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Abstract Primary malignant bone tumors are the third highest cause of cancer-related mortality among patients under the age of 20. X-ray scan is the primary tool for detecting bone tumors. However, due to the varying morphologies of bone tumors, it is challenging for radiologists to make a definitive diagnosis based on radiographs. With the recent advancement in deep learning algorithms, there is a surge of interest in computer-aided diagnosis of primary bone tumors. Nonetheless, the development in this field has been hindered by the lack of publicly available X-ray datasets for bone tumors. To tackle this challenge, we established the Bone Tumor X-ray Radiograph dataset (termed BTXRD) in collaboration with multiple medical institutes and hospitals. The BTXRD dataset comprises 3,746 bone images (1,879 normal and 1,867 tumor), with clinical information and global labels available for each image, and distinct mask and annotated bounding box for each tumor instance. This publicly available dataset can support the development and evaluation of deep learning algorithms for the diagnosis of primary bone tumors.

Abstract Phosphorus in crucial for all living organisms. In vertebrate, cellular phosphate homeostasis is partly controlled by XPR1, a poorly characterized inositol pyrophosphate-dependent phosphate exporter. Here, we report the cryo-EM structure of human XPR1, which forms a loose dimer with 10 transmembrane helices (TM) in each protomer. The structure consists of a scaffold domain (TM1, TM3-4) and a core domain (TM2, TM5-10) structurally related to ion-translocating rhodopsins. Bound phosphate is observed in a tunnel within the core domain at a narrow point that separates the tunnel into intracellular and extracellular vestibules. This site contains a cluster of basic residues that coordinate phosphate and a conserved W573 essential for export function. Loss of inositol pyrophosphate binding is accompanied by structural movements in TM9 and the W573 sidechain, closing the extracellular vestibule and blocking phosphate export. These findings provide insight into XPR1 mechanism and pave the way for further in-depth XPR1 studies.

The transformers have achieved significant accomplishments in the natural language processing as its outstanding parallel processing capabilities and highly flexible attention mechanism. In addition, increasing studies based on transformers have been proposed to model single-cell data. In this review, we attempt to systematically summarize the single-cell language models and applications based on transformers. First, we provide a detailed introduction about the structures and principles of transformers. Then, we review the single-cell language models and large language models for single-cell data analysis. Moreover, we explore the datasets and applications of single-cell language models in downstream tasks, such as batch correction, cell clustering, cell type annotation, gene regulatory network inference, and perturbation response. Further, we discuss the challenges of single-cell language models and provide promising research directions. We hope this review will serve as an up-to-date reference for researchers who are interested in the direction of single-cell language models.

With the rapidly development of biotechnology, it is now possible to obtain single-cell multi-omics data in the same cell. However, how to integrate and analyze these single-cell multi-omics data remains a great challenge. Herein, we introduce an interpretable multitask framework (scMoMtF) for comprehensively analyzing single-cell multi-omics data. The scMoMtF can simultaneously solve multiple key tasks of single-cell multi-omics data including dimension reduction, cell classification and data simulation. The experimental results shows that scMoMtF outperforms current state-of-the-art algorithms on these tasks. In addition, scMoMtF has interpretability which allowing researchers to gain a reliable understanding of potential biological features and mechanisms in single-cell multi-omics data.

Abstract The otopetrin (OTOP) proteins were recently characterized as extracellular proton-activated proton channels. Several recent OTOP channel structures demonstrated that the channels form a dimer with each subunit adopting a double-barrel architecture. However, the structural mechanisms underlying some basic functional properties of the OTOP channels remain unresolved, including extracellular pH activation, proton conducting pathway, and rapid desensitization. In this study, we performed structural and functional characterization of the Caenorhabditis elegans OTOP8 (CeOTOP8) and mouse OTOP2 (mOTOP2) and illuminated a set of conformational changes related to the proton-conducting process in OTOP. The structures of CeOTOP8 reveal the conformational change at the N-terminal part of TM12 that renders the channel in a transiently proton-transferring state, elucidating an inter-barrel, Glu/His-bridged proton passage within each subunit. The structures of mOTOP2 reveal the conformational change at the N-terminal part of TM6 that exposes the central glutamate to the extracellular solution for protonation. In addition, the structural comparison between CeOTOP8 and mOTOP2, along with the structure-based mutagenesis, demonstrates that an inter-subunit movement at the OTOP channel dimer interface plays a central role in regulating channel activity. Combining the structural information from both channels, we propose a working model describing the multi-step conformational changes during the proton conducting process.

Circulating tumour cells (CTCs) and CTC clusters are considered metastatic precursors due to their ability to seed distant metastasis. However, navigating the bloodstream presents a significant challenge for CTCs, as they must endure fluid shear forces and resist detachment-induced anoikis. Consequently, while a large number of cells from the primary tumour may enter the circulation, only a tiny fraction will result in metastasis. Nevertheless, the metastatic potency dramatically increases when CTCs travel in conjunction with other cell types to form CTC clusters, including neutrophils, myeloid-derived suppressor cells, macrophages, platelets, cancer-associated fibroblasts and red blood cells found in circulation. Such heterotypic CTC clustering events have been identified in a variety of cancer types and may serve as intriguing therapeutic targets and novel biomarkers for liquid biopsy. This review summarises recent advances in microfluidic technologies designed for the isolation of CTC clusters and explores the biological properties of distinct types of CTC clusters within the circulatory system. Investigation of the mechanisms of CTC cluster–blood microenvironment interactions may offer a promising avenue for gaining fresh insights into CTC cluster-mediated metastatic progression and reveal potential opportunities for devising personalised antimetastasis treatments.

