after segmentation to detect KOA and its severity using the Kellgren-Lawrence… More >

QIAO ZHOU^2,3,4, JIAN LIU^1,2,*, LING XIN¹, YANYAN FANG^1,2, LEI WAN^1,2, DAN HUANG^1,2, JIANTING WEN^1,2

BIOCELL, Vol.47, No.7, pp. 1623-1643, 2023, DOI:10.32604/biocell.2023.028331

Abstract Objective: On the basis of data mining, systematic pharmacology, molecular docking, and experiment validation, the oxidative-inflammatory molecular targets of Coicis Semen in the therapy of osteoarthritis (OA) were explored. Methods: The association rule analysis was effectively applied to highlight the correlation between Coicis Semen and oxidative inflammation indices. The random walk model was subsequently used to evaluate the clinical efficacy of Coicis Semen. Network pharmacology was used to predict network targets. The binding affinity of the active ingredient in Coicis Semen to the key target of OA was also successfully predicted. Results: Coicis Semen showed a significant reduction in oxidative-inflammatory… More >

CHUYAO WANG^1,2,#, CHUAN LU^2,#, LUXIANG ZOU^2,*, DONGMEI HE^2,*

BIOCELL, Vol.47, No.6, pp. 1293-1306, 2023, DOI:10.32604/biocell.2023.028199

Abstract Background: Temporomandibular joint osteoarthritis (TMJOA) is an end-stage disease that seriously affects the patients’ quality of life. Molecular insights in advancing our understanding of TMJOA are the need of the hour. Methods: We performed RNA high-throughput sequencing and bioinformatics analysis of differentially expressed (DE) long non-coding RNA (lncRNAs), microRNAs (miRNAs), and messenger RNA (mRNAs) in human synovial TMJOA tissues. Firstly, synovium samples of TMJOA patients and non-TMJOA controls were collected for highthroughput sequencing of lncRNAs, miRNAs, and mRNAs. We then performed biological function analysis of the top 100 mRNAs with more than 2-fold differential expression, and their upstream regulated miRNAs… More >

Xin Wang^*, Shuang Liu, Chang-Cai Zhou

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 221-234, 2023, DOI:10.32604/iasc.2023.036766

Abstract Knee osteoarthritis (OA) is a common disease that impairs knee function and causes pain. Currently, studies on the detection of knee OA mainly focus on X-ray images, but X-ray images are insensitive to the changes in knee OA in the early stage. Since magnetic resonance (MR) imaging can observe the early features of knee OA, the knee OA detection algorithm based on MR image is innovatively proposed to judge whether knee OA is suffered. Firstly, the knee MR images are preprocessed before training, including a region of interest clipping, slice selection, and data augmentation. Then the data set was divided… More >

QIAO ZHOU^1,2,3, JIAN LIU^2,4, LING XIN⁴, YANYAN FANG^2,4, LEI WAN^2,4, DAN HUANG^2,4, JINCHEN GUO¹, JIANTING WEN^2,4

BIOCELL, Vol.47, No.4, pp. 761-772, 2023, DOI:10.32604/biocell.2023.026698

Abstract The pathophysiology of osteoarthritis (OA) is multifactorial, with the primary risk factors being obesity, age, environmental variables, and genetic predisposition. The available evidence suggests that genetic diversity does not adequately account for all clinical characteristics and heterogeneity of OA. Genetics has emerged as a nascent and crucial area of research in OA. The epigenetic module presents a potential link between genetic and environmental risk factors and the susceptibility and pathogenesis of OA. As a critical epigenetic alteration, DNA methylation has been shown to have an important role in the etiology of OA and is a viable biomarker for predicting disease… More >

Sang-min Lee^*, Namgi Kim

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1981-1994, 2023, DOI:10.32604/cmc.2023.035245

Abstract Artificial intelligence technologies are being studied to provide scientific evidence in the medical field and developed for use as diagnostic tools. This study focused on deep learning models to classify degenerative arthritis into Kellgren–Lawrence grades. Specifically, degenerative arthritis was assessed by X-ray radiographic images and classified into five classes. Subsequently, the use of various deep learning models was investigated for automating the degenerative arthritis classification process. Although research on the classification of osteoarthritis using deep learning has been conducted in previous studies, only local models have been used, and an ensemble of deep learning models has never been applied to… More >

R. Kanthavel^1,*, R. Dhaya², Kanagaraj Venusamy³

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5783-5798, 2022, DOI:10.32604/cmc.2022.023745

Abstract Knee Osteoarthritis (OA) is a joint disease that is commonly observed in people around the world. Osteoarthritis commonly affects patients who are obese and those above the age of 60. A valid knee image was generated by Computed Tomography (CT). In this work, efficient segmentation of CT images using Elephant Herding Optimization (EHO) optimization is implemented. The initial stage employs, the CT image normalization and the normalized image is incited to image enhancement through histogram correlation. Consequently, the enhanced image is segmented by utilizing Niblack and Bernsen algorithm. The (EHO) optimized outcome is evaluated in two steps. The initial step… More >