CO2 hydrogenation over Ru hydrotalcite-derived catalysts

The hydrogenation of CO2 over Ru catalysts is structure sensitive, the selectivity of the process can be driven either to the production of CH4 or CO depending on Ru particles and support features. Herein, Ru-based MgAl-HT (HT=hydrotalcite) derived catalysts with different Ru loadings (0.5, 1.0 and 2.0 wt%) and promoted with La3+ were prepared, characterized, and tested for CO2 methanation at high Gas Hourly Space Velocity values (480 L/gcat h) feeding a CO2/H2/N2 = 1/4/1 v/v mixture. The MgAlOx mixed oxide obtained after calcination at 600 °C and reduction provided weak, medium and mainly strong basic sites, able to activate the CO2 molecule, and hosted very small Ru nanoparticles (1–3 nm). However, the catalysts displayed a low activity in the low temperature range and a poor selectivity to CH4. The addition of La3+, despite contributing to the basicity, did not have any significant effect on performance. In a comparison between Ru- and Ni-HT-derived catalysts, tested under similar reaction conditions, Ni largely overperformed Ru

IL-10, IL-13, Eotaxin and IL-10/IL-6 ratio distinguish breast implant-associated anaplastic large-cell lymphoma from all types of benign late seromas

Breast implant-associated anaplastic large-cell lymphoma (BI-ALCL) is an uncommon peripheral T cell lymphoma usually presenting as a delayed peri-implant effusion. Chronic inflammation elicited by the implant has been implicated in its pathogenesis. Infection or implant rupture may also be responsible for late seromas. Cytomorphological examination coupled with CD30 immunostaining and eventual T-cell clonality assessment are essential for BI-ALCL diagnosis. However, some benign effusions may also contain an oligo/monoclonal expansion of CD30 + cells that can make the diagnosis challenging. Since cytokines are key mediators of inflammation, we applied a multiplexed immuno-based assay to BI-ALCL seromas and to different types of reactive seromas to look for a potential diagnostic BI-ALCL-associated cytokine profile. We found that BI-ALCL is characterized by a Th2-type cytokine milieu associated with significant high levels of IL-10, IL-13 and Eotaxin which discriminate BI-ALCL from all types of reactive seroma. Moreover, we found a cutoff of IL10/IL-6 ratio of 0.104 is associated with specificity of 100% and sensitivity of 83% in recognizing BI-ALCL effusions. This study identifies promising biomarkers for initial screening of late seromas that can facilitate early diagnosis of BI-ALCL

Stabilization of mismatch repair gene PMS2 by glycogen synthase kinase 3β is implicated in the treatment of cervical carcinoma

<p>Abstract</p> <p>Background</p> <p>PMS2 expression loss was reported in a variety of human. However, its importance has not been fully understood in cervical carcinoma. The aim of this study was to determine the expression of PMS2 in cervical carcinoma and evaluate the significance of mismatch repair gene PMS2 regulated by glycogen synthase kinase 3β (GSK-3β) in chemosensitivity.</p> <p>Methods</p> <p>We examined PMS2 and phosphorylated GSK-3β(<it>s</it>9) expression in cervical carcinoma tissues using immunohistochemical staining. Furthermore, we detected PMS2 expression in HeLa cells and evaluate the interaction with GSK-3β after transfection with GSK-3β by small interference RNA (siRNA), co-immunoprecipitation and immunoblotting. We also evaluated the effect of PMS2 transfection on HeLa cells' chemosensitivity to cisplatin treatment.</p> <p>Results</p> <p>We found significant downregulation of PMS2 in cervical carcinoma, which was negatively associated with phosphorylated GSK-3β (<it>s</it>9). Furthermore, we demonstrated GSK-3β transfection was able to interact with PMS2 and enhance PMS2 production in HeLa cells, and increased PMS2 production was responsible for enhanced chemosensitivity.</p> <p>Conclusions</p> <p>Our results provide the evidence that stabilization of PMS2 production by GSK-3β was important to improve chemosensitization, indicating the significance of GSK-3β-related PMS2 downregulation in the development of cervical carcinoma and in developing a potential strategy for chemotherapy.</p

Microarray comparative genomic hybridization detection of chromosomal imbalances in uterine cervix carcinoma

BACKGROUND: Chromosomal Comparative Genomic Hybridization (CGH) has been applied to all stages of cervical carcinoma progression, defining a specific pattern of chromosomal imbalances in this tumor. However, given its limited spatial resolution, chromosomal CGH has offered only general information regarding the possible genetic targets of DNA copy number changes. METHODS: In order to further define specific DNA copy number changes in cervical cancer, we analyzed 20 cervical samples (3 pre-malignant lesions, 10 invasive tumors, and 7 cell lines), using the GenoSensor microarray CGH system to define particular genetic targets that suffer copy number changes. RESULTS: The most common DNA gains detected by array CGH in the invasive samples were located at the RBP1-RBP2 (3q21-q22) genes, the sub-telomeric clone C84C11/T3 (5ptel), D5S23 (5p15.2) and the DAB2 gene (5p13) in 58.8% of the samples. The most common losses were found at the FHIT gene (3p14.2) in 47% of the samples, followed by deletions at D8S504 (8p23.3), CTDP1-SHGC- 145820 (18qtel), KIT (4q11-q12), D1S427-FAF1 (1p32.3), D9S325 (9qtel), EIF4E (eukaryotic translation initiation factor 4E, 4q24), RB1 (13q14), and DXS7132 (Xq12) present in 5/17 (29.4%) of the samples. CONCLUSION: Our results confirm the presence of a specific pattern of chromosomal imbalances in cervical carcinoma and define specific targets that are suffering DNA copy number changes in this neoplasm

A calibrated multiexit neural network for detecting urothelial cancer cells

Deep convolutional networks have become a powerful tool for medical imaging diagnostic. In pathology, most efforts have been focused in the subfield of histology, while cytopathology (which studies diagnostic tools at the cellular level) remains underexplored. In this paper, we propose a novel deep learning model for cancer detection from urinary cytopathology screening images. We leverage recent ideas from the field of multioutput neural networks to provide a model that can efficiently train even on small-scale datasets, such as those typically found in real-world scenarios. Additionally, we argue that calibration (i.e., providing confidence levels that are aligned with the ground truth probability of an event) has been a major shortcoming of prior works, and we experiment a number of techniques to provide a well-calibrated model. We evaluate the proposed algorithm on a novel dataset, and we show that the combination of focal loss, multiple outputs, and temperature scaling provides a model that is significantly more accurate and calibrated than a baseline deep convolutional network