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Microcystin (MC) -producing and non-MC-producing Microcystisstrains typically coexist in Microcystis-dominated blooms. However, the interaction between submerged plants and Microcystis at strain level is not clear. This study aimed to assess the effects of a submerged macrophyte Myriophyllum spicatum on one MC-producing versus one non- MC-producing strains of the cyanobacterium Microcystis using plant-Microcystis co-culture experiments. The impacts of Microcystis on M. spicatum were also examined. It showed that the MC-producing Microcystis strain had a higher resistance to negative impacts by the cocultured submerged plant M. spicatum than the non-MC-producing strain. By contrast, the plant M. spicatum was impacted more by the MC-producing Microcystis than the non-MC-producer. The associated bacterioplankton community was affected more by the MC-producing Microcystis than the cocultured M. spicatum. The MC cell quotas were significantly higher in the coculture treatment (the PM+ treatment, p < 0.05), indicating that the production and release of MCs might be a key factor responsible for the reduced impact of M. spicatum. The higher concentrations of dissolved organic and reducing inorganic compounds might eventually exacerbate the recovering capacity of co-existing submerged plants. Overall, this study indicated that the capacity to produce MCs, as well as the density of Microcystis should be taken into account when attempting to re-establish submerged vegetation to undertake remediation works.

This study provides the first empirical evidence that the Federal Reserve (the Fed) systematically retrieves micro-level accounting reports to aid its understanding of the state of the macroeconomy. Using unique data identifying its direct access of corporate SEC filings, we show that the Fed tracks firms that are bellwethers and industry leaders, or that can engender systemic risk. The qualitative information in the Fed-accessed periodic reports explains the Fed’s GDP growth forecasts for up to four quarters, after controlling for contemporaneous aggregate earnings and other economic information. However, professional forecasts fail to incorporate such qualitative accounting information. In addition, this qualitative information is reflected in the tone of the ensuing FOMC meeting discussions as well as in Fed forecasts of key macro demand and supply factors. Overall, our evidence suggests important externalities of micro-level accounting reports, especially qualitative disclosures, on the central bank’s macroeconomic forecasts and, by extension, monetary policy. Data Availability: Data are available from public sources. JEL Classifications: E58; G20; M41; M45.

Background Fecal DNA and occult blood testing have been gradually developed for colorectal cancer (CRC) screening. Comparison of different testing strategies for these methods in CRC screening is in urgent need. This study aims to examine the efficacy of different testing strategies including multi-target fecal DNA testing, qualitative and quantitative fecal immunoassay tests (FITs). Methods Fecal samples were collected from patients diagnosed by colonoscopy. Tests using fecal DNA, quantitative FIT or qualitative FIT were performed on same fecal samples. Efficiency of different testing strategies within different populations was investigated. Results For high-risk populations (CRC and advanced adenoma), the positive rate of the three methods alone was 74.3–80%; the positive predictive values (PPVs) ranged from 37.3% to 77.8%, and the negative predictive values (NPVs) ranged from 86.3% to 92.2%. For combined testing strategies, the positive rate was 71.4–88.6%, PPVs ranged from 38.3% to 86.2%, and NPVs ranged from 89.6% to 92.9%. Parallel fecal multi-target DNA test and quantitative FIT appears to be superior when using a combined testing strategy. For the normal population, no significant difference was identified in efficacy between these methods when used alone and in combination. Conclusions Single testing strategy among the three methods is more suitable for the general population screening, and the combined testing strategy is more suitable for high-risk populations screening. The use of different combination strategies may have superiority in CRC high-risk population screening, but cannot conclude significant differences which may be attributed to the small sample size, large samples controlled trials are needed.

