CDI-NSTSEG: A clinical diagnosis-inspired effective and efficient framework for non-salient small tumor segmentation.

To accurately segment various clinical lesions from computed tomography(CT) images is a critical task for the diagnosis and treatment of many diseases. However, current segmentation frameworks are tailored to specific diseases, and limited frameworks can detect and segment different types of lesions. Besides, it is another challenging problem for current segmentation frameworks to segment visually inconspicuous and small-scale tumors (such as small intestinal stromal tumors and pancreatic tumors). Our proposed framework, CDI-NSTSEG, efficiently segments small non-salient tumors using multi-scale visual information and non-local target mining. CDI-NSTSEG follows the diagnostic process of clinicians, including preliminary screening, localization, refinement, and segmentation. Specifically, we first explore to extract the unique features at three different scales (1×, 0.5×, and 1.5×) based on the scale space theory. Our proposed scale fusion module (SFM) hierarchically fuses features to obtain a comprehensive representation, similar to preliminary screening in clinical diagnosis. The global localization module (GLM) is designed with a non-local attention mechanism. It captures the long-range semantic dependencies of channels and spatial locations from the fused features. GLM enables us to locate the tumor from a global perspective and output the initial prediction results. Finally, we design the layer focusing module (LFM) to gradually refine the initial results. LFM mainly conducts context exploration based on foreground and background features, focuses on suspicious areas layer-by-layer, and performs element-by-element addition and subtraction to eliminate errors. Our framework achieves state-of-the-art segmentation performance on small intestinal stromal tumor and pancreatic tumor datasets. CDI-NSTSEG outperforms the best comparison segmentation method by 7.38% Dice on small intestinal stromal tumors.

Construction of Functionalized Oxindoles by Quinone-Carbonate Synergistically Triggered Intermolecular Radical Coupling.

We disclose a rapid and nontoxic procedure to construct various oxindoles. This method harnesses the power of a catalytic amount of quinone in synergy with Cs2CO3, showcasing remarkable compatibility with a wide range of functional groups. Mechanistic investigations reveal that it operates via a radical pathway, likely initiated by the single-electron transfer from quinone-Cs2CO3 complexes. This pivotal electron transfer event leads to the generation of a crucial alkyl radical intermediate, contributing to the overall success and efficacy of the transformation.

Location method for emergency rescue node on expressways based on spatio-temporal characteristics of vehicle operation.

Expressway networks are continuously developing and emergency rescue demand is increasing proportionately. The location of expressway emergency rescue nodes needs refinement to meet changing requirements. In this study, the expressway was modeled as an expressway network. The differences in the origin destination (OD) distribution matrices for working days and major holidays were used as the bases for determining the need for temporary emergency rescue nodes. Overlapping and non-overlapping community detection algorithms were used to extract the distribution characteristics of OD during both day categories. These distributions were used to determine permanent and temporary emergency rescue sites. In this study, we considered the differences in traffic volume, distance, and impact of four vehicle types on traffic accidents to select the location of emergency rescue nodes, and allocate emergency resources. An emergency rescue node selection model for an expressway network was established based on spatio-temporal characteristics. The results based on a regional example determined that 22 permanent and 25 temporary emergency rescue nodes were appropriate. The average rescue time for traffic accidents during working days and major holidays compared to the P-center location model, was reduced by approximately 27.08% and 6.70%, respectively. The coefficient of variation of emergency rescue time was reduced by approximately 28.22% and 21.41%, respectively. The results indicated that the model satisfied the expressway emergency rescue demand requirements, and improved the rationality of the rescue center node layout.

Study of tree shrew biology and models: A booming and prosperous field for biomedical research.

