Radial nerve palsy in the newborn combined with congenital radial head dislocation: Case report and literature review.

RATIONALE: Radial nerve palsy in the newborn and congenital radial head dislocation (CRHD) are both rare disorders, and early diagnosis is challenging. We reported a case of an infant with concurrent presence of these 2 diseases and provide a comprehensive review of the relevant literature. The purpose of the study is to share diagnostic and treatment experiences and provide potentially valuable insights. PATIENT CONCERNS: A newborn has both radial nerve palsy and CRHD, characterized by limited wrist and fingers extension but normal flexion, normal shoulder and elbow movement on the affected side, characteristic skin lesions around the elbow, and an "audible click" at the radial head. The patient achieved significant improvement solely through physical therapy and observation. DIAGNOSES: The patient was diagnosed with radial nerve palsy in the newborn combined with CRHD. INTERVENTIONS: The patient received regular physical therapy including joint function training, low-frequency pulse electrical therapy, acupuncture, paraffin treatment, as well as overnight splint immobilization. OUTCOMES: The child could actively extend the wrist to a neutral position and extend all fingers. LESSONS: If a neonate exhibits limited extension in the wrist and fingers, but normal flexion, along with normal shoulder and elbow movement, and is accompanied by skin lesions around the elbow, there should be a high suspicion of radial nerve palsy in the newborn.

PANet: Patch-Aware Network for Light Field Salient Object Detection.

Most existing light field saliency detection methods have achieved great success by exploiting unique light field data-focus information in focal slices. However, they process light field data in a slicewise way, leading to suboptimal results because the relative contribution of different regions in focal slices is ignored. How we can comprehensively explore and integrate focused saliency regions that would positively contribute to accurate saliency detection. Answering this question inspires us to develop a new insight. In this article, we propose a patch-aware network to explore light field data in a regionwise way. First, we excavate focused salient regions with a proposed multisource learning module (MSLM), which generates a filtering strategy for integration followed by three guidances based on saliency, boundary, and position. Second, we design a sharpness recognition module (SRM) to refine and update this strategy and perform feature integration. With our proposed MSLM and SRM, we can obtain more accurate and complete saliency maps. Comprehensive experiments on three benchmark datasets prove that our proposed method achieves competitive performance over 2-D, 3-D, and 4-D salient object detection methods. The code and results of our method are available at https://github.com/OIPLab-DUT/IEEE-TCYB-PANet.