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ABSTRACT Yersinia enterocolitica, a species within the genus Yersinia, thrives optimally at 22–25°C but can also grow at the mammalian core body temperature of 37°C. This dual temperature adaptability necessitates establishing both temperature conditions in research to examine the effects on various biological processes. In quantitative real-time PCR (qRT-PCR) assays, the selection of appropriate housekeeping genes is vital for data accuracy. Nevertheless, the lack of alternatives and information often leads to the default use of the 16S rRNA gene despite potential limitations. This investigation sourced 16 potential reference genes through a comprehensive review of the literature and transcriptome sequencing data analysis. We validated the expression stability of these genes via qRT-PCR across 12 Y. enterocolitica strains, representing the four prevalent serotypes O:3, O:5,27, O:8, and O:9, isolated from diarrheal patient stool samples. This approach aimed to minimize the impact of serotype heterogeneity. After acquiring Cq values, gene stability was evaluated using four established algorithms—ΔCq, geNorm, NormFinder, and BestKeeper—and subsequently synthesized into a consolidated ranking through the Robust Rank Aggregation (RRA) method. Our study suggests that the genes glnS, nuoB, glmS, gyrB, dnaK, and thrS maintain consistent expression across varying culture temperatures, supporting their candidacy as robust housekeeping genes. We advise against the exclusive use of 16S rRNA for this purpose. Should tradition prevail in its utilization, it must be employed with discernment, preferably alongside one or two of the housekeeping genes identified in this study as internal controls.IMPORTANCEIn our study, we focused on identifying stable reference genes for quantitative real-time PCR (qRT-PCR) experiments on Y. enterocolitica cultured at different temperatures (22°C and 37°C). After thoroughly evaluating 16 candidate genes, we identified six genes—glnS, nuoB, glmS, gyrB, dnaK, and thrS—as exhibiting stable expression across these temperature conditions, making them ideal reference genes for Y. enterocolitica studies. This discovery is crucial for ensuring the accuracy and reliability of qRT-PCR data, as the choice of appropriate reference genes is key to normalizing expression data and minimizing experimental variability. Importantly, our research extended beyond bioinformatics analysis by incorporating validation with clinical strains, bridging the gap between theoretical predictions and practical application. This approach not only underscores the robustness and reliability of our findings but also directly addresses the critical need for experimental validation in the field. By providing a set of validated, stably expressed reference genes, our work offers valuable guidance for designing experiments involving Y. enterocolitica, enhancing the reliability of research outcomes, and advancing our understanding of this significant pathogen.

Abstract Dislocation of anti‐adhesion materials, non‐specific tissue adhesion, and the induction of secondary fibrinolysis disorders are the main challenges faced by postoperative anti‐adhesion materials. Herein, a self‐leveling transient unilateral adhesive hydrogel is custom‐designed to conquer these challenges with a theoretically calculated and dual‐step tailored gellan gum (GG) as the sole agent. First, the maximum gelation temperature of GG is lowered from 42–25 °C through controlled perturbation of intra‐ and inter‐molecular hydrogen bonds, which is achieved by employing the methacrylic anhydride as a “hydrogen bond's perturbator” to form methacrylate GG (MeGG). Second, the “self‐leveling” injectability and wound shape adaptably are endowed by the formation of borate‐diol complexed MeGG (BMeGG). Finally, the transient unilateral tissue‐adhesive hydrogel (BMeGG‐H) barrier is prepared through photo‐controlled cross‐linking of reactive alkenyl groups. This degradable hydrogel demonstrates favorable rheological properties, light‐controlled unilateral adhesion properties, biocompatibility, anti‐fibrin adhesion, and anti‐cell adhesion properties in vitro. Comprehensive regulation of the fibrinolysis balance toward non‐adhesion is conformed in a rat model after intra‐abdominal surgery via anti‐autoinflammatory response, intestinal wall integrity repair, and Tissue plasminogen activator (t‐PA) and plasminogen activator inhibitor‐1 (PAI‐1) balance adjustment. Notably, the 14th day anti‐adhesion effective rate is 100%, indicating its significant potential in clinical applications for postoperative anti‐adhesion.