How abnormal neurodevelopment relates to the tumour aggressiveness of medulloblastoma (MB), the most common type of embryonal tumour, remains elusive. Here we uncover a neurodevelopmental epigenomic programme that is hijacked to induce MB metastatic dissemination. Unsupervised analyses of integrated publicly available datasets with our newly generated data reveal that SMARCD3 (also known as BAF60C) regulates Disabled 1 (DAB1)-mediated Reelin signalling in Purkinje cell migration and MB metastasis by orchestrating cis-regulatory elements at the DAB1 locus. We further identify that a core set of transcription factors, enhancer of zeste homologue 2 (EZH2) and nuclear factor I X (NFIX), coordinates with the cis-regulatory elements at the SMARCD3 locus to form a chromatin hub to control SMARCD3 expression in the developing cerebellum and in metastatic MB. Increased SMARCD3 expression activates Reelin–DAB1-mediated Src kinase signalling, which results in a MB response to Src inhibition. These data deepen our understanding of how neurodevelopmental programming influences disease progression and provide a potential therapeutic option for patients with MB.

Consensus clustering provides an elegant framework to aggregate multiple weak clustering results to learn a consensus one that is more robust and stable than a single result. However, most of the existing methods usually use all data for consensus learning, whereas ignoring the side effects caused by some unreliable or difficult data. To address this issue, in this article, we propose a novel self-paced consensus clustering method with adaptive bipartite graph learning to gradually involve data from more reliable to less reliable ones in consensus learning. At first, we construct an initial bipartite graph from the base results, where the nodes represent the clusters and instances, and the edges indicate that an instance belongs to a cluster. Then, we adaptively learn a structured bipartite graph from this initial one by self-paced learning, i.e., we automatically determine the reliability of each edge with adaptive cluster similarity measuring and involve the edges in bipartite graph learning in order of their reliability. At last, we obtain the final consensus result from the learned structured bipartite graph. We conduct extensive experiments on both toy and benchmark datasets, and the results show the effectiveness and superiority of our method. The codes of this article are released in http://Doctor-Nobody.github.io/codes/code_SCCABG.zip.

Uncontrolled hyperglycemia, hypercholesterolemia, and hypertension are common in persons with diabetes.To compare the effectiveness of team-based care with and without a clinical decision support system (CDSS) in controlling glycemia, lipids, and blood pressure (BP) among patients with type 2 diabetes.Cluster randomized trial. (ClinicalTrials.gov: NCT02835287).38 community health centers in Xiamen, China.11 132 persons aged 50 years or older with uncontrolled diabetes and comorbid conditions, 5475 receiving team-based care with a CDSS and 5657 receiving team-based care alone.Team-based care was delivered by primary care physicians, health coaches, and diabetes specialists in all centers. In addition, a computerized CDSS, which generated individualized treatment recommendations based on clinical guidelines, was implemented in 19 centers delivering team-based care with a CDSS.Coprimary outcomes were mean reductions in hemoglobin ADuring the 18-month intervention, HbAThere was no usual care control, and clinical outcome assessors were unblinded; the analysis did not account for multiple comparisons.Compared with team-based care alone, team-based care with a CDSS significantly reduced cardiovascular risk factors in patients with diabetes, but the effect was modest.Xiamen Municipal Health Commission.

Brain magnetic resonance images (MRI) are widely used for the classification of Alzheimer's disease (AD). The size of 3D images is, however, too large. Some of the sliced image features are lost, which results in conflicting network size and classification performance. This article uses key components in the transformer model to propose a new lightweight method, ensuring the lightness of the network and achieving highly accurate classification. First, the transformer model is imitated by using image patch input to enhance feature perception. Second, the Gaussian error linear unit (GELU), commonly used in transformer models, is used to enhance the generalization ability of the network. Finally, the network uses MRI slices as learning data. The depthwise separable convolution makes the network more lightweight. Experiments are carried out on the ADNI public database. The accuracy rate of AD vs. normal control (NC) experiments reaches 98.54%. The amount of network parameters is 1.3% of existing similar networks.