Abstract In multiple myeloma, abnormal plasma cells establish oncogenic niches within the bone marrow by engaging the NF-κB pathway to nurture their survival while they accumulate pro-proliferative mutations. Under these conditions, many cases eventually develop genetic abnormalities endowing them with constitutive NF-κB activation. Here, we find that sustained NF-κB/p52 levels resulting from such mutations favours the recruitment of enhancers beyond the normal B-cell repertoire. Furthermore, through targeted disruption of p52, we characterise how such enhancers are complicit in the formation of super-enhancers and the establishment of cis-regulatory interactions with myeloma dependencies during constitutive activation of p52. Finally, we functionally validate the pathological impact of these cis-regulatory modules on cell and tumour phenotypes using in vitro and in vivo models, confirming RGS1 as a p52-dependent myeloma driver. We conclude that the divergent epigenomic reprogramming enforced by aberrant non-canonical NF-κB signalling potentiates transcriptional programs beneficial for multiple myeloma progression.

The steel box girder bridge is a structure composed of mutually vertical stiffening ribs (longitudinal ribs and transverse ribs) that carry the loads of vehicles. Since the external loads are usually complex and variable, the rational design of the bridge components is a topic that deserves more attention. The purpose of this study is to explore the optimal range of some of the component design parameters, expecting to reduce costs while ensuring the stress-carrying capacity. A finite element model (FEM) based on ABAQUS was built and the results were verified by laboratory experiments. The varied thicknesses of the bridge deck, diaphragm, and U-rib were explored based on the validated FEM. The simulation results fit well with the experimental results, which proved that the FEM was quite reliable. The stress analysis results demonstrated an optimal range of 18–20 mm for bridge deck thickness, 14–16 mm for diaphragm thickness, and 8–10 mm for U-rib thickness. The present study holds significant reference value for the design and optimization of multiple steel box girder bridge components, which could further provide a theoretical foundation for related research in this field.

ObjectiveThis study aimed to construct a machine learning model using clinical variables and ultrasound radiomics features for the prediction of the benign or malignant nature of pancreatic tumors.Methods242 pancreatic tumor patients who were hospitalized at the First Affiliated Hospital of Guangxi Medical University between January 2020 and June 2023 were included in this retrospective study. The patients were randomly divided into a training cohort (n=169) and a test cohort (n=73). We collected 28 clinical features from the patients. Concurrently, 306 radiomics features were extracted from the ultrasound images of the patients’ tumors. Initially, a clinical model was constructed using the logistic regression algorithm. Subsequently, radiomics models were built using SVM, random forest, XGBoost, and KNN algorithms. Finally, we combined clinical features with a new feature RAD prob calculated by applying radiomics model to construct a fusion model, and developed a nomogram based on the fusion model.ResultsThe performance of the fusion model surpassed that of both the clinical and radiomics models. In the training cohort, the fusion model achieved an AUC of 0.978 (95% CI: 0.96–0.99) during 5-fold cross-validation and an AUC of 0.925 (95% CI: 0.86–0.98) in the test cohort. Calibration curve and decision curve analyses demonstrated that the nomogram constructed from the fusion model has high accuracy and clinical utility.ConclusionThe fusion model containing clinical and ultrasound radiomics features showed excellent performance in predicting the benign or malignant nature of pancreatic tumors.

Abstract Background and objectives Comprehensive analysis of multi-omics data is crucial for accurately formulating effective treatment plans for complex diseases. Supervised ensemble methods have gained popularity in recent years for multi-omics data analysis. However, existing research based on supervised learning algorithms often fails to fully harness the information from unlabeled nodes and overlooks the latent features within and among different omics, as well as the various associations among features. Here, we present a novel multi-omics integrative method MOSEGCN, based on the Transformer multi-head self-attention mechanism and Graph Convolutional Networks(GCN), with the aim of enhancing the accuracy of complex disease classification. MOSEGCN first employs the Transformer multi-head self-attention mechanism and Similarity Network Fusion (SNF) to separately learn the inherent correlations of latent features within and among different omics, constructing a comprehensive view of diseases. Subsequently, it feeds the learned crucial information into a self-ensembling Graph Convolutional Network (SEGCN) built upon semi-supervised learning methods for training and testing, facilitating a better analysis and utilization of information from multi-omics data to achieve precise classification of disease subtypes. Results The experimental results show that MOSEGCN outperforms several state-of-the-art multi-omics integrative analysis approaches on three types of omics data: mRNA expression data, microRNA expression data, and DNA methylation data, with accuracy rates of 83.0% for Alzheimer's disease and 86.7% for breast cancer subtyping. Furthermore, MOSEGCN exhibits strong generalizability on the GBM dataset, enabling the identification of important biomarkers for related diseases. Conclusion MOSEGCN explores the significant relationship information among different omics and within each omics' latent features, effectively leveraging labeled and unlabeled information to further enhance the accuracy of complex disease classification. It also provides a promising approach for identifying reliable biomarkers, paving the way for personalized medicine.

Abstract Widespread lake drainage can lead to large-scale drying in Arctic lake-rich areas, affecting hydrology, ecosystems and permafrost carbon dynamics. To date, the spatio-temporal distribution, driving factors, and post-drainage dynamics of lake drainage events across the Arctic remain unclear. Using satellite remote sensing and surface water products, we identify over 35,000 (~0.6% of all lakes) lake drainage events in the northern permafrost zone between 1984 and 2020, with approximately half being relatively understudied non-thermokarst lakes. Smaller, thermokarst, and discontinuous permafrost area lakes are more susceptible to drainage compared to their larger, non-thermokarst, and continuous permafrost area counterparts. Over time, discontinuous permafrost areas contribute more drained lakes annually than continuous permafrost areas. Following drainage, vegetation rapidly colonizes drained lake basins, with thermokarst drained lake basins showing significantly higher vegetation growth rates and greenness levels than their non-thermokarst counterparts. Under warming, drained lake basins are likely to become more prevalent and serve as greening hotspots, playing an important role in shaping Arctic ecosystems.

