A simple, robust and rapid approach to detect carbapenemases in Gram-negative isolates by MALDI-TOF mass spectrometry: validation with triple quadripole tandem mass spectrometry, microarray and PCR.

Carbapenemases should be accurately and rapidly detected, given their possible epidemiological spread and their impact on treatment options. Here, we developed a simple, easy and rapid matrix-assisted laser desorption ionization-time of flight (MALDI-TOF)-based assay to detect carbapenemases and compared this innovative test with four other diagnostic approaches on 47 clinical isolates. Tandem mass spectrometry (MS-MS) was also used to determine accurately the amount of antibiotic present in the supernatant after 1 h of incubation and both MALDI-TOF and MS-MS approaches exhibited a 100% sensitivity and a 100% specificity. By comparison, molecular genetic techniques (Check-MDR Carba PCR and Check-MDR CT103 microarray) showed a 90.5% sensitivity and a 100% specificity, as two strains of Aeromonas were not detected because their chromosomal carbapenemase is not targeted by probes used in both kits. Altogether, this innovative MALDI-TOF-based approach that uses a stable 10-μg disk of ertapenem was highly efficient in detecting carbapenemase, with a sensitivity higher than that of PCR and microarray.

Fast Geodesic Active Fields for Image Registration Based on Splitting and Augmented Lagrangian Approaches.

In this paper, we present an efficient numerical scheme for the recently introduced geodesic active fields (GAF) framework for geometric image registration. This framework considers the registration task as a weighted minimal surface problem. Hence, the data-term and the regularization-term are combined through multiplication in a single, parametrization invariant and geometric cost functional. The multiplicative coupling provides an intrinsic, spatially varying and data-dependent tuning of the regularization strength, and the parametrization invariance allows working with images of nonflat geometry, generally defined on any smoothly parametrizable manifold. The resulting energy-minimizing flow, however, has poor numerical properties. Here, we provide an efficient numerical scheme that uses a splitting approach; data and regularity terms are optimized over two distinct deformation fields that are constrained to be equal via an augmented Lagrangian approach. Our approach is more flexible than standard Gaussian regularization, since one can interpolate freely between isotropic Gaussian and anisotropic TV-like smoothing. In this paper, we compare the geodesic active fields method with the popular Demons method and three more recent state-of-the-art algorithms: NL-optical flow, MRF image registration, and landmark-enhanced large displacement optical flow. Thus, we can show the advantages of the proposed FastGAF method. It compares favorably against Demons, both in terms of registration speed and quality. Over the range of example applications, it also consistently produces results not far from more dedicated state-of-the-art methods, illustrating the flexibility of the proposed framework.

Personalized approaches to active immunotherapy in cancer

Immunotherapy is emerging as a promising anti-cancer curative modality. However, in contrast to recent advances obtained employing checkpoint blockade agents and T cell therapies, clinical efficacy of therapeutic cancer vaccines is still limited. Most vaccination attempts in the clinic represent "off-the shelf" approaches since they target common "self" tumor antigens, shared among different patients. In contrast, personalized approaches of vaccination are tailor-made for each patient and in spite being laborious, hold great potential. Recent technical advancement enabled the first steps in the clinic of personalized vaccines that target patient-specific mutated neo-antigens. Such vaccines could induce enhanced tumor-specific immune response since neo-antigens are mutation-derived antigens that can be recognized by high affinity T cells, not limited by central tolerance. Alternatively, the use of personalized vaccines based on whole autologous tumor cells, overcome the need for the identification of specific tumor antigens. Whole autologous tumor cells could be administered alone, pulsed on dendritic cells as lysate, DNA, RNA or delivered to dendritic cells in-vivo through encapsulation in nanoparticle vehicles. Such vaccines may provide a source for the full repertoire of the patient-specific tumor antigens, including its private neo-antigens. Furthermore, combining next-generation personalized vaccination with other immunotherapy modalities might be the key for achieving significant therapeutic outcome.