Multi-view attribute learning and context relationship encoding enhanced segmentation of lung tumors from CT images.

Graph convolutional neural networks (GCN) have shown the promise in medical image segmentation due to the flexibility of representing diverse range of image regions using graph nodes and propagating knowledge via graph edges. However, existing methods did not fully exploit the various attributes of image nodes and the context relationship among their attributes. We propose a new segmentation method with multi-similarity view enhancement and node attribute context learning (MNSeg). First, multiple views were formed by measuring the similarities among the image nodes, and MNSeg has a GCN based multi-view image node attribute learning (MAL) module to integrate various node attributes learnt from multiple similarity views. Each similarity view contains the specific similarities among all the image nodes, and it was integrated with the node attributes from all the channels to form the enhanced attributes of image nodes. Second, the context relationships among the attributes of image nodes are formulated by a transformer-based context relationship encoding (CRE) strategy to propagate these relationships across all the image nodes. During the transformer-based learning, the relationships were estimated based on the self-attention on all the image nodes, and then they were encoded into the learned node features. Finally, we design an attention at attribute category level (ACA) to discriminate and fuse the learnt diverse information from MAL, CRE, and the original node attributes. ACA identifies the more informative attribute categories by adaptively learn their importance. We validate the performance of MNSeg on a public lung tumor CT dataset and an in-house non-small cell lung cancer (NSCLC) dataset collected from the hospital. The segmentation results show that MNSeg outperformed the compared segmentation methods in terms of spatial overlap and the shape similarities. The ablation studies demonstrated the effectiveness of MAL, CRE, and ACA. The generalization ability of MNSeg was proved by the consistent improved segmentation performances using different 3D segmentation backbones.

Optimization and mechanisms of proteolytic enzyme immobilization onto large-pore mesoporous silica nanoparticles: Enhanced tumor penetration.

Immobilization of proteolytic enzymes onto nanocarriers is effective to improve drug diffusion in tumors through degrading the dense extracellular matrix (ECM). Herein, immobilization and release behaviors of hyaluronidase, bromelain, and collagenase (Coll) on mesoporous silica nanoparticles (MSNs) were explored. A series of cationic MSNs (CMSNs) with large and adjustable pore sizes were synthesized, and investigated together with two anionic MSNs of different pore sizes. CMSNs4.0 exhibited the highest enzyme loading capacity for hyaluronidase and bromelain, and CMSNs4.5 was the best for Coll. High electrostatic interaction, matched pore size, and large pore volume and surface area favor the immobilization. Changes of the enzyme conformations and surface charges with pH, existence of a space around the immobilized enzymes, and the depth of the pore structures, affect the release ratio and tunability. The optimal CMSNs-enzyme complexes exhibited deep and homogeneous penetration into pancreatic tumors, a tumor model with the densest ECM, with CMSNs4.5-Coll as the best. Upon loading with doxorubicin (DOX), the CMSNs-enzyme complexes induced high anti-tumor efficiencies. Conceivably, the DOX/CMSNs4.5-NH2-Coll nanodrug exhibited the most effective tumor therapy, with a tumor growth inhibition ratio of 86.1 %. The study provides excellent nanocarrier-enzyme complexes, and offers instructive theories for enhanced tumor penetration and therapy.

New α-Glucosidase Inhibitors from the Whole Plant of Hypericum beanii Based on Ligand Fishing and Molecular Networking Analysis.

In this work, a new rapid and targeted method for screening α-glucosidase inhibitors from Hypericum beanii was developed and verified. Ten new polycyclic polyprenylated acylphloroglucinols (PPAPs), hyperlagarol A-J (1-10), and nine known PPAPs (11-19) were obtained from H. beanii. Their structures were identified by using comprehensive analyses involving mass spectrometry, ultraviolet spectroscopy, infrared spectroscopy, nuclear magnetic resonance spectroscopy, and electron capture dissociation calculations. 1 and 2 are two new rare 2,3-seco-spirocyclic PPAPs, 3 and 4 are two novel 12,13-seco-spirocyclic PPAPs, 5 and 6 are two novel spirocyclic PPAPs, 7 and 8 are two new unusual spirocyclic PPAPs with complex bridged ring systems, and 9 and 10 are two novel nonspirocyclic PPAPs. α-GC inhibitory activities of all isolated compounds were tested. Most of them displayed inhibitory activities against α-glucosidase, with the IC50 values ranging from 6.85 ± 0.65 to 112.5 ± 9.03 µM. Moreover, the inhibitory type and mechanism of the active compounds were further analyzed using kinetic studies and molecular docking.

