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Three porous nanosphere-structured manganese oxide (MnOx) catalysts were prepared and used for the low-temperature catalytic oxidation of methanol using different organic reagents as structure-directing agents to modulate the surface morphology of the catalysts. The structure-effect relationship between MnOx catalysts with different microsphere structures and the catalytic oxidation activity of methanol were analyzed using various characterization techniques. The MnOx–J catalyst had significantly higher catalytic oxidation activity for methanol (T90 = 130 °C) than the other MnOx catalysts. The characterization results indicate that the regulation of 2-methylimidazole promotes the formation of structural defects, surface-adsorbed oxygen, and Mn3+ on the surface of the manganese-based catalysts and enhances the low-temperature reduction capacity of the catalysts—the primary reason for the high reactivity of the MnOx–J samples. Three MnOx catalysts with porous nanosphere structures were synthesized using different organic reagents as structure-directing agents. Among them, the 2-methylimidazole organo-reagent promoted the formation of structural defects, surface adsorbed oxygen and Mn3+ on the surface of the manganese-based catalysts, thus enhancing the catalytic oxidation activity of the catalysts in low-temperature methanol-catalyzed reactions. [ABSTRACT FROM AUTHOR]

• We proposed a hybrid MIL model called TGMIL, based on transformer and GAT, without adding additional resource consumption, which effectively mimics the diagnostic process of pathologists considering the contextual information around a single area and the correlation of different regions. • Mimicking pathologists who adjust microscope magnifications, we crafted a WSI feature pyramid (MMFP) with varying magnifications (20 ×, 10 ×, and 5 ×). This enhances information richness while curbing memory usage and training time compared to the 40 × magnification. • Assessed across three histopathology image datasets, TGMIL demonstrated good classification performance. Notably, TGMIL's attentional thermograms corresponded to the tumor regions identified by the pathologist. The pathological diagnosis of renal cell carcinoma is crucial for treatment. Currently, the multi-instance learning method is commonly used for whole-slide image classification of renal cell carcinoma, which is mainly based on the assumption of independent identical distribution. But this is inconsistent with the need to consider the correlation between different instances in the diagnosis process. Furthermore, the problem of high resource consumption of pathology images is still urgent to be solved. Therefore, we propose a new multi-instance learning method to solve this problem. In this study, we proposed a hybrid multi-instance learning model based on the Transformer and the Graph Attention Network, called TGMIL, to achieve whole-slide image of renal cell carcinoma classification without pixel-level annotation or region of interest extraction. Our approach is divided into three steps. First, we designed a feature pyramid with the multiple low magnifications of whole-slide image named MMFP. It makes the model incorporates richer information, and reduces memory consumption as well as training time compared to the highest magnification. Second, TGMIL amalgamates the Transformer and the Graph Attention's capabilities, adeptly addressing the loss of instance contextual and spatial. Within the Graph Attention network stream, an easy and efficient approach employing max pooling and mean pooling yields the graph adjacency matrix, devoid of extra memory consumption. Finally, the outputs of two streams of TGMIL are aggregated to achieve the classification of renal cell carcinoma. On the TCGA-RCC validation set, a public dataset for renal cell carcinoma, the area under a receiver operating characteristic (ROC) curve (AUC) and accuracy of TGMIL were 0.98±0.0015,0.9191±0.0062, respectively. It showcased remarkable proficiency on the private validation set of renal cell carcinoma pathology images, attaining AUC of 0.9386±0.0162 and ACC of 0.9197±0.0124. Furthermore, on the public breast cancer whole-slide image test dataset, CAMELYON 16, our model showed good classification performance with an accuracy of 0.8792. TGMIL models the diagnostic process of pathologists and shows good classification performance on multiple datasets. Concurrently, the MMFP module efficiently diminishes resource requirements, offering a novel angle for exploring computational pathology images. [ABSTRACT FROM AUTHOR]