Abstract The Dicke model describes the cooperative interaction of an ensemble of two-level atoms with a single-mode photonic field and exhibits a quantum phase transition as a function of light–matter coupling strength. Extending this model by incorporating short-range atom–atom interactions makes the problem intractable but is expected to produce new physical phenomena and phases. Here, we simulate such an extended Dicke model using a crystal of ErFeO3, where the role of atoms (photons) is played by Er3+ spins (Fe3+ magnons). Through terahertz spectroscopy and magnetocaloric effect measurements as a function of temperature and magnetic field, we demonstrated the existence of a novel atomically ordered phase in addition to the superradiant and normal phases that are expected from the standard Dicke model. Further, we elucidated the nature of the phase boundaries in the temperature–magnetic-field phase diagram, identifying both first-order and second-order phase transitions. These results lay the foundation for studying multiatomic quantum optics models using well-characterized many-body solid-state systems.

Abstract Named Entity Recognition (NER) plays a crucial role in the field of Natural Language Processing, holding significant value in applications such as information extraction, knowledge graphs, and question–answering systems. However, Chinese NER faces challenges such as semantic complexity, uncertain entity boundaries, and nested structures. To address these issues, this study proposes an innovative approach, namely Multi-Granularity BERT Adapter and Efficient Global Pointer (MGBERT-Pointer). The semantic encoding layer adopts Multi-Granularity Adapter (MGA), while the decoding layer employs Efficient Global Pointer (EGP) network, ensuring collaborative functionality. The MGA, incorporating Character Adapter, Entity Adapter, and Lexicon Adapter through interactive mechanisms, are deeply integrated into the BERT base, significantly enhancing the model’s ability to handle complex contexts and ambiguities. The EGP, utilizing Rotary Position Embedding, resolves the issue of insufficient boundary information in traditional attention mechanisms, thereby improving the model’s understanding and recognition of nested entity structures. Experimental results on four public datasets demonstrate a significant enhancement in Chinese NER performance achieved by the MGBERT-Pointer model.

Semi-supervised medical image segmentation models often face challenges such as empirical mismatch and data imbalance. Traditional methods, like the two-stream perturbation model, tend to over-rely on strong perturbation, leaving weak perturbation and labeled images underutilized. To overcome these challenges, we propose an innovative hybrid copy-paste (HCP) method within the strong perturbation branch, encouraging unlabeled images to learn more comprehensive semantic information from labeled images and narrowing the empirical distribution gap. Additionally, we integrate contrastive learning into the weak perturbation branch, where contrastive learning samples are selected through semantic grouping contrastive sampling (SGCS) to address memory and variance issues. This sampling strategy ensures more effective use of weak perturbation data. This approach is particularly advantageous for pixel segmentation tasks with severely limited labels. Finally, our approach is validated on the public ACDC (Automated Cardiac Diagnosis Challenge) dataset, achieving a 90.6% DICE score, with just 7% labeled data. These results demonstrate the effectiveness of our method in improving segmentation performance with limited labeled data.

High-quality development is the primary task of building a modern socialist country in an all-round way. Under the requirements of China to accelerate the construction of a new development pattern with the domestic cycle as the main body and the domestic and international dual cycles promoting each other, the development of the marine economy is facing unprecedented opportunities and challenges. As the only coastal province in Northeast China, Liaoning borders the Yellow Sea and the Bohai Sea. Six cities including Dalian, Dandong, Jinzhou, Yingkou, Panjin, and Huludao constitute the Liaoning Coastal Economic Belt, which plays a pivotal role in the revitalization of Northeast China. The paper used PEST and SWOT analysis methods to analyze the influencing factors and development status of the high-quality development of Liaoning Coastal Economic Belt. It faced challenges in the aspects of the legal system related to the sea, coordinated regional development, coordinated development of land and sea, and comprehensive opening to the outside world. Under the perspective of revitalizing Northeast China, based on the current situation of Liaoning's development, leveraging Liaoning's unique advantages, this paper provided effective suggestions for accelerating the high-quality development of Liaoning Coastal Economic Belt.

Abstract Photonic Random-Access Memories (P-RAM) are an essential component for the on-chip non-von Neumann photonic computing by eliminating optoelectronic conversion losses in data links. Emerging Phase-Change Materials (PCMs) have been showed multilevel memory capability, but demonstrations still yield relatively high optical loss and require cumbersome WRITE-ERASE approaches increasing power consumption and system package challenges. Here we demonstrate a multistate electrically programmed low-loss nonvolatile photonic memory based on a broadband transparent phase-change material (Ge2Sb2Se5, GSSe) with ultralow absorption in the amorphous state. A zero-static-power and electrically programmed multi-bit P-RAM is demonstrated on a silicon-on-insulator platform, featuring efficient amplitude modulation up to 0.2 dB/μm and an ultralow insertion loss of total 0.12 dB for a 4-bit memory showing a 100× improved signal to loss ratio compared to other phase-change-materials based photonic memories. We further optimize the positioning of dual microheaters validating performance tradeoffs. Experimentally we demonstrate a half-a-million cyclability test showcasing the robust approach of this material and device. Low-loss photonic retention-of-state adds a key feature for photonic functional and programmable circuits impacting many applications including neural networks, LiDAR, and sensors for example.

