Background: Lung cancer remains a major global health concern due to its high incidence and mortality rates. With advancements in medical treatments, an increasing number of early-stage lung cancer cases are being detected, making surgical treatment the primary option for such cases. However, this presents challenges to the physical and mental recovery of patients.
View Article and Find Full Text PDFAims: Long noncoding RNAs (lncRNAs) may be associated with the development of type 2 diabetes mellitus and its complications; however, the findings remain controversial. We aimed to synthesize the available data to assess the diagnostic utility of lncRNAs for identification of type 2 diabetes mellitus and its consequences.
Materials And Methods: We performed a systematic review and meta-analysis, searching PubMed, Embase, and Web of Science for articles published from September 11, 2015 to December 27, 2022.
Despite efforts to diagnose diabetic nephropathy (DN) using biochemical data or ultrasound imaging separately, a significant gap exists regarding the development of integrated models combining both modalities for enhanced early DN diagnosis. Therefore, we aimed to assess the ability of machine learning models containing two-dimensional ultrasound imaging and biochemical data to diagnose early DN in patients with type 2 diabetes mellitus (T2DM). This retrospective study included 219 patients, divided into a training or test group at an 8:2 ratio.
View Article and Find Full Text PDFNon‑small cell lung cancer (NSCLC) accounts for ~85% of lung cancer cases and has high morbidity and mortality rates. Over the past decade, treatment strategies for NSCLC have progressed rapidly, particularly with the increasing use of screening programs, leading to improvements in the initial diagnosis and treatment of early‑stage and preinvasive tumors. Surgical intervention remains the primary treatment for early‑stage NSCLC.
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