Explore the vast range of titles available.

In the actual image segmentation tasks in the medical field, the phenomenon of limited labeled data accompanied by domain shifts often occurs and such domain shifts may exist in homologous or even heterologous data. In the study, a novel method was proposed to deal with this challenging phenomenon. Firstly, a model was trained with labeled data in source and target domains so as to adapt to unlabeled data. Then, the alignment at two main levels was realized. At the style level, based on multi-scale stylistic features, the alignment of unlabeled target images was maximized and unlabeled target image features were enhanced. At the inter-domain level, the similarity of the category centroids between target domain data and mixed image data was also maximized. Additionally, a fused supervised loss and alignment loss computation method was proposed. In validation experiments, two cross-domain medical image datasets were constructed: homologous and heterologous datasets. Experimental results showed that the proposed method had the more advantageous comprehensive performance than common semi-supervised and domain adaptation methods.

In neuroimaging analysis, accurately delineating stroke lesion areas is crucial for assessing rehabilitation outcomes. However, the lesion areas typically exhibit irregular shapes and unclear boundaries, and the signal intensity of the lesion may closely resemble that of the surrounding healthy brain tissue. This makes it difficult to distinguish lesions from normal tissues, thereby increasing the complexity of the lesion segmentation task. To address these challenges, we propose a novel method called the Spatial-Frequency Multi-Scale Attention Network (SFMANet). Based on the UNet architecture, SFMANet incorporates Spatial-Frequency Gating Units (SFGU) and Dual-axis Multi-scale Attention Units (DMAU) to tackle the segmentation difficulties posed by irregular lesion shapes and blurred boundaries. SFGU enhances feature representation through gating mechanisms and effectively uses redundant information, while DMAU improves the positioning accuracy of image edges by integrating multi-scale context information and better allocates the weights of global and local information to strengthen the interaction between features. Additionally, we introduce an Information Enhancement Module (IEM) to reduce information loss during deep network propagation and establish long-range dependencies. We performed extensive experiments on the ISLES 2022 and ATLAS datasets and compared our model’s performance with that of existing methods. The experimental results demonstrate that SFMANet effectively captures the edge details of stroke lesions and outperforms other methods in lesion segmentation tasks.

Neuroinflammation and mitochondrial impairment play important roles in the neuropathogenesis of Parkinson's disease (PD). The activation of NLRP3 inflammasome and the accumulation of α-synuclein (α-Syn) are strictly correlated to neuroinflammation. Therefore, the regulation of NLRP3 inflammasome activation and α-Syn aggregation might have therapeutic potential. It has been indicated that Dl-3-n-butylphthalide (NBP) produces neuroprotection against some neurological diseases such as ischemic stroke. We here intended to explore whether NBP suppressed NLRP3 inflammasome activation and reduced α-Syn aggregation, thus protecting dopaminergic neurons against neuroinflammation. In our study, we established a MPTP-induced mouse model and 6-OHDA-induced SH-SY5Y cell model to examine the neuroprotective actions of NBP. We then performed behavioral tests to examine motor dysfunction in MPTP-exposed mice after NBP treatment. Western blotting, immunofluorescence staining, flow cytometry and RT-qPCR were conducted to investigate the expression of NLRP3 inflammasomes, neuroinflammatory cytokines, PARP1, p-α-Syn, and markers of microgliosis and astrogliosis. The results showed that NBP exerts a neuroprotective effect on experimental PD models. , NBP ameliorated behavioral impairments and reduced dopaminergic neuron loss in MPTP-induced mice. , treatment of SH-SY5Y cells with 6-OHDA (100uM,24 h) significantly decreased cell viability, increased intracellular ROS production, and induced apoptosis, while pretreatment with 5uM NBP could alleviated 6-OHDA-induced cytotoxicity, ROS production and cell apoptosis to some extent. Importantly, both and , NBP suppressed the activation of the NLRP3 inflammasome and the aggregation of α-Syn, thus inhibited neuroinflammation ameliorated mitochondrial impairments. In summary, NBP rescued dopaminergic neurons by reducing NLRP3 inflammasome activation and ameliorating mitochondrial impairments and increases in p-α-Syn levels. This current study may provide novel neuroprotective mechanisms of NBP as a potential therapeutic agent.