The tree shrew ( Tupaia belangeri) has long been proposed as a suitable alternative to non-human primates (NHPs) in biomedical and laboratory research due to its close evolutionary relationship with primates. In recent years, significant advances have facilitated tree shrew studies, including the determination of the tree shrew genome, genetic manipulation using spermatogonial stem cells, viral vector-mediated gene delivery, and mapping of the tree shrew brain atlas. However, the limited availability of tree shrews globally remains a substantial challenge in the field. Additionally, determining the key questions best answered using tree shrews constitutes another difficulty. Tree shrew models have historically been used to study hepatitis B virus (HBV) and hepatitis C virus (HCV) infection, myopia, and psychosocial stress-induced depression, with more recent studies focusing on developing animal models for infectious and neurodegenerative diseases. Despite these efforts, the impact of tree shrew models has not yet matched that of rodent or NHP models in biomedical research. This review summarizes the prominent advancements in tree shrew research and reflects on the key biological questions addressed using this model. We emphasize that intensive dedication and robust international collaboration are essential for achieving breakthroughs in tree shrew studies. The use of tree shrews as a unique resource is expected to gain considerable attention with the application of advanced techniques and the development of viable animal models, meeting the increasing demands of life science and biomedical research.

Ultrasensitive Wearable Pressure Sensors with Stress-Concentrated Tip-Array Design for Long-Term Bimodal Identification.

The great challenges for existing wearable pressure sensors are the degradation of sensing performance and weak interfacial adhesion owing to the low mechanical transfer efficiency and interfacial differences at the skin-sensor interface. Here, an ultrasensitive wearable pressure sensor is reported by introducing a stress-concentrated tip-array design and self-adhesive interface for improving the detection limit. A bipyramidal microstructure with various Young's moduli is designed to improve mechanical transfer efficiency from 72.6% to 98.4%. By increasing the difference in modulus, it also mechanically amplifies the sensitivity to 8.5 V kPa-1 with a detection limit of 0.14 Pa. The self-adhesive hydrogel is developed to strengthen the sensor-skin interface, which allows stable signals for long-term and real-time monitoring. It enables generating high signal-to-noise ratios and multifeatures when wirelessly monitoring weak pulse signals and eye muscle movements. Finally, combined with a deep learning bimodal fused network, the accuracy of fatigued driving identification is significantly increased to 95.6%.

Mechanochemical Synthesis of α-halo Alkylboronic Esters.

α-halo alkylboronic esters, acting as ambiphilic synthons, play a pivotal role as versatile intermediates in fields like pharmaceutical science and organic chemistry. The sequential transformation of carbon-boron and carbon-halogen bonds into a broad range of carbon-X bonds allows for programmable bond formation, facilitating the incorporation of multiple substituents at a single position and streamlining the synthesis of complex molecules. Nevertheless, the synthetic potential of these compounds is constrained by limited reaction patterns. Additionally, the conventional methods often necessitate the use of bulk toxic solvents, exhibit sensitivity to air/moisture, rely on expensive metal catalysts, and involve extended reaction times. In this report, a ball milling technique is introduced that overcomes these limitations, enabling the external catalyst-free multicomponent coupling of aryl diazonium salts, alkenes, and simple metal halides. This approach offers a general and straightforward method for obtaining a diverse array of α-halo alkylboronic esters, thereby paving the way for the extensive utilization of these synthons in the synthesis of fine chemicals.

Preoperative prediction of microvascular invasion: new insights into personalized therapy for early-stage hepatocellular carcinoma.

Owing to advances in diagnosis and treatment methods over past decades, a growing number of early-stage hepatocellular carcinoma (HCC) diagnoses has enabled a greater of proportion of patients to receive curative treatment. However, a high risk of early recurrence and poor prognosis remain major challenges in HCC therapy. Microvascular invasion (MVI) has been demonstrated to be an essential independent predictor of early recurrence after curative therapy. Currently, biopsy is not generally recommended before treatment to evaluate MVI in HCC according clinical guidelines due to sampling error and the high risk of tumor cell seeding following biopsy. Therefore, the postoperative histopathological examination is recognized as the gold standard of MVI diagnosis, but this lagging indicator greatly impedes clinicians in selecting the optimal effective treatment for prognosis. As imaging can now noninvasively and completely assess the whole tumor and host situation, it is playing an increasingly important role in the preoperative assessment of MVI. Therefore, imaging criteria for MVI diagnosis would be highly desirable for optimizing individualized therapeutic decision-making and achieving a better prognosis. In this review, we summarize the emerging image characteristics of different imaging modalities for predicting MVI. We also discuss whether advances in imaging technique have generated evidence that could be practice-changing and whether advanced imaging techniques will revolutionize therapeutic decision-making of early-stage HCC.

