Epigallocatechin-3-Gallate (EGCG) Suppresses Pancreatic Cancer Cell Growth, Invasion, and Migration partly through the Inhibition of Akt Pathway and Epithelial-Mesenchymal Transition: Enhanced Efficacy when Combined with Gemcitabine.

Most pancreatic cancers are usually diagnosed at an advanced stage when they have already metastasized. Epigallocatechin-3-gallate (EGCG), a major polyphenolic constituent of green tea, has been shown to reduce pancreatic cancer growth, but its effect on metastasis remains elusive. This study evaluated the capacity of EGCG to inhibit pancreatic cancer cell migration and invasion and the underlying mechanisms. EGCG reduced pancreatic cancer cell growth, migration, and invasion in vitro and in vivo. EGCG prevented "Cadherin switch" and decreased the expression level of TCF8/ZEB1, β-Catenin, and Vimentin. Mechanistically, EGCG inhibited the Akt pathway in a time-dependent manner, by suppressing IGFR phosphorylation and inducing Akt degradation. Co-treatment with catalase or N-Acetyl-L-cysteine did not abrogate EGCG's effect on the Akt pathway or cell growth. Moreover, EGCG synergized with gemcitabine to suppress pancreatic cancer cell growth, migration, and invasion, through modulating epithelial-mesenchymal transition markers and inhibiting Akt pathway. In summary, EGCG may prove beneficial to improve gemcitabine sensitivity in inhibiting pancreatic cancer cell migration and invasion, to some extent through the inhibition of Akt pathway and epithelial-mesenchymal transition

Study of the performance of the LHCb MWPC with cosmic rays

In this note we report the results of measurements performed with cosmic rays on different LHCb Muon Chambers. The main characteristics of the chambers have been investigated as a function of the high voltage value in order to achieve a better comprehension of the detector performance both for optimizing the chamber working conditions on the experimental apparatus and for providing useful information for the Monte Carlo simulation

A new method based on noise counting to monitor the frontend electronics of the LHCb muon detector

A new method has been developed to check the correct behaviour of the frontend electronics of the LHCb muon detector. This method is based on the measurement of the electronic noise rate at different thresholds of the frontend discriminator. The method was used to choose the optimal discriminator thresholds. A procedure based on this method was implemented in the detector control system and allowed the detection of a small percentage of frontend channels which had deteriorated. A Monte Carlo simulation has been performed to check the validity of the method

Dependence of the energy resolution of a scintillating crystal on the readout integration time

The possibilty of performing high-rate calorimetry with a slow scintillating crystal is studied. In this experimental situation, to avoid pulse pile-up, it can be necessary to base the energy measurement on only a fraction of the emitted light, thus spoiling the energy resolution. This effect was experimentally studied with a BGO crystal and a photomultiplier followed by an integrator, by measuring the maximum amplitude of the signals. The experimental data show that the energy resolution is exclusively due to the statistical fluctuations of the number of photoelectrons contributing to the maximum amplitude. When such number is small its fluctuations are even smaller than those predicted by Poisson statistics. These results were confirmed by a Monte Carlo simulation which allows to estimate, in a general case, the energy resolution, given the total number of photoelectrons, the scintillation time and the integration time

Scintillating properties of frozen new liquid scintillators

The light emission from scintillators which are liquid at room temperature was studied in the interval between $+20$~$^{\circ}$C and $-120$~$^{\circ}$C, where the phase transition from liquid to solid takes place. The light yield measured at $-120$~$^{\circ}$C is about twice as much as that observed at $+20$~$^{\circ}$C. By cooling the scintillator from $+20$~$^{\circ}$C to $-120$~$^{\circ}$C and then heating it from $-120$~$^{\circ}$C to $+20$~$^{\circ}$C, the light yield varies in steps at well defined temperatures, which are different for the cooling and heating processes. These hysteresis phenomena appear to be related to the solvent rather than to the dopant. The decay time of scintillation light was measured at $+20$~$^{\circ}$C and $-120$~$^{\circ}$C. Whilst at room temperature most of the light is emitted with a decay time of 6--8 ns, at $-120$~$^{\circ}$C a slower component, with a decay time of 25--35 ns, becomes important

Heritability of peach tree resistance to bacterial leaf spot.

