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A novel disialoganglioside 2 (GD2)-specific chimeric antigen receptor (CAR)-modified T cell therapy against retinoblastoma (RB) were generated. GD2-CAR consists of a single-chain variable fragment (scFv) derived from a monoclonal antibody, hu3F8, that is linked with the cytoplasmic signaling domains of CD28, 41BB, a CD3ζ, and an inducible caspase 9 death fusion partner. GD2 antigen is highly expressed in Y79RB cell line and in several surgical RB tumor specimens. In vitro co-culture experiments revealed the effective killing of Y79RB cells by GD2-CAR T cells, but not by control CD19-CAR T cells. The killing activities of GD2-CAR T cells were diminished when repeatedly exposed to the tumor, due to an attenuated expression of GD2 antigen on tumor cells and upregulation of inhibitory molecules of the PD1 and PD-L1 axis in the CAR T cells and RB tumor cells respectively. This is the first report to describe the potential of GD2-CAR T cells as a promising therapeutic strategy for RB with the indication of potential benefit of combination therapy with immune checkpoint inhibitors.EGFR mutation-positive NSCLC tumors are highly heterogeneous, therefore, exploring an agent simultaneously targeting multiple EGFR mutations may be valuable for clinical practice. Compared with osimertinib, BEBT-109 shows more sensitive and extensive antitumor activity in EGFR mutant NSCLC, while sparing wild-type EGFR cell lines. Meanwhile, unlike the metabolite of osimertinib AZ5104, the main metabolites of BEBT-109 are found lacking in activity against wild-type EGFR cell lines. Preclinical and clinical studies demonstrate a unique pharmacokinetic profiles of BEBT-109 with rapid absorption and quick in vivo clearance without accumulation, which are conducive to minimizing the off-target toxicity of the covalent irreversible EGFR inhibitor. Oral administration of BEBT-109 induces tumor regression in EGFR exon 20 insertion xenografts, and even tumor disappearance in PC-9, HCC827 and H1975 xenograft models. Furthermore, in clinical trials, the objective responses were observed in NSCLC patients with EGFR T790M mutation in the first and second dosing cohorts. These findings demonstrate that BEBT-109, a potent pan-mutant-selective EGFR inhibitor with improved pharmacokinetic properties, might offer a promising new option for the treatment of multiple mutant-EGFR-driven NSCLC.The NitroSpeed-Carba NP test was used to rapidly detect and discriminate between the different types of carbapenemases (classes A, B, and D) within 30 minutes among a collection of 202 Pseudomonas sp. strains (mostly Pseudomonas aeruginosa). A total of 99 carbapenemase-(including enzymes exhibiting weak carbapenemase activity such as several Guyana Extended-Spectrum (GES)-ß-lactamases) and 103 non-carbapenemase producers were tested, and the overall specificity and sensitivity were 100% and 99%, respectively. The NitroSpeed-Carba NP test is a rapid, specific, sensitive, and easy-to-implement technique for identification of carbapenemase-producing Pseudomonas spp.Boron carbide powder was hot-pressed at 2070 °C with 30 MPa uniaxial pressure and 90 min soaking. The mechanical, microstructure and other related properties were evaluated. XRD of the boron carbide powder and sintered samples, shows the presence of B13C2 phase of high electrical conductivity. Crystal lattice parameters, space group, cell angle, cell parameters, etc. were found from Rietveld refinement. The micro Vicker's hardness was 26.98 ± 0.98 GPa at 4.9 N load, fracture toughness 3.54 ± 0.26 MPa m and Young's modulus 461.50 ± 4.5 GPa. The hot-pressed boron carbide was found to be electrically conducting, which can be machined using a wire electrical discharge machine (WEDM).HAADF-STEM tomography is a widely used experimental technique for analyzing nanometer-scale structures of a large variety of materials in three dimensions. It is especially useful for studying crystalline nanoparticles, where conventional TEM tomography suffers from diffraction-related artefacts. Unfortunately, the acquisition of a HAADF-STEM tilt series can easily take up one hour or more, depending on the complexity of the experiment. It is therefore challenging to investigate samples that do not withstand long electron beam illumination or to acquire a large number of tilt series during a single TEM experiment. The latter would facilitate obtaining more statistically representative 3D data, and enable performing dynamic in situ 3D characterizations with a finer time resolution. Various HAADF-STEM acquisition strategies have been proposed to accelerate the tomographic acquisition and reduce the required electron dose. These methods include tilting the holder continuously while acquiring a projection "movie" and a hybrid, incremental, methodology which combines the benefits of the conventional and continuous technique. However, until now an experimental evaluation of these techniques has been lacking. In this paper, the different acquisition strategies will be experimentally compared in terms of speed, resolution and electron dose. This evaluation will be performed based on experimental tilt series, acquired for various metallic nanoparticles with different shapes and sizes. We discuss the necessary data processing and provide a general guideline that can be used to determine the most optimal acquisition strategy for specific electron tomography experiments.Electron Energy-Loss Spectroscopy (EELS) is a powerful and versatile spectroscopic technique used to study the composition and local optoelectronic properties of nanometric materials. Currently, this technique is generating large amounts of spectra per experiment, producing a huge quantity of data to analyse. Several strategies can be applied in order to classify these data to map physical properties at the nanoscale. EGFR inhibitor In the present study, the Support Vector Machine (SVM) algorithm is applied to EELS, and its effectiveness identifying EEL spectra is assessed. Our results evidence the capacity of SVM to determine the oxidation state of iron and manganese in iron and manganese oxides, based on the ELNES of the white lines of the transition metal. The SVM algorithm is first trained with given datasets and then the resulting models are tested through noisy test data sets. We demonstrate that SVM exhibits a very good performance classifying these EEL spectra, despite the usual level of noise and instrumental energy shifts.