Hepatocellular carcinoma (HCC) is the third leading cause of cancer death worldwide. Ginsenoside Rk3, an important and rare saponin in heat-treated ginseng, is generated from Rg1 and has a smaller molecular weight. However, the anti-HCC efficacy and mechanisms of ginsenoside Rk3 have not yet been characterized. Here, we investigated the mechanism by which ginsenoside Rk3, a tetracyclic triterpenoid rare ginsenoside, inhibits the growth of HCC. We first explored the possible potential targets of Rk3 through network pharmacology. Both in vitro (HepG2 and HCC-LM3 cells) and in vivo (primary liver cancer mice and HCC-LM3 subcutaneous tumor-bearing mice) studies revealed that Rk3 significantly inhibits the proliferation of HCC. Meanwhile, Rk3 blocked the cell cycle in HCC at the G1 phase and induced autophagy and apoptosis in HCC. Further proteomics and siRNA experiments showed that Rk3 regulates the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT) pathway to inhibit HCC growth, which was validated by molecular docking and surface plasmon resonance. In conclusion, we report the discovery that ginsenoside Rk3 binds to PI3K/AKT and promotes autophagy and apoptosis in HCC. Our data strongly support the translation of ginsenoside Rk3 into novel PI3K/AKT-targeting therapeutics for HCC treatment with low toxic side effects.

The split delivery vehicle routing problem (SDVRP) is a classic combinatorial optimization problem, which is usually solved using a heuristic algorithm. The ant colony optimization algorithm is an excellent heuristic algorithm that has been successfully applied to solve various practical problems, and it has achieved good results. However, in the existing ant colony optimization algorithms, there are issues with weak targeting of different customer selection strategies, difficulty in balancing convergence speed and global search ability, and a predisposition to become trapped in local optima. To solve these problems, this paper proposes an improved ant colony algorithm (IACA). First, in terms of customer point selection, the initial customer and noninitial customer selection strategies are proposed for different customers, and the adaptive selection threshold is designed. Second, in terms of pheromone processing, an initial pheromone distribution method based on a greedy strategy, a pheromone backtracking mechanism, and an adaptive pheromone volatile factor are proposed. Finally, based on the 2-opt local search method, vehicle path self-search and intervehicle path search are proposed to further improve the quality of the solution. This paper tests the performance of the IACA on datasets of different scales. The experimental results show that compared with the clustering algorithm, artificial bee colony algorithm, particle swarm optimization algorithm, traditional ant colony algorithm, and other algorithms, the IACA can achieve more competitive results. Specifically, compared to the path length calculated by other algorithms, the path length calculated by IACA decreased by an average of 1.58%, 4.28%, and 3.64% in small, medium, and large-scale tests, respectively.

Antibody-cell conjugation (ACC) technology is a new research direction in medicine and biotechnology in recent years. The concept of ACC was proposed by Hsiao et al. and developed into a viable cell therapy technology, which refers to the cells with specific functions. Such as natural killer cells (NK cells), cytokine induced killer cells (CIK) and other immune cells and monoclonal antibodies through the linker together formed conjugate. ACC directly modifies specific antibodies on the cell surface through a simple and effective chemical coupling method to enable cells to have new functions. ACC has been developed for the treatment of various diseases, including cancers of the blood system and solid tumors. This paper reviews the current ACC construction methods, challenges and future development directions.

