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Lymph-node status is important in decision-making during early gastric cancer (EGC) treatment. Currently, endoscopic submucosal dissection is the mainstream treatment for EGC. However, it is challenging for even experienced endoscopists to accurately diagnose and treat EGC. Multiphoton microscopy can extract the morphological features of collagen fibers from tissues. The characteristics of collagen fibers can be used to assess the lymph-node metastasis status in patients with EGC. First, we compared the accuracy of four deep learning models (VGG16, ResNet34, MobileNetV2, and PVTv2) in training preprocessed images and test datasets. Next, we integrated the features of the best-performing model, which was PVTv2, with manual and clinical features to develop a novel model called AutoLNMNet. The prediction accuracy of AutoLNMNet for the no metastasis (Ly0) and metastasis in lymph nodes (Ly1) stages reached 0.92, which was 0.3% higher than that of PVTv2. The receiver operating characteristics of AutoLNMNet in quantifying Ly0 and Ly1 stages were 0.97 and 0.97, respectively. Therefore, AutoLNMNet is highly reliable and accurate in detecting lymph-node metastasis, providing an important tool for the early diagnosis and treatment of EGC.

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Large Language Models (LLMs) have shown great potential in intelligent visualization systems, especially for domainspecific applications. Integrating LLMs into visualization systems presents challenges, and we categorize these challenges into three alignments: domain problems with LLMs, visualization with LLMs, and interaction with LLMs. To achieve these alignments, we propose a framework and outline a workflow to guide the application of fine-tuned LLMs to enhance visual interactions for domain-specific tasks. These alignment challenges are critical in education because of the need for an intelligent visualization system to support beginners' self-regulated learning. Therefore, we apply the framework to education and introduce Tailor-Mind, an interactive visualization system designed to facilitate self-regulated learning for artificial intelligence beginners. Drawing on insights from a preliminary study, we identify self-regulated learning tasks and fine-tuning objectives to guide visualization design and tuning data construction. Our focus on aligning visualization with fine-tuned LLM makes Tailor-Mind more like a personalized tutor. Tailor-Mind also supports interactive recommendations to help beginners better achieve their learning goals. Model performance evaluations and user studies confirm that Tailor-Mind improves the self-regulated learning experience, effectively validating the proposed framework.

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The fundamental challenge in electron-transporting organic mixed ionic-electronic conductors (OMIECs) is simultaneous optimization of electron and ion transport. Beginning from Y6-type/U-shaped non-fullerene solar cell acceptors, we systematically synthesize and characterize molecular structures that address the aforementioned challenge, progressively introducing increasing numbers of oligoethyleneglycol (OEG; g) sidechains from 1g to 3g, affording OMIECs 1gY, 2gY, and 3gY, respectively. The crystal structure of 1gY preserves key structural features of the Yn series: a U-shaped/planar core, close π-π molecular stacking, and interlocked acceptor groups. Versus inactive Y6 and Y11, all of the new glycolated compounds exhibit mixed ion-electron transport in both conventional organic electrochemical transistor (cOECT) and vertical OECT (vOECT) architectures. Notably, 3gY with the highest OEG density achieves a high normalized transconductance of 25.29 S cm-1, an on/off current ratio of ~106, and a turn-on/off response time of 94.7/5.7 ms in vOECTs. Systematic optoelectronic, electrochemical, architectural, and crystallographic analysis explains the superior 3gY-based OECT performance in terms of denser ngY OEG content, increased crystallite dimensions with decreased long-range crystalline order, and enhanced film hydrophilicity which facilitates ion transport and efficient redox processes. Finally, we demonstrate an efficient small-molecule-based complementary inverter using 3gY vOECTs, showcasing the bioelectronic applicability of these new small-molecule OMIECs.