Cytotoxic indole diterpenoids and rare pyridoxatin atropisomers from the endophytic fungus Tolypocladium sp. SHJJ1.

Phytochemical investigation on the extract of endophytic fungus Tolypocladium sp. SHJJ1 resulted in the identification of a pair of previously undescribed pyridoxatin atropisomers [1 (M/P)] and three new indole diterpenoids (3-5), together with a pair of known pyridoxatin atropisomers [2 (M/P)] and ten known indole diterpenoids (6-15). Their structures, including their absolute configurations were elucidated by extensive spectroscopic analysis, quantum chemical calculations, and X-ray diffraction. Among the undescribed natural products, [1 (M/P)] that two rapidly interconverting atropisomers are the third example to report in the pyridoxatin atropisomers. Except for compounds 1 (M/P) and 2 (M/P), all other compounds were tested for their cytotoxicity using HepG2, A549, and MCF-7 human cell lines. Compound 9 displayed moderate cytotoxicity against the HepG2, A549, and MCF-7 cell lines with IC50 values of 32.39 ± 1.48 µM, 26.06 ± 1.14 µM, and 31.44 ± 1.94 µM, respectively, which was similar to the positive drug cisplatin (with IC50 values of 32.55 ± 1.76 µM, 18.40 ± 1.43 µM, and 27.31 ± 1.22 µM, respectively).

Exploiting Geometric Features via Hierarchical Graph Pyramid Transformer for Cancer Diagnosis using Histopathological Images.

Cancer is widely recognized as the primary cause of mortality worldwide, and pathology analysis plays a pivotal role in achieving accurate cancer diagnosis. The intricate representation of features in histopathological images encompasses abundant information crucial for disease diagnosis, regarding cell appearance, tumor microenvironment, and geometric characteristics. However, recent deep learning methods have not adequately exploited geometric features for pathological image classification due to the absence of effective descriptors that can capture both cell distribution and gathering patterns, which often serve as potent indicators. In this paper, inspired by clinical practice, a Hierarchical Graph Pyramid Transformer (HGPT) is proposed to guide pathological image classification by effectively exploiting a geometric representation of tissue distribution which was ignored by existing state-of-the-art methods. First, a graph representation is constructed according to morphological feature of input pathological image and learn geometric representation through the proposed multi-head graph aggregator. Then, the image and its graph representation are feed into the transformer encoder layer to model long-range dependency. Finally, a locality feature enhancement block is designed to enhance the 2D local representation of feature embedding, which is not well explored in the existing vision transformers. An extensive experimental study is conducted on Kather-5K, MHIST, NCT-CRC-HE, and GasHisSDB for binary or multi-category classification of multiple cancer types. Results demonstrated that our method is capable of consistently reaching superior classification outcomes for histopathological images, which provide an effective diagnostic tool for malignant tumors in clinical practice.

Two successful pregnancies in a membranous nephropathy patient: Case report and literature review.

BACKGROUND: Pregnancy in patients with nephrotic syndrome presents enormous challenges to both the mother and fetus, and there are no treatment guidelines for these patients. METHODS: We show a case of a woman with anti-PLA2R antibody-positive membranous nephropathy who did not have a kidney biopsy. Her clinical course during both pregnancies was closely followed and her medications were guided. RESULTS: She gave birth to 2 healthy babies and her condition was very well controlled with the help of medication. CONCLUSION: Patients with nephrotic syndrome can have successful pregnancies after drug treatment. In addition, similar to the non-pregnant population, percutaneous kidney biopsy is not required for the diagnosis of idiopathic membranous nephropathy (IMN) in pregnant nephrotic syndrome patients with anti-PLA2R antibody positive, but the etiology of secondary MN should be excluded.

Mutually enhanced multi-view information learning for segmentation of lung tumor in CT images.

