PAULINA CHMIEL^1,2, ALEKSANDRA SłOWIKOWSKA^1,2, ŁUKASZ BANASZEK^1,2, ANNA SZUMERA-CIEćKIEWICZ³, BARTłOMIEJ SZOSTAKOWSKI¹, MATEUSZ J. SPAłEK^1,4,*, TOMASZ ŚWITAJ¹, PIOTR RUTKOWSKI¹, ANNA M. CZARNECKA¹

Oncology Research, Vol.32, No.7, pp. 1141-1162, 2024, DOI:10.32604/or.2024.050350

Abstract Inflammatory myofibroblastic tumor (IMT) is a rare neoplasm with intermediate malignancy characterized by a propensity for recurrence but a low metastatic rate. Diagnostic challenges arise from the diverse pathological presentation, variable symptomatology, and lack of different imaging features. However, IMT is identified by the fusion of the anaplastic lymphoma kinase (ALK) gene, which is present in approximately 70% of cases, with various fusion partners, including ran-binding protein 2 (RANBP2), which allows confirmation of the diagnosis. While surgery is the preferred approach for localized tumors, the optimal long-term treatment for advanced or metastatic disease is difficult… More >

ELHAM SHAKIBA¹, SETI BOROOMAND², SIMA KHERADMAND KIA³, MEHDI HEDAYATI^4,*

Oncology Research, Vol.32, No.6, pp. 1011-1019, 2024, DOI:10.32604/or.2024.049235

Abstract This review aimed to describe the inculpation of microRNAs (miRNAs) in thyroid cancer (TC) and its subtypes, mainly medullary thyroid carcinoma (MTC), and to outline web-based tools and databases for bioinformatics analysis of miRNAs in TC. Additionally, the capacity of miRNAs to serve as therapeutic targets and biomarkers in TC management will be discussed. This review is based on a literature search of relevant articles on the role of miRNAs in TC and its subtypes, mainly MTC. Additionally, web-based tools and databases for bioinformatics analysis of miRNAs in TC were identified and described. MiRNAs can… More > Graphic Abstract

Umar Rashid^1,2,*, Arfan Jaffar^1,2, Muhammad Rashid³, Mohammed S. Alshuhri⁴, Sheeraz Akram^1,4,5

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3377-3390, 2024, DOI:10.32604/cmc.2024.046320

Abstract Pulmonary nodules are small, round, or oval-shaped growths on the lungs. They can be benign (noncancerous) or malignant (cancerous). The size of a nodule can range from a few millimeters to a few centimeters in diameter. Nodules may be found during a chest X-ray or other imaging test for an unrelated health problem. In the proposed methodology pulmonary nodules can be classified into three stages. Firstly, a 2D histogram thresholding technique is used to identify volume segmentation. An ant colony optimization algorithm is used to determine the optimal threshold value. Secondly, geometrical features such as More >

Jinbo Yang¹, Hai Huang¹, Lailai Yin², Jiaxing Qu³, Wanjuan Xie^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3085-3099, 2024, DOI:10.32604/cmes.2023.045417

Abstract Diagnosing multi-stage diseases typically requires doctors to consider multiple data sources, including clinical symptoms, physical signs, biochemical test results, imaging findings, pathological examination data, and even genetic data. When applying machine learning modeling to predict and diagnose multi-stage diseases, several challenges need to be addressed. Firstly, the model needs to handle multimodal data, as the data used by doctors for diagnosis includes image data, natural language data, and structured data. Secondly, privacy of patients’ data needs to be protected, as these data contain the most sensitive and private information. Lastly, considering the practicality of the… More >

Xinlong Diao, Xiao Wang^*, Junkang Qin, Qinmu Wu, Zhiqin He, Xinghong Fan

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2617-2665, 2024, DOI:10.32604/cmc.2024.047377

Abstract In recent years, Artificial Intelligence (AI) has revolutionized people’s lives. AI has long made breakthrough progress in the field of surgery. However, the research on the application of AI in orthopedics is still in the exploratory stage. The paper first introduces the background of AI and orthopedic diseases, addresses the shortcomings of traditional methods in the detection of fractures and orthopedic diseases, draws out the advantages of deep learning and machine learning in image detection, and reviews the latest results of deep learning and machine learning applied to orthopedic image detection in recent years, describing… More >

Mohamed Abdel-Basset¹, Hossam Hawash¹, Mohamed Abouhawwash^2,3,*, S. S. Askar⁴, Alshaimaa A. Tantawy¹

