Subtype prediction of intrahepatic cholangiocarcinoma using dynamic contrast-enhanced ultrasound.

OBJECTIVE: The study aimed to investigate the predictive value of dynamic contrast-enhanced ultrasound (DCE-US) in differentiating small-duct (SD) and large-duct (LD) types of intrahepatic cholangiocarcinoma (ICC). METHODS: This study retrospectively enrolled 110 patients with pathologically confirmed ICC lesions who were subject to preoperative contrast-enhanced ultrasound (CEUS) examinations between January 2022 and February 2023. Patients were further classified according to the subtype: SD-type and LD-type, and an optimal predictive model was established and validated using the above pilot cohort. The test cohort, consisting of 48 patients prospectively enrolled from March 2023 to September 2023, was evaluated. RESULTS: In the pilot cohort, compared with SD-type ICCs, more LD-type ICCs showed elevated carcinoembryonic antigen (p < 0.001), carbohydrate antigen 19-9 (p = 0.004), ill-defined margin (p = 0.018), intrahepatic bile duct dilation (p < 0.001). Among DCE-US quantitative parameters, the wash-out area under the curve (WoAUC), wash-in and wash-out area under the curve (WiWoAUC), and fall time (FT) at the margin of lesions were higher in the SD-type group (all p < 0.05). Meanwhile, the mean transit time (mTT) and wash-out rate (WoR) at the margin of the lesion were higher in the LD-type group (p = 0.041 and 0.007, respectively). Logistic regression analysis showed that intrahepatic bile duct dilation, mTT, and WoR were significant predictive factors for predicting ICC subtypes, and the AUC of the predictive model achieved 0.833 in the test cohort. CONCLUSIONS: Preoperative DCE-US has the potential to become a novel complementary method for predicting the pathological subtype of ICC. CRITICAL RELEVANCE STATEMENT: DCE-US has the potential to assess the subtypes of ICC lesions quantitatively and preoperatively, which allows for more accurate and objective differential diagnoses, and more appropriate treatments and follow-up or additional examination strategies for the two subtypes. KEY POINTS: Preoperative determination of intrahepatic cholangiocarcinoma (ICC) subtype aids in surgical decision-making. Quantitative parameters from dynamic contrast-enhanced US (DCE-US) allow for the prediction of the ICC subtype. DCE-US-based imaging has the potential to become a novel complementary method for predicting ICC subtypes.

Few-Shot Face Stylization via GAN Prior Distillation.

Face stylization has made notable progress in recent years. However, when training on limited data, the performance of existing approaches significantly declines. Although some studies have attempted to tackle this problem, they either failed to achieve the few-shot setting (less than 10) or can only get suboptimal results. In this article, we propose GAN Prior Distillation (GPD) to enable effective few-shot face stylization. GPD contains two models: a teacher network with GAN Prior and a student network that fulfills end-to-end translation. Specifically, we adapt the teacher network trained on large-scale data in the source domain to the target domain using a handful of samples, where it can learn the target domain's knowledge. Then, we can achieve few-shot augmentation by generating source domain and target domain images simultaneously with the same latent codes. We propose an anchor-based knowledge distillation module that can fully use the difference between the training and the augmented data to distill the knowledge of the teacher network into the student network. The trained student network achieves excellent generalization performance with the absorption of additional knowledge. Qualitative and quantitative experiments demonstrate that our method achieves superior results than state-of-the-art approaches in a few-shot setting.

Ag supraparticles with 3D hot spots to actively capture molecules for sensitive detection by surface enhanced Raman spectroscopy.

To achieve highly sensitive detection using surface-enhanced Raman spectroscopy (SERS), it is imperative to fabricate a substrate with a high density of hot spots and facilitate the entry of target molecules into these hot spot regions. However, steric hindrance arising from the presence of surfactants and ligands on the SERS substrate may impede the access of target molecules to the hot spots. Here, we fabricate non-close-packed three-dimensional (3D) supraparticles with high-density hot spots to actively capture molecules. The formation of 3D supraparticles is attributed to the minimization of free energy during the gradual contraction of the droplet. The numerous capillaries present in non-close-packed supraparticles induce the movement of target molecules into the hot spot region through capillary force along with the solution. The results demonstrate that the SERS enhancement effect of 3D supraparticles is at least one order of magnitude higher than that of multi-layered nanoparticle structures formed under natural drying conditions. In addition, the SERS performance of 3D supraparticles is evaluated with diverse target molecules, including antimicrobial agents and drugs. Hence, this work provides a new idea for the preparation of non-close-packed substrates for SERS sensitive detection.