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BACKGROUND: Chronic myeloid leukemia (CML) is driven primarily by the constitutively active BCR-ABL fusion oncoprotein. Although the development of tyrosine kinase inhibitors has markedly improved the prognosis of CML patients, it remains a significant challenge to overcome drug-resistant mutations, such as the T315I mutation of BCR-ABL, and achieve treatment-free remission in the clinic. PURPOSE: The identification of new intervention targets beyond BCR-ABL could provide new perspectives for future research and therapeutic intervention. A network pharmacology analysis was conducted to identify the most promising natural product with anti-CML activity. Celastrol was selected for further analysis to gain insights into its mechanism of action (MoA), with the aim of identifying potential new intervention targets for BCR-ABL T315I-mutant CML. METHODS: Transcriptomic and proteomic analyses were conducted to systematically investigate the molecular MoA of celastrol in K562T315I cells. To identify the target proteins of celastrol, mass spectrometry-coupled cellular thermal shift assay (MS-CETSA) was carried out, followed by validations with genetic knockdown and overexpression, cell proliferation assay, comet assay, Western blotting, celastrol probe-based in situ labeling and pull-down assay, molecular docking, and biolayer interferometry. RESULTS: Our multi-omics analyses revealed that celastrol primarily induces DNA damage accumulation and the unfolded protein response in K562T315I cells. Among the twelve most potential celastrol targets, experimental evidence demonstrated that the direct interaction of celastrol with YY1 and HMCES increases the levels of DNA damage, leading to cell death. CONCLUSION: This study represents the first investigation utilizing a proteome-wide label-free target deconvolution approach, MS-CETSA, to identify the protein targets of celastrol. This study also develops a new systems pharmacology strategy. The findings provide new insights into the multifaceted mechanisms of celastrol and, more importantly, highlight the potential of targeting proteins in DNA damage and repair pathways, particularly YY1 and HMCES, to combat drug-resistant CML.

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Carotid artery plaque is a key factor in stroke and other cardiovascular diseases. Accurate detection and localization of carotid artery plaque are essential for early prevention and treatment of diseases. However, current carotid artery ultrasound image anomaly detection algorithms face several challenges, such as scarcity of anomaly data in carotid arteries and traditional convolutional neural networks (CNNs) overlooking long-distance dependencies in image processing. To address these issues, we propose an anomaly detection algorithm for carotid artery plaques based on ultrasound images. The algorithm innovatively introduces an anomaly sample pair generation method to increase dataset diversity. Moreover, it employs an improved adaptive recursive gating pyramid pooling module to extract image features. This module significantly enhances the model's capacity for high-order spatial interactions and adaptive feature fusion, thereby greatly improving the neural network's feature extraction ability. The algorithm uses a Sigmoid layer to map each pixel's feature vector to a probability distribution between 0 and 1, and anomalies are detected through probability threshold binarization. Experimental results show that our algorithm's AUROC index reached 90.7% on a carotid artery dataset, improving by 2.1% compared to the FPI method. This research is expected to provide robust support for the early prevention and treatment of cardiovascular diseases.

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Aromatase inhibitors are effective in treating hormone receptor-positive breast cancer, particularly in postmenopausal women. However, the challenges of inconsistent dissolution, variable absorption and side effects with oral administration persist. To address these issues, transdermal delivery has emerged as a viable alternative. In our study, we have developed nanoemulsion-based transdermal creams containing third-generation aromatase inhibitors Exemestane (EXE) or Letrozole (LE) and evaluated their toxicity, anti-tumour effects and androgenic potency using preclinical models including Bama minipigs, DMBA-induced breast cancer rats and orchidectomized male rats. The results of our study are significant, suggesting that both creams effectively penetrated the skin, demonstrating an impressive anti-breast cancer effect. Importantly, EXE cream had no organ toxicity at the tested dose, providing a reassuring safety profile for its use. In contrast, LE cream displayed reversible toxicity from drug molecule itself in animals at the given dose, dissipating after 3 weeks of withdrawal and recovery. This study establishes a solid foundation for the safe clinical use of third-generation aromatase inhibitors. It highlights transdermal creams as a promising drug delivery carrier for administering them.