Objective.The accurate automatic segmentation of tumors from computed tomography (CT) volumes facilitates early diagnosis and treatment of patients. A significant challenge in tumor segmentation is the integration of the spatial correlations among multiple parts of a CT volume and the context relationship across multiple channels.Approach.We proposed a mutually enhanced multi-view information model (MEMI) to propagate and fuse the spatial correlations and the context relationship and then apply it to lung tumor CT segmentation. First, a feature map was obtained from segmentation backbone encoder, which contained many image region nodes. An attention mechanism from the region node perspective was presented to determine the impact of all the other nodes on a specific node and enhance the node attribute embedding. A gated convolution-based strategy was also designed to integrate the enhanced attributes and the original node features. Second, transformer across multiple channels was constructed to integrate the channel context relationship. Finally, since the encoded node attributes from the gated convolution view and those from the channel transformer view were complementary, an interaction attention mechanism was proposed to propagate the mutual information among the multiple views.Main results.The segmentation performance was evaluated on both public lung tumor dataset and private dataset collected from a hospital. The experimental results demonstrated that MEMI was superior to other compared segmentation methods. Ablation studies showed the contributions of node correlation learning, channel context relationship learning, and mutual information interaction across multiple views to the improved segmentation performance. Utilizing MEMI on multiple segmentation backbones also demonstrated MEMI's generalization ability.Significance.Our model improved the lung tumor segmentation performance by learning the correlations among multiple region nodes, integrating the channel context relationship, and mutual information enhancement from multiple views.

O2-Generation-Enhanced Responsive Starvation/Photothermal Synergistic Tumor Therapy Based on the AuNRs@MnO2@SiO2 Nanocarrier and Thermosensitive Biomimetic Camouflaging.

Cancer starvation/photothermal combined tumor therapy (CST/PTT) has attracted great interest attributed to their mutual compensation and synergistically enhanced effect. However, the very low O2 supply in the tumor microenvironment (TME) greatly limits the CST efficiency of glucose oxidase (GOx). Additionally, the easy degradation in blood circulation and significant off-target effects are big challenges for clinical applications of the GOx-based CST. In this study, a drug delivery system (DDS) with specific tumor-targeted GOx delivery, near-infrared (NIR) light and TME responsive O2 generation, NIR-responsive glucose consumption, high GOx loading, and efficient NIR photothermia was developed. Positively charged AuNRs@MnO2@SiO2 nanoparticles (named AMS+ NPs) were synthesized. GOx was covalently loaded with a high loading ratio of 36.0%. Finally, a thermosensitive biomimetic hybrid membrane composed of a thermosensitive lipid (TSL) membrane, red blood cell membrane (RBCM), and 4T1 cancer cell membrane (CCM) was coated on the NPs through a double-layer strategy. The AMS+-G@TSL@[RBC-CC-TSL]M NPs consumed 32.7 times glucose at 50 °C as that at 37 °C and generated 4.9 times O2 upon NIR laser irradiation. The thermosensitive biomimetic NPs showed an efficient targeting capability to the homotypic 4T1 cancer cells/tumors accompanied by good biocompatibility, macrophage evading capability, high cancer cell cytotoxicity, and excellent antitumor efficacy. The tumor growth inhibition ratio with NIR laser irradiation reached 92.8%. The AMS+-GOx@TSL@[RBC-CC-TSL]M NPs provide a smart, efficient, safe, PTT/CST combined DDS for highly efficient tumor therapy.

pH-Sensitive Biomimetic Nanosystem Based on Large-Pore Mesoporous Silica Nanoparticles with High Hyaluronidase Loading for Tumor Deep Penetration.

Loading hyaluronidase (Hyal) in a nanocarrier is a potent strategy to degrade the tumor extracellular matrix for tumor deep penetration and enhanced tumor therapy. Herein, a pH-sensitive biomimicking nanosystem with high Hyal loading, effective tumor targeting, and controllable release is constructed. Specifically, cationic mesoporous silica nanoparticles (CMSNs) with large pores 13.52 nm in diameter were synthesized in a one-pot manner by adding N-[3-trimethoxysilylpropyl]-N,N,N-trimethylammonium to a reversed microemulsion reaction system. The Hyal loading rate was as high as 19.47% owing to matched pore size and the cationic surface charge. Subsequently, a pH-sensitive biomimetic hybrid membrane (pHH) composed of pH-sensitive liposome (pHL), red blood cell membrane, and pancreatic cancer cell membrane was camouflaged on the pHL-coated and doxorubicin/Hyal-loaded CMSNs (shortened as DHCM). The DHCM@pHL@pHH is stable at neutral pH while it releases the payloads smoothly in the tumor acidic microenvironment. Consequently, it can escape from macrophage clearance, be specifically taken up by pancreatic cancer cells, and efficiently accumulate at the tumor site. More importantly, it can penetrate deeply in pancreatic tumors with a tumor growth inhibition ratio of 80.46%. The nanosystem is biocompatible and has potential for clinical transformation, and the nanocarrier is promisingly applicable as a platform for encapsulation of various macromolecules for smart and tumor-targeted delivery.

