Medicine
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Long noncoding RNA (lncRNA) is reported to be upregulated in many tumors. Although the expression of lncRNA in oral squamous cell carcinoma has been assessed, the association between lncRNA expression and prognosis or clinicopathological feature still remains controversial. Therefore, we conducted a meta-analysis to verify whether lncRNA expression was related to prognosis or clinicopathological features in patients with oral squamous cell carcinoma. ⋯ The conclusion of our study will provide the updated evidence to judge the lncRNA on the prognosis and clinicopathological features of oral squamous cell carcinoma.
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Gerstmann-Sträussler-Scheinker syndrome (GSS) is a rare autosomal dominant disease caused by a mutation in the prion protein gene (PRNP) that is not well known among neurologists and is therefore easily misdiagnosed. ⋯ Cerebral and cerebellar atrophy are neuroimaging features symptomatic of GSS that become more apparent as the disease progresses. This atrophy is positively correlated with the severity of symptoms and reduced quality of life. Neurologists treating middle-aged patients with progressive ataxia, cognitive impairment or dysarthria, and brain atrophy need to consider the possibility of GSS.
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Long noncoding RNAs play vital roles in development and progression of lung cancers. Nuclear paraspeckle assembly transcript 1 (NEAT1) polymorphisms were reported to be closely related to lung cancer susceptibility. Recently, numerous studies have been performed to detect the association between NEAT1 polymorphisms and lung cancer susceptibility. However, their results were inconsistent and controversial. So, we carried out a meta-analysis aiming to define the association exactly. ⋯ This meta-analysis will summarize the relationship between NEAT1 polymorphism and lung cancer.
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Comparative Study
Evaluation of a deep learning-based computer-aided detection algorithm on chest radiographs: Case-control study.
Along with recent developments in deep learning techniques, computer-aided diagnosis (CAD) has been growing rapidly in the medical imaging field. In this work, we evaluate the deep learning-based CAD algorithm (DCAD) for detecting and localizing 3 major thoracic abnormalities visible on chest radiographs (CR) and to compare the performance of physicians with and without the assistance of the algorithm. A subset of 244 subjects (60% abnormal CRs) was evaluated. ⋯ For the image classification, the overall AUC of the pooled physicians was 0.8679 without DCAD and 0.9112 with DCAD. Regarding lesion detection, the pooled observers exhibited a weighted JAFROC figure of merit (FOM) of 0.8426 without DCAD and 0.9112 with DCAD. DCAD for CRs could enhance physicians' performance in the detection of 3 major thoracic abnormalities.
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Tarlov or perineurial cysts are nerve root lesions often found in the sacral region. Most perineural cysts (PCs) remain asymptomatic throughout a patient's life. While their pathogenesis is still unclear, trauma resulting in hemorrhaging into subarachnoid space has been put forward as a possible cause of these cysts. Recently, we worked with a patient experiencing symptomatic PCs after spontaneous subarachnoid hemorrhage. ⋯ Subarachnoid hemorrhage can be the source of the development of pain from asymptomatic PCs, making them symptomatic. Surgical extirpation is 1 treatment option for these symptomatic PCs.