CT-based radiomics for predicting radio-chemotherapy response and overall survival in nonsurgical esophageal carcinoma

BackgroundTo predict treatment response and 2 years overall survival (OS) of radio-chemotherapy in patients with esophageal cancer (EC) by radiomics based on the computed tomography (CT) images.MethodsThis study retrospectively collected 171 nonsurgical EC patients treated with radio-chemotherapy from Jan 2010 to Jan 2019. 80 patients were randomly divided into training (n=64) and validation (n=16) cohorts to predict the radiochemotherapy response. The models predicting treatment response were established by Lasso and logistic regression. A total of 156 patients were allocated into the training cohort (n=110), validation cohort (n=23) and test set (n=23) to predict 2-year OS. The Lasso Cox model and Cox proportional hazards model established the models predicting 2-year OS.ResultsTo predict the radiochemotherapy response, WFK as a radiomics feature, and clinical stages and clinical M stages (cM) as clinical features were selected to construct the clinical-radiomics model, achieving 0.78 and 0.75 AUC (area under the curve) in the training and validation sets, respectively. Furthermore, radiomics features called WFI and WGI combined with clinical features (smoking index, pathological types, cM) were the optimal predictors to predict 2-year OS. The AUC values of the clinical-radiomics model were 0.71 and 0.70 in the training set and validation set, respectively.ConclusionsThis study demonstrated that planning CT-based radiomics showed the predictability of the radiochemotherapy response and 2-year OS in nonsurgical esophageal carcinoma. The predictive results prior to treatment have the potential to assist physicians in choosing the optimal therapeutic strategy to prolong overall survival

Predicting the recurrence and overall survival of patients with glioma based on histopathological images using deep learning

BackgroundA deep learning (DL) model based on representative biopsy tissues can predict the recurrence and overall survival of patients with glioma, leading to optimized personalized medicine. This research aimed to develop a DL model based on hematoxylin-eosin (HE) stained pathological images and verify its diagnostic accuracy.MethodsOur study retrospectively collected 162 patients with glioma and randomly divided them into a training set (n = 113) and a validation set (n = 49) to build a DL model. The HE-stained slide was segmented into a size of 180 × 180 pixels without overlapping. The patch-level features were extracted by the pre-trained ResNet50 to predict the recurrence and overall survival. Additionally, a light-strategy was introduced where low-size digital biopsy images with clinical information were inputted into the DL model to ensure minimum memory occupation.ResultsOur study extracted 512 histopathological features from the HE-stained slides of each glioma patient. We identified 36 and 18 features as significantly related to disease-free survival (DFS) and overall survival (OS), respectively, (P < 0.05) using the univariate Cox proportional-hazards model. Pathomics signature showed a C-index of 0.630 and 0.652 for DFS and OS prediction, respectively. The time-dependent receiver operating characteristic (ROC) curves, along with nomograms, were used to assess the diagnostic accuracy at a fixed time point. In the validation set (n = 49), the area under the curve (AUC) in the 1- and 2-year DFS was 0.955 and 0.904, respectively, and the 2-, 3-, and 5-year OS were 0.969, 0.955, and 0.960, respectively. We stratified the patients into low- and high-risk groups using the median hazard score (0.083 for DFS and−0.177 for OS) and showed significant differences between these groups (P < 0.001).ConclusionOur results demonstrated that the DL model based on the HE-stained slides showed the predictability of recurrence and survival in patients with glioma. The results can be used to assist oncologists in selecting the optimal treatment strategy in clinical practice

Multi-Objective Optimization of Cell Voltage Based on a Comprehensive Index Evaluation Model in the Aluminum Electrolysis Process

In the abnormal situation of an aluminum electrolysis cell, the setting of cell voltage is mainly based on manual experience. To obtain a smaller cell voltage and optimize the operating parameters, a multi-objective optimization method for cell voltage based on a comprehensive index evaluation model is proposed. Firstly, a comprehensive judgment model of the cell state based on the energy balance, material balance, and stability of the aluminum electrolysis process is established. Secondly, a fuzzy neural network (FNN) based on the autoregressive moving average (ARMA) model is designed to establish the cell-state prediction model in order to finish the real-time monitoring of the process. Thirdly, the optimization goal of the process is summarized as having been met when the difference between the average cell voltage and the target value reaches the minimum, and the condition of the cell is excellent. And then, the optimization setting model of cell voltage is established under the constraints of the production and operation requirements. Finally, a multi-objective antlion optimization algorithm (MOALO) is used to solve the above model and find a group of optimized values of the electrolysis cell, which is used to realize the optimization control of the cell state. By using actual production data, the above method is validated to be effective. Moreover, optimized operating parameters are used to verify the prediction model of cell voltage, and the cell state is just excellent. The method is also applied to realize the optimization control of the process. It is of guiding significance for stabilizing the electrolytic aluminum production and achieving energy saving and consumption reduction

Assessment on gas‐polyethylene terephthalate solid interface partial discharge properties of C4F7N/CO2 gas mixture for eco‐friendly gas insulating transformer

Abstract The eco‐friendly insulating gas perfluoroisobutyronitrile (C4F7N) is potentially used in gas‐insulated transformers (GIT) to replace sulphur hexafluoride (SF6). However, evaluation of the long‐term insulation reliability and gas–solid interface discharge decomposition characteristics of the gas–solid film insulation structure in GIT is indispensable. The authors simulated the gas–solid film insulation structure in GIT and explored the interface partial discharge (PD) characteristics of C4F7N/CO2 gas mixture with polyethylene terephthalate (PET). The effect of gas pressure, mixing ratio on gas–solid interface gas decomposition, PET degradation was investigated, and the interaction mechanism was analysed. It is found that the interface PD generated three degradation regions on a PET film. The gas–solid interface reaction in the electrode contact region and the discharge development trace was significantly higher than that of halation region. The content of gas decomposition products decreases with the increase of gas pressure and the PD intensity of SF6‐PET is inferior to that of C4F7N/CO2 under the same condition. Relevant results provide reference for the development and application of C4F7N/CO2 based GIT