Specific topology and topological connection sensitivity enhanced graph learning for lncRNA-disease association prediction.

Predicting disease-related candidate long noncoding RNAs (lncRNAs) is beneficial for exploring disease pathogenesis due to the close relations between lncRNAs and the occurrence and development of human diseases. It is a long-term and challenging task to adequately extract specific and local topologies in individual lncRNA network and individual disease network, and integrate the information of the connection relationships. We propose a new graph learning-based prediction method to encode specific and local topologies from each individual network, neighbor topologies with different connection relationships, and pairwise attributes. We first construct a lncRNA network composed of all the lncRNA nodes and their similarities, and a single disease network that contains all the disease nodes and disease similarities. Then, a network-aware graph convolutional autoencoder is constructed to encode the specific and local topologies of each network. Secondly, a heterogeneous network is established to embed all lncRNA, disease, and miRNA nodes and their various connections. Afterwards, a connection-sensitive graph neural network is designed to deeply integrate the neighbor node attributes and connection characteristics in the heterogeneous network and learn neighbor topological representations. We also construct both connection-level and topology representation-level attention mechanisms to extract informative connections and topological representations. Finally, we build a multi-layer convolutional neural networks with weighted residuals to adaptively complement the detailed features to pairwise attribute encoding. Comprehensive experiments and comparison results demonstrated that NCPred outperforms seven state-of-the-art prediction methods. The ablation studies demonstrated the importance of local topology learning, neighbor topology learning, and pairwise attribute encoding. Case studies on prostate, lung, and breast cancers further revealed NCPred's capacity to screen potential candidate disease-related lncRNAs.

Traditional Chinese medicine in the era of immune checkpoint inhibitor: theory, development, and future directions.

Immune checkpoint inhibitors (ICIs) have revolutionized cancer management and have been widely applied; however, they still have some limitations in terms of efficacy and toxicity. There are multiple treatment regimens in Traditional Chinese Medicine (TCM) that play active roles in combination with Western medicine in the field of oncology treatment. TCM with ICIs works by regulating the tumor microenvironment and modulating gut microbiota. Through multiple targets and multiple means, TCM enhances the efficacy of ICIs, reverses resistance, and effectively prevents and treats ICI-related adverse events based on basic and clinical studies. However, there have been few conclusions on this topic. This review summarizes the development of TCM in cancer treatment, the mechanisms underlying the combination of TCM and ICIs, existing studies, ongoing trials, and prospects for future development.

Nigrospora musae Causing Taxus chinensis var. mairei stem blight in Sichuan, China.

