Kawasaki disease with peritonsillar abscess as the first symptom: A case report.

BACKGROUND: Kawasaki disease (KD), also known as mucocutaneous lymph node syndrome, is an acute, self-limiting vasculitis of unknown aetiology that mainly involves the medium and small arteries and can lead to serious cardiovascular complications, with a 25% incidence of coronary artery aneurysms. Periton-Sillar abscesses are a rare symptom of KD and is easily misdiagnosed at its early stages. CASE SUMMARY: A 5-year-old boy who presented to a community hospital with a 3-d fever, difficulty in opening his mouth, and neck pain and was originally treated for throat infection without improvement. On the basis of laboratory tests, ultrasound of submandibular and superficial lymph nodes and computed tomography of the neck, the clinician diagnosed the periamygdala abscess and sepsis that did not resolve after antibiotic therapy. On the fifth day of admission, the child developed conjunctival congestion, prune tongue, perianal congestion and desquamation, and slightly stiff and swollen bunions on both feet. A diagnosis of KD was reached with complete remission after intravenous immunoglobulin treatment. CONCLUSION: Children with neck pain, lymph node enlargement, or airway obstruction as the main manifestations are poorly treated with intravenous broad-spectrum antibiotics. Clinicians should not rush invasive operations such as neck puncture, incision, and drainage and should be alert for KD when it cannot be explained by deep neck space infection and early treatment with aspirin combined with gammaglobulin.

Recursive-NeRF: An Efficient and Dynamically Growing NeRF.

View synthesis methods using implicit continuous shape representations learned from a set of images, such as the Neural Radiance Field (NeRF) method, have gained increasing attention due to their high quality imagery and scalability to high resolution. However, the heavy computation required by its volumetric approach prevents NeRF from being useful in practice; minutes are taken to render a single image of a few megapixels. Now, an image of a scene can be rendered in a level-of-detail manner, so we posit that a complicated region of the scene should be represented by a large neural network while a small neural network is capable of encoding a simple region, enabling a balance between efficiency and quality. Recursive-NeRF is our embodiment of this idea, providing an efficient and adaptive rendering and training approach for NeRF. The core of Recursive-NeRF learns uncertainties for query coordinates, representing the quality of the predicted color and volumetric intensity at each level. Only query coordinates with high uncertainties are forwarded to the next level to a bigger neural network with a more powerful representational capability. The final rendered image is a composition of results from neural networks of all levels. Our evaluation on public datasets and a large-scale scene dataset we collected shows that Recursive-NeRF is more efficient than NeRF while providing state-of-the-art quality. The code will be available at https://github.com/Gword/Recursive-NeRF.

Proteomic analysis of Citrus sinensis roots and leaves in response to long-term magnesium-deficiency.

BACKGROUND: Magnesium (Mg)-deficiency is frequently observed in Citrus plantations and is responsible for the loss of productivity and poor fruit quality. Knowledge on the effects of Mg-deficiency on upstream targets is scarce. Seedlings of 'Xuegan' [Citrus sinensis (L.) Osbeck] were irrigated with Mg-deficient (0 mM MgSO4) or Mg-sufficient (1 mM MgSO4) nutrient solution for 16 weeks. Thereafter, we first investigated the proteomic responses of C. sinensis roots and leaves to Mg-deficiency using two-dimensional electrophoresis (2-DE) in order to (a) enrich our understanding of the molecular mechanisms of plants to deal with Mg-deficiency and (b) understand the molecular mechanisms by which Mg-deficiency lead to a decrease in photosynthesis. RESULTS: Fifty-nine upregulated and 31 downregulated protein spots were isolated in Mg-deficient leaves, while only 19 upregulated and 12 downregulated protein spots in Mg-deficient roots. Many Mg-deficiency-responsive proteins were involved in carbohydrate and energy metabolism, followed by protein metabolism, stress responses, nucleic acid metabolism, cell wall and cytoskeleton metabolism, lipid metabolism and cell transport. The larger changes in leaf proteome versus root one in response to Mg-deficiency was further supported by our observation that total soluble protein concentration was decreased by Mg-deficiency in leaves, but unaffected in roots. Mg-deficiency had decreased levels of proteins [i.e. ribulose-1,5-bisphosphate carboxylase (Rubisco), rubisco activase, oxygen evolving enhancer protein 1, photosynthetic electron transfer-like protein, ferredoxin-NADP reductase (FNR), aldolase] involved in photosynthesis, thus decreasing leaf photosynthesis. To cope with Mg-deficiency, C. sinensis leaves and roots might respond adaptively to Mg-deficiency through: improving leaf respiration and lowering root respiration, but increasing (decreasing) the levels of proteins related to ATP synthase in roots (leaves); enhancing the levels of proteins involved in reactive oxygen species (ROS) scavenging and other stress-responsive proteins; accelerating proteolytic cleavage of proteins by proteases, protein transport and amino acid metabolism; and upregulating the levels of proteins involved in cell wall and cytoskeleton metabolism. CONCLUSIONS: Our results demonstrated that proteomics were more affected by long-term Mg-deficiency in leaves than in roots, and that the adaptive responses differed between roots and leaves when exposed to long-term Mg-deficiency. Mg-deficiency decreased the levels of many proteins involved in photosynthesis, thus decreasing leaf photosynthesis.