Discrepancies in ASPECTS obtained by artificial intelligence and experts: Associated factors and prognostic implications.

PURPOSE: The differences between the Alberta Stroke Program Early CT Score (ASPECTS) obtained by experts and artificial intelligence (AI) software require elucidation. We aimed to characterize the discrepancies between the ASPECTS obtained by AI and experts and determine the associated factors and prognostic implications. METHODS: This multicenter, retrospective, observational cohort study included patients showing acute ischemic stroke caused by large-vessel occlusion in the anterior circulation. ASPECTS was determined by AI software (RAPID ASPECTS) and experts from the core laboratory. Interclass correlation coefficients (ICCs) and Bland-Altman plots were used to illustrate the consistency and discrepancies; logistic regression analyses were used to assess the correlates of inconsistency; and receiver operating characteristic analyses were performed to assess the diagnostic performance for predicting unfavorable clinical outcomes. RESULTS: The study population included 491 patients. The ICC for the expert and AI ASPECTS was 0.63 (95 % confidence interval [CI]: 0.25-0.79).The mean difference between expert and AI ASPECTS was 2.24. Chronic infarcts (odds ratio [OR], 1.9; 95 % CI, 1.1-3.4; P=0.021) and expert scores in the internal capsule (OR, 2.9; 95 % CI, 1.1-7.7; P=0.034) and lentiform (OR, 2.4; 95 % CI, 1.3-4.7; P=0.008) were significant correlates of inconsistency. The ASPECTS obtained by AI showed a significantly higher area under the curve for unfavorable outcomes (0.68 vs. 0.63, P=0.04). CONCLUSIONS: In comparison with expert ASPECTS, AI ASPECTS overestimated the infarct extent. Future studies should aim to determine whether AI ASPECTS assessments should use a lower threshold to screen patients for endovascular therapy.

Chemoarchitectural signatures of subcortical shape alterations in generalized epilepsy.

Genetic generalized epilepsies (GGE) exhibit widespread morphometric alterations in the subcortical structures. Subcortical structures are essential for understanding GGE pathophysiology, but their fine-grained morphological diversity has yet to be comprehensively investigated. Furthermore, the relationships between macroscale morphological disturbances and microscale molecular chemoarchitectures are unclear. High-resolution structural images were acquired from patients with GGE (n = 97) and sex- and age-matched healthy controls (HCs, n = 184). Individual measurements of surface shape features (thickness and surface area) of seven bilateral subcortical structures were quantified. The patients and HCs were then compared vertex-wise, and shape anomalies were co-located with brain neurotransmitter profiles. We found widespread morphological alterations in GGE and prominent disruptions in the thalamus, putamen, and hippocampus. Shape area dilations were observed in the bilateral ventral, medial, and right dorsal thalamus, as well as the bilateral lateral putamen. We found that the shape area deviation pattern was spatially correlated with the norepinephrine transporter and nicotinic acetylcholine (Ach) receptor (α4ß2) profiles, but a distinct association was seen in the muscarinic Ach receptor (M1). The findings provided a comprehensive picture of subcortical morphological disruptions in GGE, and further characterized the associated molecular mechanisms. This information may increase our understanding of the pathophysiology of GGE.

Precise Identification of Glioblastoma Micro-Infiltration at Cellular Resolution by Raman Spectroscopy.

Precise identification of glioblastoma (GBM) microinfiltration, which is essential for achieving complete resection, remains an enormous challenge in clinical practice. Here, the study demonstrates that Raman spectroscopy effectively identifies GBM microinfiltration with cellular resolution in clinical specimens. The spectral differences between infiltrative lesions and normal brain tissues are attributed to phospholipids, nucleic acids, amino acids, and unsaturated fatty acids. These biochemical metabolites identified by Raman spectroscopy are further confirmed by spatial metabolomics. Based on differential spectra, Raman imaging resolves important morphological information relevant to GBM lesions in a label-free manner. The area under the receiver operating characteristic curve (AUC) for Raman spectroscopy combined with machine learning in detecting infiltrative lesions exceeds 95%. Most importantly, the cancer cell threshold identified by Raman spectroscopy is as low as 3 human GBM cells per 0.01 mm2. Raman spectroscopy enables the detection of previously undetectable diffusely infiltrative cancer cells, which holds potential value in guiding complete tumor resection in GBM patients.

Unifying emotion-oriented and cause-oriented predictions for emotion-cause pair extraction.