A telomere-targeting drug depletes cancer initiating cells and promotes anti-tumor immunity in small cell lung cancer.

There are few effective treatments for small cell lung cancer (SCLC) underscoring the need for innovative therapeutic approaches. This study focuses on exploiting telomerase, a critical SCLC dependency as a therapeutic target. A prominent characteristic of SCLC is their reliance on telomerase activity, a key enzyme essential for their continuous proliferation. Here we utilize a nucleoside analog, 6-Thio-2'-deoxyguanosine (6TdG) currently in phase II clinical trials, that is preferentially incorporated by telomerase into telomeres leading to telomere dysfunction. Using preclinical mouse and human derived models we find low intermittent doses of 6TdG inhibit tumor growth and reduce metastatic burden. Anti-tumor efficacy correlates with a reduction in a subpopulation of cancer initiating like cells (CICs) identified by their expression of L1CAM/CD133 and highest telomerase activity. 6TdG treatment also leads to activation of innate and adaptive anti-tumor responses. Mechanistically, 6TdG depletes CICs and induces type-I interferon signaling leading to tumor immune visibility by activating tumor cell STING signaling. We also observe increased sensitivity to irradiation after 6TdG treatment in both syngeneic and humanized SCLC xenograft models both of which are dependent on the presence of host immune cells. This study underscores the immune-enhancing and metastasis-reducing effects of 6TdG, employing a range of complementary in vitro and in vivo SCLC preclinical models providing a potential therapeutic approach to SCLC.

Steel Strip Surface Defect Detection Method Based on Improved YOLOv5s.

Steel strip is an important raw material for the engineering, automotive, shipbuilding, and aerospace industries. However, during the production process, the surface of the steel strip is prone to cracks, pitting, and other defects that affect its appearance and performance. It is important to use machine vision technology to detect defects on the surface of a steel strip in order to improve its quality. To address the difficulties in classifying the fine-grained features of strip steel surface images and to improve the defect detection rate, we propose an improved YOLOv5s model called YOLOv5s-FPD (Fine Particle Detection). The SPPF-A (Spatial Pyramid Pooling Fast-Advance) module was constructed to adjust the spatial pyramid structure, and the ASFF (Adaptively Spatial Feature Fusion) and CARAFE (Content-Aware ReAssembly of FEatures) modules were introduced to improve the feature extraction and fusion capabilities of strip images. The CSBL (Convolutional Separable Bottleneck) module was also constructed, and the DCNv2 (Deformable ConvNets v2) module was introduced to improve the model's lightweight properties. The CBAM (Convolutional Block Attention Module) attention module is used to extract key and important information, further improving the model's feature extraction capability. Experimental results on the NEU_DET (NEU surface defect database) dataset show that YOLOv5s-FPD improves the mAP50 accuracy by 2.6% before data enhancement and 1.8% after SSIE (steel strip image enhancement) data enhancement, compared to the YOLOv5s prototype. It also improves the detection accuracy of all six defects in the dataset. Experimental results on the VOC2007 public dataset demonstrate that YOLOv5s-FPD improves the mAP50 accuracy by 4.6% before data enhancement, compared to the YOLOv5s prototype. Overall, these results confirm the validity and usefulness of the proposed model.

Loss of p53 and mutational heterogeneity drives immune resistance in an autochthonous mouse lung cancer model with high tumor mutational burden.

The role of tumor mutational burden (TMB) in shaping tumor immunity is a key question that has not been addressable using genetically engineered mouse models (GEMMs) of lung cancer. To induce TMB in lung GEMMs, we expressed an ultra-mutator variant of DNA polymerase-E (POLE)P286R in lung epithelial cells. Introduction of PoleP286R allele into KrasG12D and KrasG12D; p53L/L (KP) models significantly increase their TMB. Immunogenicity and sensitivity to immune checkpoint blockade (ICB) induced by Pole is partially dependent on p53. Corroborating these observations, survival of NSCLC patients whose tumors have TP53truncating mutations is shorter than those with TP53WT with immunotherapy. Immune resistance is in part through reduced antigen presentation and in part due to mutational heterogeneity. Total STING protein levels are elevated in Pole mutated KP tumors creating a vulnerability. A stable polyvalent STING agonist or p53 induction increases sensitivity to immunotherapy offering therapeutic options in these polyclonal tumors.