The metal-support interaction in Pt/MnO 2 hetero-catalysts regulates the physical and chemical properties of local environment on the surface of MnO 2 materials, which is the key to improve the catalytic activity of methanol oxidation. [Display omitted] • Pt/MnO 2 hetero-catalysts with different morphologies were synthesized. • Metal-support interaction plays a pivotal role in improving catalyst performance. • Loaded Pt nanoparticles (NPs) improve the surface local environment of MnO 2 support. • Specifically, the increase of Mn4+ and surface lattice oxygen on catalyst surface. • Morphology obviously affects the fixation and regulation of Pt NPs on the support surface. Rational use of the strong metal-support interaction (SMSI) is an important way to achieve the complete destruction of volatile organic compounds (VOCs). In order to investigate the influence of SMSI on the catalytic oxidation of VOCs, three Pt/MnO 2 hetero-catalysts, i.e., Pt/α-MnO 2 , Pt/γ-MnO 2 and Pt/δ-MnO 2 , were synthesized and then applied to the catalytic oxidation of methanol. The results showed that, compared with their corresponding pure MnO 2 , the temperatures (T90) at which 90% of methanol is converted to CO 2 for the above three synthesized catalysts were reduced by 54 °C, 67 °C and 32 °C, respectively. The Pt/α-MnO 2 catalyst has the best reducibility at lower temperature (T 90 = 91 °C). Multiple characterization analyses confirm that the interaction between Pt and MnO 2 support can increase the amount of Mn4+ and surface lattice oxygen on the catalyst surface. Thanks to the optimization of local reaction environment on the catalyst surface, Pt/MnO 2 hetero-catalysts have better low temperature reduction capacity and stronger VOC deep oxidation capacity than corresponding pure MnO 2 catalysts. [ABSTRACT FROM AUTHOR]

Objectives: The present study aimed to investigate the clinical prognostic significance of radiomics signature (R-signature) in patients with gastric cancer who had undergone radical resection.Methods: A total of 181 patients with gastric cancer who had undergone radical resection were enrolled in this retrospective study. The association between the R-signature and overall survival (OS) was assessed in the primary cohort and verified in the validation cohort. Furthermore, the performance of a radiomics nomogram integrating the R-signature and significant clinicopathological risk factors was evaluated.Results: The R-signature, which consisted of six imaging features, stratified patients with gastric cancer who had undergone radical resection into two prognostic risk groups in both cohorts. The radiomics nomogram incorporating R-signature and significant clinicopathological risk factors (T stage, N stage, and differentiation) exhibited significant prognostic superiority over clinical nomogram and R-signature alone (Harrell concordance index, 0.82 vs 0.71 and 0.82 vs 0.74, respectively, p < 0.001 in both analyses). All calibration curves showed remarkable consistency between predicted and actual survival, and decision curve analysis verified the usefulness of the radiomics nomogram for clinical practice.Conclusions: The R-signature could be used to stratify patients with gastric cancer following radical resection into high- and low-risk groups. Furthermore, the radiomics nomogram provided better predictive accuracy than other predictive models and might aid clinicians with therapeutic decision-making and patient counseling.Key Points: • Radiomics can stratify the gastric cancer patients following radical resection into high- and low-risk groups. • Radiomics can improve the prognostic value of TNM staging system. • Radiomics may facilitate personalized treatment of gastric cancer patients. [ABSTRACT FROM AUTHOR]