Abstract Background Mesenchymal stromal cells (MSCs) have broad potential as a cell therapy including for the treatment of drug-resistant inflammatory conditions with abnormal T cell proliferation such as graft-versus-host disease (GVHD). Clinical success, however, has been complicated by the heterogeneity of culture-expanded MSCs as well as donor variability. Here, we devise culture conditions that promote expansion of MSCs with enhanced immunomodulatory functions both in vitro and in animal models of GVHD. Methods Human bone marrow-derived MSCs were expanded at high-confluency (MSCHC) and low-confluency state (MSCLC). Their immunomodulatory properties were evaluated with in vitro co-culture assays based on suppression of activated T cell proliferation and secretion of pro-inflammatory cytokines from activated T cells. Metabolic state of these cells was determined, while RNA sequencing was performed to explore transcriptome of these MSCs. Ex vivo expanded MSCHC or MSCLC was injected into human peripheral blood mononuclear cells (PBMC)-induced GVHD mouse model to determine their in vivo therapeutic efficacy based on clinical grade scoring, human CD45+ blood count and histopathological examination. Results As compared to MSCLC, MSCHC significantly reduced both the proliferation of anti-CD3/CD28-activated T cells and secretion of pro-inflammatory cytokines upon MSCHC co-culture across several donors even in the absence of cytokine priming. Mechanistically, metabolic analysis of MSCHC prior to co-culture with activated T cells showed increased glycolytic metabolism and lactate secretion compared to MSCLC, consistent with their ability to inhibit T cell proliferation. Transcriptome analysis further revealed differential expression of immunomodulatory genes including TRIM29, BPIFB4, MMP3 and SPP1 in MSCHC as well as enriched pathways including cytokine–cytokine receptor interactions, cell adhesion and PI3K-AKT signalling. Lastly, we demonstrate in a human PBMC-induced GVHD mouse model that delivery of MSCHC showed greater suppression of inflammation and improved outcomes compared to MSCLC and saline controls. Conclusion Our study provides evidence that ex vivo expansion of MSCs at high confluency alters the metabolic and transcriptomic states of these cells. Importantly, this approach maximizes the production of MSCs with enhanced immunomodulatory functions without priming, thus providing a non-invasive and generalizable strategy for improving the use of MSCs for the treatment of inflammatory diseases.

Abstract Background Preclinical models are often used for cancer studies and evaluation of novel therapeutics. The relevance of these models has vastly improved with mice bearing a human immune system, especially in the context of immunotherapy. Nonetheless, cancer is an age-related disease, and studies often overlook the effects of aging. Here we have established a humanized mouse model of human immune aging to investigate the role of this phenomenon on liver tumor dynamics. Methods Multiple organs and tissues (blood, thymus, lung, liver, spleen and bone marrow) were harvested from NOD-scid IL2rγ −/− (NIKO) mice reconstituted with human immune cells, over a period of 60 weeks post-birth, for immune profiling. Young and aging immune cells were compared for transcriptomic changes and functional differences. Effect of immune aging was investigated in a liver cancer humanized mouse model. Results Focusing on the T cell population, which is central to cancer immunosurveillance and immunotherapy, we showed that the proportion of naïve T cells declined while memory subsets and senescent-like cells increased with age. RNA-sequencing revealed that downregulated genes were related to immune responses and processes, and this was corroborated by reduced cytokine production in aging T cells. Finally, we showed faster liver tumor growth in aging than younger humanized mice, which could be attributed to specific pathways of aging T cell exhaustion. Conclusion Our work improves on existing humanized (immune) mouse model and highlights the importance of considering immune aging in liver cancer modeling.

Summary: Interleukin-37 (IL-37) has been shown to inhibit tumor growth in various cancer types. However, the immune regulatory function of IL-37 in the tumor microenvironment is unclear. Here, we established a human leukocyte antigen-I (HLA-I)-matched humanized patient-derived xenograft hepatocellular carcinoma (HCC) model and three murine orthotopic HCC models to study the function of IL-37 in the tumor microenvironment. We found that IL-37 inhibited HCC growth and promoted T cell activation. Further study revealed that IL-37 impaired the immunosuppressive capacity of myeloid-derived suppressor cells (MDSCs). Pretreatment of MDSCs with IL-37 before adoptive transfer attenuated their tumor-promoting function in HCC tumor-bearing mice. Moreover, IL-37 promoted both glycolysis and oxidative phosphorylation in MDSCs, resulting in the upregulation of ATP release, which impaired the immunosuppressive capacity of MDSCs. Collectively, we demonstrated that IL-37 inhibited tumor development through dampening MDSCs’ immunosuppressive capacity in the tumor microenvironment via metabolic reprogramming, making it a promising target for future cancer immunotherapy.

Abstract The COVID-19 pandemic has sickened millions, cost lives and has devastated the global economy. Various animal models for experimental infection with SARS-CoV-2 have played a key role in many aspects of COVID-19 research. Here, we describe a humanized hACE2 (adenovirus expressing hACE2) NOD-SCID IL2Rγ−/− (NIKO) mouse model and compare infection with ancestral and mutant (SARS-CoV-2-∆382) strains of SARS-CoV-2. Immune cell infiltration, inflammation, lung damage and pro-inflammatory cytokines and chemokines was observed in humanized hACE2 NIKO mice. Humanized hACE2 NIKO mice infected with the ancestral and mutant SARS-CoV-2 strain had lung inflammation and production of pro-inflammatory cytokines and chemokines. This model can aid in examining the pathological basis of SARS-CoV-2 infection in a human immune environment and evaluation of therapeutic interventions.

Engineered T cells are used for tumour immunotherapy but can have side effects and need multiple treatment rounds. Here during expansion of T cells from patients, the authors use an inhibitor of the epigenetic regulator G9a/GLP and show that this increases T cell cytotoxic function and tumour reduction in vitro and in vivo respectively.

