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Use of 3-azidoindoles in organic synthesis remains a difficult task owing to their instabilities. Herein, we report a general and concise approach for tackling this problem by using 3-azidoindole surrogates. The surrogates are bench-stable, presumably due to the observed intramolecular O-Nβ bonding. The resultant fleeting intermediates undergo capturing in situ to afford 3-substitued indoles through formal ipso-substitution of the azide group by nucleophiles. In these investigations, we found that the fleeting 3-azidoindoles show a C3-electrophilic character for the first time.Hyperspectral imaging is an emergent technique in viticulture that can potentially detect bacterial diseases in a non-destructive manner. However, the main problem is to handle the substantial amount of information obtained from this type of data, for which reliable data analysis tools are necessary. In this work, a combination of multivariate curve resolution-alternating least squares (MCR-ALS) and factorial discriminant analysis (FDA) is proposed to detect the flavescence dorée grapevine disease from hyperspectral imaging. The main purpose of MCR-ALS in this work was to provide chemically meaningful basic spectral signatures and distribution maps of the constituents needed to describe both healthy and infected leaf images by flavescence dorée. MCR scores (distribution maps) were used as the starting information for FDA to distinguish between healthy and infected pixels/images. Such an approach is presumably more powerful than the direct use of FDA on the raw imaging data, since MCR scores are compressed and noise-filtered information on pixel properties, which makes them more suitable for discrimination analysis. High levels of correct pixel discrimination rates (CR = 85.1%) for the MCR-ALS/FDA discrimination model were obtained. The model presents a lesser ability to determine infected leaves than healthy leaves. Nevertheless, only two images were misclassified. Therefore, the proposed strategy constitutes a good approach for the detection of flavescence dorée that could be potentially used to detect other phytopathologies.Anti-vascular endothelial growth factors currently are the first-line treatment option for neovascular age-related macular degeneration (nAMD) and other retinal vascular disorders, and their clinical use is associated with high financial burden. Biosimilars are a type of biological product highly similar to referral biologic drugs; they are increasing competition among biologics and have the potential to reduce the overall expenditures on biologics. AHPN agonist chemical structure In this comprehensive literature review, the current investigational biosimilars acting on retinal diseases are discussed. The authors review the results of clinical studies and highlight ongoing trials. Several biosimilar candidates are under evaluation and the pipeline will rapidly change in the future, as soon as each patent expires. Clinicians have to know these new therapeutic agents, which might come in the mainstream clinical practice as a more cost-efficient option.Therapy for high-risk neuroblastoma (HR NBL) is comprised of multimodal therapy including chemotherapy, surgery, radiation therapy, myeloablative therapy followed by autologous hematopoietic stem cell transplant, and immunotherapy. GD2 is a disialoganglioside that is highly expressed on the surface of neuroblastoma cells, with limited expression on normal tissues, which makes it an attractive target for immunologic therapy. The combination of immunotherapy with murine and chimeric anti-GD2 antibody formulations has improved outcomes compared with standard therapy in HR NBL patients. Naxitamab (Danyelza), a fully humanized anti-GD2 antibody, was developed at Memorial Sloan Kettering Cancer Center (MSKCC) to mitigate adverse reactions related to intolerance of foreign murine and chimeric antigens. Phase I and II studies demonstrating the tolerability and efficacy of naxitamab in patients with relapsed/refractory (r/r) HR NBL prompted its approval by the U.S. Food and Drug Administration (FDA) in 2020 for HR NBL with bone or bone marrow involvement. Initial outcomes with naxitamab are encouraging; however, future trials to maximize drug tolerance and elucidate its optimal role in neuroblastoma therapy in conjunction with other treatment strategies are needed. This review discusses the use of naxitamab in combination with granulocyte-macrophage colony-stimulating factor (GM-CSF) for the treatment of r/r HR NBL.Isatuximab is an IgG1 monoclonal antibody targeting CD38 that has received regulatory approval in combination regimens for patients with relapsed/refractory multiple myeloma. CD38 is an antigen with high surface expression on multiple myeloma cells. While daratumumab holds most of the market share for this drug class, isatuximab offers several unique aspects including a mechanism of action that may involve more direct myeloma-cell inhibition and killing and less reliance on cross-linking and immune effector cells, as well as subgroup data from pivotal trials showing notable efficacy in populations with renal impairment, high-risk cytogenetics and the elderly. While the administration of the drug remains intravenous, studies of fixed-volume infusion and rapid infusion may improve drug administration convenience. link2 Ongoing studies are examining isatuximab in combination with other immune therapies and cellular therapies, conventional chemotherapy and across other disease entities.Multiple myeloma is the second most common hematologic malignancy worldwide. Despite the growing number of available therapeutic options and advances in the treatment since the 2000s, relapse of multiple myeloma is inevitable. Currently, the main therapeutic agents for multiple myeloma treatment include proteasome inhibitors, immunomodulatory