Taxus chinensis var. mairei is the endemic, endangered, and first-class protected tree species in China. This species is considered as an important resource plant because it can produce Taxol which is an effective medicinal compound against various cancers (Zhang et al., 2010). Stem blight was observed in two plant nurseries in Ya'an (102°44'E,30°42'N), Sichuan province in April 2021. The symptoms first appeared as round brown spots on the stem. As the disease progressed, the damaged area gradually expanded into an oval or irregular shape, which was dark brown. About 800 square meters of planting area were investigated and the disease incidence was up to approximately 64.8%. Twenty obviously symptomatic stems which exhibited the same symptoms as above were collected from 5 different trees in the nursery. To isolate the pathogen, the symptom margin was cut into small blocks (5 x 5 mm), and the blocks were surface sterilized in 75% ethanol for 90 s and 3% NaClO solution for 60 s . Finally incubated on Potato Dextrose Agar (PDA) at 28℃ for 5 days. Ten pure cultures were isolated by transferring hyphal and the three strains (HDS06, HDS07 and HDS08) were selected as representative isolates for further study. Initially, colonies on the PDA of three isolates were white and cotton-like, and then gradually turned gray-black from the center. After 21 days, conidia were produced and were smooth-walled, single-celled, black, oblate, or spherical, measuring 9.3 to 13.6 × 10.1 to 14.5 µm in size (n = 50). Conidia were present at the tip of conidiophores on hyaline vesicles. These morphological features were generally consistent with those of N. musae (Wang et al., 2017). To validate the identification, DNA were extracted from the three isolates, followed by the amplification of transcribed spacer region of rDNA (ITS), the translation elongation factor EF-1 (TEF-1), and the Beta-tubulin (TUB2) sequences with the respective primer pairs ITS1/ITS4 (White et al., 1990), EF-728F/EF-986R (Vieira et al., 2014) and Bt2a/Bt2b (O'Donnell et al., 1997) .The sequences were deposited in GenBank with the accession numbers ON965533, OP028064, OP028068, OP060349, OP060353, OP060354, OP060350, OP060351 and OP060352, respectively. Phylogenetic analysis of combined ITS, TUB2, and TEF genes using the Mrbayes inference method showed that the three isolates clustered with Nigrospora musae as a distinct clade (Fig. 2). Combine with morphological characteristics and phylogenetic analysis, three isolates were identified as N. musae. 30 2-year-old healthy potted plants of T. chinensis were used for pathogenicity test. 25 of these plants were inoculated by injecting 10 µL of the conidia suspension (1 × 106 conidia/mL) into stems and then wrap around the seal to moisturize. The remaining 5 plants were injected with the same amount of sterilized distilled water as a control. Finally, all potted plants were placed in a greenhouse at 25°C and 80% relative humidity. After 2 weeks, the inoculated stems developed lesions similar to those observed in the field, whereas controls were asymptomatic. N. musae was re-isolated from the infected stem and identified by both morphological characteristics and DNA sequence analysis. The experiments repeated three times showed similar results. As far as we know, this is the first report of N. musae causing T. chinensis stem blight in the world. The identification of N. musae could provide a certain theoretical basis for field management and further research of T. chinensis.

Aptamer-based technology for gastric cancer theranostics.

Gastric cancer is one of the most common causes of cancer death worldwide. This cancer exhibits high molecular and phenotype heterogeneity. The overall survival rate for gastric cancer is very low because it is always diagnosed in the advanced stages. Therefore, early detection and treatment are of great significance. Currently, biomedical studies have tapped the potential clinical applicability of aptamer-based technology for gastric cancer diagnosis and targeted therapy. Herein, we summarize the enrichment and evolution of relevant aptamers, followed by documentation of the recent developments in aptamer-based techniques for early diagnosis and precision therapy for gastric cancers.

Topological structure and global features enhanced graph reasoning model for non-small cell lung cancer segmentation from CT.

Objective.Accurate and automated segmentation of lung tumors from computed tomography (CT) images is critical yet challenging. Lung tumors are of various sizes and locations and have indistinct boundaries adjacent to other normal tissues.Approach.We propose a new segmentation model that can integrate the topological structure and global features of image region nodes to address the challenges. Firstly, we construct a weighted graph with image region nodes. The graph topology reflects the complex spatial relationships among these nodes, and each node has its specific attributes. Secondly, we propose a node-wise topological feature learning module based on a new graph convolutional autoencoder (GCA). Meanwhile, a node information supplementation (GNIS) module is established by integrating specific features of each node extracted by a convolutional neural network (CNN) into each encoding layer of GCA. Afterwards, we construct a global feature extraction model based on multi-layer perceptron (MLP) to encode the features learnt from all the image region nodes which are crucial complementary information for tumor segmentation.Main results.Ablation study results over the public lung tumor segmentation dataset demonstrate the contributions of our major technical innovations. Compared with other segmentation methods, our new model improves the segmentation performance and has generalization ability on different 3D image segmentation backbones. Our model achieved Dice of 0.7827, IoU of 0.6981, and HD of 32.1743 mm on the public dataset 2018 Medical Segmentation Decathlon challenge, and Dice of 0.7004, IoU of 0.5704 and HD of 64.4661 mm on lung tumor dataset from Shandong Cancer Hospital.Significance. The novel model improves automated lung tumor segmentation performance especially the challenging and complex cases using topological structure and global features of image region nodes. It is of great potential to apply the model to other CT segmentation tasks.

Integrating Preoperative Computed Tomography and Clinical Factors for Lymph Node Metastasis Prediction in Esophageal Squamous Cell Carcinoma by Feature-Wise Attentional Graph Neural Network.