Abstract In keeping with the rule of “form follows function”, morphological aspects of a cell can reflect its role. Here, it is shown that the cellular granularity of a lymphocyte, represented by its intrinsic side scatter (SSC), is a potent indicator of its cell state and function. The granularity of a lymphocyte increases from naïve to terminal effector state. High‐throughput cell‐sorting yields a SSChigh population that can mediate immediate effector functions, and a highly prolific SSClow population that can give rise to the replenishment of the memory pool. CAR‐T cells derived from the younger SSClow population possess desirable attributes for immunotherapy, manifested by increased naïve‐like cells and stem cell memory (TSCM)‐like cells together with a balanced CD4/CD8 ratio, as well as enhanced target‐killing in vitro and in vivo. Altogether, lymphocyte segregation based on biophysical properties is an effective approach for label‐free selection of cells that share collective functions and can have important applications for cell‐based immunotherapies.

Abstract Background Currently available anti-leukemia drugs have shown limited success in the treatment of acute myeloid leukemia (AML) due to their poor access to bone marrow niche supporting leukemic cell proliferation. Results Herein, we report a bone marrow-targetable green tea catechin-based micellar nanocomplex for synergistic AML therapy. The nanocomplex was found to synergistically amplify the anti-leukemic potency of sorafenib via selective disruption of pro-survival mTOR signaling. In vivo biodistribution study demonstrated about 11-fold greater bone marrow accumulation of the nanocomplex compared to free sorafenib. In AML patient-derived xenograft (AML-PDX) mouse model, administration of the nanocomplex effectively eradicated bone marrow-residing leukemic blasts and improved survival rates without noticeable off-target toxicity. Conclusion This study may provide insights into the rational design of nanomedicine platforms enabling bone marrow-targeted delivery of therapeutic agents for the treatment of AML and other bone marrow diseases. Graphical Abstract

Abstract Hepatitis B Virus (HBV) is a hepadnavirus that is the principal pathogen underlying viral liver disease in human populations. In this study, we describe the isolation and characterization of a fully human monoclonal antibody for HBV. This HuMab was isolated by a combinatorial screen of the memory B-cell repertoire from an acute/recovered HBV-infected patient. Lead candidate selection was based upon strong binding and neutralizing activity for live HBV. We provide a detailed biochemical/biophysical, and subclass characterization of its specificity and affinity against all of the principal HBV genotypes combined with a functional analysis of its in vitro activity. We also demonstrate its potential as a prophylaxis/therapy in vivo using human liver chimeric mouse models for HBV infection. These data have important implications for our understanding of natural human immunity to HBV and suggest that this potentially represents a new antibody-based anti-viral candidate for prophylaxis and/or therapy for HBV infection.

During the erythrocyte (RBC) stage of P. falciparum infection variant surface antigens (VSAs) such as PfEMP1s and RIFINs expressed on RBCs are important for infection and evasion of host innate immune system. Here, Chew et al. use a NSG mouse model, which is deficient in B, T and NK cells but retains macrophages, to show that PfEMP1 surface expression is required for in vivo adaptation as well as in vitro evasion of macrophage phagocytosis.

Hypoxia can promote tumor escape from immune surveillance and immunotherapy. Here, the authors show that hypoxia induces T and NK cell dysfunction through HIF1α-mediated epigenetic suppression of effector gene expression, conferring resistance to anti-PD1 blockade in triple negative breast cancer models.

IntroductionHepatocellular carcinoma (HCC) is an aggressive malignancy with steadily increasing incidence rates worldwide and poor therapeutic outcomes. Studies show that metabolic reprogramming plays a key role in tumor genesis and progression. In this study, we analyzed the metabolic heterogeneity of epithelial cells in the HCC and screened for potential biomarkers.MethodsThe hepatic single-cell RNA sequencing (scRNA-seq) datasets of HCC patients and healthy controls were obtained from the Gene Expression Omnibus (GEO) database. Based on data intergration and measurement of differences among groups, the metabolic epithelial cell subpopulations were identified. The single-cell metabolic pathway was analyzed and the myeloid subpopulations were identified. Cell-cell interaction analysis and single-cell proliferation analysis were performed. The gene expression profiles of HCC patients were obtained from the GSE14520 dataset of GEO and TCGA-LIHC cohort of the UCSC Xena website. Immune analysis was performed. The differentially expressed genes (DEGs) were identified and functionally annotated. Tumor tissues from HCC patients were probed with anti-ALDOA, anti-CD68, anti-CD163, anti-CD4 and anti-FOXP3 antibodies. Results We analyzed the scRNA-seq data from 48 HCC patients and 14 healthy controls. The epithelial cells were significantly enriched in HCC patients compared to the controls (p = 0.011). The epithelial cells from HCC patients were classified into two metabolism-related subpopulations (MRSs) – pertaining to amino acid metabolism (MRS1) and glycolysis (MRS2). Depending on the abundance of these metabolic subpopulations, the HCC patients were also classified into the MRS1 and MRS2 subtype distinct prognoses and immune infiltration. The MRS2 group had significantly worse clinical outcomes and more inflamed tumor microenvironment (TME), as well as a stronger crosstalk between MRS2 cells and immune subpopulations that resulted in an immunosuppressive TME. We also detected high expression levels of ALDOA in the MRS2 cells and HCC tissues. In the clinical cohort, HCC patients with higher ALDOA expression showed greater enrichment of immunosuppressive cells including M2 macrophages and T regulatory cells.DiscussionThe glycolytic subtype of HCC cells with high ALDOA expression is associated with an immunosuppressive TME and predicts worse clinical outcomes, providing new insights into the metabolism and prognosis of HCC.

The unequal area facility layout problem (UA-FLP) refers to the reasonable arrangement of a certain number of facilities in a given layout area. The facility layout should satisfy given layout constraints and optimize given optimization objectives as far as possible. In this paper, a method combining improved lowest horizontal line method and particle swarm optimization algorithm is proposed to solve UA-FLP, which achieves multi-objective optimization of material handling cost, the adjacent value and the utilization rate of floor shop. On the one hand, the algorithm formulates facility packing rules through the improved lowest horizontal line method, which simplifies the legalization of facility layout. On the other hand, a modified particle swarm optimization (PSO) algorithm combining objective space division method (OSD) and niche technology is used for multi-objective optimization. The proposed algorithm overcomes the shortcomings of previous facility layout methods such as complex overlapping interference process, large amount of calculation and long time for multi-objective optimization. Compared with the comparison methods, the results show that material handling cost is reduced by 1%–6% and the utilization ratio of floor shop is increased by 2%–7%.

