Cheryl Dickson^1,2,4, Danielle Osborn¹, David Baker^1,4, Judith Fethney³, David S. Celermajer^1,4, Rachael Cordina^1,4,*

Congenital Heart Disease, Vol.19, No.1, pp. 49-63, 2024, DOI:10.32604/chd.2023.044874

Abstract Background: Much has been written about the loss to follow-up in the transition between pediatric and adult Congenital Heart Disease (CHD) care centers. Much less is understood about the loss to follow-up (LTF) after a successful transition. This is critical too, as patients lost to specialised care are more likely to experience morbidity and premature mortality. Aims: To understand the prevalence and reasons for loss to follow-up (LTF) at a large Australian Adult Congenital Heart Disease (ACHD) centre. Methods: Patients with moderate or highly complex CHD and gaps in care of >3 years (defined as LTF) were identified from a… More >

Irfan Haider¹, Muhammad Attique Khan^1,*, Muhammad Nazir¹, Taerang Kim², Jae-Hyuk Cha²

Computer Systems Science and Engineering, Vol.48, No.2, pp. 529-554, 2024, DOI:10.32604/csse.2023.042080

Abstract Fruit infections have an impact on both the yield and the quality of the crop. As a result, an automated recognition system for fruit leaf diseases is important. In artificial intelligence (AI) applications, especially in agriculture, deep learning shows promising disease detection and classification results. The recent AI-based techniques have a few challenges for fruit disease recognition, such as low-resolution images, small datasets for learning models, and irrelevant feature extraction. This work proposed a new fruit leaf leaf leaf disease recognition framework using deep learning features and improved pathfinder optimization. Three fruit types have been employed in this work for… More >

Nazik Alturki¹, Abdulaziz Altamimi², Muhammad Umer^3,*, Oumaima Saidani¹, Amal Alshardan¹, Shtwai Alsubai⁴, Marwan Omar⁵, Imran Ashraf^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3513-3534, 2024, DOI:10.32604/cmes.2023.045868

Abstract Chronic kidney disease (CKD) is a major health concern today, requiring early and accurate diagnosis. Machine learning has emerged as a powerful tool for disease detection, and medical professionals are increasingly using ML classifier algorithms to identify CKD early. This study explores the application of advanced machine learning techniques on a CKD dataset obtained from the University of California, UC Irvine Machine Learning repository. The research introduces TrioNet, an ensemble model combining extreme gradient boosting, random forest, and extra tree classifier, which excels in providing highly accurate predictions for CKD. Furthermore, K nearest neighbor (KNN) imputer is utilized to deal… 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 model, the computational requirements should… More >

Chirag Chandrashekar¹, K. P. Vijayakumar^1,*, K. Pradeep¹, A. Balasundaram^1,2

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2511-2533, 2024, DOI:10.32604/cmc.2024.047697

Abstract The most widely farmed fruit in the world is mango. Both the production and quality of the mangoes are hampered by many diseases. These diseases need to be effectively controlled and mitigated. Therefore, a quick and accurate diagnosis of the disorders is essential. Deep convolutional neural networks, renowned for their independence in feature extraction, have established their value in numerous detection and classification tasks. However, it requires large training datasets and several parameters that need careful adjustment. The proposed Modified Dense Convolutional Network (MDCN) provides a successful classification scheme for plant diseases affecting mango leaves. This model employs the strength… 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 the contributions, strengths and weaknesses,… More >

Afnan M. Alhassan^*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2207-2223, 2024, DOI:10.32604/cmc.2024.046427

Abstract Breast Arterial Calcification (BAC) is a mammographic decision dissimilar to cancer and commonly observed in elderly women. Thus identifying BAC could provide an expense, and be inaccurate. Recently Deep Learning (DL) methods have been introduced for automatic BAC detection and quantification with increased accuracy. Previously, classification with deep learning had reached higher efficiency, but designing the structure of DL proved to be an extremely challenging task due to overfitting models. It also is not able to capture the patterns and irregularities presented in the images. To solve the overfitting problem, an optimal feature set has been formed by Enhanced Wolf… More >

XIAN ZHANG¹, ZHUANG ZHOU², JUNZHE WANG¹, MENGMENG HAN¹, HAN LIU¹, MEIRONG ZANG¹, JIANNING LIU¹, JIAPEI LU¹, JINQIAO ZHANG¹, GUOCHUAN ZHANG^2,*, LIXIA SUN^1,#,*

BIOCELL, Vol.48, No.2, pp. 303-311, 2024, DOI:10.32604/biocell.2023.046640

Abstract Background: Prognosis of multiple myeloma (MM) patients with extramedullary disease (EMD) remains poor. T cell dysfunction and an immunosuppressive environment have been reported in the bone marrow (BM) of MM patients. However, the immunosuppressive microenvironment and immune checkpoint receptors (ICRs) on CD8 T cells in the EMD tissue of newly diagnosed MM (NDMM) patients have not been thoroughly studied. Methods: We investigated the expression levels of T cell immunoglobulin mucin-domain-containing-3 (TIM-3) and T-cell immunoglobulin and ITIM domain (TIGIT) on CD8 T cells and the expression of their ligands (Galectin-9 and CD155) on myeloma cells in EMD tissue of NDMM patients.… More > Graphic Abstract

Yuxin Wang^1,2,*, Jia Shi^1,2

International Journal of Mental Health Promotion, Vol.26, No.1, pp. 69-80, 2024, DOI:10.32604/ijmhp.2023.044641

Abstract In the 21st century, the rapid growth of the Internet has presented a significant avenue for China to respond actively to the aging population and promote the “Healthy China” strategy in an orderly manner. This study uses panel data from the China Health and Retirement Longitudinal Study (CHARLS) to empirically investigate the influence of Internet use on the mental health of older adults, particularly those who lack intergenerational emotional support and suffer from chronic diseases. This study employs a multi-period difference-in-differences (DID) method and a two-stage instrumental variable approach to address the endogenous problem. Results show that Internet use has… More >

ZEHRA GÜL MORÇIMEN¹, ŞEYMA TAŞDEMIR², AYLIN ŞENDEMIR^3,4,*

BIOCELL, Vol.48, No.1, pp. 79-96, 2024, DOI:10.32604/biocell.2023.045361

Abstract Neurodegeneration is a catastrophic process that develops progressive damage leading to functional and structural loss of the cells of the nervous system and is among the biggest unavoidable problems of our age. Animal models do not reflect the pathophysiology observed in humans due to distinct differences between the neural pathways, gene expression patterns, neuronal plasticity, and other disease-related mechanisms in animals and humans. Classical in vitro cell culture models are also not sufficient for pre-clinical drug testing in reflecting the complex pathophysiology of neurodegenerative diseases. Today, modern, engineered techniques are applied to develop multicellular, intricate in vitro models and to… More >