PURPOSE: This study aimed to propose a regional lymph node (LN) metastasis prediction model for patients with esophageal squamous cell carcinoma (ESCC) that can learn and adaptively integrate preoperative computed tomography (CT) image features and nonimaging clinical parameters. METHODS AND MATERIALS: Contrast-enhanced CT scans taken 2 weeks before surgery and 20 clinical factors, including general, pathologic, hematological, and diagnostic information, were collected from 357 patients with ESCC between October 2013 and November 2018. There were 999 regional LNs (857 negative, 142 positive) with pathologically confirmed status after surgery. All LNs were randomly divided into a training set (n = 738) and a validation set (n = 261) for testing. The feature-wise attentional graph neural network (FAGNN) was composed of (1) deep image feature extraction by the encoder of 3-dimensional UNet and high-level nonimaging factor representation by the clinical parameter encoder; (2) a feature-wise attention module for feature embedding with learnable adaptive weights; and (3) a graph attention layer to integrate the embedded features for final LN level metastasis prediction. RESULTS: Among the 4 models we constructed, FAGNN using both CT and clinical parameters as input is the model with the best performance, and the area under the curve (AUC) reaches 0.872, which is better than manual CT diagnosis method, multivariable model using CT only (AUC = 0.797), multivariable model with combined CT and clinical parameters (AUC = 0.846), and our FAGNN using CT only (AUC = 0.853). CONCLUSIONS: Our adaptive integration model improved the metastatic LN prediction performance based on CT and clinical parameters. Our model has the potential to foster effective fusion of multisourced parameters and to support early prognosis and personalized surgery or radiation therapy planning in patients with ESCC.

Lipolysis and gestational diabetes mellitus onset: a case-cohort genome-wide association study in Chinese.

BACKGROUND: Genetic knowledge of gestational diabetes mellitus (GDM) in Chinese women is quite limited. This study aimed to identify the risk factors and mechanism of GDM at the genetic level in a Chinese population. METHODS: We conducted a genome-wide association study (GWAS) based on single nucleotide polymorphism (SNP) array genotyping (ASA-CHIA Bead chip, Illumina) and a case-cohort study design. Variants including SNPs, copy number variants (CNVs), and insertions-deletions (InDels) were called from genotyping data. A total of 2232 pregnant women were enrolled in their first/second trimester between February 2018 and December 2020 from Anqing Municipal Hospital in Anhui Province, China. The GWAS included 193 GDM patients and 819 subjects without a diabetes diagnosis, and risk ratios (RRs) and their 95% confidence intervals (CIs) were estimated by a regression-based method conditional on the population structure. The calling and quality control of genotyping data were performed following published guidelines. CNVs were merged into CNV regions (CNVR) to simplify analyses. To interpret the GWAS results, gene mapping and overexpression analyses (ORAs) were further performed to prioritize the candidate genes and related biological mechanisms. RESULTS: We identified 14 CNVRs (false discovery rate corrected P values < 0.05) and two suggestively significant SNPs (P value < 0.00001) associated with GDM, and a total of 19 candidate genes were mapped. Ten genes were significantly enriched in gene sets related to lipase (triglyceride lipase and lipoprotein lipase) activity (LIPF, LIPK, LIPN, and LIPJ genes), oxidoreductase activity (TPH1 and TPH2 genes), and cellular components beta-catenin destruction complex (APC and GSK3B genes), Wnt signalosome (APC and GSK3B genes), and lateral element in the Gene Ontology resource (BRCA1 and SYCP2 genes) by two ORA methods (adjusted P values < 0.05). CONCLUSIONS: Genes related to lipolysis, redox reaction, and proliferation of islet ß-cells are associated with GDM in Chinese women. Energy metabolism, particularly lipolysis, may play an important role in GDM aetiology and pathology, which needs further molecular studies to verify.

Remarkably Selective Propylene-Propane Separation Using a Copper Scorpionate.

Propylene is a crucial building block to produce many industrial-scale chemicals including polypropylene. The separation of propylene from propane to reach the high-purity levels needed for downstream applications is a difficult task due to the close similarities in their physical properties. The olefin/paraffin separation including that involving propylene mainly relies on highly energy-intensive distillation processes and accounts for nearly 0.3% of the global energy consumption. The utility of a copper complex supported by a fluorinated bis(pyrazolyl)borate is demonstrated to accomplish the separation of propylene from propane repeatedly, under mild conditions with high selectivity. Complete characterization of a rare, copper(I) propylene complex is also reported including the molecular structure.

Human platelet mitochondria improve the mitochondrial and cardiac function of donor heart.