Genetically predicted TWEAK mediates the association between lipidome and Keratinocyte Carcinomas.

BACKGROUND: Reports suggest that lipid profiles may be linked to the likelihood of developing skin cancer, yet the exact causal relationship is still unknown. OBJECTIVE: This study aimed to examine the connection between lipidome and skin cancers, as well as investigate any possible mediators. METHODS: A two-sample Mendelian randomization (MR) analysis was conducted on 179 lipidomes and each skin cancer based on a genome-wide association study (GWAS), including melanoma, basal cell carcinoma (BCC), and squamous cell carcinoma (SCC). Then, Bayesian weighted MR was performed to verify the analysis results of two-sample MR. Moreover, a two-step MR was employed to investigate the impact of TNF-like weak inducer of apoptosis (TWEAK)-mediated lipidome on skin cancer rates. RESULTS: MR analysis identified higher genetically predicted phosphatidylcholine (PC) (17:0_18:2) could reduce the risk of skin tumors, including BCC (OR = 0.9149, 95% CI: 0.8667-0.9658), SCC (OR = 0.9343, 95% CI: 0.9087-0.9606) and melanoma (OR = 0.9982, 95% CI: 0.9966-0.9997). The proportion of PC (17:0_18:2) predicted by TWEAK-mediated genetic prediction was 6.6 % in BCC and 7.6% in SCC. The causal relationship between PC (17:0_18:2) and melanoma was not mediated by TWEAK. CONCLUSION: This study identified a negative causal relationship between PC (17:0_18:2) and keratinocyte carcinomas, a small part of which was mediated by TWEAK, and most of the remaining mediating factors are still unclear. Further research on other risk factors is needed in the future.

CRHR1 antagonist alleviated depression-like behavior by downregulating p62 in a rat model of post-stroke depression.

Post-stroke depression (PSD) is a complication of cerebrovascular disease, which can increase mortality after stroke. CRH is one of the main signaling peptides released after activation of the hypothalamic-pituitary-adrenal (HPA) axis in response to stress. It affects synaptic plasticity by regulating inflammation, oxidative stress and autophagy in the central nervous system. And the loss of spines exacerbates depression-like behavior. Therefore, synaptic deficits induced by CRH may be related to post-stroke depression. However, the underlying mechanism remains unclear. The Keap1-Nrf2 complex is one of the core components of the antioxidant response. As an autophagy associated protein, p62 participates in the Keap1-NrF2 pathway through its Keap1 interaction domain. Oxidative stress is involved in the feedback regulation between Keap1-Nrf2 pathway and p62.However, whether the relationship between CRH and the Keap1-Nrf2-p62 pathway is involved in PSD remains unknown. This study found that serum levels of CRH in 22 patients with PSD were higher than those in healthy subjects. We used MCAO combined with CUMS single-cage SD rats to establish an animal model of PSD. Animal experiments showed that CRHR1 antagonist prevented synaptic loss in the hippocampus of PSD rats and alleviated depression-like behavior. CRH induced p62 accumulation in the prefrontal cortex of PSD rats through CRHR1. CRHR1 antagonist inhibited Keap1-Nrf2-p62 pathway by attenuating oxidative stress. In addition, we found that abnormal accumulation of p62 induces PSD. It alleviates depression-like behavior by inhibiting the expression of p62 and promoting the clearance of p62 in PSD rats. These findings can help explore the pathogenesis of PSD and design targeted treatments for PSD.