As the world's demand for rare earths soars, the supply of rare earths is in danger of being depleted. The recycling rate of rare earth is only 1 % of all rare earth. Therefore, the recovery of rare earth is crucial and challenging. A phosphoric acid modified kaolin (P-K) was prepared by a simple mechanochemical method, and the selective recovery of rare earth element lanthanum (La) was studied. The P-K was characterized and analyzed by SEM, EDS, and FT-IR. Additionally, the different modification conditions of P-K preparation such as milling speed, time, and mass ratio of H3PO4/kaolin on La adsorption were investigated, and the effects of operating conditions such as solution initial pH, adsorption time, and adsorbent concentration on La adsorption were studied. The adsorption behavior of La by P-K was consistent with the Langmuir isothermal adsorption model with a saturated adsorption capacity of 18.55 mg/g, and the adsorption kinetics accorded with the pseudo-second-order kinetic model. Additionally, La in the mixture of La and magnesium (Mg) can be selectively adsorbed through P-K, while the adsorption rate of Mg is almost 0. P-K load with La could be eluted by 0.03 mol/L HCl, and the elution rate of La can reach 99.9 %. As a result, this study demonstrates a method for the recovery of La from rare earth wastewater.

Human hepatocyte transplantation for liver disease treatment have been hampered by the lack of quality human hepatocytes. Pigs with their large body size, longevity and physiological similarities with human are appropriate animal models for the in vivo expansion of human hepatocytes. Here we report on the generation of RAG2-/-IL2Rγ-/YFAH-/- (RGFKO) pigs via CRISPR/Cas9 system and somatic cell nuclear transfer. We showed that thymic and splenic development in RGFKO pigs was impaired. V(D)J recombination processes were also inactivated. Consequently, RGFKO pigs had significantly reduced numbers of porcine T, B and NK cells. Moreover, due to the loss of FAH, porcine hepatocytes continuously undergo apoptosis and consequently suffer hepatic damage. Thus, RGFKO pigs are both immune deficient and constantly suffer liver injury in the absence of NTBC supplementation. These results suggest that RGFKO pigs have the potential to be engrafted with human hepatocytes without immune rejection, thereby allowing for large scale expansion of human hepatocytes.

Secondary structures have been proved to relate with the great functional diversity of RNA. There have been many studies to predict and compare the RNA secondary structures. However, fast and accurate comparison of RNA secondary structures with arbitrary pseudoknots is a challenging issue due to the hidden but important structural properties, such as the distribution of stems and branches. In this paper, we construct a novel RNA secondary structure model called modified adjoining grammars binary tree (BTMGCSP). It can not only represent the complex RNA secondary structure including arbitrary pseudoknots intuitively, but also reserve RNA secondary structure properties. Further, we propose vector-edit distance to measure the structure similarity between BTMGCSP trees converted from RNA sequences and their secondary structures for classifying conserved stem pattern. The experimental results show that our method substantially reduces the memory and time consumption in contrast to previous algorithms, such as O((n/k)2) and O(n/k) for time and space complexity, respectively. In particular, the AUC value of our method achieves 0.949 in PseudoBase.

Cancer therapeutics have undergone immense research over the past decade. While chemotherapies remain the mainstay treatments for many cancers, the advent of new molecular techniques has opened doors for more targeted modalities towards cancer cells. Although immune checkpoint inhibitors (ICIs) have demonstrated therapeutic efficacy in treating cancer, adverse side effects related to excessive inflammation are often reported. There is a lack of clinically relevant animal models to probe the human immune response towards ICI-based interventions. Humanized mouse models have emerged as valuable tools for pre-clinical research to evaluate the efficacy and safety of immunotherapy. This review focuses on the establishment of humanized mouse models, highlighting the challenges and recent advances in these models for targeted drug discovery and the validation of therapeutic strategies in cancer treatment. Furthermore, the potential of these models in the process of uncovering novel disease mechanisms is discussed.

BackgroundThe objective of this study is to compare clinical and surgical outcomes of appendectomy among elderly and non-elderly subjects.MethodsA systematic search was conducted on PubMed, Scopus, and Google academic databases. Studies, observational in design, that compared peri-and postoperative outcomes of appendectomy, in patients with acute appendicitis, between elderly and non-elderly/younger subjects were considered for inclusion. Statistical analysis was performed using STATA software.ResultsA total of 15 studies were included. Compared to non-elderly patients, those that were elderly had an increased risk of complicated appendicitis [relative risk (RR), 2.38; 95% CI: 2.13, 2.66], peritonitis [RR, 1.88; 95% CI: 1.36, 2.59], and conversion from laparoscopic to open appendectomy [RR, 3.02; 95% CI: 2.31, 3.95]. The risk of overall postoperative complications [RR, 2.59; 95% CI: 2.19, 3.06], intra-abdominal abscess [RR, 1.84; 95% CI: 1.15, 2.96], wound infection [RR, 3.80; 95% CI: 2.57, 5.61], and use of postoperative drainage [RR, 1.14; 95% CI: 1.09, 1.19] was higher among the elderly. The risk of readmission (30 days) [RR, 1.61; 95% CI: 1.16, 2.24] and mortality (30 days) [RR, 12.48; 95% CI: 3.65, 42.7] was also higher among elderly.ConclusionsFindings suggest an increased risk of peri-and postoperative complications among elderly subjects undergoing appendectomy, compared to non-elderly subjects.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/, identifier: CRD42021286157.

