Altered intra- and inter-network connectivity in autism spectrum disorder.

OBJECTIVE: A neurodevelopmental illness termed as the autism spectrum disorder (ASD) is described by social interaction impairments. Previous studies employing resting-state functional imaging (rs-fMRI) identified both hyperconnectivity and hypoconnectivity patterns in ASD people. However, specific patterns of connectivity within and between networks linked to ASD remain largely unexplored. METHODS: We utilized a meticulously selected subset of high-quality data, comprising 45 individuals diagnosed with ASD and 47 HCs, obtained from the ABIDE dataset. The pre-processed rs-fMRI time series signals were partitioned into ninety regions of interest. We focused on eight intrinsic connectivity networks and further performed intra- and inter-network analysis. Finally, support vector machine was used to discriminate ASD from HC. RESULTS: Through different sparsities, ASD exhibited significantly decreased intra-network connectivity within default mode network and dorsal attention network, increased connectivity between limbic network and subcortical network, and decreased connectivity between default mode network and limbic network. Using the classifier trained on altered intra- and inter-network connectivity, multivariate pattern analyses classified the ASD from HC with 71.74% accuracy, 70.21% specificity and 75.56% sensitivity in 10% sparsity of functional connectivity. CONCLUSIONS: ASD showed characteristic reorganization of the brain networks and this provided new insight into the underlying process of the functional connectome dysfunction in ASD.

Multipattern graph convolutional network-based autism spectrum disorder identification.

The early diagnosis of autism spectrum disorder (ASD) has been extensively facilitated through the utilization of resting-state fMRI (rs-fMRI). With rs-fMRI, the functional brain network (FBN) has gained much attention in diagnosing ASD. As a promising strategy, graph convolutional networks (GCN) provide an attractive approach to simultaneously extract FBN features and facilitate ASD identification, thus replacing the manual feature extraction from FBN. Previous GCN studies primarily emphasized the exploration of topological simultaneously connection weights of the estimated FBNs while only focusing on the single connection pattern. However, this approach fails to exploit the potential complementary information offered by different connection patterns of FBNs, thereby inherently limiting the performance. To enhance the diagnostic performance, we propose a multipattern graph convolution network (MPGCN) that integrates multiple connection patterns to improve the accuracy of ASD diagnosis. As an initial endeavor, we endeavored to integrate information from multiple connection patterns by incorporating multiple graph convolution modules. The effectiveness of the MPGCN approach is evaluated by analyzing rs-fMRI scans from a cohort of 92 subjects sourced from the publicly accessible Autism Brain Imaging Data Exchange database. Notably, the experiment demonstrates that our model achieves an accuracy of 91.1% and an area under ROC curve score of 0.9742. The implementation codes are available at https://github.com/immutableJackz/MPGCN.

Towards an accurate autism spectrum disorder diagnosis: multiple connectome views from fMRI data.

Functional connectome has revealed remarkable potential in the diagnosis of neurological disorders, e.g. autism spectrum disorder. However, existing studies have primarily focused on a single connectivity pattern, such as full correlation, partial correlation, or causality. Such an approach fails in discovering the potential complementary topology information of FCNs at different connection patterns, resulting in lower diagnostic performance. Consequently, toward an accurate autism spectrum disorder diagnosis, a straightforward ambition is to combine the multiple connectivity patterns for the diagnosis of neurological disorders. To this end, we conduct functional magnetic resonance imaging data to construct multiple brain networks with different connectivity patterns and employ kernel combination techniques to fuse information from different brain connectivity patterns for autism diagnosis. To verify the effectiveness of our approach, we assess the performance of the proposed method on the Autism Brain Imaging Data Exchange dataset for diagnosing autism spectrum disorder. The experimental findings demonstrate that our method achieves precise autism spectrum disorder diagnosis with exceptional accuracy (91.30%), sensitivity (91.48%), and specificity (91.11%).

Few-shot segmentation with duplex network and attention augmented module.

Establishing the relationship between a limited number of samples and segmented objects in diverse scenarios is the primary challenge in few-shot segmentation. However, many previous works overlooked the crucial support-query set interaction and the deeper information that needs to be explored. This oversight can lead to model failure when confronted with complex scenarios, such as ambiguous boundaries. To solve this problem, a duplex network that utilizes the suppression and focus concept is proposed to effectively suppress the background and focus on the foreground. Our network includes dynamic convolution to enhance the support-query interaction and a prototype match structure to fully extract information from support and query. The proposed model is called dynamic prototype mixture convolutional networks (DPMC). To minimize the impact of redundant information, we have incorporated a hybrid attentional module called double-layer attention augmented convolutional module (DAAConv) into DPMC. This module enables the network to concentrate more on foreground information. Our experiments on PASCAL-5i and COCO-20i datasets suggested that DPMC and DAAConv outperform traditional prototype-based methods by up to 5-8% on average.