Emotion-cause pair extraction (ECPE) is an extraction task aiming to simultaneously identify the emotions and causes from the text without emotion annotations. Let ci and cj represent the emotion clause and the cause clause of a document, respectively, and we can predict one from the other and vice versa. Previous works fail to take advantage of this bidirectional opportunity. We refer to the prediction from ci to cj, i.e., ci→cj, as an emotion-oriented cause prediction (EoCP) task and the prediction from cj to ci, i.e., cj→ci, as a cause-oriented emotion prediction (CoEP) task. After redefining the ECPE task, we propose a novel unified architecture for ECPE, which incorporates EoCP and CoEP as cells and unifies them into a single-chain architecture. Additionally, we redefine emotion-cause pair extraction as a closed-loop structure detection problem to alleviate the mismatch between emotion and cause clauses. To enhance the training of the architecture, we provide a procedure for estimating the confidence of the extraction system for its emotion-cause pairs. We demonstrate the superiority of our proposed model through extensive experiments on two public datasets, achieving a new state-of-the-art performance. Furthermore, our method particularly achieves significant improvements in multiple emotion-cause pair extraction.

Remarkable flexibility in freestanding single-crystalline antiferroelectric PbZrO3 membranes.

The ultrahigh flexibility and elasticity achieved in freestanding single-crystalline ferroelectric oxide membranes have attracted much attention recently. However, for antiferroelectric oxides, the flexibility limit and fundamental mechanism in their freestanding membranes are still not explored clearly. Here, we successfully fabricate freestanding single-crystalline PbZrO3 membranes by a water-soluble sacrificial layer technique. They exhibit good antiferroelectricity and have a commensurate/incommensurate modulated microstructure. Moreover, they also have good shape recoverability when bending with a small radius of curvature (about 2.4 µm for the thickness of 120 nm), corresponding to a bending strain of 2.5%. They could tolerate a maximum bending strain as large as 3.5%, far beyond their bulk counterpart. Our atomistic simulations reveal that this remarkable flexibility originates from the antiferroelectric-ferroelectric phase transition with the aid of polarization rotation. This study not only suggests the mechanism of antiferroelectric oxides to achieve high flexibility but also paves the way for potential applications in flexible electronics.

Structural and functional disruption of subcortical limbic structures related with executive function in pediatric bipolar disorder.

BACKGROUND: Impaired cognition has been demonstrated in pediatric bipolar disorder (PBD). The subcortical limbic structures play a key role in PBD. However, alternations of anatomical and functional characteristics of subcortical limbic structures and their relationship with neurocognition of PBD remain unclear. METHODS: Thirty-six PBD type I (PBD-I) (15.36 ± 0.32 years old), twenty PBD type II (PBD-II) (14.80 ± 0.32 years old) and nineteen age-gender matched healthy controls (HCs) (14.16 ± 0.36 years old) were enlisted. Primarily, the volumes of the subcortical limbic structures were obtained and differences in the volumes were evaluated. Then, these structures served as seeds of regions of interest to calculate the voxel-wised functional connectivity (FC). After that, correlation analysis was completed between volumes and FC of brain regions showing significant differences and neuropsychological tests. RESULTS: Compared to HCs, both PBD-I and PBD-II patients showed a decrease in the Stroop color word test (SCWT) and digit span backward test scores. Compared with HCs, PBD-II patients exhibited a significantly increased volume of right septal nuclei, and PBD-I patients presented increased FC of right nucleus accumbens and bilateral pallidum, of right basal forebrain with right putamen and left pallidum. Both the significantly altered volumes and FC were negatively correlated with SCWT scores. SIGNIFICANCE: The study revealed the role of subcortical limbic structural and functional abnormalities on cognitive impairments in PBD patients. These may have far-reaching significance for the etiology of PBD and provide neuroimaging clues for the differential diagnosis of PBD subtypes. CONCLUSIONS: Distinctive features of neural structure and function in PBD subtypes may contribute to better comprehending the potential mechanisms of PBD.

Tannic acid-poloxamer self-assembled nanoparticles for advanced atherosclerosis therapy by regulation of macrophage polarization.

Atherosclerosis (AS) is a significant contributor to cardiovascular events. Advanced AS is particularly concerning, as it leads to the formation of high-risk vulnerable plaques. Current treatments for AS focus on antithrombotic and lipid-lowering interventions, which are effective in treating early-stage AS. Recent studies have shown that macrophage polarization plays a crucial role in the development of AS. This study presents a new biomedical application of natural tannic acid as an anti-inflammatory nanoplatform for advanced AS. Tannic acid-poloxamer nanoparticles (TPNP) are fabricated through self-assembly of tannic acid (TA) and poloxamer. TPNP has the potential to provide effective treatment for advanced AS. According to in vitro studies, TPNP has been found to suppress the inflammatory response in lipopolysaccharide-stimulated macrophages by scavenging reactive oxygen species (ROS), downregulating the expression levels of inflammatory cytokines (such as interleukin-10 and tumor necrosis factor-α) and regulating polarization of macrophages. In vivo studies further reveal that TPNP can retard the development of advanced atherosclerotic plaques by reducing ROS production and promoting M2 macrophage polarization in the aorta of ApoE-/- mice. Overall, these findings suggest that TPNP could be used to develop natural multifunctional nanoplatforms for molecular therapy of AS and other inflammation-related diseases.