PMSGAN: Parallel Multistage GANs for Face Image Translation.

In this article, we address the face image translation task, which aims to translate a face image of a source domain to a target domain. Although significant progress has been made by recent studies, face image translation is still a challenging task because it has more strict requirements for texture details: even a few artifacts will greatly affect the impression of generated face images. Targeting to synthesize high-quality face images with admirable visual appearance, we revisit the coarse-to-fine strategy and propose a novel parallel multistage architecture on the basis of generative adversarial networks (PMSGAN). More specifically, PMSGAN progressively learns the translation function by disintegrating the general synthesis process into multiple parallel stages that take images with gradually decreasing spatial resolution as inputs. To prompt the information exchange between various stages, a cross-stage atrous spatial pyramid (CSASP) structure is specially designed to receive and fuse the contextual information from other stages. At the end of the parallel model, we introduce a novel attention-based module that leverages multistage decoded outputs as in situ supervised attention to refine the final activations and yield the target image. Extensive experiments on several face image translation benchmarks show that PMSGAN performs considerably better than state-of-the-art approaches.

Tunable magnetic and electronic properties of armchair BeN4 nanoribbons.

Recently, layered BeN4 as a novel Dirac semimetal has been fabricated (M. Bykov, T. Fedotenko, S. Chariton et al. Phys. Rev. Lett., 2021, 126, 175501). Motivated by the experiment, we perform first-principles calculations to predict the stability, magnetic configurations, and electronic structures of unsaturated BeN4 nanoribbons with an armchair-terminated edge. The magnetic interactions and electronic properties of BeN4 nanoribbons are sensitively influenced by the edge morphology. The BeN4 nanoribbons with both edges occupied by Be atoms undergo a transition from a ferromagnetic (FM) metal to an antiferromagnetic (AFM) semiconductor with the increase of ribbon width. The configurations with edges situated by Be and N atoms are FM/ferrimagnetic (FIM) metals or nearly half-metals, and the spin polarizability is as high as 85% when the ribbon width is N = 5. The nanoribbons with both edge sites occupied by pentagonal N atoms are nonmagnetic (NM), while the nanoribbons terminated by N atoms in a hexagonal ring are FM metals. We also explore the magnetic properties and band structures of BeN4 nanoribbons with hydrogen passivation. Our results open up a versatile edge engineering avenue to design BeN4-based spintronic and nanoelectronic devices.

Energy sensor AMPK gamma regulates translation via phosphatase PPP6C independent of AMPK alpha.

Maintenance of energy level to drive movements and material exchange with the environment is a basic principle of life. AMP-activated protein kinase (AMPK) senses energy level and is a major regulator of cellular energy responses. The gamma subunit of AMPK senses elevated ratio of AMP to ATP and allosterically activates the alpha catalytic subunit to phosphorylate downstream effectors. Here, we report that knockout of AMPKγ, but not AMPKα, suppressed phosphorylation of eukaryotic translation elongation factor 2 (eEF2) induced by energy starvation. We identified PPP6C as an AMPKγ-regulated phosphatase of eEF2. AMP-bound AMPKγ sequesters PPP6C, thereby blocking dephosphorylation of eEF2 and thus inhibiting translation elongation to preserve energy and to promote cell survival. Further phosphoproteomic analysis identified additional targets of PPP6C regulated by energy stress in an AMPKγ-dependent manner. Thus, AMPKγ senses cellular energy availability to regulate not only AMPKα kinase, but also PPP6C phosphatase and possibly other effectors.

Mitophagy in Traumatic Brain Injury: A New Target for Therapeutic Intervention.

