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Diagnosis of lung cancer using salivary miRNAs expression and clinical characteristics.
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Universal Scientific Education and Research Network (USERN), Tehran, Iran.
Objective: Lung cancer (LC), the primary cause for cancer-related death globally is a diverse illness with various characteristics. Saliva is a readily available biofluid and a rich source of miRNA. It can be collected non-invasively as well as transported and stored easily.
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Deep-learning models have shown promise in differentiating between benign and malignant lesions. Previous studies have primarily focused on specific anatomical regions, overlooking tumors occurring throughout the body with highly heterogeneous whole-body backgrounds. Using neurofibromatosis type 1 (NF1) as an example, this study developed highly accurate MRI-based deep-learning models for the early automated screening of malignant peripheral nerve sheath tumors (MPNSTs) against complex whole-body background.
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Department of Obstetrics and Gynecology, Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE, West China Second Hospital, State Key Laboratory of Biotherapy, and Department of Neurosurgery, West China Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, P. R. China.
Genome-wide functional genetic screening has been widely used in the biomedicine field, which makes it possible to find a needle in a haystack at the genetic level. In cancer research, gene mutations are closely related to tumor development, metastasis, and recurrence, and the use of state-of-the-art powerful screening technologies, such as clustered regularly interspaced short palindromic repeat (CRISPR), to search for the most critical genes or coding products provides us with a new possibility to further refine the cancer mapping and provide new possibilities for the treatment of cancer patients. The use of CRISPR screening for the most critical genes or coding products has further refined the cancer atlas and provided new possibilities for the treatment of cancer patients.
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Department of Biomedical Engineering, School of Life Science and Technology, Changchun University of Science and Technology, Changchun, 130022, China.
The cervical cell classification technique can determine the degree of cellular abnormality and pathological condition, which can help doctors to detect the risk of cervical cancer at an early stage and improve the cure and survival rates of cervical cancer patients. Addressing the issue of low accuracy in cervical cell classification, a deep convolutional neural network A2SDNet121 is proposed. A2SDNet121 takes DenseNet121 as the backbone network.
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College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China.
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