G protein subunit Gγ13-mediated signaling pathway is critical to the inflammation resolution and functional recovery of severely injured lungs.

Tuft cells are a group of rare epithelial cells that can detect pathogenic microbes and parasites. Many of these cells express signaling proteins initially found in taste buds. It is, however, not well understood how these taste signaling proteins contribute to the response to the invading pathogens or to the recovery of injured tissues. In this study, we conditionally nullified the signaling G protein subunit Gγ13 and found that the number of ectopic tuft cells in the injured lung was reduced following the infection of the influenza virus H1N1. Furthermore, the infected mutant mice exhibited significantly larger areas of lung injury, increased macrophage infiltration, severer pulmonary epithelial leakage, augmented pyroptosis and cell death, greater bodyweight loss, slower recovery, worsened fibrosis and increased fatality. Our data demonstrate that the Gγ13-mediated signal transduction pathway is critical to tuft cells-mediated inflammation resolution and functional repair of the damaged lungs.To our best knowledge, it is the first report indicating subtype-specific contributions of tuft cells to the resolution and recovery.

DAPK1 mediates cognitive dysfunction and neuronal apoptosis in PSD rats through the ERK/CREB/BDNF signaling pathway.

Post-stroke depression (PSD) is one of the most common mental sequelae after a stroke and can damage the brain. Although PSD has garnered increasing attention in recent years, the precise mechanism remains unclear. Studies have indicated that the expression of DAPK1 is elevated in various neurodegenerative conditions, including depression, ischemic stroke, and Alzheimer's disease. However, the specific molecular mechanism of DAPK1-mediated cognitive dysfunction and neuronal apoptosis in PSD rats is unclear. In this study, we established a rat model of PSD, and then assessed depression-like behaviors and cognitive dysfunction in rats using behavioral tests. In addition, we detected neuronal apoptosis and analyzed the expression of DAPK1 protein and proteins related to the ERK/CREB/BDNF signaling pathway. The findings revealed that MCAO combined with CUMS can induce more severe depression-like behaviors and cognitive dysfunction in rats, while overexpression of DAPK1 may hinder the downstream ERK/CREB/BDNF signaling pathways, resulting in neuronal loss and exacerbation of brain tissue damage. In this study, we will focus on DAPK1 and explore its role in PSD.

Event-related prefrontal activations during online video game playing are modulated by game mechanics, physiological arousal and the amount of daily playing.

There is a trend to study human brain functions in ecological contexts and in relation to human factors. In this study, functional near-infrared spectroscopy (fNIRS) was used to record real-time prefrontal activities in 42 male university student habitual video game players when they played a round of multiplayer online battle arena game, League of Legends. A content-based event coding approach was used to analyze regional activations in relation to event type, physiological arousal indexed by heart rate (HR) change, and individual characteristics of the player. Game events Slay and Slain were found to be associated with similar HR and prefrontal responses before the event onset, but differential responses after the event onset. Ventrolateral prefrontal cortex (VLPFC) activation preceding the Slay onset correlated positively with HR change, whereas activations in dorsolateral prefrontal cortex (DLPFC) and rostral frontal pole area (FPAr) preceding the Slain onset were predicted by self-reported hours of weekly playing (HoWP). Together, these results provide empirical evidence to support the notion that event-related regional prefrontal activations during online video game playing are shaped by game mechanics, in-game dynamics of physiological arousal and individual characteristics the players.

GateView: A Multi-Omics Platform for Gene Feature Analysis of Virus Receptors within Human Normal Tissues and Tumors.