Ethylene has an important role in regulating plant growth and development as well as responding to adversity stresses. The 1-aminocyclopropane-1-carboxylate synthase (ACS) is the rate-limiting enzyme for ethylene biosynthesis. However, the role of the ACS gene family in wheat has not been examined. In this study, we identified 12 ACS members in wheat. According to their position on the chromosome, we named them TaACS1-TaACS12, which were divided into four subfamilies, and members of the same subfamilies had similar gene structures and protein-conserved motifs. Evolutionary analysis showed that fragment replication was the main reason for the expansion of the TaACS gene family. The spatiotemporal expression specificity showed that most of the members had the highest expression in roots, and all ACS genes contained W box elements that were related to root development, which suggested that the ACS gene family might play an important role in root development. The results of the gene expression profile analysis under stress showed that ACS members could respond to a variety of stresses. Protein interaction prediction showed that there were four types of proteins that could interact with TaACS. We also obtained the targeting relationship between TaACS family members and miRNA. These results provided valuable information for determining the function of the wheat ACS gene, especially under stress.

Ischaemic stroke is a common neurological disease and a leading cause of severe disability and death in developed countries. In most cases, stroke is thought to be a multifactorial disorder or complex trait for which classic patterns of inheritance cannot be shown. Xuesaitong is one of the most commonly used medicines for treating ischemic stroke in China. However, compared to the conventional therapy, the effectiveness and safety of Xuesaitong for ischemic stroke needs to be further systematically reviewed and determined.Relevant randomized controlled trials (RCTs) examining the use of the Xuesaitong soft capsule in the treatment of patients with ischemic stroke were identified from databases, including the China National Knowledge Infrastructure, Wanfang, PubMed, Embase, and Web of Science databases. Next, 2 researchers independently extracted information from the included studies, analyzed the data using STATA 15.0 software, and evaluated the quality of the included studies using RevMan 5.3.A total of 17 RCTs (comprising 1,942 patients with ischemic stroke) were included in the meta-analysis. The meta-analysis results showed that the Xuesaitong soft capsule treatment increased patients' total effective rate compared to conventional or other drug treatments, and improved patients' Clinical Severity Score (CSS scores) or Barthel index (BI) score. A further subgroup analysis stratified by different treatment times showed that Xuesaitong soft capsule treatment at 4 and 8 weeks improved CSS scores more than treatment at 2 weeks in patients with ischemic stroke. Additionally, the Xuesaitong soft capsule also significantly improved plasma viscosity, whole-blood viscosity at high and low shear rates, fibrinogen, hematocrit, and the effect on traditional Chinese medicine (TCM) single symptoms or signs in patients with ischemic stroke.In summary, compared to conventional or other drug treatments, the Xuesaitong soft capsule treatment was beneficial in improving patients' TCM symptoms (e.g., crooked mouth and tongue, and dizziness) and various indicators. Further, Xuesaitong soft capsule may be a safe and effective drug for the treatment of ischemic stroke. And large-scale randomized clinical trials are needed to further confirm our findings.

Background Nutritient intake of pregnant women with gestational diabetes mellitus (GDM) is important to ensure satisfactory birth outcomes. The aim of this study is to explore the dietary profiles of patients with GDM, compare the results with the Chinese dietary guidelines or Dietary Reference Intakes (DRIs) from China and investigate the relationship between maternal dietary intake and pregnancy outcomes. Methods A total of 221 patients with GDM in the second trimester were included in the cohort. Dietary intake data were collected using a 24-hour recall method for 3 consecutive days. Pregnancy outcomes of these participants were followed up. Univariate logistic regression and multivariate logistic regression analyses were conducted to explore the associations between dietary intake variables or general characteristics variables and adverse pregnancy outcomes. Results Participants with adverse pregnancy outcomes showed lower percentage of dietary energy intake from carbohydrate, iodine and Vitamin D and higher percentage of dietary energy intake from fat compared to participants without adverse pregnancy outcomes. The gestational weight gain and family history of diabetes were associated with increased risk of adverse pregnancy outcomes. Exercise, the percentage of dietary energy intake from carbohydrate, the intake of iodine and Vitamin D were associated with a decreased risk of adverse pregnancy outcomes. Conclusion The daily diet of pregnant women with GDM in China did not meet the dietary guidelines or DRIs. The low dietary carbohydrate ratio and intake of Vitamin D and iodine, family history of diabetes, lack of exercise, and high gestational weight gain were associated with increased risk of adverse pregnancy outcomes in pregnant women with GDM.