Specific Generation of Singlet Oxygen through the Russell Mechanism in Hypoxic Tumors and GSH Depletion by Cu-TCPP Nanosheets for Cancer Therapy.

The generation of singlet oxygen (1 O2 ) during photodynamic therapy is limited by the precise cooperation of light, photosensitizer, and oxygen, and the therapeutic efficiency is restricted by the elevated glutathione (GSH) levels in cancer cells. Herein, we report that an ultrathin two-dimensional metal-organic framework of Cu-TCPP nanosheets (TCPP=tetrakis(4-carboxyphenyl)porphyrin) can selectively generate 1 O2 in a tumor microenvironment. This process is based on the peroxidation of the TCPP ligand by acidic H2 O2 followed by reduction to peroxyl radicals under the action of the peroxidase-like nanosheets and Cu2+ , and their spontaneous recombination reaction by the Russell mechanism. In addition, the nanosheets can also deplete GSH. Consequently, the Cu-TCPP nanosheets can selectively destroy tumor cells with high efficiency, constituting an attractive way to overcome current limitations of photodynamic therapy.

Methylene Blue Loaded Cu-Tryptone Complex Nanoparticles: A New Glutathione-Reduced Enhanced Photodynamic Therapy Nanoplatform.

The concentration of intracellular reactive oxygen species directly determines the effect of photodynamic therapy. Reducing intracellular glutathione (GSH) content can increase reactive oxygen species (ROS) level. Therefore, it is extremely important to construct a nanoplatform that can promote photodynamic therapy by consuming GSH. In this study, we synthesized Cu-typtone complex nanoparticles (Cu-Try NPs) by a simple green method and demonstrated their ability to consume GSH to increase intracellular ROS for the first time. Photosensitizer methylene blue was loaded onto Cu-Try NPs (Cu-Try/MB NPs) for enhanced photodynamic therapy. Studies in vitro and in vivo illustrated that enhanced photodynamic therapy based on Cu-Try/MB NPs can kill cancer cells effectively.

Mn-Fe layered double hydroxide nanosheets: a new photothermal nanocarrier for O2-evolving phototherapy.

Iron-manganese layered double hydroxide nanosheets were developed as an effective photothermal nanocarrier for loading a photosensitizer. The catalase-like activity enables the nanosheets to decompose H2O2 into O2, overcoming tumor hypoxia and enhancing O2-dependent photodynamic therapy (PDT). The combination of PDT and photothermal therapy (PTT) can almost completely eliminate tumor tissues.

Photosensitizer-Conjugated Bi2Te3 Nanosheets as Theranostic Agent for Synergistic Photothermal and Photodynamic Therapy.

Phototherapy, including photothermal therapy (PTT)/photodynamic therapy (PDT), is usually considered as a promising strategy for cancer treatment due to its noninvasive and selective therapeutic effect by laser irradiation. A light-activatable nanoplatform based on bovine serum albumin (BSA)-coated Bi2Te3 nanosheets conjugated with methylene blue (MB) was successfully designed and constructed for bimodal PTT/PDT combination therapy. The resultant nanoconstruct (BSA-Bi2Te3/MB) exhibited high stability in various physiological solutions and excellent biocompatibility. Especially, the nanoconstruct not only possessed strong near-infrared absorption and high photothermal conversion as a photothermal agent for efficient tumor ablation but also could successfully load photosensitizer for PDT of tumor. When exposed to laser irradiation, tumors in mice with BSA-Bi2Te3/MB injection were completely eliminated without recurrence within 15 d, demonstrating the potential of the nanoconstruct as a bimodal PTT/PDT therapeutic platform for cancer treatment.

In Situ Surface-Enhanced Infrared Absorption Spectroscopy of Aqueous Molecules with Facile-Prepared Large-Area Reduced Graphene Oxide Island Film.

Midinfrared plasmons in patterned graphene could advance the development of surface-enhanced infrared absorption spectroscopy (SEIRAS). However, limitation in measuring the extinction spectra with transmission and external reflection configurations greatly restricts the analyses of aqueous samples. In addition, complicated, time- and cost-consuming preparation of patterned graphene also limits its progress. Here we demonstrate a facile-prepared large-scale reduced graphene oxide island film on a total internal reflection silicon prism, which not only shows a prominent enhancement effect in mid-infrared region but also effectively eliminates the contribution of bulk solution by optical near-field effect. As a result, the entire vibrational fingerprints of methylene blue monolayer in aqueous solution can be acquired with high sensitivity in real time. Our work extends the application of graphene-based SEIRAS to aqueous environment, breaking through previously unattainable technology.