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Although targeting the androgen signaling pathway by androgen receptor (AR) inhibitors, including enzalutamide, has shown therapeutic effectiveness, inevitable emergence of acquired resistance remains a critical challenge in the treatment of advanced prostate cancer (PCa). Recognizing targetable genomic aberrations that trigger endocrine treatment failure holds great promise for advancing therapeutic interventions. Here, we characterized PLXNA1, amplified in a subset of PCa patients, as a contributor to enzalutamide resistance (ENZR). Elevated PLXNA1 expression facilitated PCa proliferation under enzalutamide treatment due to AKT signaling activation. Mechanistically, PLXNA1 recruited NRP1 forming a PLXNA1-NRP1 complex, which in turn potentiated the phosphorylation of the AKT. Either inhibiting PLXNA1-NRP1 complex with an NRP1 inhibitor, EG01377, or targeting PLXNA1-mediated ENZR with AKT inhibitors, abolished the pro-resistance phenotype of PLXNA1. Taken together, combination of AKT inhibitor and AR inhibitors presents a promising therapeutic strategy for PCa, especially in advanced PCa patients exhibiting PLXNA1 overexpression.

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Improving clinical immunotherapy for glioblastoma (GBM) relies on addressing the immunosuppressive tumor microenvironment (TME). Enhancing CD8+ T cell infiltration and preventing its exhaustion holds promise for effective GBM immunotherapy. Here, a low-intensity focused ultrasound (LIFU)-guided sequential delivery strategy is developed to enhance CD8+ T cells infiltration and activity in the GBM region. The sequential delivery of CXC chemokine ligand 10 (CXCL10) to recruit CD8+ T cells and interleukin-2 (IL-2) to reduce their exhaustion is termed an "open-source throttling" strategy. Consequently, up to 3.39-fold of CD8+ T cells are observed with LIFU-guided sequential delivery of CXCL10, IL-2, and anti-programmed cell death 1 ligand 1 (aPD-L1), compared to the free aPD-L1 group. The immune checkpoint inhibitors (ICIs) therapeutic efficacy is substantially enhanced by the reversed immunosuppressive TME due to the expansion of CD8+ T cells, resulting in the elimination of tumor, prolonged survival time, and long-term immune memory establishment in orthotopic GBM mice. Overall, LIFU-guided sequential cytokine and ICIs delivery offers an "open-source throttling" strategy of CD8+ T cells, which may present a promising strategy for brain-tumor immunotherapy.

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Although multiplexed DNA fluorescence in situ hybridization (FISH) enables tracking the spatial localization of thousands of genomic loci using probes within individual cells, the high rates of undetected probes impede the depiction of 3D chromosome structures. Current data imputation methods neither utilize single-cell Hi-C data, which elucidate 3D genome architectures using sequencing nor leverage multimodal RNA FISH data that reflect cell-type information, limiting the effectiveness of these methods in complex tissues such as the mouse brain. To this end, a novel multiplexed DNA FISH imputation method named ImputeHiFI is proposed, which fully utilizes the complementary structural information from single-cell Hi-C data and the cell type signature from RNA FISH data to obtain a high-fidelity and complete spatial location of chromatin loci. ImputeHiFI enhances cell clustering, compartment identification, and cell subtype detection at the single-cell level in the mouse brain. ImputeHiFI improves the recognition of cell-type-specific loops in three high-resolution datasets. In short, ImputeHiFI is a powerful tool capable of imputing multiplexed DNA FISH data from various resolutions and imaging protocols, facilitating studies of 3D genome structures and functions.

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Objective: The objective is to elucidate the collaboration and current research status in the pediatric field of fNIRS using bibliometric analysis, and to discuss future directions. Method: Bibliometric analysis was conducted on publications related to pediatric fNIRS research published before June 2024 in the Web of Science Core Collection using VOSviewer software and R language. Results: A total of 761 documents were retrieved, published by 2,686 authors from 893 institutions across 44 countries in 239 journals. The number of publications has significantly increased since 2012. The United States is the country with the highest number of publications, University College London is the institution with the most publications, Lloyd-Fox Sarah is the author with the most publications and significant influence, and "Neurophotonics" is the journal with the most publications. The current hotspots mainly involve using fNIRS to study executive functions and autism spectrum disorders in children. Conclusion: The study provides useful reference information for researchers by analyzing publication numbers, collaborative networks, publishing journals, and research hotspots. In the future, there should be an emphasis on enhancing interdisciplinary and international collaboration to collectively dedicate efforts toward the advancement of fNIRS technology and the standardization of research.