High amylose wheat (HAW) has potential health benefits but its dough structure is usually inferior. Wheat dough is a complex mixture and its structure is influenced by the physicochemical properties of gluten and starch. In this study, we investigated the starch granule development, gluten structure, starch properties, pasting, and thermal properties of flour, as well as the rheological properties of dough in wheat variety Xinong 836 with high amylose content (33.57%) and its parents. The results showed that Xinong 836 wheat starch contained more small starch granules, which was consistent with the microstructural results of starch granules in grain filling stage. Moreover, Xinong 836 wheat starch showed highest swelling power and water solubility. Importantly, the flour of Xinong 836 wheat had the highest protein content and wet gluten content and Xinong 836 wheat gluten showed highest β-sheets content and disulfide bond content than its parents Zhengmai 7698 and Xinong 979, which conferring to more compact microscopic networks of dough, thereby contributing to the higher peak viscosity (PV), final viscosity (FV), and setback viscosity (SB) in the flour of Xinong 836. Our finding elucidated that the stability of gluten and properties of starch synergistically affected the pasting and thermal properties of the flour paste, and the presence of more small starch granules contributed to dough with a rather dense structure in HAW Xinong 836. Thus, superior gluten structure and more small starch granules have synergistic effects on enhancing the gluten–starch interaction, thereby contributing to better dough quality.

Intracranial pressure (ICP) is one of the most important indexes in neurosurgery. It is essential for doctors to determine the numeric value and changes of ICP, whether before or after an operation. Although external ventricular drainage (EVD) is the gold standard for monitoring ICP, more and more novel monitoring methods are being applied clinically.Invasive wired ICP monitoring is still the most commonly used in practice. Meanwhile, with the rise and development of various novel technologies, non-invasive types and invasive wireless types are gradually being used clinically or in the testing phase, as a complimentary approach of ICP management. By choosing appropriate monitoring methods, clinical neurosurgeons are able to obtain ICP values safely and effectively under particular conditions.This article introduces diverse monitoring methods and compares the advantages and disadvantages of different monitoring methods. Moreover, this review may enable clinical neurosurgeons to have a broader view of ICP monitoring.

Background: Gastric intestinal metaplasia (IM) is the key link of gastric precancerous lesions. Ferroptosis is a novel form of programmed cell death. However, its impact on IM is unclear. The focus of this study is to identify and verify ferroptosis-related genes (FRGs) that may be involved in IM by bioinformatics analysis.Materials and methods: Differentially expressed genes (DEGs) were obtained from microarray dataset GSE60427 and GSE78523 downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed ferroptosis-related genes (DEFRGs) were obtained from overlapping genes of DEGs and FRGs got from FerrDb. DAVID database was used for functional enrichment analysis. Protein-protein interaction (PPI) analysis and Cytoscape software were used to screen hub gene. In addition, we built a receiver operating characteristic (ROC) curve and verified the relative mRNA expression by quantitative reverse transcription-polymerase chain reaction (qRT-PCR). Finally, the CIBERSORT algorithm was used to analyze the immune infiltration in IM.Results: First, a total of 17 DEFRGs were identified. Second, a gene module identified by Cytoscape software was considered as hub gene: PTGS2, HMOX1, IFNG, and NOS2. Third, ROC analysis showed that HMOX1 and NOS2 had good diagnostic characteristics. qRT-PCR experiments confirmed the differential expression of HMOX1 in IM and normal gastric tissues. Finally, immunoassay showed that the proportion of T cells regulatory (Tregs) and macrophages M0 in IM was relatively higher, while the proportion of T cells CD4 memory activated and dendritic cells activated was lower.Conclusion: We found significant associations between FRGs and IM, and HMOX1 may be diagnostic biomarkers and therapeutic targets for IM. These results may enhance our understanding of IM and may contribute to its treatment.

