Language Matters: The Autism-Spectrum Quotient in English,Mandarin and Bahasa Malaysia

Journal of Autism and Developmental Disorders - The autism-spectrum quotient (AQ) measures autistic traits and has been studied in different countries, sometimes with the English version, and...     

Side effects of long-term oral anti-seizure drugs on thyroid hormones in patients with epilepsy: a systematic review and network meta-analysis

Neurological Sciences - Anti-seizure drugs have long been known to affect thyroid hormone levels in epilepsy patients. The current study is a network meta-analysis designed to produce a systematic...     

FFNet: Feature Fusion Network for Few-shot Semantic Segmentation

Cognitive Computation - Semantic segmentation aims at assigning a category label to each pixel in an image. Deep neural networks have achieved many breakthrough research achievements on this task....     

Back Pain-Inducing Test,a Novel and Sensitive Screening Test for Painful Osteoporotic Vertebral Fractures: A Prospective Clinical Study

To detect painful vertebral fractures (VFs) in back pain populations at risk of osteoporosis, we designed a physical examination test (the Back Pain-Inducing Test [BPIT]) that included three movements: lying supine, rolling over, and sitting up. If back pain is induced during any of these movements, the result is defined as positive, thereby establishing a presumptive diagnosis of painful VFs. Pain severity is quantified using a self-reported numerical rating scale (NRS). The presence or absence of painful VFs is verified by whole-spine magnetic resonance imaging (MRI), the gold standard for final diagnosis. According to the standards for reporting diagnostic accuracy, a real-world, prospective, and observational study was performed on 510 back pain patients (enrolled from a single institute) at risk of osteoporosis. The sensitivity, specificity, and accuracy of the BPIT for identifying painful VFs were 99.1% (95% CI, 97.5% to 99.8%), 67.9% (95% CI, 60.4% to 74.5%), and 89.0%, respectively. The positive and negative predictive values were 86.6% (95% CI, 82.9% to 89.6%) and 97.4% (95% CI, 92.6% to 99.3%), respectively. Cutoff NRS scores for lying supine, rolling over, and sitting up were 3, 0, and 2, respectively. The corresponding area under the receiver operating characteristic curves (AUROCs) of each movement was 0.898 (95% CI, 0.868 to 0.922), 0.884 (95% CI, 0.854 to 0.911), and 0.910 (95% CI, 0.882 to 0.933), respectively. Although the high prevalence of VFs in the enrolled cohort partially limits the external validity of the predictive value in the general population, we conclude that the BPIT is potentially effective for detecting painful VFs in back pain populations at risk of osteoporosis. This test may be used as a stratification tool in decision-making on subsequent imaging procedures: a negative BPIT rules out painful VFs and indicates that an MRI should be spared, whereas a positive BPIT means that an MRI is necessary and is likely to identify painful VFs. © 2019 American Society for Bone and Mineral Research.     

肝胆外科从数字虚拟人、三维可视化到数字智能化发展的现状

数字智能化肝胆外科的发展经历了十几年的演进过程,从数字虚拟人技术到质控化、同质化三维可视化精准诊疗体系的建立;从三维可视化到数字智能化技术临床转化及诊疗模式的转换;实现了疾病的经验性诊断到深度学习智能化诊断与治疗和经验性手术到多模态影像实时手术导航的技术创新;从肿瘤的形态学诊断深入到分子影像学精准诊断的研究。在这个过程中只有不断地进行研究创新、理论创新和技术创新才能给数字智能化外科赋予新的生命力。未来,从关键分子功能可视化实现肝癌分子、细胞层面边界界定和早诊早治,对改变肝癌患者肝切除术后的预后具有重大的临床价值。此外,为了实现智能化肝切除手术导航,突破技术瓶颈,研发具有自动导航技术、机器学习智能规划技术和多模态影像融合技术的智能化机器人实时导航肝切除手术系统具有重要的临床意义,给数字智能化肝胆外科的发展带来了新的机遇和挑战。