• The pure manganese oxides Mn 2 O 3 @δ-MnO 2 core-shell structures can be fine-manipulated via a novel synthesis method. • The synergistic effect of δ-MnO 2 nanosheets and Mn 2 O 3 microspheres is the key to enhance the deep methanol oxidation. • The Mn 2 O 3 @δ-MnO 2 interface structure exhibited more Mn4+, well redox ability and active surface lattice oxygen. • Activity study revealed that the Mn 2 O 3 @δ-MnO 2 -8h catalyst has excellent activity for low-temperature methanol oxidation. In order to elucidate the special role of interfacial effect over nanomaterials in the catalytic oxidation of volatile organic compounds (VOCs), a series of core-shell Mn 2 O 3 @δ-MnO 2 catalysts with different degree of oriented growth were prepared by a hydrothermal method, which were applied for methanol oxidation. Besides, the structure-activity relationship over the core-shell Mn 2 O 3 @δ-MnO 2 catalysts was further explained in details by multiple characterization techniques. It was found that the strong metal-support interaction promoted the formation of the interface between Mn 2 O 3 and δ-MnO 2 , which significantly changed the physicochemical properties and catalytic behaviours of core-shell Mn 2 O 3 @δ-MnO 2 catalysts. Among all the catalysts, the catalytic performances of these core-shell catalysts were obviously better than those of pure δ-MnO 2 nanosheets and Mn 2 O 3 microspheres, and the Mn 2 O 3 @δ-MnO 2 -8h catalyst exhibited the best outstanding activity for methanol oxidation (T 90 = 119 °C). In addition, the characterization analyses showed that the interfacial effect derived from the interaction between Mn 2 O 3 and δ-MnO 2 can increase the amount of Mn4+, and surface lattice oxygen, as well as optimize the low-temperature reduction ability, which was the crucial reason for the high activity of above core-shell Mn 2 O 3 @δ-MnO 2 catalysts. [Display omitted] [ABSTRACT FROM AUTHOR]

Objectives: The first treatment strategy for brain metastases (BM) plays a pivotal role in the prognosis of patients. Among all strategies, stereotactic radiosurgery (SRS) is considered a promising therapy method. Therefore, we developed and validated a radiomics-based prediction pipeline to prospectively identify BM patients who are insensitive to SRS therapy, especially those who are at potential risk of progressive disease. Methods: A total of 337 BM patients (277, 30, and 30 in the training set, internal validation set, and external validation set, respectively) were enrolled in the study. 19,377 radiomics features (3 masks × 3 MRI sequences × 2153 features) extracted from 9 ROIs were filtered through LASSO and Max-Relevance and Min-Redundancy (mRMR) algorithms. The selected radiomics features were combined with 4 clinical features to construct a two-stage cascaded model for the prediction of BM patients' response to SRS therapy using SVM and an ensemble learning classifier. The performance of the model was evaluated by its accuracy, specificity, sensitivity, and AUC curve. Results: Radiomics features were integrated with the clinical features of patients in our optimal model, which showed excellent discriminative performance in the training set (AUC: 0.95, 95% CI: 0.88–0.98). The model was also verified in the internal validation set and external validation set (AUC 0.93, 95% CI: 0.76–0.95 and AUC 0.90, 95% CI: 0.73–0.93, respectively). Conclusions: The proposed prediction pipeline could non-invasively predict the response to SRS therapy in patients with brain metastases thus assisting doctors to precisely designate individualized first treatment decisions. Clinical relevance statement: The proposed prediction pipeline combines the radiomics features of multi-modal MRI with clinical features to construct machine learning models that noninvasively predict the response of patients with brain metastases to stereotactic radiosurgery therapy, assisting neuro-oncologists to develop personalized first treatment plans. Key Points: • The proposed prediction pipeline can non-invasively predict the response to SRS therapy. • The combination of multi-modality and multi-mask contributes significantly to the prediction. • The edema index also shows a certain predictive value. [ABSTRACT FROM AUTHOR]

Graph: Figure 1: A 73-year-old patient with acute basal ganglia infarction was found to have rare and complex coronary artery fistulas through cardiovascular computed tomography angiography. Keywords: Coronary artery fistulas; Computerized tomography EN Coronary artery fistulas Computerized tomography 1 1 1 10/04/23 20230901 NES 230901 SUPPLEMENTARY MATERIAL Supplementary material is available at I EJCTS i online. The coronary artery fistulas exhibit multiple origins and drainage sites (red arrow), including pulmonary artery, aortic arch, left subclavian artery, left bronchial arteries and left atrium (Supplementary Material, Video S1). [Extracted from the article]