Traumatic brain injury (TBI) contributes to death, and disability worldwide more than any other traumatic insult and damage to cellular components including mitochondria leads to the impairment of cellular functions and brain function. In neurons, mitophagy, autophagy-mediated degradation of damaged mitochondria, is a key process in cellular quality control including mitochondrial homeostasis and energy supply and plays a fundamental role in neuronal survival and health. Conversely, defective mitophagy leads to the accumulation of damaged mitochondria and cellular dysfunction, contributing to inflammation, oxidative stress, and neuronal cell death. Therefore, an extensive characterization of mitophagy-related protective mechanisms, taking into account the complex mechanisms by which each molecular player is connected to the others, may provide a rationale for the development of new therapeutic strategies in TBI patients. Here, we discuss the contribution of defective mitophagy in TBI, and the underlying molecular mechanisms of mitophagy in inflammation, oxidative stress, and neuronal cell death highlight novel therapeutics based on newly discovered mitophagy-inducing strategies.

Knowledge Distillation for Face Photo-Sketch Synthesis.

Significant progress has been made with face photo-sketch synthesis in recent years due to the development of deep convolutional neural networks, particularly generative adversarial networks (GANs). However, the performance of existing methods is still limited because of the lack of training data (photo-sketch pairs). To address this challenge, we investigate the effect of knowledge distillation (KD) on training neural networks for the face photo-sketch synthesis task and propose an effective KD model to improve the performance of synthetic images. In particular, we utilize a teacher network trained on a large amount of data in a related task to separately learn knowledge of the face photo and knowledge of the face sketch and simultaneously transfer this knowledge to two student networks designed for the face photo-sketch synthesis task. In addition to assimilating the knowledge from the teacher network, the two student networks can mutually transfer their own knowledge to further enhance their learning. To further enhance the perception quality of the synthetic image, we propose a KD+ model that combines GANs with KD. The generator can produce images with more realistic textures and less noise under the guide of knowledge. Extensive experiments and a user study demonstrate the superiority of our models over the state-of-the-art methods.

An Efficient Transformer Based on Global and Local Self-attention for Face Photo-Sketch Synthesis.

Face photo-sketch synthesis tasks have been dominated by convolutional neural networks (CNNs), especially CNN-based generative adversarial networks (GANs), because of their strong texture modeling capabilities and thus their ability to generate more realistic face photos/sketches beyond traditional methods. However, due to CNNs' locality and spatial invariance properties, there have weaknesses in capturing the global and structural information which are extremely important for face images. Inspired by the recent phenomenal success of the Transformer in vision tasks, we propose replacing CNNs with Transformers that are able to model long-range dependencies to synthesize more structured and realistic face images. However, the existing vision Transformers are mainly designed for high-level vision tasks and lack the dense prediction ability to generate high resolution images due to the quadratic computational complexity of their self-attention mechanism. In addition, the original Transformer is not capable of modeling local correlations which is an important skill for image generation. To address these challenges, we propose two types of memory-friendly Transformer encoders, one for processing local correlations via local self-attention and another for modeling global information via global self-attention. By integrating the two proposed Transformer encoders, we present an efficient GL-Transformer for face photo-sketch synthesis, which can synthesize realistic face photo/sketch images from coarse to fine. Extensive experiments demonstrate that our model achieves a comparable or better performance beyond the state-of-the-art CNN-based methods both qualitatively and quantitatively.

Inhibition of Autophagy by a Small Molecule through Covalent Modification of the LC3 Protein.

The autophagic ubiquitin-like protein LC3 functions through interactions with LC3-interaction regions (LIRs) of other autophagy proteins, including autophagy receptors, which stands out as a promising protein-protein interaction (PPI) target for the intervention of autophagy. Post-translational modifications like acetylation of Lys49 on the LIR-interacting surface could disrupt the interaction, offering an opportunity to design covalent small molecules interfering with the interface. Through screening covalent compounds, we discovered a small molecule modulator of LC3A/B that covalently modifies LC3A/B protein at Lys49. Activity-based protein profiling (ABPP) based evaluations reveal that a derivative molecule DC-LC3in-D5 exhibits a potent covalent reactivity and selectivity to LC3A/B in HeLa cells. DC-LC3in-D5 compromises LC3B lipidation in vitro and in HeLa cells, leading to deficiency in the formation of autophagic structures and autophagic substrate degradation. DC-LC3in-D5 could serve as a powerful tool for autophagy research as well as for therapeutic interventions.