The thyroid gland is an important and essential endocrine organ for the human body that regulates metabolism, growth and development by secreting thyroid hormones. Thyroid nodules are irregular masses caused by lesions that can reflect the main clinical manifestation of thyroid abnormalities. Delineating the boundaries of thyroid nodules from ultrasound images is an indispensable part of computer-aided diagnosis systems and medical imaging diagnosis for thyroid diseases. However, automatic segmentation of thyroid nodules is still a challenging task because of the similarity between the heterogeneous appearance and its background. In this study, we propose a novel framework for thyroid nodule segmentation that exploits multiscale anatomical features to build a late-stage fusion method based on adversarial training. The introduced architecture can handle blurred and uneven tissue regions during the thyroid nodule segmentation process. By adversarial training approaches, our segmentation block S adopts a framework with three different fusion scales, and discrimination block D employs a fully convolutional encoder-decoder architecture. Extensive experimental results demonstrate that AMSeg outperforms popular methods in terms of thyroid nodule segmentation. The proposed framework achieves Dice, Hd95 (95% Hausdorff distance), Jaccard, and precision values of 83.06%, 23.13, 74.18%, and 87.82%, respectively. As an end-to-end network, the AMSeg can effectively replace manual segmentation methods and has great prospects in clinical applications.

Deep neural networks (DNNs) are susceptible to adversarial attacks, including the recently introduced locally visible adversarial patch attack, which achieves a success rate exceeding 96%. These attacks pose significant challenges to DNN security. Various defense methods, such as adversarial training, robust attention modules, watermarking, and gradient smoothing, have been proposed to enhance empirical robustness against patch attacks. However, these methods often have limitations concerning patch location requirements, randomness, and their impact on recognition accuracy for clean images.To address these challenges, we propose a novel defense algorithm called Local Adversarial Attack Empirical Defense using Gradient Optimization (LAAGO). The algorithm incorporates a low-pass filter before noise suppression to effectively mitigate the interference of high-frequency noise on the classifier while preserving the low-frequency areas of the images. Additionally, it emphasizes the original target features by enhancing the image gradients. Extensive experimental results demonstrate that the proposed method improves defense performance by 3.69% for 80 × 80 noise patches (representing approximately 4% of the images), while experiencing only a negligible 0.3% accuracy drop on clean images. The LAAGO algorithm provides a robust defense mechanism against local adversarial attacks, overcoming the limitations of previous methods. Our approach leverages gradient optimization, noise suppression, and feature enhancement, resulting in significant improvements in defense performance while maintaining high accuracy for clean images. This work contributes to the advancement of defense strategies against emerging adversarial attacks, thereby enhancing the security and reliability of deep neural networks.

Depending on the relative rates of coupling and dissipation, a light-matter coupled system is either in the weak- or strong-coupling regime. Here, we present a unique system where the coupling rate continuously increases with an externally applied magnetic field while the dissipation rate remains constant, allowing us to monitor a weak-to-strong coupling transition as a function of magnetic field. We observed a Rabi splitting of a terahertz magnon mode in yttrium orthoferrite above a threshold magnetic field of ∼14 T. Based on a microscopic theoretical model, we show that with increasing magnetic field the magnons transition into magnon polaritons through an exceptional point, which will open up new opportunities for in situ control of non-Hermitian systems.

Typically, magnetic phenomena result from the spontaneous order of the sublattices. Here, the cross-talk of two magnetic ions gives rise to an intrinsic, yet non-spontaneous ordering and manifests as emergent strong spin–phonon coupling in SmFeO3.

In order to solve the classification model's shortcomings, this study suggests a new trash classification model that is generated by altering the structure of the ResNet-50 network. The improvement is divided into two sections. The first section is to change the residual block. To filter the input features, the attention module is inserted into the residual block. Simultaneously, the downsampling process in the residual block is changed to decrease information loss. The second section is multi-scale feature fusion. To optimize feature usage, horizontal and vertical multi-scale feature fusion is integrated to the primary network structure. Because of the filtering and reuse of image features, the enhanced model can achieve higher classification performance than existing models for small data sets with few samples. The experimental results show that the modified model outperforms the original ResNet-50 model on the TrashNet dataset by 7.62% and is more robust. In the meanwhile, our model is more accurate than other advanced methods.

All-optical platforms hold potential for fast and efficient analog computing, but are limited by their size and poor reconfigurability. Here, a zero-index nanophotonic platform enables post-Moore’s law analog optical computing, processing data with high throughput and low-energy levels.