As the most important part of smart city and long-standing challenging issue, a highly efficient smart logistic system has attracted a great deal of attention in recent years. In particular, unmanned aerial vehicles (UAVs) are ideal solutions for last mile delivery scenarios in recent years due to their fast speed and easy deployment. However, because of the highly automatic delivery process, UAVs are still constrained by the limited payload, battery and computing capacity for complex computational tasks. With the aid of mobile edge computing (MEC) technology, UAVs can offload computational tasks to the MEC computational resources in various types of IoT environments. In spite of the task offloading which can enhance their task process capability, it also brings extra overhead such as data transfer time and energy consumption. These extra overheads may significantly impact the efficiency and payload of UAV-based delivery systems. Therefore, taking the UAV last mile delivery system with MEC as an example, this paper investigates the energy-aware multi-UAV task computation management problem according to a realistic autonomous delivery network (ADNET). Specifically, we propose a computation management strategy, namely, the MEC-based task offloading and scheduling strategy (TOSS), to provide an integral approach covering both the static task offloading and scheduling algorithm, as well as the dynamic resource conflict resolution algorithm. Grounded on real-world scenarios, our experimental results show that TOSS can achieve a higher payload for UAVs by using minimum energy consumption and task makespan within the given constraints of the deadline compared to the state-of-the-art methods.

The growth and photosynthetic responses of microcystin (MC)- and non-MC-producing Microcystis to the submerged macrophyte Myriophyllum spicatum were investigated under plant-Microcystis co-culture conditions (PMC + , PMC−), in comparison with their corresponding mono-culture controls (SMC + , SMC-). The OJIP chlorophyll a fluorescence transient was recorded and analyzed using JIP-test parameters. In comparison with the corresponding mono-culture controls, the quantum efficiencies of the electron transport chain expressed as parameters Ψo and φEo decreased by more than 25% in the PMC− group, much higher than those in the PMC+ group (p < 0.05). The decreasing extent of the electron transport flux ratio ETo/RC was significantly larger in the PMC− group than in the PMC+ group (p < 0.05). The performance index (PIABS) decreased by 26.98% in the PMC− group and by 16.25% in the PMC+ group. These results indicated that the non-MC-producing Microcystis was more sensitive than the MC-producing strain when co-cultured with M. spicatum, and the efficiency and energy flux of electron transport might be the main targets of Photosystem II. Future research should focus on the intraspecific composition and toxigenic levels of Microcystis populations interacting with submerged macrophytes to guide the use of submerged vegetation restoration.

Introduction: Using gene therapy to transfer specific genes to implant therapeutic proteins into damaged tissues is a more promising way to treat sports injuries. The combination of tissue engineering and gene therapy will potentially promote the regeneration and repair of various damaged tissues. Objective: This article explores the adaptive relationship between gene selection therapy and athletes in sports. Methods: We selected students of related majors in sports schools to conduct specific genetic testing and measure the muscle area, fatigue level, muscle damage, and other related indicators before and after exercise. Results: After a series of physical fitness assessments, an increase in the gene sequence, as well as changes in the biochemical indices, were confirmed Conclusions: The muscle gain of the test subject during training is better than other genotypes. Level of evidence II; Therapeutic studies - investigation of treatment results. RESUMO Introdução: A terapia gênica para transferir genes específicos para codificar proteínas terapêuticas para tecidos danificados é uma forma bastante promissora de tratar lesões esportivas. A combinação de engenharia de tecidos e terapia gênica possivelmente promoverá regeneração e reparo de vários tecidos danificados. Objetivo: Este artigo explora a relação adaptativa entre a terapia de seleção gênica e atletas no esporte. Métodos: Selecionamos alunos universitários nas escolas de esportes para realizar testes genéticos específicos e medir a área muscular, nível de fadiga, lesões musculares e outros indicadores relacionados antes e depois do exercício. Resultados: Depois de uma série de avaliações da aptidão física, verificou-se aumento da sequência gênica, assim como índices bioquímicos também apresentaram alterações. Conclusões: O ganho muscular dos indivíduos testados durante o treinamento é melhor do que outros genótipos. Nível de Evidência II; Estudos terapêuticos - Investigação dos resultados do tratamento. RESUMEN Introducción: La terapia génica para transferir genes específicos que codifiquen proteínas terapéuticas a los tejidos dañados es una forma muy prometedora de tratar las lesiones deportivas. La combinación de ingeniería tisular y terapia génica posiblemente favorecerá la regeneración y reparación de diversos tejidos dañados. Objetivo: Este artículo explora la relación adaptativa entre la terapia de selección génica y los atletas en el deporte. Métodos: Se seleccionaron estudiantes universitarios de escuelas deportivas para realizar pruebas genéticas específicas y medir el área muscular, el nivel de fatiga, las lesiones musculares y otros indicadores relacionados antes y después del ejercicio. Resultados: Tras una serie de evaluaciones de acondicionamiento físico, se comprobó un aumento de la secuencia génica, y los índices bioquímicos también presentaron cambios. Conclusiones: El aumento de la masa muscular de los individuos analizados durante el entrenamiento es mejor que el de otros genotipos. Nivel de Evidencia II; Estudios terapéuticos - Investigación de los resultados del tratamiento.