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Ion preintercalation is an effective method for fine-tuning the electrochemical characteristics of electrode materials, thereby enhancing the performance of aqueous ammonium-ion hybrid supercapacitors (A-HSCs). However, much of the current research on ion preintercalation lacks controllability, and the underlying mechanisms remain unclear. In this study, we employ a two-step electrochemical activation approach, involving galvanostatic charge-discharge and cyclic voltammetry, to modulate the preintercalation of NH4+ in MnO2. An in-depth analysis of the electrochemical activation mechanism is presented. This two-step electrochemical activation approach endows the final MnO2/AC electrode with a high capacitance of 917.4 F g-1, approximately 2.4 times higher than that of original MnO2. Furthermore, the MnO2/AC electrode retains approximately 93.4% of its capacitance after 10 000 cycles at a current density of 25 mA cm-2. Additionally, aqueous A-HSC, comprising MnO2/AC and P-MoO3, achieves a maximum energy density of 87.6 Wh kg-1. This study offers novel insights into the controllable ion preintercalation approach via electrochemical activation.

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Efgartigimod was the first-in-class neonatal Fc receptor antagonist approved for the treatment of acetylcholine receptor antibody positive (AChR+), Myasthenia Gravis Foundation of America (MGFA) Class II-IV generalized myasthenia gravis (gMG) patients. As a novel therapy, the clinical experiences are still lacking, especially for the use of efgartigimod in manifest and impending myasthenic crisis (IMC). We reported three AChR+, gMG patients, two with myasthenic crisis (MC) and one with IMC, treated with efgartigimod. MGFA class, MG-Activity of Daily Living score (MG-ADL), Quantitative MG score (QMG), and Muscle Research Council sum score (MRC), concentration of anti-AChR antibody, IgG, globulin, and albumin, subsets of T and B lymphocyte were evaluated or measured before, during and after efgartigimod treatment. All patients showed fast and robust response to efgartigimod with marked improvement in MGFA, MG-ADL, QMG, and MRC scores. Patient 1 did not respond effectively to IVIg but was successfully rescued by add-on efgartigimod. She extubated at 7 days after the first infusion and got rid of NIV after 14-days treatment. Patient 2 and patient 3 directly used efgartigimod when symptoms were not ameliorated by adjusting of oral drugs. Patient 2 wean from BiPAP at seven days after the first infusion. Patient 3 in IMC status, overcame the severe dysphagia at three days after the first infusion. Clinical symptoms continued to improve 1-2 weeks after discharge. Concentration of anti-AChR antibody, IgG and globulin were remarkably reduced by efgartigimod treatment. Our study supported that efgartigimod could act as a fast-acting rescue therapy for patients with MC or IMC. Larger studies from multicenter are required to provide further evidence.

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Neural implicit function based on signed distance field (SDF) has achieved impressive progress in reconstructing 3D models with high fidelity. However, such approaches can only represent closed surfaces. Recent works based on unsigned distance function (UDF) are proposed to handle both watertight and single-layered open surfaces. Nonetheless, as UDF is signless, its direct output is limited to the point cloud, which imposes an additional challenge on extracting high-quality meshes from discrete points. To address this challenge, we present a novel neural implicit representation coded HSDF, which is a hybrid of signed and unsigned distance fields. In particular, HSDF is able to represent arbitrary topologies containing both closed and open surfaces while being compatible with existing iso-surface extraction techniques for easy field-to-mesh conversion. In addition to predicting a UDF, we propose to learn an additional sign field. Unlike traditional SDF, HSDF is able to locate the surface of interest before level surface extraction by generating surface points following NDF [1]. We are then able to obtain open surfaces via an adaptive meshing approach that only instantiates regions containing surfaces into a polygon mesh. HSDF benefits downstream tasks like neural rendering, as it enables the rendering of back-faces of open surfaces. We also propose HSDF-Net, a dedicated learning framework that factorizes the learning of HSDF into two easier sub-problems. Experiments and evaluations show that HSDF outperforms the state-of-the-art techniques both qualitatively and quantitatively on some of the used datasets.