Genome-wide association studies (GWAS) have recently identified phosphatase and actin regulator-1 (PHACTR1) as a critical risk gene associated with polyvascular diseases. However, it remains largely unclear how PHACTR1 is involved in endothelial dysfunction. Here, by mining published datasets of human stable and vulnerable/ruptured plaque tissues, we observed upregulated expression of PHACTR1 in vulnerable/ruptured plaques. Congruent with these data, we demonstrated increased Phactr1 gene expression in aortic endothelium from ApoE-/- mice fed a western type diet compared with that in normal C57BL/6J mice. Relevantly, PHACTR1 gene expression was upregulated by pro-inflammatory and pro-atherogenic stimuli, including TNF-α, IL-1β and oxidized LDL (oxLDL). By employing next-generation RNA sequencing, we demonstrate that PHACTR1 overexpression disrupts pathways associated with endothelial homeostasis. Cell biological studies unravel that PHACTR1 mediates endothelial inflammation and monocyte adhesion by activating NF-κB dependent intercellular adhesion molecule 1 (ICAM1) and vascular cell adhesion molecule 1 (VCAM1) expression. In addition, overexpression of PHACTR1 also reduces the generation of nitric oxide (NO) by inhibiting Akt/eNOS activation. In-house compound screening of vasoprotective drugs identifies several drugs, including lipid-lowering statins, decreases PHACTR1 gene expression. However, PHACTR1 gene expression was not affected by another lipid-lowering drug-fenofibrate. We also performed a proteomic study to reveal PHACTR1 interacting proteins and validated that PHACTR1 can interact with heat shock protein A8 (HSPA8) which was reported to be associated with coronary artery disease and eNOS degradation. Further studies are warranted to confirm the precise mechanism of PHACTR1 in driving endothelial dysfunction. In conclusion, by using systems biology approach and molecular validation, we disclose the deleterious effects of PHACTR1 on endothelial function by inducing endothelial inflammation and reducing NO production, highlighting the potential to prevent endothelial dysfunction and atherosclerosis by targeting PHACTR1 expression. The precise role of endothelial cell PHACTR1 in polyvascular diseases remains to be validated in diseased conditions.

Ultrastrong light-matter interactions with dominant antiresonant terms are expected to give rise to interesting phenomena such as quantum fluctuation suppression. Here, the authors propose a system of ultrastrongly coupled magnon modes in a rare earth orthoferrite as a platform for exploring such phenomena.

Non-alcoholic fatty liver disease (NAFLD) is a series of disorders of liver metabolism caused by the accumulation of lipids in the liver, which is considered the main cause of hepatocellular carcinoma. Our previous study demonstrated the promising efficacy of ginsenoside Rh4 in improving the intestinal tract and its related metabolites. Meanwhile, many studies in the literature have investigated the gut microbiota and its metabolites, such as bile acids (BAs) and short-chain fatty acids (SCFAs), which play a key role in the pathogenesis of NAFLD. Therefore, this study focused on whether Rh4 could achieve therapeutic effects on NAFLD through the gut–liver axis. The results showed that Rh4 exhibited sound therapeutic effects on the NAFLD model induced by the Western diet and CCl4 in mice. In the liver, the degrees of hepatic steatosis, lobular inflammation levels, and bile acid in the liver tissue were improved after Rh4 treatment. At the same time, Rh4 treatment significantly increased the levels of intestinal SCFAs and BAs, and these changes were accompanied by the complementary diversity and composition of intestinal flora. In addition, correlation analysis showed that Rh4 affected the expression of proteins involved in the farnesoid X receptor (FXR) signaling pathway in the liver and intestine, which modulates hepatic lipid metabolism, inflammation, and proteins related to bile acid regulation. In conclusion, our study provides a valuable insight into how Rh4 targets the gut–liver axis for the development of NAFLD, which indicates that Rh4 may be a promising candidate for the clinical therapy of NAFLD.

Precision medicine, a popular treatment strategy, has become increasingly important to the development of targeted therapy. To correlate medical imaging with prognostic and genomic data, researches in radiomics and radiogenomics have provided many pre-defined image features to describe image information quantitatively or qualitatively. However, in previous researches, there are only statistical results which prove high correlation among multi-source medical data, but those can’t give intuitive and visual result. In this paper, a deep learning based radiogenomic framework is provided to construct the linkage from lung tumor images to genomic data and implement generation process in turn, which form a bi-direction framework to map multi-source medical data. The imaging features are extracted from autoencoder under the condition of genomic data. It can obtain much more relevant features than traditional radiogenomic methods. Finally, we use generative adversarial network to transform genomic data onto tumor images, which gives a cogent result to explain the linkage between them. As a result, our framework provides a deep learning method to do radiogenomic researches more functionally and intuitively.

