Advances in laboratory diagnosis of Sjogren's disease in children.

Sjogren's disease (SjD) in children is a rare chronic autoimmune disease not fully recognized due to clinical manifestations different from adults. As such, new objective indicators are needed to supplement existing markers and assist in diagnosis. This review summarizes pathogenesis of SjD in children, current diagnostic criteria and research progress in laboratory diagnosis including serologic testing, saliva and tear analysis, histopathological examination as well as emerging markers of interest.

Elucidating thoracic aortic dissection pathogenesis: The interplay of m1A-related gene expressions and miR-16-5p/YTHDC1 Axis in NLRP3-dependent pyroptosis.

The underlying molecular mechanisms of thoracic aortic dissection (TAD) remain incompletely understood. Recent insights into RNA methylation and microRNA-mediated gene regulation offer new avenues for exploring how these processes contribute to the pathophysiology of TAD, particularly through the modulation of pyroptosis and smooth muscle cell viability. This research aimed to elucidate the interplay of m1A-related gene expressions and miR-16-5p/YTHDC1 Axis in NLRP3-dependent pyroptosis, a mechanism implicated in the pathogenesis of TAD.

Social Determinants of Cardiovascular Risk Factors Among Asian American Subgroups.

Background: Social determinants of health (SDOH) play a significant role in the development of cardiovascular risk factors. We investigated SDOH associations with cardiovascular risk factors among Asian American subgroups.

Methods And Results: We utilized the National Health Interview Survey, a nationally representative survey of US adults, years 2013 to 2018.

Association of Acculturation with Cardiovascular Risk Factors in Asian-American Subgroups.

Objective: This cross-sectional study aims to better understand the heterogeneous associations of acculturation level on CV risk factors among disaggregated Asian subgroups. We hypothesize that the association between acculturation level and CV risk factors will differ significantly by Asian subgroup.

Methods: We used the National Health Interview Survey (NHIS), a nationally representative US survey, years 2014-18.

Automated Machine Learning Segmentation and Measurement of Urinary Stones on CT Scan.

Objectives: To evaluate the performance of an engineered machine learning algorithm to identify kidney stones and measure stone characteristics without the need for human input.

Methods: We performed a cross-sectional study of 94 children and adults who had kidney stones identified on non-contrast CT. A previously developed deep learning algorithm was trained to segment renal anatomy and kidney stones and to measure stone features.

Can virtual events achieve co-benefits for climate, participation, and satisfaction? Comparative evidence from five international Agriculture, Nutrition and Health Academy Week conferences.

The advancement of science and evidence-based solutions for planetary health increasingly require interdisciplinary and international learning and sharing. Yet aviation travel to academic conferences is carbon-intensive and expensive, thus perpetuating planetary health and equity challenges. Using data from five annual international Agriculture, Nutrition and Health Academy Week conferences from 2016 to 2020, we explore whether moving to virtual conferencing produced co-benefits for climate, participation, attendee interaction, and satisfaction.

Adaptive convolutional neural networks for accelerating magnetic resonance imaging via k-space data interpolation.

Deep learning in k-space has demonstrated great potential for image reconstruction from undersampled k-space data in fast magnetic resonance imaging (MRI). However, existing deep learning-based image reconstruction methods typically apply weight-sharing convolutional neural networks (CNNs) to k-space data without taking into consideration the k-space data's spatial frequency properties, leading to ineffective learning of the image reconstruction models. Moreover, complementary information of spatially adjacent slices is often ignored in existing deep learning methods.

Fabrication of 3D-Printed Ceramic Structures for Portable Solar Desalination Devices.

This paper proposes the fabrication process of the first fully 3D-printed ceramic core structures for portable solar desalination devices optimized to tackle water scarcity from an energy and sustainability perspective. Robocasting, a 3D printing technique, is utilized to fabricate a fully ceramic structure of an integrated solar absorber/thermal insulator/water transporter based on the two-layered structure of modified graphene on silica (MG@Silica) and the porous silica structure. Robocasting has demonstrated its flexibility in tailoring structural designs, combining nanopores and microchannels that exhibit uniform water transport delivery and thermal insulation.