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Eighteen nitrogen-containing compounds (1-18) were isolated from cultures of the lichen-associated Streptomyces flavidovirens collected from the Qinghai-Tibet Plateau, including seven phenazine derivatives with three new ones, named subphenazines A-C (2-4), two new furan pyrrolidones (8-9), and nine known alkaloids. The structures were elucidated by spectroscopic data analysis, and absolute configurations were determined by single-crystal X-ray diffraction and ECD calculations. The phenazine-type derivatives, in particular compound 3, exhibited significantly better antineuroinflammatory activity than other isolated compounds (8-18). Compound 3 inhibited the release of proinflammatory cytokines including IL-6, TNF-α, and PGE2, and the nuclear translocation of NF-κB; it also reduced the oxidative stress and activated the Nrf2 signaling pathway in LPS-induced BV2 microglia cells. In vivo anti-inflammatory activity in zebrafish indicated that 3 inhibited LPS-stimulated ROS generation. These findings suggested that compound 3 might be a potent antineuroinflammatory agent through the regulation of the NF-κB/Nrf2 signaling pathways.

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Gliomas are a diverse group of primary central nervous system neoplasms with no curative therapies available. Brain macrophages comprise microglia in the brain parenchyma, border-associated macrophages in the meningeal-choroid plexus-perivascular space and monocyte-derived macrophages infiltrating the brain. With the great improvement of our recognition of brain macrophages, diverse macrophage populations have been found in the context of glioma, which exhibit functional and phenotypic heterogeneity. We have long thought that brain macrophage senescence is detrimental, manifested by specialized forms of persistent cell cycle arrest and chronic low-grade inflammation. Persistent senescence of macrophages may result in immune dysfunction, potentially contributing to glioma initiation and development. Given the crucial roles played by brain macrophages in glioma, we unravel how brain macrophages undergo reprogramming and their contribution to glioma. We outline general molecular alterations and specific biomarkers in senescent brain macrophages, as well as functional changes (such as metabolism, autophagy, phagocytosis, antigen presentation, and infiltration and recruitment). In addition, recent advances in genetic regulation and mechanisms linked to senescent brain macrophages are discussed. In particular, this review emphasizes the contribution of senescent brain macrophages to glioma, which may drive translational efforts to utilize brain macrophages as a prognostic marker or/and treatment target in glioma. An in-depth comprehending of how brain macrophage senescence functionally influences the tumor microenvironment will be key to our development of innovative therapeutics for glioma.

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Biomimetic neuromorphic sensing systems, inspired by the structure and function of biological neural networks, represent a major advancement in the field of sensing technology and artificial intelligence. This review paper focuses on the development and application of electrolyte gated transistors (EGTs) as the core components (synapses and neuros) of these neuromorphic systems. EGTs offer unique advantages, including low operating voltage, high transconductance, and biocompatibility, making them ideal for integrating with sensors, interfacing with biological tissues, and mimicking neural processes. Major advances in the use of EGTs for neuromorphic sensory applications such as tactile sensors, visual neuromorphic systems, chemical neuromorphic systems, and multimode neuromorphic systems are carefully discussed. Furthermore, the challenges and future directions of the field are explored, highlighting the potential of EGT-based biomimetic systems to revolutionize neuromorphic prosthetics, robotics, and human-machine interfaces. Through a comprehensive analysis of the latest research, this review is intended to provide a detailed understanding of the current status and future prospects of biomimetic neuromorphic sensory systems via EGT sensing and integrated technologies.