Mitochondria transplantation emerges as an effective therapeutic strategy for ischemic-related diseases but the roles in the donor hearts for transplant remain unidentified. Here, we investigated whether the preservation of the donor heart with human platelet-derived mitochondria (pl-MT) could improve mitochondrial and cardiac function. Incubation with pl-MT resulted in the internalization of pl-MT and the enhancement of ATP production in primary cardiomyocytes. In addition, incubation of rat hearts with pl-MT ex vivo for 9 h clearly demonstrated pl-MT transfusion into the myocardium. Mitochondria isolated from the hearts incubated with pl-MT showed increased mitochondrial membrane potential and greater ATP synthase activity and citrate synthase activity. Importantly, the production of reactive oxygen species from cardiac mitochondria was not different with and without pl-MT incubation. Functionally, the heartbeat and the volume of coronary circulation perfusate were significantly increased in the Langendorff perfusion system and the viability of cardiomyocytes was increased from pl-MT hearts.Taken together, these results suggest that incubation with Pl-MT improves mitochondrial activity and maintains the cardiac function of rat hearts with prolonged preservation time. The study provides the proof of principle for pl-MT application as an enhancer of the donor heart.

Graph based multi-scale neighboring topology deep learning for kidney and tumor segmentation.

Objective.Effective learning and modelling of spatial and semantic relations between image regions in various ranges are critical yet challenging in image segmentation tasks.Approach.We propose a novel deep graph reasoning model to learn from multi-order neighborhood topologies for volumetric image segmentation. A graph is first constructed with nodes representing image regions and graph topology to derive spatial dependencies and semantic connections across image regions. We propose a new node attribute embedding mechanism to formulate topological attributes for each image region node by performing multi-order random walks (RW) on the graph and updating neighboring topologies at different neighborhood ranges. Afterwards, multi-scale graph convolutional autoencoders are developed to extract deep multi-scale topological representations of nodes and propagate learnt knowledge along graph edges during the convolutional and optimization process. We also propose a scale-level attention module to learn the adaptive weights of topological representations at multiple scales for enhanced fusion. Finally, the enhanced topological representation and knowledge from graph reasoning are integrated with content features before feeding into the segmentation decoder.Main results.The evaluation results over public kidney and tumor CT segmentation dataset show that our model outperforms other state-of-the-art segmentation methods. Ablation studies and experiments using different convolutional neural networks backbones show the contributions of major technical innovations and generalization ability.Significance.We propose for the first time an RW-driven MCG with scale-level attention to extract semantic connections and spatial dependencies between a diverse range of regions for accurate kidney and tumor segmentation in CT volumes.

Thermosensitive Biomimetic Hybrid Membrane Camouflaged Hollow Gold Nanoparticles for NIR-Responsive Mild-Hyperthermia Chemo-/Photothermal Combined Tumor Therapy.

As an appealing biomimetic strategy for various medical applications, cell membrane coating lacks sensitive on-demand breaking capability. Herein, we incorporated thermosensitive lipid (TSL) membrane into red blood cell (RBC) and MCF-7 cancer cell (MC) hybrid membrane ([RBC-MC]M) vesicles. The [RBC-MC-TSL]M was coated onto doxorubicin (Dox)-loaded hollow gold nanoparticles to enhance chemo-/photothermal combined tumor therapy at a mild hyperthermia temperature (≤49 °C). Double-layer coating with TSL and [RBC-MC-TSL]M as the inner and outer layer, respectively, presented better antileakage and higher NIR-responsivity than single-layer coating. The Dox release ratio upon NIR laser irradiation (≤49 °C) was 74.6%, much higher than that (33.5%) without NIR laser. The nanodrug can be efficiently and specifically taken up by MCF-7 cells. In addition, the nanodrug exhibited excellent tumor-targeting property, with 4.08- and 1.12-times Dox accumulation in MCF-7 tumors compared to free Dox and [RBC-MC]M-coated counterpart, respectively. Most importantly, TSL incorporation significantly enhanced NIR-responsive antitumor efficiency, with tumor growth inhibition ratio increased from 35.1% to 48.6% after a single dose administration. Besides, the nanodrug exhibited very good biocompatibility. Camouflaging nanoparticles with the thermosensitive biomimetic hybrid membrane provides a painless and promisingly clinical-applicable approach for effective chemo-/photothermal combined mild-hyperthermia tumor therapy.