Efficacy of alanyl glutamine in nutritional support therapy for patients with sepsis: A protocol for systematic review and meta-analysis.

Background: Sepsis is a systemic inflammatory response caused by infection, which is a common complication after severe infection, trauma, shock, and surgery, and is also an important factor in inducing septic shock and multiple organ dysfunction syndrome (MODS), and has become one of the important causes of death in critically ill patients. Septic patients with gastrointestinal transport function weakened, are prone to malnutrition, resulting in decreased immune function, thereby affecting the therapeutic effect. Clinical practice shows that the nutritional metabolism and immune response of patients with sepsis can be effectively improved by giving alanyl glutamine nutritional support treatment, but there is no evidence of evidence-based medicine.

ACEnet: Anatomical context-encoding network for neuroanatomy segmentation.

Segmentation of brain structures from magnetic resonance (MR) scans plays an important role in the quantification of brain morphology. Since 3D deep learning models suffer from high computational cost, 2D deep learning methods are favored for their computational efficiency. However, existing 2D deep learning methods are not equipped to effectively capture 3D spatial contextual information that is needed to achieve accurate brain structure segmentation.

DeepSEED: 3D Squeeze-and-Excitation Encoder-Decoder Convolutional Neural Networks for Pulmonary Nodule Detection.

Pulmonary nodule detection plays an important role in lung cancer screening with low-dose computed tomography (CT) scans. It remains challenging to build nodule detection deep learning models with good generalization performance due to unbalanced positive and negative samples. In order to overcome this problem and further improve state-of-the-art nodule detection methods, we develop a novel deep 3D convolutional neural network with an Encoder-Decoder structure in conjunction with a region proposal network.

Adaptive Sparsity Regularization Based Collaborative Clustering for Cancer Prognosis.

Radiomic approaches have achieved promising performance in prediction of clinical outcomes of cancer patients. Particularly, feature dimensionality reduction plays an important role in radiomic studies. However, conventional feature dimensionality reduction techniques are not equipped to suppress data noise or utilize latent supervision information of patient data under study ( difference in patients) for learning discriminative low dimensional representations.

Robust Collaborative Clustering of Subjects and Radiomic Features for Cancer Prognosis.

Feature dimensionality reduction plays an important role in radiomic studies with a large number of features. However, conventional radiomic approaches may suffer from noise, and feature dimensionality reduction techniques are not equipped to utilize latent supervision information of patient data under study, such as differences in patients, to learn discriminative low dimensional representations. To achieve robustness to noise and feature dimensionality reduction with improved discriminative power, we develop a robust collaborative clustering method to simultaneously cluster patients and radiomic features into distinct groups respectively under adaptive sparse regularization.

SLICR super-voxel algorithm for fast, robust quantification of myocardial blood flow by dynamic computed tomography myocardial perfusion imaging.

We created and evaluated a processing method for dynamic computed tomography myocardial perfusion imaging (CT-MPI) of myocardial blood flow (MBF), which combines a modified simple linear iterative clustering algorithm (SLIC) with robust perfusion quantification, hence the name SLICR. SLICR adaptively segments the myocardium into nonuniform super-voxels with similar perfusion time attenuation curves (TACs). Within each super-voxel, an α-trimmed-median TAC was computed to robustly represent the super-voxel and a robust physiological model (RPM) was implemented to semi-analytically estimate MBF.

A facile oxidation-dehydration reaction-driven robust porous copper oxide nanobelt coating on copper foam for an energy-saving and low-cost urea oxidization reaction.

We fabricated a robust porous copper oxide nanobelt coating on copper foam by a facile oxidation-dehydration reaction, which is firstly reported as a low-cost pure copper-based urea oxidization catalyst. This catalyst has enriched electrochemically active surface area, abudant nanopores and micropores for gas and electrolyte diffusion, and high conductivity from copper foam for electron transfer and herein shows superior UOR performance, outperforming noble metal catalysts or most of the as-reported nonprecious metal UOR catalysts especially at high current density.

Room temperature thiosulfate ion redox reaction-driven synthesis of a robust porous copper-cobalt-sulfur-oxygen nanowire coating on copper foam for highly-efficient and low-cost oxygen evolution reaction.