The crack in the rock is a system that coexists with multiscale and interaction cracks, and it is necessary to evaluate the deformation, stability, and strength of rock mass in many engineering projects and the failure of rock are related to the distribution of cracks in the rock. Cracks of different lengths were set in rock-like materials, and uniaxial loading experiments were carried out under 30 test conditions by changing the length of the rock bridge and the short crack as well as the crack angle. During the experiment, a high-speed camera system was used to record the failure process of the specimens. The following conclusions were obtained: when the crack angle is in the range of 30°to 60° with the loading direction, the shorter cracks are more likely to propagate and coalesce. Most of the specimens initiate cracking from the outer tip of the longer crack and the key point of crack instability is the outer tip of the longer crack. The coalescence mode of the cracks is mainly related to the length of the rock bridge length and the crack angle.

Osteosarcoma is a kind of primary malignant tumor of bone. In recent years, its therapeutic effect and prognostic survival are dissatisfactory. The tumor immune microenvironment (TIME) reflects immune status of patients, but it is little known in osteosarcoma. Therefore, this study attempts to conduct a comprehensive analysis to explore TIME of osteosarcoma and identify TIME-related subtypes for clinical management and treatment. We successfully established two novel tumor immune infiltration clusters (TIIC) which are characterized by difference of microenvironment and immune-related biological processes. High tumor immune infiltration cluster (H-TIIC) subtypes with higher immune infiltration score shows a better overall survival. Further, the two immune subtypes are shown to differ in immunotherapy and chemotherapy. The results would be helpful for clinical decision in osteosarcoma.

Cancer immunotherapy is a type of treatment that restores and stimulates human immune system to inhibit cancer growth or eradicate cancer. It serves as one of the latest systemic therapies, which has been approved to treat different types of cancer in patients. Nevertheless, the clinical response rate is unsatisfactory and the response observed is mostly a partial response in patients. Despite the continuous improvement and identification of novel cancer immunotherapy, there is a pressing need to establish a robust platform to evaluate the efficacy and safety of pre-clinical drugs, simulate the interaction between patients’ tumor and immune system, and predict patients’ responses to the treatment. In this review, we summarize the pros and cons of existing immuno-oncology assay platforms, especially the humanized mouse models for the screening of cancer immunotherapy drugs. In addition, various emerging trends and progress of utilizing humanized mouse models as the screening tool are discussed. Of note, humanized mouse models can also be used for further development of personalized precision medicines to treat cancer. Collectively, these highlight the significance of humanized mouse models as the important platform for the screening of next generation cancer immunotherapy in vivo.

Industrial emissions of volatile organic compounds (VOCs) directly impact air quality downwind of facilities and contribute to regional ozone and secondary organic aerosol production. Positive matrix factorization (PMF) is often used to apportion VOCs to their respective sources using measurement data collected at fixed sites, for example air quality monitoring stations. Here, we apply PMF analysis to high time-resolution VOC measurement data collected both while stationary and while moving using a mobile monitoring platform. The stationary monitoring periods facilitated the extraction of representative industrial VOC source profiles while the mobile monitoring periods were critical for the spatial identification of VOC hotspots. Data were collected over five days in a heavily industrialized region of southwestern Ontario containing several refineries, petrochemical production facilities and a chemical waste disposal facility. Factors associated with petroleum, chemical waste and rubber production were identified and ambient mixing ratios of selected aromatic, unsaturated and oxygenated VOCs were apportioned to local and background sources. Fugitive emissions of benzene, highly localized and predominantly associated with storage, were found to be the dominant local contributor to ambient benzene mixing ratios measured while mobile. Toluene and substituted aromatics were predominantly associated with refining and traffic, while methyl ethyl ketone was linked to chemical waste handling. The approach described here facilitates the apportionment of VOCs to their respective local industrial sources at high spatial and temporal resolution. This information can be used to identify problematic source locations and to inform VOC emission abatement strategies.

Identifying drug–target interaction (DTI) is the basis for drug development. However, the method of using biochemical experiments to discover drug-target interactions has low coverage and high costs. Many computational methods have been developed to predict potential drug-target interactions based on known drug-target interactions, but the accuracy of these methods still needs to be improved. In this article, a graph autoencoder approach for DTI prediction (GADTI) was proposed to discover potential interactions between drugs and targets using a heterogeneous network, which integrates diverse drug-related and target-related datasets. Its encoder consists of two components: a graph convolutional network (GCN) and a random walk with restart (RWR). And the decoder is DistMult, a matrix factorization model, using embedding vectors from encoder to discover potential DTIs. The combination of GCN and RWR can provide nodes with more information through a larger neighborhood, and it can also avoid over-smoothing and computational complexity caused by multi-layer message passing. Based on the 10-fold cross-validation, we conduct three experiments in different scenarios. The results show that GADTI is superior to the baseline methods in both the area under the receiver operator characteristic curve and the area under the precision–recall curve. In addition, based on the latest Drugbank dataset (V5.1.8), the case study shows that 54.8% of new approved DTIs are predicted by GADTI.

Soil microbial communities are an important part of the soil ecosystem in greenhouse vegetable fields, where ammonia-oxidising microorganisms play a key role in nitrogen conversion. The health and stability of the ecological environment of greenhouse vegetable fields are affected by the number of years of continuous cultivation. We used real-time PCR amplification and 16S rRNA gene sequencing to analyse the changes in soil microbial community structure and diversity in different planting years (0, 3, 9, and 13). The content of environmental factors increased with the increase of planting year; the NO3−-N content in the 0–20 cm soil layer showed a cumulative trend, peaking to 1290–1390 mg/kg in year 13. The abundance of operational taxonomic units (OTUs) in the microbial community gradually decreased, and the OTUs of 0–20 cm soil layer in year 13 decreased by 52.2% compared to year 0. The Shannon and Simpson indices indicated a substantial decrease in microbial diversity after year 9. The dominant phyla in the soil microbial community mainly included Firmicutes (23.6%), Actinobacteria (23.2%), Proteobacteria (17.6%), Crenarchaeota (83.4%), and Euryarchaeota (2.7%). Nitrosopumilus and Nitrososphaera in the ammonia-oxidising archaeal (AOA) community and Nitrolancea and Nitrospira in the ammonia-oxidising bacterial (AOB) community dominated the ammonia-oxidising microorganisms. With the increase in planting years in greenhouse vegetable fields, the structure of soil microbial community changed significantly, with soil biomass and diversity significantly decreasing in years 9 and 13. Reasonable fertilization and planting year would improve microbial activity and provide a basis for sustainable utilization and high-quality production in greenhouse vegetable fields.