Clustering ensemble integrates multiple base clustering results to obtain a consensus result and thus improves the stability and robustness of the single clustering method. Since it is natural to use a hypergraph to represent the multiple base clustering results, where instances are represented by nodes and base clusters are represented by hyperedges, some hypergraph based clustering ensemble methods are proposed. Conventional hypergraph based methods obtain the final consensus result by partitioning a pre-defined static hypergraph. However, since base clusters may be imperfect due to the unreliability of base clustering methods, the pre-defined hypergraph constructed from the base clusters is also unreliable. Therefore, directly obtaining the final clustering result by partitioning the unreliable hypergraph is inappropriate. To tackle this problem, in this paper, we propose a clustering ensemble method via structured hypergraph learning, i.e., instead of being constructed directly, the hypergraph is dynamically learned from base results, which will be more reliable. Moreover, when dynamically learning the hypergraph, we enforce it to have a clear clustering structure, which will be more appropriate for clustering tasks, and thus we do not need to perform any uncertain postprocessing, such as hypergraph partitioning. Extensive experiments show that, our method not only performs better than the conventional hypergraph based ensemble methods, but also outperforms the state-of-the-art clustering ensemble methods.

In many real-world unsupervised learning applications, given data with balanced distribution, that is, there are an approximately equal number of instances in each class, we often need to construct a model to reveal such balance. However, in many data, especially the high-dimensional ones, the data in the original feature space often do not present such balance due to the redundant and noisy features. To tackle this problem, we apply an unsupervised spectral feature selection method to select some informative features, which can better reveal the balanced structure of data. Although spectral feature selection is one of the most popular unsupervised feature selection methods and has been widely studied, none of the existing spectral feature selection methods consider the balance property of data. To address this issue, in this article, we propose a novel balanced spectral feature selection (BSFS) method, which not only selects the discriminative features but also picks those to reveal the balanced structure of data. To the best of our knowledge, this is the first spectral feature selection method considering balance structure of data. By introducing a balanced regularization term, we integrate the balanced spectral clustering and feature selection into a unified framework seamlessly. At last, the experiments on benchmark datasets show that the proposed one outperforms the conventional feature selection methods in both clustering performance and balance, which demonstrates the effectiveness and efficiency of the proposed method.

PDF file, 66KB, Serum biochemistry panel.

XLS file, 83KB.

Purpose: Hypoxia is an important characteristic of the solid tumor microenvironment and constitutes a barrier for effective radiotherapy. Here, we studied the effects of darinaparsin (an arsenic cytotoxin) on survival and radiosensitivity of tumor cells in vitro under normoxia and hypoxia and in vivo using xenograft models, compared to effects on normal tissues.Experimental Design: The cytotoxicity and radiosensitization of darinaparsin were first tested in vitro in a variety of solid tumor cell lines under both normoxia and hypoxia and compared with arsenic trioxide (ATO, an arsenical with reported cytotoxic and radiosensitizing activities on tumor cells). The effects were then tested in mouse models of xenograft tumors derived from tumor cell lines and clinical tumor specimens. The potential mechanisms of darinaparsin effects, including reactive oxygen species (ROS) generation, cellular damage, and changes in global gene expression, were also investigated.Results: In comparison with ATO, darinaparsin had significantly higher in vitro cytotoxic and radiosensitizing activities against solid tumor cells under both normoxia and hypoxia. In vivo experiments confirmed these activities at doses that had no systemic toxicities. Importantly, darinaparsin did not radiosensitize normal bone marrow and actually radioprotected normal intestinal crypts. The darinaparsin-mediated antitumor effects under hypoxia were not dependent on ROS generation and oxidative damage, but were associated with inhibition of oncogene (RAS and MYC)-dependent gene expression.Conclusion: Darinaparsin has significant and preferential cytotoxic and radiosensitizing effects on solid tumors as compared with normal cells. Darinaparsin may therefore increase the therapeutic index of radiation therapy and has near term translational potential. Clin Cancer Res; 18(12); 3366–76. ©2012 AACR.