A robust porous copper-cobalt-sulfur-oxygen nanowire coating (Cu-Co-S-O NWC) was fabricated for the first time on copper foam using a mild thiosulfate ion redox reaction-driven chemical bath synthesis (CBS) strategy. Cu-Co-S-O NWC has a large ECAS, enriched tunnels for gas and electrolyte diffusion and good electron transfer performance from the highly conductive copper foam substrate, and herein, shows improved overall OER activity, outperforming the precious IrO2 catalyst and almost all the as-reported Cu-based or Co-based catalysts, especially at high current density.

Anti-aging effects exerted by Tetramethylpyrazine enhances self-renewal and neuronal differentiation of rat bMSCs by suppressing NF-kB signaling.

In order to improve the therapeutic effects of mesenchymal stem cell (MSC)-based therapies for a number of intractable neurological disorders, a more favorable strategy to regulate the outcome of bone marrow MSCs (bMSCs) was examined in the present study. In view of the wide range of neurotrophic and neuroprotective effects, Tetramethylpyrazine (TMP), a biologically active alkaloid isolated from the herbal medicine , was used. It was revealed that treatment with 30-50 mg/l TMP for 4 days significantly increased cell viability, alleviated senescence by suppressing NF-κB signaling, and promoted bMSC proliferation by regulating the cell cycle.

Robust and superwetting island-shaped phytate bimetallic oxyhydroxide porous nanoclusters via a mild self-assembly-etching-catching-electrochemical oxidization strategy for an enhanced oxygen evolution reaction.

Robust and superwetting island-shaped phytate bimetallic oxyhydroxide (PBMO) porous nanoclusters were fabricated by a mild self-assembly-etching-catching-electrochemical oxidization strategy, which show enhanced water oxidation catalytic activity, outperforming the benchmark noble metal IrO2 catalyst and most of the organic metal or NiFe-based catalysts especially at high current density.

The role of acquisition and quantification methods in myocardial blood flow estimability for myocardial perfusion imaging CT.

In this work, we clarified the role of acquisition parameters and quantification methods in myocardial blood flow (MBF) estimability for myocardial perfusion imaging using CT (MPI-CT). We used a physiologic model with a CT simulator to generate time-attenuation curves across a range of imaging conditions, i.e.

SLIC robust (SLICR) processing for fast, robust CT myocardial blood flow quantification.

There are several computational methods for estimating myocardial blood flow (MBF) using CT myocardial perfusion imaging (CT-MPI). Previous work has shown that model-based deconvolution methods are more accurate and precise than model-independent methods such as singular value decomposition and max-upslope. However, iterative optimization is computationally expensive and models are sensitive to image noise, thus limiting the utility of low x-ray dose acquisitions.

[The application of popliteal vein anatomy study and valve construction procedure].

Objective: To analyze anatomy data of popliteal veins (PV), with the purpose of selection of popliteal venous valves construction segment via venography, and to evaluate the surgical results.

Methods: From February 1998 to November 2010, after analyzing the popliteal vessel anatomy data of 39 limbs and related phlebography research of 862 cases, 102 patients (69 male and 33 female patients, aged from 48 to 71 years, mean 59 years) with severe deep venous insufficiency were selected for popliteal venous valve construction procedures. Doppler ultrasound, continuous dynamic venography, and intraoperative venous pressure measurements were used to assess the hemodynamic changes pre- and postoperatively.

[Treatment of long-segment iliofemoral occlusive disease: two extra-anatomic bypass surgeries].

Objective: To report and evaluate the clinical results of surgical treatment for long-segment iliofemoral arteriosclerosis obliterans, including external iliac-popliteal (EIP) and femoral-deep femoral (FDF) crossover bypass surgeries.

Methods: From July 1995 to December 2009, 85 patients (61 male, 24 female, aged from 64 to 91 years, mean age 75 years) with comprehensive unilateral iliac-superficial femoral arteriosclerosis obliterans were involved in this research. According to Fontaine classification, the 85 patients could be graded as 62 class IIb-III patients (72.