With the continuous development of wireless communication and artificial intelligence technology, Internet of Things (IoT) technology has made great progress. Deep learning methods are currently used in IoT technology, but deep neural networks (DNNs) are notoriously susceptible to adversarial examples, and subtle pixel changes to images can result in incorrect recognition results from DNNs. In the real‐world application, the patches generated by the recent physical attack methods are larger or less realistic and easily detectable. To address this problem, a Generative Adversarial Network based on Visual attention model and Style transfer network (GAN‐VS) is proposed, which reduces the patch area and makes the patch more natural and less noticeable. A visual attention model combined with generative adversarial network is introduced to detect the critical regions of image recognition, and only generate patches within the critical regions to reduce patch area and improve attack efficiency. For any type of seed patch, an adversarial patch can be generated with a high degree of stylistic and content similarity to the attacked image by generative adversarial network and style transfer network. Experimental evaluation shows that the proposed GAN‐VS has good camouflage and outperforms state‐of‐the‐art adversarial patch attack methods. A Generative Adversarial Network based on Visual attention model and Style transfer network (GAN‐VS) is proposed, which reduces the patch area and makes the patch more natural and less noticeable. A visual attention model combined with generative adversarial network is introduced to detect the critical regions of image recognition, and only generate patches within the critical regions to reduce patch area and improve attack efficiency. For any type of seed patch, an adversarial patch can be generated with a high degree of stylistic and content similarity to the attacked image by generative adversarial network and style transfer network.

A network analysis model was used to explore the complex associations between college students' perceived social support and depressive symptoms and problematic phone use from a specific symptom perspective;. A total of 3869 study participants were included in this study using convenience sampling from five different universities in five different provinces in China. Perceived Social Support Scale was been used to measure the perceived social support level of college students, the extent of problematic smartphone use was evaluated using the Smartphone Addiction Scale-Short Version, and the Patient Health Questionnaire-9 scale to assess the depressive symptoms. A network analysis model was used to explore the intrinsic associations between their specific symptoms of perceived social support, depressive symptoms and problematic phone use;. The results revealed that social support from family and from friends played a potentially critical role in alleviating depressive symptoms and reducing problematic phone use among college students;. The support given by family and peers is of great practical importance in reducing problematic cell phone use and controlling the development of depressive symptoms in the college population.

6-monoacetylmorphine (6-MAM), a primary active metabolite of heroin that reaches the human brain, plays a crucial role in producing heroin-associated physiological and lethal effects. Therefore, 6-MAM has emerged as a key target for alleviating the adverse consequences of heroin abuse. In this study, the proposed 6-MAM hydrolase E. coli aminopeptidase N (eAPN) was recombinantly produced, and its biochemical and functional profiles were investigated. eAPN’s biochemical properties, with respect to pH, metal ions, and temperature, and catalytic functions toward peptidase substrates and 6-MAM were thoroughly examined. Extensive experiments reveal that incorporation of an N-terminal His-tag notably affects eAPN’s aminopeptidase activity. This cost-effective recombinant eAPN exhibits favorable thermostability and optimal activity at pH 7.5. Kinetic analysis toward peptidase substrates reveals that eAPN preferentially cleaves peptides following amino acid residues in the order of Ala > Arg >> Met, Gly > Leu > Pro, indicating a preference for small or basic amino acid residues as substrates. Computational and experimental studies have, for the first time, discovered that eAPN is capable of catalyzing the hydrolysis of heroin and 6-MAM, which has shed light on its functional versatility and potential applications. This work elucidates the biochemical properties of eAPN and expands its catalytic functions, thereby laying the groundwork for a deep understanding and further reengineering of eAPN to enhance its activity toward 6-MAM for heroin detoxification.