The existing image completion algorithms may result in problems of poor completion in the missing parts, excessive smoothing or chaotic structure of the completed areas, and large training cycle when processing more complex images. Therefore, a two-stage adversarial image completion model based on edge prediction and improved generator structure has been put forward to solve the existing problems. Firstly, Canny edge detection is utilized to extract the damaged edge image, to predict and to complete the edge information of the missing area of the image in the edge prediction network. Secondly, the predicted edge image is taken as a priori information by the Image completion network to complete the damaged area of the image, so as to make the structure information of the completed area more accurate. A-JPU module is designed to ensure the completion result and speed up training for existing models due to the enormous number of computations caused by the large use of extended convolution in the self-coding structure. Finally, the experimental results on Places 2 dataset show that the PSNR and SSIM of the image using the image completion model are higher and the subjective visual effect is closer to the real image than some other image completion models.

Worldwide, drug-induced acute liver failure is associated with a high rate of morbidity and mortality. Acetaminophen (APAP), a widely used analgesic, is one of the most common causes of acute liver injury. The purpose of this study was to determine the protective effect of protopanaxatriol-type saponin (PTS) against APAP-induced liver injury and to elucidate the mechanisms underlying this effect. Mice were pretreated with PTS via gavage for 7 days prior to receiving APAP. The results indicated that PTS was highly effective in preventing hepatotoxicity caused by APAP. Meanwhile, pretreatment with PTS resulted in a decrease in inflammation and oxidative stress. Furthermore, PTS reversed the histological change and reduced hepatic injury. Additionally, PTS inhibited the expression of proteins associated with APAP induced hepatic apoptosis in the liver. Taken together, our findings indicated that PTS protected against liver injury caused by APAP by alleviating oxidative stress, inflammation, and apoptosis via the ROS-mediated JNK pathway. Our findings suggest that PTS may have the potential as a novel therapeutic agent for APAP-induced liver injury.

Traditional Chinese Medicine is the crystallization of Chinese people’s exploration of the way of life and health for thousands of years, which contains rich philosophical ideas, medical theories and treatment methods. Therefore, by clarifying the connotation and significance of the excellent traditional culture of TCM and analyzing the realistic dilemmas in its integration into the civic education of colleges and universities, we aim to enhance the ideology and affinity of the teaching of TCM on the basis of insisting on diversified teaching measures and aligning with the teaching objectives, so as to achieve the integration of TCM culture into the whole process of civic education of young students.

Determining the adaptive mechanisms by which bumblebees adapt to high altitudes can help us to better understand their distribution, providing a basis for the future protection and utilization of bumblebee resources. For this study, the adaptive mechanisms of two dominant bumblebee species in the northeastern Qinghai-Tibet Plateau—Bombus kashmirensis and B. waltoni—were studied through transcriptomics methods. For each species, enrichment analysis of the differentially expressed genes and gene set enrichment analysis were carried out between samples collected at different altitudes (4000 m, 4500 m, and 5000 m). The results indicate that these bumblebees tend to up-regulate energy metabolism-related genes when facing extremely high-altitude environments. Of the enriched pathways up-regulated in higher altitudes, the pentose and glucuronate interconversions pathway presented the most severe up-regulation in multiple comparisons of different altitudes for B. kashmirensis, as well as the AMPK signaling pathway, which was found to be up-regulated in both species. Notably, limited by the extreme hypoxic conditions in this study, oxidative phosphorylation was found to be down-regulated with increasing altitude, which is uncommon in studies on bumblebee adaptation to high altitudes.

In this work, we formulate the image in-painting as a matrix completion problem. Traditional matrix completion methods are generally based on linear models, assuming that the matrix is low rank. When the original matrix is large scale and the observed elements are few, they will easily lead to over-fitting and their performance will also decrease significantly. Recently, researchers have tried to apply deep learning and nonlinear techniques to solve matrix completion. However, most of the existing deep learning-based methods restore each column or row of the matrix independently, which loses the global structure information of the matrix and therefore does not achieve the expected results in the image in-painting. In this paper, we propose a deep matrix factorization completion network (DMFCNet) for image in-painting by combining deep learning and a traditional matrix completion model. The main idea of DMFCNet is to map iterative updates of variables from a traditional matrix completion model into a fixed depth neural network. The potential relationships between observed matrix data are learned in a trainable end-to-end manner, which leads to a high-performance and easy-to-deploy nonlinear solution. Experimental results show that DMFCNet can provide higher matrix completion accuracy than the state-of-the-art matrix completion methods in a shorter running time.

