Spatial-Temporal Characteristics of Brain Activity in Autism Spectrum Disorder Based on Hidden Markov Model and Dynamic Graph Theory: A Resting-State fMRI Study

Autism spectrum disorder (ASD) is a common neurodevelopmental disorder. Functional magnetic resonance imaging (fMRI) can be used to measure the temporal correlation of blood-oxygen-level-dependent (BOLD) signals in the brain to assess the brain’s intrinsic connectivity and capture dynamic changes in the brain. In this study, the hidden Markov model (HMM) and dynamic graph (DG) theory are used to study the spatial-temporal characteristics and dynamics of brain networks based on dynamic functional connectivity (DFC). By using HMM, we identified three typical brain states for ASD and healthy control (HC). Furthermore, we explored the correlation between HMM time-varying properties and clinical autism scale scores. Differences in brain topological characteristics and dynamics between ASD and HC were compared by DG analysis. The experimental results indicate that ASD is more inclined to enter a strongly connected HMM brain state, leading to the isolation of brain networks and alterations in the topological characteristics of brain networks, such as default mode network (DMN), ventral attention network (VAN), and visual network (VN). This work suggests that using different data-driven methods based on DFC to study brain network dynamics would have better information complementarity, which can provide a new direction for the extraction of neuro-biomarkers in the early diagnosis of ASD

Multi-source coordinated stochastic restoration for SOP in distribution networks with a two-stage algorithm

After suffering from a grid blackout, distributed energy resources (DERs), such as local renewable energy and controllable distributed generators and energy storage can be used to restore loads enhancing the system’s resilience. In this study, a multi-source coordinated load restoration strategy was investigated for a distribution network with soft open points (SOPs). Here, the flexible regulation ability of the SOPs is fully utilized to improve the load restoration level while mitigating voltage deviations. Owing to the uncertainty, a scenario-based stochastic optimization approach was employed, and the load restoration problem was formulated as a mixed-integer nonlinear programming model. A computationally efficient solution algorithm was developed for the model using convex relaxation and linearization methods. The algorithm is organized into a two-stage structure, in which the energy storage system is dispatched in the first stage by solving a relaxed convex problem. In the second stage, an integer programming problem is calculated to acquire the outputs of both SOPs and power resources. A numerical test was conducted on both IEEE 33-bus and IEEE 123-bus systems to validate the effectiveness of the proposed strategy

Linear Dextrin as Potential Insulin Delivery System: Effect of Degree of Polymerization on the Physicochemical Properties of Linear Dextrin–Insulin Inclusion Complexes

In the current study, linear dextrin (LD) was prepared using waxy potato starch debranched with pullulanase, which has attracted immense interest in the food, pharmaceutical, and cosmetic industries as a versatile ingredient. Various LDs were separated on the basis of their differential solubility in aqueous/ethanol solutions of different volumetric ratios. Three LD products—LD Fabrications with 40% ethanol (F-40); LD Fabrications with 50% ethanol (F-50); and LD Fabrications with 60%, 70%, and 80% ethanol (F-M)—were obtained with an average degree of polymerization (DP) values of 31.44, 21.84, and 16.10, respectively. The results of Fourier transform infrared spectroscopy (FT-IR) analysis revealed that the reaction mainly involved hydrogen bonding and a hydrophobic interaction between LD and insulin in the process of inclusion complex formation. X-ray diffraction (XRD) results indicated that insulin was encapsulated in LD. The results of circular dichroism (CD) showed that the changes in the secondary structure of insulin were negligible during the release from the inclusion complexes. The order of encapsulation capacity is as follows: the complex composed of F-M and insulin (F-M-INS) > the complex composed of LD and insulin (LD-INS) > the complex composed of F-50 and insulin (F-50-INS) > and the complex composed of F-40 and insulin (F-40-INS). F-M-INS inclusion complexes showed a better effect on reducing the release of insulin in gastric juice and promoting the release of insulin in intestinal juice and blood plasma than LD-INS

An Optimized Strategy Based on Conventional Ultrasound for Diagnosing Metabolic Dysfunction-Associated Steatotic Liver Disease

