Coactivation pattern analysis reveals altered whole-brain functional transient dynamics in autism spectrum disorder.

Recent studies on autism spectrum disorder (ASD) have identified recurring states dominated by similar coactivation pattern (CAP) and revealed associations between dysfunction in seed-based large-scale brain networks and clinical symptoms. However, the presence of abnormalities in moment-to-moment whole-brain dynamics in ASD remains uncertain. In this study, we employed seed-free CAP analysis to identify transient brain activity configurations and investigate dynamic abnormalities in ASD. We utilized a substantial multisite resting-state fMRI dataset consisting of 354 individuals with ASD and 446 healthy controls (HCs, from HC groups and 2). CAP were generated from a subgroup of all HC subjects (HC group 1) through temporal K-means clustering, identifying four CAPs. These four CAPs exhibited either the activation or inhibition of the default mode network (DMN) and were grouped into two pairs with opposing spatial CAPs. CAPs for HC group 2 and ASD were identified by their spatial similarity to those for HC group 1. Compared with individuals in HC group 2, those with ASD spent more time in CAPs involving the ventral attention network but less time in CAPs related to executive control and the dorsal attention network. Support vector machine analysis demonstrated that the aberrant dynamic characteristics of CAPs achieved an accuracy of 74.87% in multisite classification. In addition, we used whole-brain dynamics to predict symptom severity in ASD. Our findings revealed whole-brain dynamic functional abnormalities in ASD from a single transient perspective, emphasizing the importance of the DMN in abnormal dynamic functional activity in ASD and suggesting that temporally dynamic techniques offer novel insights into time-varying neural processes.

Wearable dual-drug controlled release patch for psoriasis treatment.

Wearable drug delivery systems (DDS) have made significant advancements in the field of precision medicine, offering precise regulation of drug dosage, location, and timing. The performance qualities that wearable DDS has always strived for are simplicity, efficiency, and intelligence. This paper proposes a wearable dual-drug synergistic release patch. The patch is powered by a built-in magnesium battery and utilizes a hydrogel containing viologen-based hyperbranched polyamidoamine as both a cathode material and an integrated drug reservoir. This design allows for the simultaneous release of both dexamethasone and tannic acid, overcoming the limitations of monotherapy and ensuring effective synergy for on-demand therapy. In a mouse model with praziquimod-induced psoriasis, the patch demonstrated therapeutic efficacy at a low voltage. The inflammatory skin returned to normal after 5 days with the on-demand release of dual drugs. This work provides a promising treatment option considering its straightforward construction and the therapeutic advantages of dual-drug synergy.

Relationship among low baseline muscle mass, skeletal muscle quality, and mortality in critically ill children.

BACKGROUND: Studies in adults have shown that low baseline muscle mass at intensive care unit (ICU) admission was associated with poor clinical outcomes. However, no information on the relationship between baseline muscle quality or mass and clinical outcomes in critically ill children was found. METHODS: 3775 children were admitted to the pediatric ICU (PICU), 262 were eligible for inclusion. Abdominal computed tomography was performed to assess baseline skeletal muscle mass and quality. Patients were categorized to normal or low group based on the cutoff value for predicting hospital mortality of the skeletal muscle index (SMI; 30.96 cm2 /m2 ) and skeletal muscle density (SMD; 41.21 Hounsfield units). RESULTS: Body mass index (BMI) (18.07 ± 4.44 vs 15.99 ± 4.51) and BMI-for-age z score (0.46 [-0.66 to 1.74] vs -0.87 [-1.69 to 0.05]) were greater in the normal-SMI group, the length of PICU stay was longer in the low-SMI group (16.00 days [8.50-32.50] vs 13.00 days [7.50-20.00]), and the in-PICU mortality rate in the normal-SMI group (10.00%) was lower than the low-SMI group (22.6%). Children with low SMD had a higher in-PICU mortality rate (25.6% vs 7.7%), were younger (36.00 months [12.00-120.00] vs 84.00 months [47.50-147.50]) and weighed less (16.40 kg [10.93-37.25] vs 23.00 kg [16.00-45.00]). Mortality was greater in patients with lower SMD and prolonged hospital stay (log-rank, P = 0.007). SMD was an independent predictor for length of PICU stay and in-PICU mortality. CONCLUSIONS: Low baseline skeletal muscle quality in critically ill children is closely tied with a higher in-PICU mortality and longer PICU stay and is an independent risk factor for unfavorable clinical outcomes.

Automatic lumbar spinal MRI image segmentation with a multi-scale attention network.

Lumbar spinal stenosis (LSS) is a lumbar disease with a high incidence in recent years. Accurate segmentation of the vertebral body, lamina and dural sac is a key step in the diagnosis of LSS. This study presents an lumbar spine magnetic resonance imaging image segmentation method based on deep learning. In addition, we define the quantitative evaluation methods of two clinical indicators (that is the anteroposterior diameter of the spinal canal and the cross-sectional area of the dural sac) to assist LSS diagnosis. To improve the segmentation performance, a dual-branch multi-scale attention module is embedded into the network. It contains multi-scale feature extraction based on three 3 × 3 convolution operators and vital information selection based on attention mechanism. In the experiment, we used lumbar datasets from the spine surgery department of Shengjing Hospital of China Medical University to evaluate the effect of the method embedded the dual-branch multi-scale attention module. Compared with other state-of-the-art methods, the average dice similarity coefficient was improved from 0.9008 to 0.9252 and the average surface distance was decreased from 6.40 to 2.71 mm.