Lung cancer has a high mortality rate and is very common. One of the main reasons for the poor prognosis of patients with lung cancer is the high incidence of metastasis. Ginsenoside Rh3, a rare ginsenoside extracted from Panax notoginseng, exhibits excellent anti-inflammatory and anti-tumor effects. Nonetheless, the inhibitory potential of Rh3 against lung cancer remains unknown. The target genes of Rh3 were screened by the PharmMapper database; the proliferation of lung cancer cells was detected by MTT assay; the migration and invasion of cells were detected by the Transwell method; and the expression of extracellular signal-regulated kinase (ERK) and EMT-related proteins in vivo and in vitro were detected by Western blotting. In addition, we established a lung metastasis model in nude mice using A549 cells to assess the effect of Rh3 on NSCLC tumor metastasis in vivo. Our findings suggest that Rh3 significantly inhibited lung cancer metastasis both in vivo and in vitro. It was determined by flow cytometry analysis that Rh3 notably inhibited cell proliferation by blocking the G1 phase. In addition, Rh3 inhibited metastasis in lung cancer cells and regulated the expression of metastasis-related proteins under hypoxia. Mechanistic studies suggested that Rh3 targeted ERK to inhibit lung cancer metastasis. The ERK inhibitor U0126 or siRNA-mediated knockdown of ERK had an enhanced effect on Rh3’s ability to inhibit lung cancer metastasis. The studies revealed that the inhibitory effect of Rh3 on the metastatic ability of lung cancer cells may be supported by ERK-related signaling pathways.

Hepatocellular carcinoma (HCC) is a kind of malignant tumor with high morbidity and mortality rates worldwide. Epithelial–mesenchymal transformation (EMT) is crucial for HCC progression and prognosis. Characteristics of the tumor microenvironment, such as hypoxia, and excessive activation of the NF-κB signaling pathway have been identified as the key inducers of EMT in HCC. In our study, we verified the crosstalk between HIF-1α signaling and NF-κB pathway and their effects on EMT in HCC cells. The results show that CoCl2-induced hypoxia could promote IκB phosphorylation to activate NF-κB signaling and vice versa. Moreover, we found that ginsenoside CK, a metabolite of protopanaxadiol saponins, could inhibit the proliferation and colony formation of different HCC cell lines. Furthermore, ginsenoside CK could impair the metastatic potential of HCC cell lines under hypoxic conditions. Mechanistically, ginsenoside CK suppressed HIF-1α/NF-κB signaling and expression level of EMT-related proteins and cytokines in hypoxia-induced or TNFα-stimulated HCC cell lines. An in vivo study revealed that the oral delivery of ginsenoside CK could inhibit the growth of xenograft tumors and block HIF-1α and NF-κB signaling as well as EMT marker expression. Our study suggests that ginsenoside CK is a potential therapy for HCC patients that functions by targeting the HIF-1α/NF-κB crosstalk.

Background Research on the function of HGH1 in breast cancer remains lacking.Methods TCGAand GEO (GSE45827) datasets investigated discrepancies in HGH1 expression in BC. An aggregate of 106 clinical samples were gathered through immunohistochemistry, KM curves were drawn for prognostic analysis, and the function of HGH1 of BC was predicted. Finally, the effects of HGH1 knockdown on MDA-MB-231 and MCF-7 BC cells were verified via CCK8, invasion, wound healing and colony formation assays.Results HGH1 is highly expressed in BC and is linked to unfavorable prognosis. HGH1 overexpression is connected to keratinization and the cell cycle and is closely related to ER and PR expression and tumor stage in BC patients. Knocking down HGH1 in BC cells inhibited the viability, invasion and migration.Conclusion Knockdown of HGH1 in breast cancer cell lines can inhibit the viability, invasion and migration of tumor cells.