Diagnostic and prognostic performance of artificial intelligence-based fully-automated on-site CT-FFR in patients with CAD.

Currently, clinically available coronary CT angiography (CCTA) derived fractional flow reserve (CT-FFR) is time-consuming and complex. We propose a novel artificial intelligence-based fully-automated, on-site CT-FFR technology, which combines the automated coronary plaque segmentation and luminal extraction model with reduced order 3 dimentional (3D) computational fluid dynamics. A total of 463 consecutive patients with 600 vessels from the updated China CT-FFR study in Cohort 1 undergoing both CCTA and invasive fractional flow reserve (FFR) within 90 d were collected for diagnostic performance evaluation. For Cohort 2, a total of 901 chronic coronary syndromes patients with index CT-FFR and clinical outcomes at 3-year follow-up were retrospectively analyzed. In Cohort 3, the association between index CT-FFR from triple-rule-out CTA and major adverse cardiac events in patients with acute chest pain from the emergency department was further evaluated. The diagnostic accuracy of this CT-FFR in Cohort 1 was 0.82 with an area under the curve of 0.82 on a per-patient level. Compared with the manually dependent CT-FFR techniques, the operation time of this technique was substantially shortened by 3 times and the number of clicks from about 60 to 1. This CT-FFR technique has a highly successful (> 99%) calculation rate and also provides superior prediction value for major adverse cardiac events than CCTA alone both in patients with chronic coronary syndromes and acute chest pain. Thus, the novel artificial intelligence-based fully automated, on-site CT-FFR technique can function as an objective and convenient tool for coronary stenosis functional evaluation in the real-world clinical setting.

Evidence of the Monopolar-Dipolar Crossover Regime: A Multiscale Study of Ferroelastic Domains by In Situ Microscopy Techniques.

The elastic interaction between kinks (and antikinks) within domain walls plays a pivotal role in shaping the domain structure, and their dynamics. In bulk materials, kinks interact as elastic monopoles, dependent on the distance between walls (d-1) and typically characterized by a rigid and straight domain configuration. In this work the evolution of the domain structure is investigated, as the sample size decreases, by the means of in situ heating microscopy techniques on free-standing samples. As the sample size decreases, a significant transformation is observed: domain walls exhibit pronounced curvature, accompanied by an increase in both domain wall and junction density. This transformation is attributed to the pronounced influence of kinks, inducing sample warping, where "dipole-dipole" interactions are dominant (d-2). Moreover, a critical thickness range that delineates a crossover between the monopolar and dipolar regimens is experimentally identified and corroborated by atomic simulations. These findings are relevant for in situ TEM studies and for the development of novel devices based on free-standing ferroic thin films and nanomaterials.

Focus Affinity Perception and Super-Resolution Embedding for Multifocus Image Fusion.

Despite the fact that there is a remarkable achievement on multifocus image fusion, most of the existing methods only generate a low-resolution image if the given source images suffer from low resolution. Obviously, a naive strategy is to independently conduct image fusion and image super-resolution. However, this two-step approach would inevitably introduce and enlarge artifacts in the final result if the result from the first step meets artifacts. To address this problem, in this article, we propose a novel method to simultaneously achieve image fusion and super-resolution in one framework, avoiding step-by-step processing of fusion and super-resolution. Since a small receptive field can discriminate the focusing characteristics of pixels in detailed regions, while a large receptive field is more robust to pixels in smooth regions, a subnetwork is first proposed to compute the affinity of features under different types of receptive fields, efficiently increasing the discriminability of focused pixels. Simultaneously, in order to prevent from distortion, a gradient embedding-based super-resolution subnetwork is also proposed, in which the features from the shallow layer, the deep layer, and the gradient map are jointly taken into account, allowing us to get an upsampled image with high resolution. Compared with the existing methods, which implemented fusion and super-resolution independently, our proposed method directly achieves these two tasks in a parallel way, avoiding artifacts caused by the inferior output of image fusion or super-resolution. Experiments conducted on the real-world dataset substantiate the superiority of our proposed method compared with state of the arts.