Viral hepatitis particularly Hepatitis B Virus (HBV) is still an ongoing health issue worldwide. Despite the vast technological advancements in research and development, only HBV vaccines, typically given during early years, are currently available as a preventive measure against acquiring the disease from a secondary source. In general, HBV can be cleared naturally by the human immune system if detected at low levels early. However, long term circulation of HBV in the peripheral blood may be detrimental to the human liver, specifically targeting human hepatocytes for cccDNA integration which inevitably supports HBV life cycle for the purpose of reinfection in healthy cells. Although there is some success in using nucleoside analogs or polyclonal antibodies targeting HBV surface antigens (HBsAg) in patients with acute or chronic HBV+ (CHB), majority of them would either respond only partially or succumb to the disease entirely unless they undergo liver transplants from a fully matched healthy donor and even so may not necessarily guarantee a 100% chance of survival. Indeed, in vitro/ex vivo cultures and various transgenic animal models have already provided us with a good understanding of HBV but they primarily lack human specificity or virus-host interactions in the presence of human immune surveillance. Therefore, the demand of utilizing humanized mice has increased over the last decade as a pre-clinical platform for investigating human-specific immune responses against HBV as well as identifying potential immunotherapeutic strategies in eradicating the virus. Basically, this review covers some of the recent developments and key advantages of humanized mouse models over other conventional transgenic mice platforms.

Differentiation of endometrial fibroblasts into specialized decidual cells controls embryo implantation and transforms the cycling endometrium into a semi-permanent, immune-protective matrix that accommodates the placenta throughout pregnancy. This process starts during the midluteal phase of the menstrual cycle with decidual transformation of perivascular cells (PVC) surrounding the terminal spiral arterioles and endometrial stromal cells (EnSC) underlying the luminal epithelium. Decidualization involves extensive cellular reprogramming and acquisition of a secretory phenotype, essential for coordinated placental trophoblast invasion. Secreted metabolites are an emerging class of signaling molecules, collectively known as the exometabolome. Here, we used liquid chromatography-mass spectrometry to characterize and analyze time-resolved changes in metabolite secretion (exometabolome) of primary PVC and EnSC decidualized over 8 days. PVC were isolated using positive selection of the cell surface marker SUSD2. We identified 79 annotated metabolites differentially secreted upon decidualization, including prostaglandin, sphingolipid, and hyaluronic acid metabolites. Secreted metabolites encompassed 21 metabolic pathways, most prominently glycerolipid and pyrimidine metabolism. Although temporal exometabolome changes were comparable between decidualizing PVC and EnSC, 32 metabolites were differentially secreted across the decidualization time-course. Further, targeted metabolomics demonstrated significant differences in secretion of purine pathway metabolites between decidualized PVC and EnSC. Taken together, our findings indicate that the metabolic footprints generated by different decidual subpopulations encode spatiotemporal information that may be important for optimal embryo implantation.

circRNA is a novel class of noncoding RNA with closed-loop structure. Increasing biological experiments have shown that circRNAs play an important role in many diseases by acting as a miRNA sponge to indirectly regulate the expression of miRNA target genes. Therefore, predicting associations between circRNAs and miRNAs can promote the understanding of pathogenesis of disease. In this paper, we propose a new computational method, NECMA, based on network embedding to predict potential associations between circRNAs and miRNAs. In our method, the Gaussian interaction profile (GIP) kernel similarities of circRNA and miRNA are calculated based on the known circRNA-miRNA associations, respectively. Then, the circRNA-miRNA association network, circRNA GIP kernel similarity network, and miRNA GIP kernel similarity network are utilized to construct the heterogeneous network. Furthermore, the network embedding algorithm is used to extract potential features of circRNA and miRNA from the heterogeneous network, respectively. Finally, the associations between circRNAs and miRNAs are predicted by using neighborhood regularization logic matrix decomposition and inner product. The performance of NECMA is evaluated by using ten-fold cross-validation. The results show that this method has better prediction accuracy than other state-of-the-art methods.

Advancements in science enable researchers to constantly innovate and create novel biologics. However, the use of non-human animal models during the development of biologics impedes identification of precise in vivo interactions between the human immune system and treatments. Due to lack of this understanding, adverse effects are frequently observed in healthy volunteers and patients exposed to potential biologics during clinical trials. In this study, we evaluated and compared the effects of known immunotoxic biologics, Proleukin®/IL-2 and OKT3 in humanized mice (reconstituted with human fetal cells) to published clinical outcomes. We demonstrated that humanized mice were able to recapitulate in vivo pathological changes and human-specific immune responses, such as elevated cytokine levels and modulated lymphocytes and myeloid subsets. Given the high similarities of immunological side effects observed between humanized mice and clinical studies, this model could be used to assess immunotoxicity of biologics at a pre-clinical stage, without placing research participants and/or patients at risk.

Non-alcoholic fatty liver disease (NAFLD) has been on a global rise. While animal models have rendered valuable insights to the pathogenesis of NAFLD, discrepancy with patient data still exists. Since non-alcoholic steatohepatitis (NASH) involves chronic inflammation, and CD4+ T cell infiltration of the liver is characteristic of NASH patients, we established and characterized a humanized mouse model to identify human-specific immune response(s) associated with NAFLD progression. Immunodeficient mice engrafted with human immune cells (HIL mice) were fed with high fat and high calorie (HFHC) or chow diet for 20 weeks. Liver histology and immune profile of HIL mice were analyzed and compared with patient data. HIL mice on HFHC diet developed steatosis, inflammation and fibrosis of the liver. Human CD4+ central and effector memory T cells increased within the liver and in the peripheral blood of our HIL mice, accompanied by marked up-regulation of pro-inflammatory cytokines (IL-17A and IFNγ). In vivo depletion of human CD4+ T cells in HIL mice reduced liver inflammation and fibrosis, but not steatosis. Our results highlight CD4+ memory T cell subsets as important drivers of NAFLD progression from steatosis to fibrosis and provides a humanized mouse model for pre-clinical evaluation of potential therapeutics.