PDF file, 223KB, RT-qPCR validation of microarray data.

Purpose: Isocitrate dehydrogenase (IDH) mutations in glioma patients confer longer survival and may guide treatment decision making. We aimed to predict the IDH status of gliomas from MR imaging by applying a residual convolutional neural network to preoperative radiographic data.Experimental Design: Preoperative imaging was acquired for 201 patients from the Hospital of University of Pennsylvania (HUP), 157 patients from Brigham and Women's Hospital (BWH), and 138 patients from The Cancer Imaging Archive (TCIA) and divided into training, validation, and testing sets. We trained a residual convolutional neural network for each MR sequence (FLAIR, T2, T1 precontrast, and T1 postcontrast) and built a predictive model from the outputs. To increase the size of the training set and prevent overfitting, we augmented the training set images by introducing random rotations, translations, flips, shearing, and zooming.Results: With our neural network model, we achieved IDH prediction accuracies of 82.8% (AUC = 0.90), 83.0% (AUC = 0.93), and 85.7% (AUC = 0.94) within training, validation, and testing sets, respectively. When age at diagnosis was incorporated into the model, the training, validation, and testing accuracies increased to 87.3% (AUC = 0.93), 87.6% (AUC = 0.95), and 89.1% (AUC = 0.95), respectively.Conclusions: We developed a deep learning technique to noninvasively predict IDH genotype in grade II–IV glioma using conventional MR imaging using a multi-institutional data set. Clin Cancer Res; 24(5); 1073–81. ©2017 AACR.

Supplementary Data

PDF file, 1660KB, S1 Mouse body weight change in response to DPS treatment; S2. DPS did not induce autophagy in HI-LAPC-4 cells; S3: DPS did not induce cell cycle arrest; S4: DPS did not enhance IR-induced DNA damage; S5: DPS activity on solid tumor cells was not dependent on mitochondrial damage; S6: DPS cytotoxicity depends partially on protein synthesis; S7: DPS did not modulate HIF1α protein and transcriptional activity; Fig S8: JNK was activated by DPS, but not required for DPS cytotoxicity; S9: Oncogene RAS and MYC were overexpressed in HI-LAPC-4 and PC-3 cancer cells.

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Glioblastoma (GBM) is the most common and aggressive primary brain cancer. Despite multimodal treatment including surgery, radiotherapy, and chemotherapy, median patient survival has remained at ~15 months for decades. This situation demands an outside-the-box treatment approach. Using magnetic carbon nanotubes (mCNTs) and precision magnetic field control, we report a mechanical approach to treat chemoresistant GBM. We show that GBM cells internalize mCNTs, the mobilization of which by rotating magnetic field results in cell death. Spatiotemporally controlled mobilization of intratumorally delivered mCNTs suppresses GBM growth in vivo. Functionalization of mCNTs with anti-CD44 antibody, which recognizes GBM cell surface–enriched antigen CD44, increases mCNT recognition of cancer cells, prolongs mCNT enrichment within the tumor, and enhances therapeutic efficacy. Using mouse models of GBM with upfront or therapy-induced resistance to temozolomide, we show that mCNT treatment is effective in treating chemoresistant GBM. Together, we establish mCNT-based mechanical nanosurgery as a treatment option for GBM.