Effects of the Anti-Tumorigenic Agent AT101 on Human Glioblastoma Cells in the Microenvironmental Glioma Stem Cell Niche

Glioblastoma (GBM) is a barely treatable disease due to its profound chemoresistance. A distinct inter- and intratumoral heterogeneity reflected by specialized microenvironmental niches and different tumor cell subpopulations allows GBMs to evade therapy regimens. Thus, there is an urgent need to develop alternative treatment strategies. A promising candidate for the treatment of GBMs is AT101, the R(-) enantiomer of gossypol. The present study evaluates the effects of AT101, alone or in combination with temozolomide (TMZ), in a microenvironmental glioma stem cell niche model of two GBM cell lines (U251MG and U87MG). AT101 was found to induce strong cytotoxic effects on U251MG and U87MG stem-like cells in comparison to the respective native cells. Moreover, a higher sensitivity against treatment with AT101 was observed upon incubation of native cells with a stem-like cell-conditioned medium. This higher sensitivity was reflected by a specific inhibitory influence on the p-p42/44 signaling pathway. Further, the expression of CXCR7 and the interleukin-6 receptor was significantly regulated upon these stimulatory conditions. Since tumor stem-like cells are known to mediate the development of tumor recurrences and were observed to strongly respond to the AT101 treatment, this might represent a promising approach to prevent the development of GBM recurrences.     

Ablative Radiotherapy Reprograms the Tumor Microenvironment of a Pancreatic Tumor in Favoring the Immune Checkpoint Blockade Therapy

The low overall survival rate of patients with pancreatic cancer has driven research to seek a new therapeutic protocol. Radiotherapy (RT) is frequently an option in the neoadjuvant or palliative settings for pancreatic cancer treatment. This study explored the effect of RT protocols on the tumor microenvironment (TME) and their consequent impact on anti-programmed cell death ligand-1 (PD-L1) therapy. Using a murine orthotopic pancreatic tumor model, UN-KC-6141, RT-disturbed TME was examined by immunohistochemical staining. The results showed that ablative RT is more effective than fractionated RT at recruiting T cells. On the other hand, fractionated RT induces more myeloid-derived suppressor cell infiltration than ablative RT. The RT-disturbed TME presents a higher perfusion rate per vessel. The increase in vessel perfusion is associated with a higher amount of anti-PD-L1 antibody being delivered to the tumor. Animal survival is increased by anti-PD-L1 therapy after ablative RT, with 67% of treated animals surviving more than 30 days after tumor inoculation compared to a median survival time of 16.5 days for the control group. Splenocytes isolated from surviving animals were specifically cytotoxic for UN-KC-6141 cells. We conclude that the ablative RT-induced TME is more suited than conventional RT-induced TME to combination therapy with immune checkpoint blockade.     

高岭土性质对催化裂化催化剂性能的影响

采用X射线荧光分析仪、X射线衍射仪、N2物理吸附仪、场发射扫描电子显微镜等仪器表征了苏州、广西和美国佐治亚州3个产地的高岭土，并以此3种高岭土为原料制备了模型催化裂化（FCC）催化剂，在ACE评价装置上对比了模型催化剂的反应性能。结果表明:苏州及广西高岭土主要组分为高岭石，佐治亚高岭土主要组分为地开石及珍珠陶土；苏州高岭土呈片状，还含有少量棒状颗粒；广西高岭土呈多层片状，晶粒粒径较大；佐治亚高岭土呈薄片状，晶粒粒径较小；3种高岭土制备的模型催化剂反应活性、Na2O及RE2O3质量分数相近；广西高岭土制备的模型催化剂具有最大的孔体积和磨损指数，但比表面积最小，具有较强的重油转化能力，其目标产物（液化气+汽油）和副产物（干气+焦炭）收率都高于苏州、佐治亚高岭土制备的催化剂的。     

Omics Analysis of Blood-Responsive Regulon in Bordetella pertussis Identifies a Novel Essential T3SS Substrate

Bacterial pathogens sense specific cues associated with different host niches and integrate these signals to appropriately adjust the global gene expression. Bordetella pertussis is a Gram-negative, strictly human pathogen of the respiratory tract and the etiological agent of whooping cough (pertussis). Though B. pertussis does not cause invasive infections, previous results indicated that this reemerging pathogen responds to blood exposure. Here, omics RNA-seq and LC–MS/MS techniques were applied to determine the blood-responsive regulon of B. pertussis. These analyses revealed that direct contact with blood rewired global gene expression profiles in B. pertussis as the expression of almost 20% of all genes was significantly modulated. However, upon loss of contact with blood, the majority of blood-specific effects vanished, with the exception of several genes encoding the T3SS-secreted substrates. For the first time, the T3SS regulator BtrA was identified in culture supernatants of B. pertussis. Furthermore, proteomic analysis identified BP2259 protein as a novel secreted T3SS substrate, which is required for T3SS functionality. Collectively, presented data indicate that contact with blood represents an important cue for B. pertussis cells.     