The inherent drawbacks of the conventional B-mode ultrasound for metabolic dysfunction-associated steatotic liver disease (MASLD) are poorly understood. We aimed to investigate the impact factors and optimize the screening performance of ultrasound in MASLD. In a prospective pilot cohort recruited from July 2020 to January 2022, subjects who had undergone magnetic resonance imaging-based proton density fat fraction (MRI-PDFF), ultrasound, and laboratory test-based assessments were included in the deprivation cohort. A validation cohort including 426 patients with liver histologic assessments from five medical centers in South China was also recruited. A total of 1489 Chinese subjects were enrolled in the deprivation cohort, and ultrasound misdiagnosed 62.2% of the non-MASLD patients and failed to detect 6.1% of the MASLD patients. The number of metabolic dysfunction components and the alanine aminotransferase (ALT) level were associated with a missed diagnosis by ultrasound (OR = 0.67, 95% CI 0.55–0.82 p p = 0.003, respectively). Compared with ultrasound alone, the new strategy based on ultrasound, in combination with measurements of the number of metabolic dysfunction components and ALT and uric acid levels, significantly improved the AUROC both in the research cohort and the validation cohort (0.66 vs. 0.84, 0.83 vs. 0.92, respectively). The number of metabolic dysfunction components and ALT and uric acid levels improved the screening efficacy of ultrasound for MASLD

Restoring skeletal muscle mass as an independent determinant of liver fat deposition improvement in MAFLD

Abstract Aims Cross-sectional studies have demonstrated the association of skeletal muscle mass with metabolic-associated fatty liver disease (MAFLD), while longitudinal data are scarce. We aimed to explore the impact of changes in relative skeletal muscle mass on the MAFLD treatment response. Methods MAFLD patients undergoing magnetic resonance imaging-based proton density fat fraction for liver fat content (LFC) assessments and bioelectrical impedance analysis before and after treatment (orlistat, meal replacement, lifestyle modifications) were enrolled. Appendicular muscle mass (ASM) was adjusted by weight (ASM/W). Results Overall, 256 participants were recruited and divided into two groups: with an ASM/W increase (n=166) and without an ASM/W increase (n=90). There was a great reduction in LFC in the group with an ASM/W increase (16.9% versus 8.2%, P 0.05). △ASM/W Follow-up-Baseline [odds ratio (OR)=1.48, 95% confidence interval (CI) 1.05-2.07, P = 0.024] and △total fat mass (OR=1.45, 95% CI 1.12-1.87, P = 0.004) were independent predictors for steatosis improvement (relative reduction of LFC ≥ 30%). The subgroup analysis showed that, despite without weight loss, decrease in HOMA-IR (OR=6.21, 95% CI 1.28-30.13, P=0.023), △total fat mass Baseline -Follow-up (OR=3.48, 95% CI 1.95-6.21, P <0.001 and △ASM/W Follow-up-Baseline (OR=2.13, 95% CI 1.12-4.05, P=0.022) independently predicted steatosis improvement. Conclusions ASM/W increase and loss of total fat mass benefit the resolution of liver steatosis, independent of weight loss for MAFLD

Clinical Interpretability of Deep Learning for Predicting Microvascular Invasion in Hepatocellular Carcinoma by Using Attention Mechanism

Preoperative prediction of microvascular invasion (MVI) is essential for management decision in hepatocellular carcinoma (HCC). Deep learning-based prediction models of MVI are numerous but lack clinical interpretation due to their “black-box” nature. Consequently, we aimed to use an attention-guided feature fusion network, including intra- and inter-attention modules, to solve this problem. This retrospective study recruited 210 HCC patients who underwent gadoxetate-enhanced MRI examination before surgery. The MRIs on pre-contrast, arterial, portal, and hepatobiliary phases (hepatobiliary phase: HBP) were used to develop single-phase and multi-phase models. Attention weights provided by attention modules were used to obtain visual explanations of predictive decisions. The four-phase fusion model achieved the highest area under the curve (AUC) of 0.92 (95% CI: 0.84–1.00), and the other models proposed AUCs of 0.75–0.91. Attention heatmaps of collaborative-attention layers revealed that tumor margins in all phases and peritumoral areas in the arterial phase and HBP were salient regions for MVI prediction. Heatmaps of weights in fully connected layers showed that the HBP contributed the most to MVI prediction. Our study firstly implemented self-attention and collaborative-attention to reveal the relationship between deep features and MVI, improving the clinical interpretation of prediction models. The clinical interpretability offers radiologists and clinicians more confidence to apply deep learning models in clinical practice, helping HCC patients formulate personalized therapies