A novel drug carrier for brain delivery, maleimide–poly(ethyleneglycol)–poly(lactide) (maleimide–PEG–PLA) nanoparticles (NPs) conjugated with mouse–anti-rat monoclonal antibody OX26 (OX26–NPs), was developed and its brain delivery property was evaluated. The diblock copolymers of maleimide–PEG–PLA were synthesized and applied to α-cobrotoxin (αCT)-loaded NPs which were characterized by transmission electron micrograph imaging, Fourier-transform IR, and X-ray diffraction. The NPs encapsulating αCT had a round and vesicle-like shape with a mean diameter around 100 nm, and the OX26 had covalently conjugated to the surface of NPs. MTT studies in brain microvascular endothelial cells (BMEC) revealed a moderate decrease in the cell viability of αCT, when incorporated in OX26–NPs compared to free αCT in solution. A higher affinity of the OX26–αCT–NPs to the BMEC was shown in comparison to αCT–NPs. Then, OX26–αCT–NPs were intranasally (i.n.) administered to rats, and αCT in the periaqueductal gray was monitored for up to 480 min using microdialysis technique in free-moving rats, with i.n. αCT–NPs, i.n. OX26–αCT–NPs, intramuscular injection (i.m.) αCT–NPs, and i.m. OX26–αCT–NPs. The brain transport results showed that the corresponding absolute bioavailability ( Fabs) of i.n. OX26–αCT–NPs were about 125 and 155 % with i.n. αCT–NPs and i.m. OX26–αCT–NPs, respectively, and it was found that both the Cmax and AUC of the four groups were as follows: i.n. OX26–αCT–NPs > i.n. αCT–NPs > i.m. OX26–αCT–NPs > i.m. αCT–NPs, while αCT solution, as control groups, could hardly enter the brain. These results indicated that OX26–NPs are promising carriers for peptide brain delivery. [ABSTRACT FROM AUTHOR]

Hemsleya amabilis Diels , belongs to cucurbitaceae, was traditional Chinese medicine (TCM). It is widely used to treat various diseases. However, these diseases may contribute to the development of RCC. investigated the anticancer activities of root extract of Hemsleya amabilis Diels (HRE), and elucidated the underlying molecular mechanism in vivo and in vitro. Dried Hemsleya amabilis Diels roots were extracted by ethyl acetate and used to treat RCC4, OS-RC-2 and ACHN cells. UHPLC-MS was used to analyze the chemical composition of the extract. CCK-8 and colony formation assay were used to investigate proliferation. PI staining was used to detect cell cycle. Annexin-V-FITC, AO/EB and TEM were used to evaluate apoptosis. Transwell and wound healing assays were used to evaluate migration and invasion. RNA-seq, Network pharmacology, autodocking for virtual screening and molecular dynamics simulation were used to analyze potential molecular mechanisms and active components of HRE inhibiting proliferation of RCC. LY294002 and UC2288 were used to inhibit PI3K and P21 expression, respectively. IGF-1 was used to activate PI3K. Xenograft tumor model was established to evaluate its anti-tumor potential in vivo. Immunohistochemistry and Western blot were used to test protein expression levels. H&E staining was used to explore the side effects of HRE in vivo. Applying bioinformatics to analyze the effect of P21 on RCC. HRE consists of 739 compounds. CCK-8 and colony formation assay showed that HRE significantly inhibited RCC cells proliferation. PI staining indicated that HRE caused G 2 /M phase arrest. Annexin-V-FITC, AO/EB and TEM experiments revealed that HRE significantly promoted apoptosis of RCC cells. Transwell and wound healing assays showed that HRE can inhibit the migration and invasion of RCC cells. RNA-seq showed that HRE induced 230 gene changes. Network pharmacology analysis found the relationship between HRE-component-target-RCC. Auto-docking found that Epitulipinolide diepoxide in HRE can stably bind to PIK3CA (−7.22 kJ/mol), and molecular dynamics simulation verified the combination between Epitulipinolide diepoxide of PIK3CA. In RCC4 cells, pretreatment with IGF-1, attenuated HRE-induced apoptosis and G 2 /M arrest. When pretreated with PIK3 inhibitor LY294002, the opposite result appears. Pretreatment with CDKN1A (P21) inhibitor UC2288 attenuated HRE-induced G 2 /M arrest. Xenograft tumor model showed that HRE inhibited tumor growth. Western blot analysis indicated that HRE can regulating Bax, Bcl-2, PARP, cleared-PARP, Caspase-9, Caspase-8, Caspase-3, Survivin, Cyclin-B1, CDK1, N-cadherin, snail, slug, E-cadherin, MMP-9. Immunohistochemical staining showed that in the treated group, expression of E-cadherin, Bax, P21 was up-regulated, while N-cadherin, PI3K, AKT and Bcl-2 were down-regulated. H&E staining showed that compared to control groups, the main organs in the HRE-treated groups showed no histological abnormalities. The overall survival rate of RCC patients in the high-expression group of P21 was higher than in the low-expression group of P21 on bioinformatics analysis. HRE inhibited RCC migration and invasion through EMT, and inhibited proliferation in vivo and in vitro. In addition, HRE inhibited proliferation through promoting apoptosis and P21-induced G 2 /M phase arrest via PI3K/AKT signaling pathway. Overall, these results suggest that HRE may be a promising chemotherapy agent for RCC. [Display omitted] • Exploring the anti-renal cancer effect of HRE in vitro and in vivo. • Exploring the potential mechanism of anti-renal cancer action of HRE. • Epitulipinolide diepoxide in HRE may directly act on PIK3CA to inhibit the proliferation of renal cancer cells. [ABSTRACT FROM AUTHOR]