Viruses are obligate intracellular parasites that rely on cell surface receptor molecules to complete the first step of invading host cells. The experimental method for virus receptor screening is time-consuming, and receptor molecules have been identified for less than half of known viruses. This study collected known human viruses and their receptor molecules. Through bioinformatics analysis, common characteristics of virus receptor molecules (including sequence, expression, mutation, etc.) were obtained to study why these membrane proteins are more likely to become virus receptors. An in-depth analysis of the cataloged virus receptors revealed several noteworthy findings. Compared to other membrane proteins, human virus receptors generally exhibited higher expression levels and lower sequence conservation. These receptors were found in multiple tissues, with certain tissues and cell types displaying significantly higher expression levels. While most receptor molecules showed noticeable age-related variations in expression across different tissues, only a limited number of them exhibited gender-related differences in specific tissues. Interestingly, in contrast to normal tissues, virus receptors showed significant dysregulation in various types of tumors, particularly those associated with dsRNA and retrovirus receptors. Finally, GateView, a multi-omics platform, was established to analyze the gene features of virus receptors in human normal tissues and tumors. Serving as a valuable resource, it enables the exploration of common patterns among virus receptors and the investigation of virus tropism across different tissues, population preferences, virus pathogenicity, and oncolytic virus mechanisms.

Direct visualization of intraparotid facial nerve assisting in parotid tumor resection.

Precise recognition of the intraparotid facial nerve (IFN) is crucial during parotid tumor resection. We aimed to explore the application effect of direct visualization of the IFN in parotid tumor resection. Fifteen patients with parotid tumors were enrolled in this study and underwent specific radiological scanning in which the IFNs were displayed as high-intensity images. After image segmentation, IFN could be preoperatively directly visualized. Mixed reality combined with surgical navigation were applied to intraoperatively directly visualize the segmentation results as real-time three-dimensional holograms, guiding the surgeons in IFN dissection and tumor resection. Radiological visibility of the IFN, accuracy of image segmentation and postoperative facial nerve function were analyzed. The trunks of IFN were directly visible in radiological images for all patients. Of 37 landmark points on the IFN, 36 were accurately segmented. Four patients were classified as House-Brackmann Grade I postoperatively. Two patients with malignancies had postoperative long-standing facial paralysis. Direct visualization of IFN was a feasible novel method with high accuracy that could assist in recognition of IFN and therefore potentially improve the treatment outcome of parotid tumor resection.

Anion-Cation Competition Chemistry for Comprehensive High-Performance Prussian Blue Analogs Cathodes.

The extensively studied Prussian blue analogs (PBAs) in various batteries are limited by their low discharge capacity, or subpar rate etc., which are solely reliant on the cation (de)intercalation mechanism. In contrast to the currently predominant focus on cations, we report the overlooked anion-cation competition chemistry (Cl-, K+, Zn2+) stimulated by high-voltage scanning. With our designed anion-cation combinations, the KFeMnHCF cathode battery delivers comprehensively superior discharge performance, including voltage plateau >2.0 V (vs. Zn/Zn2+), capacity >150 mAh g-1, rate capability with capacity maintenance above 96 % from 0.6 to 5 A g-1, and cyclic stability exceeding 3000 cycles. We further verify that such comprehensive improvement of electrochemical performance utilizing anion-cation competition chemistry is universal for different types of PBAs. Our work would pave a new and efficient road towards the next-generation high-performance PBAs cathode batteries.

Exploration of the causality of frailty index on psoriasis: A Mendelian randomization study.

BACKGROUND: Frailty is associated with a variety of diseases, but the relationship between frailty and psoriasis remains unclear. METHODS: First, we conducted a two-sample Mendelian randomization based on genome-wide association studies (GWAS) to investigate genetic causality between frailty index and common diseases in dermatology. Inverse variance weighted was used to estimate causality. Second, expression quantitative trait locus (eQTLs) analysis was conducted to identify the genes affected by Single nucleotide polymorphisms (SNPs). Third, we performed function and pathway enrichment, transcriptome-wide association studies (TWAS) analysis based on eQTLs. RESULTS: It was shown that the rise of frailty index could increase the risk of psoriasis (IVW, beta = 0.916, OR = 2.500, 95%CI:1.418-4.408, p = 0.002) through Mendelian randomization (MR), and there was no heterogeneity and pleiotropy. There was no causality between the frailty index and other common diseases in dermatology. We found 31 eQTLs based on strongly correlated SNPs in the causality. TWAS analysis found that the expressions of four genes were closely related to psoriasis, including HLA-DQA1, HLA-DQA2, HLA-DRB1 and HLA-DQB1. CONCLUSION: It suggested that the frailty index had a significant positive causality on the risk of psoriasis, which was well documented by combined genomic, transcriptome, and proteome analyses.