The objective of this work was to evaluate the broad-sense heritability reaction to bacterial leaf spot (Xanthomonas arboricola pv. pruni), in peach tree populations obtained from directed crosses. Disease severity and defoliation of the genotypes were evaluated in field conditions, with posterior measurement of the healthy leaf area duration (HAD). The observed average heritability (0.51) indicates that the use of the evaluated genitors can be effective for the development of cultivars with higher resistance to the disease.Notas Científicas. Título em português: Herdabilidade de resistência de pessegueiro à bacteriose foliar

Machine learning prediction models for mitral valve repairability and mitral regurgitation recurrence in patients undergoing surgical mitral valve repair

Background: Mitral valve regurgitation (MR) is the most common valvular heart disease and current variables associated with MR recurrence are still controversial. We aim to develop a machine learning-based prognostic model to predict causes of mitral valve (MV) repair failure and MR recurrence. Methods: 1000 patients who underwent MV repair at our institution between 2008 and 2018 were enrolled. Patients were followed longitudinally for up to three years. Clinical and echocardiographic data were included in the analysis. Endpoints were MV repair surgical failure with consequent MV replacement or moderate/severe MR (>2+) recurrence at one-month and mod-erate/severe MR recurrence after three years. Results: 817 patients (DS1) had an echocardiographic examination at one-month while 295 (DS2) also had one at three years. Data were randomly divided into training (DS1: n = 654; DS2: n = 206) and validation (DS1: n = 164; DS2 n = 89) cohorts. For intra-operative or early MV repair failure assessment, the best area under the curve (AUC) was 0.75 and the complexity of mitral valve prolapse was the main predictor. In predicting moderate/severe recurrent MR at three years, the best AUC was 0.92 and residual MR at six months was the most important predictor. Conclusions: Machine learning algorithms may improve prognosis after MV repair procedure, thus improving indications for correct candidate selection for MV surgical repair

A token-mixer architecture for CAD-RADS classification of coronary stenosis on multiplanar reconstruction CT images

Background and objective: In patients with suspected Coronary Artery Disease (CAD), the severity of stenosis needs to be assessed for precise clinical management. An automatic deep learning-based algorithm to classify coronary stenosis lesions according to the Coronary Artery Disease Reporting and Data System (CAD-RADS) in multiplanar reconstruction images acquired with Coronary Computed Tomography Angiography (CCTA) is proposed. Methods: In this retrospective study, 288 patients with suspected CAD who underwent CCTA scans were included. To model long-range semantic information, which is needed to identify and classify stenosis with challenging appearance, we adopted a token-mixer architecture (ConvMixer), which can learn structural relationship over the whole coronary artery. ConvMixer consists of a patch embedding layer followed by repeated convolutional blocks to enable the algorithm to learn long-range dependences between pixels. To visually assess ConvMixer performance, Gradient-Weighted Class Activation Mapping (Grad-CAM) analysis was used. Results: Experimental results using 5-fold cross-validation showed that our ConvMixer can classify significant coronary artery stenosis (i.e., stenosis with luminal narrowing ≥50%) with accuracy and sensitivity of 87% and 90%, respectively. For CAD-RADS 0 vs. 1–2 vs. 3–4 vs. 5 classification, ConvMixer achieved accuracy and sensitivity of 72% and 75%, respectively. Additional experiments showed that ConvMixer achieved a better trade-off between performance and complexity compared to pyramid-shaped convolutional neural networks. Conclusions: Our algorithm might provide clinicians with decision support, potentially reducing the interobserver variability for coronary artery stenosis evaluation

Off-shell effects in dilepton production from hot interacting mesons

The production of dielectrons in reactions involving a_1 mesons and pions is studied. We compare results obtained with different phenomenological Lagrangians that have been used in connection with hadronic matter and finite nuclei. We insist on the necessity for those interactions to satisfy known empirical properties of the strong interaction. Large off-shell effects in dielectron production are found and some consequences for the interpretation of heavy ion data are outlined. We also compare with results obtained using experimentally-extracted spectral functions.Comment: 14 pages, LaTeX2e, 2 figure

Measurement of the front-end dead-time of the LHCb muon detector and evaluation of its contribution to the muon detection inefficiency

A method is described which allows to deduce the dead-time of the front-end electronics of the LHCb muon detector from a series of measurements performed at different luminosities at a bunch-crossing rate of 20 MHz. The measured values of the dead-time range from 70 ns to 100 ns. These results allow to estimate the performance of the muon detector at the future bunch-crossing rate of 40 MHz and at higher luminosity