Large-Size Linear and Star-Shaped Dihydropyrazine Fused Pyrazinacenes

Linear and star-shaped pyrazinacenes <b>1a</b>–<b>b</b> and <b>2</b> were synthesized via condensation between a new building block <b>11</b> and pyrene tetraones or cyclohexaone. Compound <b>2</b> represents the largest star-shaped dihydropyrazine fused pyrazinacene reported so far. These largely expanded pyrazinacenes show good solubility and have a strong tendency to aggregate in both solution and thin films, indicating their potential applications for organic electronic devices

Potent, Selective, and Cell Active Protein Arginine Methyltransferase 5 (PRMT5) Inhibitor Developed by Structure-Based Virtual Screening and Hit Optimization

PRMT5 plays important roles in diverse cellular processes and is upregulated in several human malignancies. Besides, PRMT5 has been validated as an anticancer target in mantle cell lymphoma. In this study, we found a potent and selective PRMT5 inhibitor by performing structure-based virtual screening and hit optimization. The identified compound <b>17</b> (IC₅₀ = 0.33 μM) exhibited a broad selectivity against a panel of other methyltransferases. The direct binding of <b>17</b> to PRMT5 was validated by surface plasmon resonance experiments, with a <i>K_d</i> of 0.987 μM. Kinetic experiments indicated that <b>17</b> was a SAM competitive inhibitor other than the substrate. In addition, <b>17</b> showed selective antiproliferative effects against MV4-11 cells, and further studies indicated that the mechanism of cellular antitumor activity was due to the inhibition of PRMT5 mediated SmD3 methylation. <b>17</b> may represent a promising lead compound to understand more about PRMT5 and potentially assist the development of treatments for leukemia indications

Potent, Selective, and Cell Active Protein Arginine Methyltransferase 5 (PRMT5) Inhibitor Developed by Structure-Based Virtual Screening and Hit Optimization

PRMT5 plays important roles in diverse cellular processes and is upregulated in several human malignancies. Besides, PRMT5 has been validated as an anticancer target in mantle cell lymphoma. In this study, we found a potent and selective PRMT5 inhibitor by performing structure-based virtual screening and hit optimization. The identified compound <b>17</b> (IC₅₀ = 0.33 μM) exhibited a broad selectivity against a panel of other methyltransferases. The direct binding of <b>17</b> to PRMT5 was validated by surface plasmon resonance experiments, with a <i>K_d</i> of 0.987 μM. Kinetic experiments indicated that <b>17</b> was a SAM competitive inhibitor other than the substrate. In addition, <b>17</b> showed selective antiproliferative effects against MV4-11 cells, and further studies indicated that the mechanism of cellular antitumor activity was due to the inhibition of PRMT5 mediated SmD3 methylation. <b>17</b> may represent a promising lead compound to understand more about PRMT5 and potentially assist the development of treatments for leukemia indications

Development of Potent Type I Protein Arginine Methyltransferase (PRMT) Inhibitors of Leukemia Cell Proliferation

Protein Arginine Methyltransferases (PRMTs) are crucial players in diverse biological processes, and dysregulation of PRMTs has been linked to various human diseases, especially cancer. Therefore, small molecules targeting PRMTs have profound impact for both academic functional studies and clinical disease treatment. Here, we report the discovery of <i>N</i>¹-(2-((2-chlorophenyl)­thio)­benzyl)-<i>N</i>¹-methylethane-1,2-diamine (<b>28d</b>, DCPR049_12), a highly potent inhibitor of type I PRMTs that has good selectivity against a panel of other methyltransferases. Compound <b>28d</b> effectively inhibits cell proliferation in several leukemia cell lines and reduces the cellular asymmetric arginine dimethylation levels. Serving as an effective inhibitor, <b>28d</b> demonstrates the mechanism of cell killing in both cell cycle arrest and apoptotic effect as well as downregulation of the pivotal mixed lineage leukemia (MLL) fusion target genes such as <i>HOXA9</i> and <i>MEIS1</i>, which reflects the critical roles of type I PRMTs in MLL leukemia. These studies present <b>28d</b> as a valuable inhibitor to investigate the role of type I PRMTs in cancer and other diseases

Biochemical Studies and Molecular Dynamic Simulations Reveal the Molecular Basis of Conformational Changes in DNA Methyltransferase‑1

DNA methyltransferase-1 (DNMT1) plays a crucial role in the maintenance of genomic methylation patterns. The crystal structure of DNMT1 was determined in two different states in which the helix that follows the catalytic loop was either kinked (designated helix-kinked) or well folded (designated helix-straight state). Here, we show that the proper structural transition between these two states is required for DNMT1 activity. The mutations of N1248A and R1279D, which did not affect interactions between DNMT1 and substrates or cofactors, allosterically reduced enzymatic activities <i>in vitro</i> by decreasing <i>k</i>_cat/<i>K</i>_m for AdoMet. The crystallographic data combined with molecular dynamic (MD) simulations indicated that the N1248A and R1279D mutants bias the catalytic helix to either the kinked or straight conformation. In addition, genetic complementation assays for the two mutants suggested that disturbing the conformational transition reduced DNMT1 activity in cells, which could act additively with existing DNMT inhibitors to decrease DNA methylation. Collectively, our studies provide molecular insights into conformational changes of the catalytic helix, which is essential for DNMT1 catalytic activity, and thus aid in better understanding the relationship between DNMT1 dynamic switching and enzymatic activity