Heroin is a highly addictive drug that exerts its primary effects through activation of μ-opioid receptors. Its principal active metabolite, 6-monoacetylmorphine (6-MAM), significantly contributes to heroin’s neurological effects and acute toxicity. Current pharmacotherapies for heroin use disorder, employing opioid receptor agonist or antagonist, are often limited by risks of dependence, tolerance, and/or adverse side effects. In this context, enzyme-based therapy emerges as a promising alternative by rapidly converting drugs into inactive or less harmful metabolites in the blood. As a macromolecule, the enzyme does not cross the blood–brain barrier, thereby avoiding side effects in CNS. Through structure-based computational screening, Xoo-PepA (PDB ID: 3JRU), a leucine aminopeptidase from Xanthomonas oryzae pv. oryzae, was identified as a potential enzyme capable of hydrolyzing heroin and 6-MAM. Computational and experimental analyses confirm that Xoo-PepA hydrolyzes heroin sequentially to 6-MAM and subsequently to morphine. Enzymatic properties including dependence on metal ions, optimal pH, thermal stability, and substrate specificity were characterized accordingly. Notably, supplementation with Ni2+ or Zn2+ and TCEP extended Xoo-PepA’s half-life at 37 °C from 1 h to over 24 h, highlighting the essential role of metal ions in maintaining structural stability. Moreover, Ni2+ enhanced Xoo-PepA’s hydrolysis toward peptidase substrate L-leucine-p-nitroaniline by 770-fold, yet conferred no significant activation toward heroin. Mutations in metal ion-coordination residues (e.g., K262A, D267A/E346L) exhibited different activity profiles toward these two types of substrates, suggesting a distinct regulatory mechanism of metal ions may be involved in these activities. This study provides the first demonstration that Xoo-PepA, a non-mammalian, metal-dependent aminopeptidase, can hydrolyze heroin and 6-MAM, shedding light on its functional versatility and biochemical characteristics.

Objective: To characterize the clinical phenotypes associated with the “hot cross bun” sign (HCBs) on MRI and identify correlations between neuroimaging and clinical characteristics. Methods: Firstly, we screened a cohort of patients with HCBs from our radiologic information system (RIS) in our center. Secondly, we systematically reviewed published cases on HCBs and classified all these cases according to their etiologies. Finally, we characterized all HCBs cases in detail and classified the disease spectra and their clinical heterogeneity. Results : Out of a total of 3,546 patients who were screened, we identified 40 patients with HCBs imaging sign in our cohort; systemic literature review identified 39 cases, which were associated with 14 diseases. In our cohort, inflammation [neuromyelitis optica spectrum disorders (NMOSD), multiple sclerosis (MS), and acute disseminated encephalomyelitis (ADEM)] and toxicants [toxic encephalopathy caused by phenytoin sodium (TEPS)] were some of the underlying etiologies. Published cases by systemic literature review were linked to metabolic abnormality, degeneration, neoplasm, infection, and stroke. We demonstrated that the clinical phenotype, neuroimaging characteristics, and HCBs response to therapy varied greatly depending on underlying etiologies. Conclusion : This is the first to report HCBs spectra in inflammatory and toxication diseases. Our study and systemic literature review demonstrated that the underpinning disease spectrum may be broader than previously recognized.