Direct Injection of CRISPR/Cas9-Related mRNA into Cytoplasm of Parthenogenetically Activated Porcine Oocytes Causes Frequent Mosaicism for Indel Mutations

Some reports demonstrated successful genome editing in pigs by one-step zygote microinjection of mRNA of CRISPR/Cas9-related components. Given the relatively long gestation periods and the high cost of housing, the establishment of a single blastocyst-based assay for rapid optimization of the above system is required. As a proof-of-concept, we attempted to disrupt a gene (GGTA1) encoding the α-1,3-galactosyltransferase that synthesizes the α-Gal epitope using parthenogenetically activated porcine oocytes. The lack of α-Gal epitope expression can be monitored by staining with fluorescently labeled isolectin BS-I-B₄ (IB4), which binds specifically to the α-Gal epitope. When oocytes were injected with guide RNA specific to GGTA1 together with enhanced green fluorescent protein (EGFP) and human Cas9 mRNAs, 65% (24/37) of the developing blastocysts exhibited green fluorescence, although almost all (96%, 23/24) showed a mosaic fluorescent pattern. Staining with IB4 revealed that the green fluorescent area often had a reduced binding activity to IB4. Of the 16 samples tested, six (five fluorescent and one non-fluorescent blastocysts) had indel mutations, suggesting a correlation between EGFP expression and mutation induction. Furthermore, it is suggested that zygote microinjection of mRNAs might lead to the production of piglets with cells harboring various mutation types.     

黑龙江省节水增粮行动(2012—2015年)实施方案

文章通过对黑龙江省高效节水现状,存在问题的分析,结合黑龙江省高效节水发展的支撑能力,阐述黑龙江省节水增粮目标、任务及总体布局,为黑龙江省2012—2015年节水增粮实施提供依据。     

高密度聚乙烯/丁苯橡胶热塑性硫化胶的形状记忆性能

采用动态硫化法制备了高密度聚乙烯(HDPE)/丁苯橡胶(SBR)热塑性硫化胶(TPV),研究了橡塑比、定型温度、回复温度及增容剂苯乙烯-丁二烯-苯乙烯嵌段共聚物(SBS)对HDPE/SBR TPV形状记忆性能的影响,观察了其微观结构,提出了形状记忆行为机制。结果表明,HDPE/SBR TPV具有典型的"海-岛"结构,拉伸后其表面存在明显的取向结构;SBS能显著提高HDPE/SBR TPV的形状记忆性能,当SBS用量为6份、定型温度和回复温度均为120℃时,HDPE/SBR TPV的形状记忆性能最佳。     

Deep Learning Based Process Analytics Model for Predicting Type 2 Diabetes Mellitus

Process analytics is one of the popular research domains that advanced in the recent years. Process analytics encompasses identification, monitoring, and improvement of the processes through knowledge extraction from historical data. The evolution of Artificial Intelligence (AI)-enabled Electronic Health Records (EHRs) revolutionized the medical practice. Type 2 Diabetes Mellitus (T2DM) is a syndrome characterized by the lack of insulin secretion. If not diagnosed and managed at early stages, it may produce severe outcomes and at times, death too. Chronic Kidney Disease (CKD) and Coronary Heart Disease (CHD) are the most common, long-term and life-threatening diseases caused by T2DM. Therefore, it becomes inevitable to predict the risks of CKD and CHD in T2DM patients. The current research article presents automated Deep Learning (DL)-based Deep Neural Network (DNN) with Adagrad Optimization Algorithm i.e., DNN-AGOA model to predict CKD and CHD risks in T2DM patients. The paper proposes a risk prediction model for T2DM patients who may develop CKD or CHD. This model helps in alarming both T2DM patients and clinicians in advance. At first, the proposed DNN-AGOA model performs data preprocessing to improve the quality of data and make it compatible for further processing. Besides, a Deep Neural Network (DNN) is employed for feature extraction, after which sigmoid function is used for classification. Further, Adagrad optimizer is applied to improve the performance of DNN model. For experimental validation, benchmark medical datasets were used and the results were validated under several dimensions. The proposed model achieved a maximum precision of 93.99%, recall of 94.63%, specificity of 73.34%, accuracy of 92.58%, and F-score of 94.22%. The results attained through experimentation established that the proposed DNN-AGOA model has good prediction capability over other methods.