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Objectives: To explore the relationship between estradiol (E2) and the incidence of hyperuricemia (HUA) in adult women and to explore whether glucolipid metabolism disorders play a mediating role in mediating this relationship. Methods: A total of 2,941 participants aged 20-65 years were included in the National Health and Nutrition Examination Survey (NHANES) 2013-2016. Multivariate logistic regression analysis was performed to evaluate the correlations of E2 with HUA. Multivariate linear regression analysis was performed to evaluate the associations between E2 and triglyceride (TG), total cholesterol (TC), and the triglyceride-glucose index (TyG). The restricted cubic spline (RCS) model was used to further explore the association between E2 and HUA and between TG, TC, and TyG and HUA. Mediation analyses were performed to examine whether TC, TG, and TyG mediated the relationship between E2 and HUA. Results: After adjusting for covariates, logistic regression revealed that ln(E2) was significantly associated with HUA in the female subgroup (p = 0.035) and that the incidence of HUA tended to increase with decreasing ln(E2) (p for trend = 0.026). Linear regression showed that E2 was significantly associated with TC (p = 0.032), TG (p = 0.019), and TyG (p = 0.048). The RCS model showed that ln(E2) was linearly correlated with the incidence of HUA (p-overall = 0.0106, p-non-linear = 0.3030). TC and TyG were linearly correlated with HUA (TC: p-overall = 0.0039, p-non-linear = 0.4774; TyG: p-overall = 0.0082, p-non-linear = 0.0663), whereas TG was non-linearly correlated with HUA. Mediation analyses revealed that TC, TG, and TyG significantly mediated the relationship between ln(E2) and HUA (TC, indirect effect: -0.00148, 7.5%, p = 0.008; TG, indirect effect: -0.00062, 3.1%, p = 0.004; TyG, indirect effect: -0.00113, 5.6%, p = 0.016). Conclusion: In conclusion, this study demonstrated that compared with women aged 20-45 years, women aged 45-55 years and 55-65 years had lower E2 levels and a greater incidence of HUA. E2 levels and the incidence of HUA were negatively associated in female individuals but not in male individuals. In addition, TC, TG, and TyG, which are markers of glucolipid metabolism, played a mediating role in the association between E2 and HUA.

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[This corrects the article DOI: 10.3389/fcimb.2022.1021320.].

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Timely and effective diagnosis of fungal keratitis (FK) is necessary for suitable treatment and avoiding irreversible vision loss for patients. In vivo confocal microscopy (IVCM) has been widely adopted to guide the FK diagnosis. We present a deep learning framework for diagnosing fungal keratitis using IVCM images to assist ophthalmologists. Inspired by the real diagnostic process, our method employs a two-stage deep architecture for diagnostic predictions based on both image-level and sequence-level information. To the best of our knowledge, we collected the largest dataset with 96,632 IVCM images in total with expert labeling to train and evaluate our method. The specificity and sensitivity of our method in diagnosing FK on the unseen test set achieved 96.65% and 97.57%, comparable or better than experienced ophthalmologists. The network can provide image-level, sequence-level and patient-level diagnostic suggestions to physicians. The results show great promise for assisting ophthalmologists in FK diagnosis.

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CO2 capture from coal power plants is an important and necessary solution to realizing carbon neutrality in China, but CCS demonstration deployment in power sector is far behind expectations. Hence, the reduction potential of energy consumption and cost for CCS and its competitiveness to renewable powers are very important to make roadmaps and policies toward carbon neutrality. Unlike the popular recognition that capturing CO2 from flue gases is technically and commercially mature, this paper notes that it has been proved to be technically feasible but far beyond technology maturity and high energy penalty leads to its immaturity and therefore causes high cost. Additionally, the potential energy penalty reduction of capture is investigated thermodynamically, and future CO2 avoidance cost is predicted and compared to renewable power (solar PV and onshore wind power). Results show that energy penalty for CO2 capture can be reduced by 48%-57%. When installation capacity reaches a similar scale to that of solar PV in China (250 GW), CO2 capture cost in coal power plants can be reduced from the current 28-40 US$/ton to 10-20 US$/ton, and efficiency upgrade contributes to 67%-75% in cost reduction for high coal price conditions. In China, CO2 capture in coal power plants can be cost competitive with solar PV and onshore wind power. But it is worth noting that the importance and share of CCS role in CO2 emission reduction is decreasing since renewable power is already well deployed and there is still a lack of large-scale CO2 capture demonstrations in China. Innovative capture technologies with low energy penalties need to be developed to promote CCS. Results in this work can provide informative references for making roadmaps and policies regarding CO2 emission reductions that contribute towards carbon neutrality.