Ginsenoside is a major active component of ginseng, which exhibits various pharmacological properties such as hepatoprotection, tumor suppression and diabetes resistance. In this study, the anti-diabetic effects of protopanaxadiol (PPD) and protopanaxatriol (PPT)-type saponins were explored and compared in high-fat diet/streptozocin-induced type 2 diabetes mellitus (T2DM) mice. Our results showed that low or high dose (50 mg/kg bodyweight or 150 mg/kg bodyweight) PPD and PPT significantly reduced fasting blood glucose, improved glucose tolerance and insulin resistance in T2DM mice. PPD and PPT also regulated serum lipid-related markers such as reduced total cholesterol (TC), triglyceride (TG), and low-density lipoprotein cholesterol in T2DM mice. In addition, PPD and PPT dramatically ameliorated the inflammatory responses by suppressing the secretion of pro-inflammatory cytokines like tumor necrosis factor-alpha and interleukin-6 in serum level and gene expression in liver level, and improved the antioxidant capacity by increasing the superoxide dismutase and decreasing malondialdehyde levels in the serum of T2DM mice. Moreover, the anti-diabetic effect of PPD and PPT appeared to be partially mediated by the suppression of hepatic metabolism genes expression such as peroxisome proliferator-activated receptor gamma coactivator 1-alpha, phosphoenolpyruvate carboxykinase, and glucose-6-phosphatase, as well as facilitating lipid metabolism genes expression such as microsomal TG transfer protein in the liver tissues of T2DM mice. Taken together, our results indicated that PPD and PPT might potentially act as natural anti-diabetic compounds to be used for preventing and treating the T2DM and its complications in the future.

Designing the “ideal” hydrogel/matrix which can load bone morphogenetic protein-2 (BMP-2) in a low dose and with a sustained release is the key for its successful therapeutic application to enhance osteogenesis. The current use of natural collagen sponges as hydrogel/matrix is limited due to the collagen matrix showing weak mechanical strength and unmanageable biodegradability. Furthermore, the efficiency and safe dose usage of the BMP-2 has never been seriously considered other than purely chasing the lowest dose usage and extended-release time. In this paper, we customized a novel enzymatically cross-linked recombinant human-like collagen (HLC) sponge with low immunogenicity, little risk from hidden viruses, and easy production. We obtained a unique vertical pore structure and the porosity of the HLC, which are beneficial for Mesenchymal stem cells (MSCs) migration into the HLC sponge and angiopoiesis. This HLC sponge loading with low dose BMP-2 (1 µg) possessed high mechanical strength along with a burst and a sustained release profile. These merits overcome previous limitations of HLC in bone repair and are safer and more sensitive than commercial collagens. For the first time, we identified that a 5 µg dose of BMP-2 can bring about the side effect of bone overgrowth through this sensitive delivery system. Osteoinduction of the HLC-BMP sponges was proved by an in vivo mouse ectopic bone model and a rat cranial defect repair model. The method and the HLC-BMP sponge have the potential to release other growth factors and aid other tissue regeneration. Additionally, the ability to mass-produce HLC in our study overcomes the current supply shortage, which limits bone repair in the clinic.

Objective To assess the outcome of patients with cancer-related sepsis requiring continuous renal replacement therapy (CRRT) in a single-center pediatric intensive care unit (PICU). Method Children with sepsis who necessitate CRRT from January 2017 to December 2021 were enrolled. The patients with leukemia/lymphoma or solid tumors were defined as underlying cancer. Multivariate logistic regression analysis was performed to identify the death risk factors in patients with cancer-related sepsis. Results A total of 146 patients were qualified for inclusion. Forty-six (31.5%) patients with cancer-related sepsis and 100 (68.5%) non-cancer-related sepsis. The overall PICU mortality was 28.1% (41/146), and mortality was significantly higher in cancer-related sepsis patients compared with non-cancer patients (41.3% vs. 22.0%, p = 0.016). Need mechanical ventilation, p-SOFA, acute liver failure, higher fluid overload at CRRT initiation, hypoalbuminemia, and high inotropic support were associated with PICU mortality in cancer-related sepsis patients. Moreover, levels of IL-6, total bilirubin, creatinine, blood urea nitrogen, and international normalized ratio were significantly higher in non-survivors than survivors. In multivariate logistic regression analysis, pediatric sequential organ failure assessment (p-SOFA) score (OR:1.805 [95%CI: 1.047–3.113]) and serum albumin level (OR: 0.758 [95%CI: 0.581 -0.988]) were death risk factors in cancer-related sepsis receiving CRRT, and the AUC of combined index of p-SOFA and albumin was 0.852 (95% CI: 0.730–0.974). Conclusion The overall PICU mortality is high in cancer-related sepsis necessitating CRRT. Higher p-SOFA and lower albumin were independent risk factors for PICU mortality. [ABSTRACT FROM AUTHOR]