Case report: Aggressive progression of acute heart failure due to juvenile tuberculosis-associated Takayasu arteritis with aortic stenosis and thrombosis.

Background: Takayasu arteritis (TA) is a chronic granulomatous vasculitis with unknown pathophysiology. TA with severe aortic obstruction has a poor prognosis. However, the efficacy of biologics and appropriate timing of surgical intervention remain controversial. We report a case of tuberculosis (TB)-associated TA with aggressive acute heart failure (AHF), pulmonary hypertension (PH), thrombosis, and seizure, who failed to survive after surgery. Case presentation: A 10-year-old boy who developed a cough with chest tightness, shortness of breath, hemoptysis with reduced left ventricular ejection fraction, PH, and increased C-reactive protein and erythrocyte sedimentation rate was hospitalized at the pediatric intensive care unit of our hospital. He had strongly positive purified protein derivative skin test and interferon-gamma release assay result. Computed tomography angiography (CTA) showed occlusion of proximal left subclavian artery and stenosis of descending aorta and upper abdominal aorta. His condition did not improve after administration of milrinone, diuretics, antihypertensive agents, and intravenous methylprednisolone pulse followed by oral prednisone. Intravenous tocilizumab was administered for five doses, followed by two doses of infliximab, but his HF worsened, and CTA on day 77 showed complete occlusion of the descending aorta with large thrombus. He had a seizure on day 99 with deterioration of renal function. Balloon angioplasty and catheter-directed thrombolysis were performed on day 127. Unfortunately, the child's heart function continued to deteriorate and died on day 133. Conclusion: TB infection may be related to juvenile TA. The biologics, thrombolysis, and surgical intervention failed to achieve the anticipated effect in our case with aggressive AHF due to severe aortic stenosis and thrombosis. More studies are needed to determine the role of biologics and surgery in such dire cases.

Short-term effectiveness of baricitinib in children with refractory and/or severe juvenile dermatomyositis.

Objective: To determine the short-term effectiveness safety of baricitinib in children with refractory and/or severe juvenile dermatomyositis (rsJDM) in a real-world setting. Methods: This was a single-center retrospective study, including 20 children with rsJDM. They were all treated using baricitinib combined with steroids and other immunosuppressive agents. The childhood myositis assessment scale (CMAS) and PRINTO remission criteria were used to evaluate the disease severity and treatment outcome at 0, 4, 12, and 24 weeks after initiation of baricitinib. Results: The skin rash improved in 95% of patients (19/20) at week 24, with a significant decrease of skin-DAS at weeks 12 (6.0 vs. 2.0, p < 0.05] and week 24 [6.0 vs. 1.0, p < 0.05) by median statistics. The CMAS score increased significantly at week 12 (41.0 [29.0, 44.0] vs. 46.0 [42.0, 52.0], p < 0.05) and week 24 (41.0 [29.0, 44.0] vs. 50.0 [45.0, 52.0], p < 0.05), as did the manual muscle testing (MMT)-8 score at week 24 (73.0 [610, 76.0] vs. 79.0 [77.0, 80.0], p < 0.05). At 24 weeks, the complete response (CR) and partial response (PR) were achieved in 75% (15/20) and 15% (3/20), respectively. The dose of corticosteroids (CS) decreased by 37% from the baseline (0.53 [0.42, 1.00] mg/kg) to week 12 (0.33 [0.18, 0.40] mg/kg) (p < 0.05), and by 49% at week 24 (p < 0.05). No serious side effects were observed. Conclusion: Baricitinib combined with traditional immunosuppressants treatment was efficacious in rsJDM. Add-on therapy of baricitinib was helpful for tapering CS dose. No serious side effects were observed in this study.

FCL-Net: Towards accurate edge detection via Fine-scale Corrective Learning.

Integrating multi-scale predictions has become a mainstream paradigm in edge detection. However, most existing methods mainly focus on effective feature extraction and multi-scale feature fusion while ignoring the low learning capacity in fine-level branches, limiting the overall fusion performance. In light of this, we propose a novel Fine-scale Corrective Learning Net (FCL-Net) that exploits semantic information from deep layers to facilitate fine-scale feature learning. FCL-Net mainly consists of a Top-down Attentional Guiding (TAG) and a Pixel-level Weighting (PW) module. TAG module adopts semantic attentional cues from coarse-scale prediction into guiding the fine-scale branches by learning a top-down LSTM. PW module treats the contribution of each spatial location independently and promote fine-level branches to detect detailed edges with high confidence. Experiments on three benchmark datasets, i.e., BSDS500, Multicue, and BIPED, show that our approach significantly outperforms the baseline and achieves a competitive ODS F-measure of 0.826 on the BSDS500 benchmark. The source code and models are publicly available at https://github.com/DREAMXFAR/FCL-Net.