• Deep learning model is a potential tool for risk prediction. • Both radiomics model and deep learning model had prognostic values. • The deep learning model can classify the patients into low- and high-risk groups. • Individualized recommender is a potential tool to assist clinicians. Risk prediction of overall survival (OS) is crucial for gastric cancer (GC) patients to assess the treatment programs and may guide personalized medicine. A novel deep learning (DL) model was proposed to predict the risk for OS based on computed tomography (CT) images. We retrospectively collected 640 patients from three independent centers, which were divided into a training cohort (center 1 and center 2, n = 518) and an external validation cohort (center 3, n = 122). We developed a DL model based on the architecture of residual convolutional neural network. We augmented the size of training dataset by image transformations to avoid overfitting. We also developed radiomics and clinical models for comparison. The performance of the three models were comprehensively assessed. Totally 518 patients were prepared by data augmentation and fed into DL model. The trained DL model significantly classified patients into high-risk and low-risk groups in training cohort (P -value <0.001, concordance index (C-index): 0.82, hazard ratio (HR): 9.79) and external validation cohort (P -value <0.001, C-index: 0.78, HR: 11.76). Radiomics model was developed with selected 24 features and clinical model was developed with three significant clinical variables (P -value <0.05). The comparison illustrated DL model had the best performance for risk prediction of OS according to the C-index (training: DL vs Clinical vs Radiomics = 0.82 vs 0.73 vs 0.66; external validation: 0.78 vs 0.71 vs 0.72). The DL model is a powerful model for risk assessment, and potentially serves as an individualized recommender for decision-making in GC patients. [ABSTRACT FROM AUTHOR]

Purpose: Intravoxel incoherent motion (IVIM) magnetic resonance imaging is a potential noninvasive technique for the diagnosis of brain tumors. However, perfusion‐related parameter mapping is a persistent problem. The purpose of this paper is to investigate the IVIM parameter mapping of brain tumors using Bayesian fitting and low b‐values. Methods: Bayesian shrinkage prior (BSP) fitting method and different low b‐value distributions were used to estimate IVIM parameters (diffusion D, pseudo‐diffusion D*, and perfusion fraction F). The results were compared to those obtained by least squares (LSQ) on both simulated and in vivo brain data. Relative error (RE) and reproducibility were used to evaluate the results. The differences of IVIM parameters between brain tumor and normal regions were compared and used to assess the performance of Bayesian fitting in the IVIM application of brain tumor. Results: In tumor regions, the value of D* tended to be decreased when the number of low b‐values was insufficient, especially with LSQ. BSP required less low b‐values than LSQ for the correct estimation of perfusion parameters of brain tumors. The IVIM parameter maps of brain tumors yielded by BSP had smaller variability, lower RE, and higher reproducibility with respect to those obtained by LSQ. Obvious differences were observed between tumor and normal regions in parameters D (P < 0.05) and F (P < 0.001), especially F. BSP generated fewer outliers than LSQ, and distinguished better tumors from normal regions in parameter F. Conclusions: Intravoxel incoherent motion parameters clearly allow brain tumors to be differentiated from normal regions. Bayesian fitting yields robust IVIM parameter mapping with fewer outliers and requires less low b‐values than LSQ for the parameter estimation. [ABSTRACT FROM AUTHOR]