FeIII -Doped Two-Dimensional C3 N4 Nanofusiform: A New O2 -Evolving and Mitochondria-Targeting Photodynamic Agent for MRI and Enhanced Antitumor Therapy.

Local hypoxia in tumors, as well as the short lifetime and limited action region of 1 O2 , are undesirable impediments for photodynamic therapy (PDT), leading to a greatly reduced effectiveness. To overcome these adversities, a mitochondria-targeting, H2 O2 -activatable, and O2 -evolving PDT nanoplatform is developed based on FeIII -doped two-dimensional C3 N4 nanofusiform for highly selective and efficient cancer treatment. The ultrahigh surface area of 2D nanosheets enhances the photosensitizer (PS) loading capacity and the doping of FeIII leads to peroxidase mimetics with excellent catalytic performance towards H2 O2 in cancer cells to generate O2 . As such tumor hypoxia can be overcome and the PDT efficacy is improved, whilst at the same time endowing the PDT theranostic agent with an effective T 1 -weighted in vivo magnetic resonance imaging (MRI) ability. Conjugation with a mitochondria-targeting agent could further increase the sensitivity of cancer cells to 1 O2 by enhanced mitochondria dysfunction. In vitro and in vivo anticancer studies demonstrate an outstanding therapeutic effectiveness of the developed PDT agent, leading to almost complete destruction of mouse cervical tumor. This development offers an attractive theranostic agent for in vivo MRI and synergistic photodynamic therapy toward clinical applications.

Self-assembly of nitrogen-doped carbon nanoparticles: a new ratiometric UV-vis optical sensor for the highly sensitive and selective detection of Hg(2+) in aqueous solution.

Water-soluble nitrogen-doped carbon nanoparticles (N-CNPs) prepared by the one-step hydrothermal treatment of uric acid were found to show ratiometric changes in their UV-vis spectra due to Hg(2+)-mediated self-assembly. For the first time, such a property was developed into a UV-vis optical sensor for detecting Hg(2+) in aqueous solutions with high sensitively and selectively (detection limit = 1.4 nM). More importantly, this novel sensor exhibits a higher linear sensitivity over a wider concentration range compared with the fluorescence sensor based on the same N-CNPs. This work opens an exciting new avenue to explore the use of carbon nanoparticles in constructing UV-vis optical sensors for the detection of metal ions and the use of carbon nanoparticles as a new building block to self-assemble into superlattices.

[Simultaneous determination of ethyl carbamate and chloropropanols in flavorings by gas chromatography-triple quadrupole tandem mass spectrometry].

A simultaneous determination method for ethyl carbamate (EC) and chloropropanols (3-monochloropropane-1, 2-diol (3-MCPD) and 2-monochloropropane-1, 3-diol (2-MCPD)) in flavorings was developed by gas chromatography-triple quadrupole tandem mass spectrometry (GC-MS/MS). After spiked with internal standard, the sample was extracted by matrix solid-phase dispersion extraction technique with an Extrelut NT column. Hexane was used to wash the fat soluble matrix interferences and then an ethyl acetate-ethyl ether (20: 80, v/v) mixture was added to elute the analytes. The concentrated extract was detected by GC-MS/MS in multiple reaction monitoring (MRM) mode. The limits of detection (LODs) were 2, 5 and 5 microg/kg for EC, 3-MCPD and 2-MCPD, respectively. The linear ranges were 5 - 1 000 microg/kg (r = 0.9997), 10-1000 microg/kg (r = 0.999 1) and 10-1000 microg/kg (r = 0.999 5) for EC, 3-MCPD and 2-MCPD, respectively. In soy sauce, yellow rice wine, salami sauce and flavoring of instant noodle matrices, the recoveries (RSDs, n = 7) in MRM mode at the levels of 20, 100 and 400 microg/kg were 87.7%-104% (4.3%-10.7%), 90.1%-109% (2.6%-10.2%), and 90.9%-103% (3.0%-9.5%), respectively. EC, 3-MCPD and 2-MCPD were found in some real samples of the soy sauce, wine and flavoring of instant noodle. EC or 3-MCPD was found in some of the salami samples. The method is accurate, fast and suitable for the simultaneous determination of EC, 3-MCPD and 2-MCPD in flavorings.