Background T2-weighted imaging (T2WI) of the liver suffers from prolonged scan times and respiratory motion artifacts. Deep learning (DL)-based reconstruction can accelerate acquisition while maintaining diagnostic quality. We compared respiratory-gated (RG) and breath-hold (BH) DL-T2WI to radial k-space sampling acquisition and reconstruction with motion suppression (ARMS)-T2WI and evaluated how respiratory characteristics affect image quality. Materials and methods We prospectively enrolled 120 participants who underwent 3-T RG DL-, BH DL-, and ARMS-T2WI. Three radiologists evaluated image quality and lesion conspicuity using a 5-point scale. Respiratory characteristics were extracted from breathing curves. Results All sequences showed comparable lesion-to-liver contrast ratios ( p  = 0.139), detection rates ( p  = 0.106), and lesion conspicuity scores ( p  = 0.990). RG DL-T2WI showed higher overall image quality compared to BH DL-T2WI ( p  = 0.027), and similar scores to ARMS-T2WI ( p  = 0.106). A respiratory score calculated using four parameters predicted ARMS-T2WI image quality with an area under the receiver operating characteristic curve (AUROC) of 0.836 (95% confidence interval 0.638–0.968) in the validation set. For RG DL-T2WI, a respiratory score using seven parameters achieved an AUROC of 0.831 (0.652–0.967) in the validation set. Standard deviation of the respiratory amplitude (SD amp ) was an independent factor for BH DL-T2WI image quality (validation set, odds ratio 0.297, p  = 0.049). For patients with high SD amp , RG DL-T2WI provided better image quality compared to BH DL-T2WI (68.6% versus 14.3%, p  < 0.001). Conclusion Both RG and BH DL-T2WI offer image quality comparable to ARMS-T2WI. Respiratory metrics derived from breathing curves may facilitate personalized liver imaging. Relevance statement Both respiratory-gated and breath-hold T2WI with deep learning reconstruction showed comparable image quality to T2WI based on radial k-space sampling strategies. Respiratory parameters enable personalized magnetic resonance liver imaging workflows. Key Points Respiratory-gated and breath-hold deep learning T2WI exhibited satisfactory image quality. Respiratory curve traits variably impact T2WI quality, guiding personalized imaging workflows.‌ Respiratory-gated deep learning-reconstructed T2WI benefits patients with breath-holding difficulties in liver MRI. Graphical Abstract

Background The balance between endothelial cell survival and apoptosis during stress is an important cellular process for vessel integrity and vascular homeostasis, and it is also pivotal in angiogenesis during the development of many vascular diseases. However, the underlying molecular mechanisms remain largely unknown. Although both transcription and alternative splicing are important in regulating gene expression in endothelial cells under stress, the regulatory mechanisms underlying this state and their interactions have not yet been studied on a genome-wide basis. Results Human umbilical vein endothelial cells (HUVECs) were treated with cobalt chloride (CoCl 2 ) both to mimic hypoxia and to induce cell apoptosis and alternative splicing responses. Cell apoptosis rate analysis indicated that HUVECs exposed to 300 μM CoCl 2 for 24 hrs were initially counterbalancing apoptosis with cell survival. We therefore used the Affymetrix exon array system to determine genome-wide transcript- and exon-level differential expression. Other than 1583 differentially expressed transcripts, 342 alternatively spliced exons were detected and classified by different splicing types. Sixteen alternatively spliced exons were validated by RT-PCR. Furthermore, direct evidence for the ongoing balance between HUVEC survival and apoptosis was provided by Gene Ontology (GO) and protein function, as well as protein domain and pathway enrichment analyses of the differentially expressed transcripts. Importantly, a novel molecular module, in which the heat shock protein (HSP) families play a significant role, was found to be activated under mimicked hypoxia conditions. In addition, 46% of the transcripts containing stress-modulated exons were differentially expressed, indicating the possibility of combinatorial regulation of transcription and splicing. Conclusion The exon array system effectively profiles gene expression and splicing on the genome-wide scale. Based on this approach, our data suggest that transcription and splicing not only regulate gene expression, but also carry out combinational regulation of the balance between survival and apoptosis of HUVECs under mimicked hypoxia conditions. Since cell survival following the apoptotic challenge is pivotal in angiogenesis during the development of many vascular diseases, our results may advance the knowledge of multilevel gene regulation in endothelial cells under physiological and pathological conditions.