In the real word, optimization problems in multi-objective optimization (MOP) and dynamic optimization can be seen everywhere. During the last decade, among various swarm intelligence algorithms for multi-objective optimization problems, glowworm swarm optimization (GSO) and bacterial foraging algorithm (BFO) have attracted increasing attention from scholars. Although many scholars have proposed improvement strategies for GSO and BFO to keep a good balance between convergence and diversity, there are still many problems to be solved carefully. In this paper, a new coupling algorithm based on GSO and BFO (MGSOBFO) is proposed for solving dynamic multi-objective optimization problems (dMOP). MGSOBFO is proposed to achieve a good balance between exploration and exploitation by dividing into two parts. Part I is in charge of exploitation by GSO and Part II is in charge of exploration by BFO. At the same time, the simulation binary crossover (SBX) and polynomial mutation are introduced into the MGSOBFO to enhance the convergence and diversity ability of the algorithm. In order to show the excellent performance of the algorithm, we experimentally compare MGSOBFO with three algorithms on the benchmark function. The results suggests that such a coupling algorithm has good performance and outperforms other algorithms which deal with dMOP. [ABSTRACT FROM AUTHOR]

Intravoxel incoherent motion (IVIM) imaging is a magnetic resonance imaging (MRI) technique widely used in clinical applications for various organs. However, IVIM imaging at low b-values is a persistent problem. This paper aims to investigate in a systematic and detailed manner how the number of low b-values influences the estimation of IVIM parameters. To this end, diffusion-weighted (DW) data with different low b-values were simulated to get insight into the distributions of subsequent IVIM parameters. Then, in vivo DW data with different numbers of low b-values and different number of excitations (NEX) were acquired. Finally, least-squares (LSQ) and Bayesian shrinkage prior (BSP) fitting methods were implemented to estimate IVIM parameters. The influence of the number of low b-values on IVIM parameters was analyzed in terms of relative error (RE) and structural similarity (SSIM). The results showed that the influence of the number of low b-values on IVIM parameters is variable. LSQ is more dependent on the number of low b-values than BSP, but the latter is more sensitive to noise. [ABSTRACT FROM AUTHOR]

Because norcantharidin (NCTD) is unstable and subject to ring opening and hydrolysis, the diacid metabolite of norcantharidin (DM-NCTD) is the stable form of NCTD found in normal saline solution. Conversion of NCTD to DM-NCTD is almost 100 %, making it possible to determine and investigate the pharmacokinetics of DM-NCTD converted from NCTD. In this paper, a sensitive, simple and selective liquid chromatographic-tandem mass spectrometric method was developed and validated for determination of DM-NCTD in beagle plasma. DM-NCTD was detected in multiple-reaction monitoring (MRM) mode by using the dehydrated ion 169.3 as precursor ion and its product ion 123.1 as the detected ion. Ribavirin was used as internal standard and detected in MRM mode by use of precursor ions, resulting in a product ion transition of m/z 267.1 → 135.1. This method was successfully used for a pharmacokinetic study of DM-NCTD in beagles after intravenous administration of DM-NCTD in normal saline solution at doses of 0.39, 0.78, and 1.6 mg kg. DM-NCTD had dose-dependent kinetics across the dosage range investigated, with enhanced T and AUC and apparently decreasing V and CL with increasing dosage. After single-dose administration, T ranged from 0.20 to 0.55 h, AUC from 1.81 to 43.6 μg mL h, V from 228 to 55.9 mL kg, and CL from 220 to 36.5 mL kg h ( P < 0.01). The results indicated nonlinear pharmacokinetic behavior of DM-NCTD in beagles, suggesting that the risk of DM-NCTD in normal saline solution intoxication may be non-proportionally increased at higher doses.[Figure not available: see fulltext.] [ABSTRACT FROM AUTHOR]