Abstract The interaction between gene loci, namely epistasis, is a widespread biological genetic phenomenon. In genome-wide association studies(GWAS), epistasis detection of complex diseases is a major challenge. Although many approaches using statistics, machine learning, and information entropy were proposed for epistasis detection, the privacy preserving for single nucleotide polymorphism(SNP) data has been largely ignored. Thus, this paper proposes a novel two-stage approach. A fusion strategy assists in combining and sorting the SNPs importance scores obtained by the relief and mutual information, thereby obtaining a candidate set of SNPs. This avoids missing some SNPs with strong interaction. Furthermore, differentially private decision tree is applied to search for SNPs. This achieves the efficient epistasis detection of complex diseases on the basis of privacy preserving compared with heuristic methods. The recognition rate on simulation data set is more than 90%. Also, several susceptible loci including rs380390 and rs1329428 are found in the real data set for Age-related Macular Degeneration (AMD). This demonstrates that our method is promising in epistasis detection.

Long non-coding RNAs are known to regulate mouse white adipose tissue development and brown adipose tissue program expression. Here, the authors construct a roadmap for human long non-coding RNAs expressed in fetal brown adipose tissue, adult omental and subcutaneous white adipose tissues.

Summary: How are closely related lineages, including liver, pancreas, and intestines, diversified from a common endodermal origin? Here, we apply principles learned from developmental biology to rapidly reconstitute liver progenitors from human pluripotent stem cells (hPSCs). Mapping the formation of multiple endodermal lineages revealed how alternate endodermal fates (e.g., pancreas and intestines) are restricted during liver commitment. Human liver fate was encoded by combinations of inductive and repressive extracellular signals at different doses. However, these signaling combinations were temporally re-interpreted: cellular competence to respond to retinoid, WNT, TGF-β, and other signals sharply changed within 24 hr. Consequently, temporally dynamic manipulation of extracellular signals was imperative to suppress the production of unwanted cell fates across six consecutive developmental junctures. This efficiently generated 94.1% ± 7.35% TBX3+HNF4A+ human liver bud progenitors and 81.5% ± 3.2% FAH+ hepatocyte-like cells by days 6 and 18 of hPSC differentiation, respectively; the latter improved short-term survival in the Fah−/−Rag2−/−Il2rg−/− mouse model of liver failure.

Abstract Mitochondrial diseases are maternally inherited heterogeneous disorders that are primarily caused by mitochondrial DNA (mtDNA) mutations. Depending on the ratio of mutant to wild-type mtDNA, known as heteroplasmy, mitochondrial defects can result in a wide spectrum of clinical manifestations. Mitochondria-targeted endonucleases provide an alternative avenue for treating mitochondrial disorders via targeted destruction of the mutant mtDNA and induction of heteroplasmic shifting. Here, we generated mitochondrial disease patient-specific induced pluripotent stem cells (MiPSCs) that harbored a high proportion of m.3243A>G mtDNA mutations and caused mitochondrial encephalomyopathy and stroke-like episodes (MELAS). We engineered mitochondrial-targeted transcription activator-like effector nucleases (mitoTALENs) and successfully eliminated the m.3243A>G mutation in MiPSCs. Off-target mutagenesis was not detected in the targeted MiPSC clones. Utilizing a dual fluorescence iPSC reporter cell line expressing a 3243G mutant mtDNA sequence in the nuclear genome, mitoTALENs displayed a significantly limited ability to target the nuclear genome compared with nuclear-localized TALENs. Moreover, genetically rescued MiPSCs displayed normal mitochondrial respiration and energy production. Moreover, neuronal progenitor cells differentiated from the rescued MiPSCs also demonstrated normal metabolic profiles. Furthermore, we successfully achieved reduction in the human m.3243A>G mtDNA mutation in porcine oocytes via injection of mitoTALEN mRNA. Our study shows the great potential for using mitoTALENs for specific targeting of mutant mtDNA both in iPSCs and mammalian oocytes, which not only provides a new avenue for studying mitochondrial biology and disease but also suggests a potential therapeutic approach for the treatment of mitochondrial disease, as well as the prevention of germline transmission of mutant mtDNA.

Abstract Background Xenotransplantation of patient-derived AML (acute myeloid leukemia) cells in NOD-scid Il2rγ null (NSG) mice is the method of choice for evaluating this human hematologic malignancy. However, existing models constructed using intravenous injection in adult or newborn NSG mice have inferior engraftment efficiency, poor peripheral blood engraftment, or are difficult to construct. Methods Here, we describe an improved AML xenograft model where primary human AML cells were injected into NSG newborn pups intrahepatically. Results Introduction of primary cells from AML patients resulted in high levels of engraftment in peripheral blood, spleen, and bone marrow (BM) of recipient mice. The phenotype of engrafted AML cells remained unaltered during serial transplantation. The mice developed features that are consistent with human AML including spleen enlargement and infiltration of AML cells into multiple organs. Importantly, we demonstrated that although leukemic stem cell activity is enriched and mediated by CD34+CD117+ subpopulation, CD34+CD117− subpopulation can acquire CD34+CD117+ phenotype through de-differentiation. Lastly, we evaluated the therapeutic potential of Sorafenib and Regorafenib in this AML model and found that periphery and spleen AML cells are sensitive to these treatments, whereas BM provides a protective environment to AML. Conclusions Collectively, our improved model is robust, easy-to-construct, and reliable for pre-clinical AML studies.