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1171-1187, 2024, DOI:10.32604/cmc.2023.044425

Abstract The early implementation of treatment therapies necessitates the swift and precise identification of COVID-19 pneumonia by the analysis of chest CT scans. This study aims to investigate the indispensable need for precise and interpretable diagnostic tools for improving clinical decision-making for COVID-19 diagnosis. This paper proposes a novel deep learning approach, called Conformer Network, for explainable discrimination of viral pneumonia depending on the lung Region of Infections (ROI) within a single modality radiographic CT scan. Firstly, an efficient U-shaped transformer network is integrated for lung image segmentation. Then, a robust transfer learning technique is introduced… More >

ZEPENG DONG, CHENYE ZHAO, SHIBO HU, KUI YANG, JUNHUI YU^*, XUEJUN SUN, JIANBAO ZHENG^*

BIOCELL, Vol.47, No.12, pp. 2735-2745, 2023, DOI:10.32604/biocell.2023.043422

Abstract Objectives: Angiogenesis plays a significant role in the occurrence and development of inflammatory bowel disease (IBD). The aim of this study is to explore potential angiogenesis related genes (ARGs) in IBD through bioinformatics analysis and in vivo experiments. Methods: GSE57945, GSE87466, and GSE36807 were obtained from the Gene Expression Omnibus database. GSE57945 was used as the training set, while GSE87466 and GSE36807 were used as the validation set. The key ARGs associated with IBD were identified using the least absolute shrinkage and selection operator (LASSO) and random forest methods. These identified ARGs were then utilized to construct… More >

CONGCHEN BAI¹, QIHANG KONG², HAO TANG², SHUWEN ZHANG², JUNTENG ZHOU^3,*, XIAOJING LIU^2,4,*

BIOCELL, Vol.47, No.11, pp. 2517-2533, 2023, DOI:10.32604/biocell.2023.043864

Abstract Background: Various physiological mechanisms are linked to dilated cardiomyopathy (DCM) development, including oxidative stress, immune irregularities, inflammation, fibrosis, and genetic changes. However, precise molecular drivers of DCM, especially regarding abnormal immune responses, remain unclear. This study investigates immune-related long non-coding RNAs (lncRNAs) in DCM’s diagnostic and therapeutic potential. Methods: GSE141910, GSE135055, and GSE165303 datasets were acquired from the GEO database. LASSO, SVM-RFE, and random forest algorithms identified DCM-associated immune-related lncRNAs. Diagnostic capabilities were assessed by Nomogram and receiver operating characteristic (ROC) curves. Multivariate linear regression explored lncRNA correlations with ejection fraction. Single-sample gene set enrichment analysis… More > Graphic Abstract

Maryam Bukhari¹, Sadaf Yasmin¹, Sheneela Naz², Mehr Yahya Durrani¹, Mubashir Javaid³, Jihoon Moon⁴, Seungmin Rho^5,*

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1251-1279, 2023, DOI:10.32604/cmc.2023.040329

Abstract Effective smart healthcare frameworks contain novel and emerging solutions for remote disease diagnostics, which aid in the prevention of several diseases including heart-related abnormalities. In this context, regular monitoring of cardiac patients through smart healthcare systems based on Electrocardiogram (ECG) signals has the potential to save many lives. In existing studies, several heart disease diagnostic systems are proposed by employing different state-of-the-art methods, however, improving such methods is always an intriguing area of research. Hence, in this research, a smart healthcare system is proposed for the diagnosis of heart disease using ECG signals. The proposed… More >

WEI LIANG^1,2,*, ZONGWEI ZHANG^1,2, KEJU YANG^1,2,3, HONGTU HU^1,2, QIANG LUO^1,2, ANKANG YANG^1,2, LI CHANG⁴, YUANYUAN ZENG⁴

BIOCELL, Vol.47, No.6, pp. 1253-1263, 2023, DOI:10.32604/biocell.2023.027373

Abstract Background: Diabetic nephropathy (DN) is the most common complication of type 2 diabetes mellitus and the main cause of end-stage renal disease worldwide. Diagnostic biomarkers may allow early diagnosis and treatment of DN to reduce the prevalence and delay the development of DN. Kidney biopsy is the gold standard for diagnosing DN; however, its invasive character is its primary limitation. The machine learning approach provides a non-invasive and specific criterion for diagnosing DN, although traditional machine learning algorithms need to be improved to enhance diagnostic performance. Methods: We applied high-throughput RNA sequencing to obtain the genes… More >