No abstract

Monoclonal antibodies (mAbs) are a central component of therapy for hematologic malignancies. Widely used mAb agents in multiple myeloma (MM) include daratumumab and elotuzumab. However, not all patients respond to these agents, and resistance is a significant clinical issue. A recently discovered subset of human natural killer (NK) cells lacking expression of FcεRIγ (g-NK cells) was found to have a multifold increase in antibody-dependent effector functions after CD16 crosslinking. In this study, we tested the capacity of g-NK cells to enhance the efficacy of therapeutic mAbs against MM. In vitro, we found that g-NK cells have strikingly superior anti-myeloma cytotoxicity compared with conventional NK (cNK) cells when combined with daratumumab or elotuzumab (∼sixfold; P < .001). In addition, g-NK cells naturally expressed minimal surface CD38 and SLAMF7, which reduced the incidence of therapeutic fratricide. In tumor-naïve murine models, the persistence of g-NK cells in blood and spleen was >10 times higher than that of cNK cells over 31 days (P < .001). In vivo efficacy studies showed that the combination of daratumumab and g-NK cells led to a >99.9% tumor reduction (by flow cytometry analysis) compared with the combination of daratumumab and cNK cells (P < .001). Moreover, treatment with daratumumab and g-NK cells led to complete elimination of myeloma burden in 5 of 7 mice. Collectively, these results underscore the unique ability of g-NK cells to potentiate the activity of therapeutic mAbs and overcome limitations of current off-the-shelf NK cell therapies without the need for cellular irradiation or genetic engineering.

The ever-growing threat of new and existing infectious diseases in combination with antimicrobial resistance requires the need for innovative and effective forms of drug delivery. Optimal drug delivery systems for...

In neural spike sorting systems, the performance of the spike detector has to be maximized because it affects the performance of all subsequent blocks. Non-linear energy operator (NEO), is a popular spike detector due to its detection accuracy and its hardware friendly architecture. However, it involves a thresholding stage, whose value is usually approximated and is thus not optimal. This approximation deteriorates the performance in real-time systems where signal to noise ratio (SNR) estimation is a challenge, especially at lower SNRs. In this paper, we propose an automatic and robust threshold calculation method using an empirical gradient technique. The method is tested on two different datasets. The results show that our optimized threshold improves the detection accuracy in both high SNR and low SNR signals. Boxplots are presented that provide a statistical analysis of improvements in accuracy, for instance, the 75th percentile was at 98.7% and 93.5% for the optimized NEO threshold and traditional NEO threshold, respectively.