The Mediating Roles of Emotional Regulation on Negative Emotion and Internet Addiction Among Chinese Adolescents From a Development Perspective.

Previous researches indicated that emotional regulation can be associated with depression and anxiety, which may be an important mediating factor between emotional regulation and internet addiction. However, the mechanism between these associations has received little attention and it is still unclear. This study has examined 716 Chinese adolescents, 341 were males (47.6%), aged 13 to 18(Mean = 14.58, SD = 1.50), using a cross-sectional survey involving Young's Diagnostic Questionnaire for Internet Addiction, the nine-item Patient Health Questionnaire (PHQ-9), the seven-item Generalized Anxiety (GAD-7) scale, and the Emotion Regulation Questionnaire (ERQ). Correlation analysis, multiple-group analysis and structural equation modeling were carried out in SPSS Statistics version 23 (IBM, Armonk, NY) and AMOS version 21. Cognitive reappraisal had a significantly negative direct effect on Internet addiction (ß = -0.118, p < 0.05). Furthermore, negative emotions mediated the relationships between expression suppression and Internet addiction [ß = 0.149, 95% CI = (0.099, 0.212)] and the relationship between cognitive reappraisal and Internet addiction [ß = -0.101, 95% CI = (-0.147, -0.065)]. The differences in the structure path coefficients for different development stages demonstrated that recognitive reappraisal showed more protective roles for negative emotion (p < 0.01), and negative emotion also predict Internet addiction more effectively in high school students (p < 0.001). However, cognitive reappraisal directly predicted negative Internet addiction in junior high school students. Therefore, the intervention on adolescents for internet addiction should not only focus on emotional regulation and negative emotion, but also development stages of adolescents.

Characterization of the Immune Landscape of EGFR-Mutant NSCLC Identifies CD73/Adenosine Pathway as a Potential Therapeutic Target.

INTRODUCTION: Lung adenocarcinomas harboring EGFR mutations do not respond to immune checkpoint blockade therapy and their EGFR wildtype counterpart. The mechanisms underlying this lack of clinical response have been investigated but remain incompletely understood. METHODS: We analyzed three cohorts of resected lung adenocarcinomas (Profiling of Resistance Patterns of Oncogenic Signaling Pathways in Evaluation of Cancer of Thorax, Immune Genomic Profiling of NSCLC, and The Cancer Genome Atlas) and compared tumor immune microenvironment of EGFR-mutant tumors to EGFR wildtype tumors, to identify actionable regulators to target and potentially enhance the treatment response. RESULTS: EGFR-mutant NSCLC exhibited low programmed death-ligand 1, low tumor mutational burden, decreased number of cytotoxic T cells, and low T cell receptor clonality, consistent with an immune-inert phenotype, though T cell expansion ex vivo was preserved. In an analysis of 75 immune checkpoint genes, the top up-regulated genes in the EGFR-mutant tumors (NT5E and ADORA1) belonged to the CD73/adenosine pathway. Single-cell analysis revealed that the tumor cell population expressed CD73, both in the treatment-naive and resistant tumors. Using coculture systems with EGFR-mutant NSCLC cells, T regulatory cell proportion was decreased with CD73 knockdown. In an immune-competent mouse model of EGFR-mutant lung cancer, the CD73/adenosine pathway was markedly up-regulated and CD73 blockade significantly inhibited tumor growth. CONCLUSIONS: Our work revealed that EGFR-mutant NSCLC has an immune-inert phenotype. We identified the CD73/adenosine pathway as a potential therapeutic target for EGFR-mutant NSCLC.

Evasion of Innate Immunity Contributes to Small Cell Lung Cancer Progression and Metastasis.