drugs, monoclonal antibodies and others. Patients who relapse or are refractory to the above-mentioned treatments have poor prognosis. B-cell maturation antigen (BCMA) is a cell-surface receptor which is expressed on the membrane of multiple myeloma cells, but absent on naive and memory B cells, making it an ideal target for multiple myeloma treatment. Belantamab mafodotin (GSK-2857916) is a first-in-class BCMA antibody-drug conjugate with an overall response rate of 32% in the phase II clinical trial DREAMM-2, which is a phase II study designed to investigate the efficacy and safety of belantamab mafodotin in relapsed/refractory patients with multiple myeloma. In August 2020, based on the results of this pivotal DREAMM-2 study, the U.S. Food and Drug Administration (FDA) approved belantamab mafodotin as a monotherapy for relapsed/refractory multiple myeloma. Thereafter, the European Medicines Agency (EMA) also approved this indication. Although belantamab mafodotin has demonstrated single-agent activity in relapsed/refractory multiple myeloma, further studies to evaluate its efficacy and its combinational use with other drugs are necessary and ongoing.An innovative inverse scattering (IS) method is proposed for the quantitative imaging of pixel-sparse scatterers buried within a lossy half-space. On the one hand, such an approach leverages on the wide-band nature of ground penetrating radar (GPR) data by jointly processing the multi-frequency (MF) spectral components of the collected radargrams. On the other hand, it enforces sparsity priors on the problem unknowns to yield regularized solutions of the fully non-linear scattering equations. Towards this end, a multi-task Bayesian compressive sensing (MT-BCS) methodology is adopted and suitably customized to take full advantage of the available frequency diversity and of the a-priori information on the class of imaged targets. Representative results are reported to assess the proposed MF-MT-BCS strategy also in comparison with competitive state-of-the-art alternatives.A mathematical framework and accompanying numerical algorithm exploiting the continuity equation for 4D reconstruction of spatiotemporal attenuation fields from multi-angle full-field transmission measurements is presented. The algorithm is geared towards rotation-free dynamic multi-beam X-ray tomography measurements, for which angular information is sparse but the temporal information is rich. link3 3D attenuation maps are recovered by propagating an initial discretized density volume in time according to the advection equations using the Finite Volumes method with a total variation diminishing monotonic upstream-centered scheme (TVDMUSCL). The benefits and limitations of the algorithm are explored using dynamic granular system phantoms modelled via discrete elements and projected by an analytical ray model independent from the numerical ray model used in the reconstruction scheme. Three phantom scenarios of increasing complexity are presented and it is found that projections from only a few (unknownsequations > 10) angles can be sufficient for characterisation of the 3D attenuation field evolution in time. It is shown that the artificial velocity field produced by the algorithm sub-iteration, which is used to propagate the attenuation field, can to some extent approximate the true kinematics of the system. Furthermore, it is found that the selection of a temporal interpolation scheme for projection data can have a significant impact on error build up in the reconstructed attenuation field.Colour and texture are two perceptual stimuli that determine, to a great extent, the appearance of objects, materials and scenes. The ability to process texture and colour is a fundamental skill in humans as well as in animals; therefore, reproducing such capacity in artificial ('intelligent') systems has attracted considerable research attention since the early 70s. Whereas the main approach to the problem was essentially theory-driven ('hand-crafted') up to not long ago, in recent years the focus has moved towards data-driven solutions (deep learning). In this overview we retrace the key ideas and methods that have accompanied the evolution of colour and texture analysis over the last five decades, from the 'early years' to convolutional networks. Specifically, we review geometric, differential, statistical and rank-based approaches. Advantages and disadvantages of traditional methods vs. deep learning are also critically discussed, including a perspective on which traditional methods have already been subsumed by deep learning or would be feasible to integrate in a data-driven approach.JPEG is the most commonly utilized image coding standard for storage and transmission purposes. It achieves a good rate-distortion trade-off, and it has been adopted by many, if not all, handheld devices. However, often information loss occurs due to transmission error or damage to the storage device. To address this problem, various coefficient recovery methods have been proposed in the past, including a divide-and-conquer approach to speed up the recovery process. However, the segmentation technique considered in the existing method operates with the assumption of a bi-modal distribution for the pixel values, but most images do not satisfy this condition. Therefore, in this work, an adaptive method was employed to perform more accurate segmentation, so that the real potential of the previous coefficient recovery methods can be unleashed. In addition, an improved rewritable adaptive data embedding method is also proposed that exploits the recoverability of coefficients. Discrete cosine transformation (DCT) patches and blocks for data hiding are judiciously selected based on the predetermined precision to control the embedding capacity and image distortion.
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