Exploring the Effect of Xiao-Chai-Hu Decoction on Treating Psoriasis Based on Network Pharmacology and Experiment Validation.

BACKGROUND: Psoriasis is a chronic, inflammatory and recurrent skin disease. Xiao-Chai-Hu Decoction (XCHD) has shown good effects against some inflammatory diseases and cancers. However, the pharmacological effect and mechanisms of XCHD on psoriasis are not yet clear. OBJECTIVE: To uncover the effect and mechanisms of XCHD on psoriasis by integrating network pharmacology, molecular docking, and in vivo experiments. METHODS: The active ingredients and corresponding targets of XCHD were screened through Traditional Chinese Medicine Systems Pharmacology Database and Analysis (TCMSP) and Traditional Chinese Medicine Integrated Database (TCMID). Differentially expressed genes (DEGs) of psoriasis were obtained from the gene expression omnibus (GEO) database. The XCHD-psoriasis intersection targets were obtained by intersecting XCHD targets, and DEGs were used to establish the "herb-active ingredient-target" network and Protein-Protein Interaction (PPI) Network. The hub targets were identified based on the PPI network by Cytoscape software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed next. Molecular docking was executed via AutoDockTools-1.5.6. Finally, in vivo experiments were carried out further to validate the therapeutic effects of XCHD on psoriasis. RESULTS: 58 active components and 219 targets of XCHD were screened. 4 top-active components (quercetin, baicalein, wogonin and kaempferol) and 7 hub targets (IL1B, CXCL8, CCND1, FOS, MMP9, STAT1 and CCL2) were identified. GO and KEGG pathway enrichment analyses indicated that the TNF signaling pathway, IL-17 signaling pathway and several pathways were involved. Molecular docking results indicated that hub genes had a good affinity to the corresponding key compounds. In imiquimod (IMQ)-induced psoriasis mouse models, XCHD could significantly improve psoriasis-like skin lesions, downregulate KRT17 and Ki67, and inhibit inflammation cytokines and VEGF. CONCLUSION: XCHD showed the therapeutic effect on psoriasis by regulating keratinocyte differentiation, and suppressing inflammation and angiogenesis, which provided a theoretical basis for further experiments and clinical research.

Duplex-Hierarchy Representation Learning for Remote Sensing Image Classification.

Remote sensing image classification (RSIC) is designed to assign specific semantic labels to aerial images, which is significant and fundamental in many applications. In recent years, substantial work has been conducted on RSIC with the help of deep learning models. Even though these models have greatly enhanced the performance of RSIC, the issues of diversity in the same class and similarity between different classes in remote sensing images remain huge challenges for RSIC. To solve these problems, a duplex-hierarchy representation learning (DHRL) method is proposed. The proposed DHRL method aims to explore duplex-hierarchy spaces, including a common space and a label space, to learn discriminative representations for RSIC. The proposed DHRL method consists of three main steps: First, paired images are fed to a pretrained ResNet network for extracting the corresponding features. Second, the extracted features are further explored and mapped into a common space for reducing the intra-class scatter and enlarging the inter-class separation. Third, the obtained representations are used to predict the categories of the input images, and the discrimination loss in the label space is minimized to further promote the learning of discriminative representations. Meanwhile, a confusion score is computed and added to the classification loss for guiding the discriminative representation learning via backpropagation. The comprehensive experimental results show that the proposed method is superior to the existing state-of-the-art methods on two challenging remote sensing image scene datasets, demonstrating that the proposed method is significantly effective.