Small cell lung cancer (SCLC) is a pulmonary neuroendocrine cancer with very poor prognosis and limited effective therapeutic options. Most patients are diagnosed at advanced stages, and the exact reason for the aggressive and metastatic phenotype of SCLC is completely unknown. Despite a high tumor mutational burden, responses to immune checkpoint blockade are minimal in patients with SCLC. This may reflect defects in immune surveillance. Here we illustrate that evading natural killer (NK) surveillance contributes to SCLC aggressiveness and metastasis, primarily through loss of NK-cell recognition of these tumors by reduction of NK-activating ligands (NKG2DL). SCLC primary tumors expressed very low level of NKG2DL mRNA and SCLC lines express little to no surface NKG2DL at the protein level. Chromatin immunoprecipitation sequencing showed NKG2DL loci in SCLC are inaccessible compared with NSCLC, with few H3K27Ac signals. Restoring NKG2DL in preclinical models suppressed tumor growth and metastasis in an NK cell-dependent manner. Likewise, histone deacetylase inhibitor treatment induced NKG2DL expression and led to tumor suppression by inducing infiltration and activation of NK and T cells. Among all the common tumor types, SCLC and neuroblastoma were the lowest NKG2DL-expressing tumors, highlighting a lineage dependency of this phenotype. In conclusion, these data show that epigenetic silencing of NKG2DL results in a lack of stimulatory signals to engage and activate NK cells, highlighting the underlying immune avoidance of SCLC and neuroblastoma. SIGNIFICANCE: This study discovers in SCLC and neuroblastoma impairment of an inherent mechanism of recognition of tumor cells by innate immunity and proposes that this mechanism can be reactivated to promote immune surveillance.

CircR2Cancer: a manually curated database of associations between circRNAs and cancers.

Accumulating evidences have shown that the deregulation of circRNA has close association with many human cancers. However, these experimental verified circRNA-cancer associations are not collected in any database. Here, we develop a manually curated database (circR2Cancer) that provides experimentally supported associations between circRNAs and cancers. The current version of the circR2Cancer contains 1439 associations between 1135 circRNAs and 82 cancers by extracting data from existing literatures and databases. In addition, circR2Cancer contains the information of cancer exacted from Disease Ontology and basic biological information of circRNAs from circBase. At the same time, circR2Cancer provides a simple and friendly interface for users to conveniently browse, search and download the data. It will be a useful and valuable resource for researchers to understanding the regulation mechanism of circRNA in cancers. DATABASE URL: http://www.biobdlab.cn:8000.

S-Adenosyl-l-Methionine Promotes Metabolism of Fungicides in Cabernet Sauvignon (Vitis vinifera L.) Berries.

Fungicides are commonly used to prevent and treat grape (Vitis vinifera L.) diseases; however, they are potentially toxic to humans. Herein, we show that the application of S-adenosyl-l-methionine (SAM) accelerated the metabolism of various fungicides in Cabernet Sauvignon berries. The substances and enzymes involved in the metabolism of fungicides were analyzed to elucidate the effects of SAM. Results showed that SAM improved the production rate of superoxide anion, the hydrogen peroxide content, and the activities of superoxide dismutase, catalase, and peroxidase in azoxystrobin-treated berries. Additionally, SAM had a positive effect on the content of reduced glutathione and on the activities of glutathione S-transferase, glutathione reductase, and glutathione peroxidase. Importantly, the stimulatory effect of SAM on fungicide metabolism was also observed for metalaxyl and thiophanate-methyl. These results suggest that SAM can be used to improve food safety.

Covalent Inhibitors Allosterically Block the Activation of Rho Family Proteins and Suppress Cancer Cell Invasion.

The Rho family GTPases are crucial drivers of tumor growth and metastasis. However, it is difficult to develop GTPases inhibitors due to a lack of well-characterized binding pockets for compounds. Here, through molecular dynamics simulation of the RhoA protein, a groove around cysteine 107 (Cys107) that is relatively well-conserved within the Rho family is discovered. Using a combined strategy, the novel inhibitor DC-Rhoin is discovered, which disrupts interaction of Rho proteins with guanine nucleotide exchange factors (GEFs) and guanine nucleotide dissociation inhibitors (GDIs). Crystallographic studies reveal that the covalent binding of DC-Rhoin to the Cys107 residue stabilizes and captures a novel allosteric pocket. Moreover, the derivative compound DC-Rhoin04 inhibits the migration and invasion of cancer cells, through targeting this allosteric pocket of RhoA. The study reveals a novel allosteric regulatory site within the Rho family, which can be exploited for anti-metastasis drug development, and also provides a novel strategy for inhibitor discovery toward "undruggable" protein targets.