A multifunctional nanoseaurchin probe in which mesoporous silica nanobeads with iron oxide nanoparticles embedded and multi-gold nanorods crystal-seeded are fabricated and labeled with umbilical cord mesenchymal stem cells through endocytosis. This nanoplatform enables efficient magnetic remote-controlled guiding for stem cell homing, and provides dual modalities of photoacoustic imaging and magnetic resonance imaging for in situ tracking and long-term monitoring to achieve therapeutic efficacy.
Delivery efficiency with gene transfection is a pivotal point in achieving maximized therapeutic efficacy and has been an important challenge with central nervous system (CNS) diseases. In this study, neurotensin (NT, a neuro-specific peptide)-conjugated polyethylenimine (PEI)-modified reduced graphene oxide (rGO) nanoparticles with precisely controlled two-stage near-infrared (NIR)-laser photothermal treatment to enhance the ability to target neurons and achieve high gene transfection in neurons. First-stage NIR laser irradiation on the cells with nanoparticles attached on the surface can increase the permeability of the cell membrane, resulting in an apparent increase in cellular uptake compared to untreated cells. In addition, second-stage NIR laser irradiation on the cells with nanoparticles inside can further induce endo/lysosomal cavitation, which not only helps nanoparticles escape from endo/lysosomes but also prevents plasmid DNA (pDNA) from being digested by DNase I. At least double pDNA amount can be released from rGO-PEI-NT/pDNA under NIR laser trigger release compared to natural release. Moreover, in vitro differentiated PC-12 cell and in vivo mice (C57BL/6) brain transfection experiments have demonstrated the highest transfection efficiency occurring when NT modification is combined with external multi-stage stimuli-responsive NIR laser treatment. The combination of neuro-specific targeting peptide and external NIR-laser-triggered aid provides a nanoplatform for gene therapy in CNS diseases.
This study documents our efforts to provide computer support for the diagnosis of congestive heart failure (CHF). That computer support takes the form of an index value. A high index value indicates a low probability of CHF, and an index value below a threshold of 25.6 suggests a high probability of CHF. To create that index, we have designed a sophisticated algorithm chain which takes electrocardiogram signals as input. The signals are pre-processed before they are sent to a range of nonlinear feature extraction algorithms. The top 10 feature extraction methods were used to create the CHF index. By using objective feature extraction algorithms, we avoid the problem of inter- and intra-observer variability. We observed that the nonlinear feature extraction methods reflect the nature of the human heart very well. That observation is based on the fact that the nonlinear features achieved low [Formula: see text]-values and high feature ranking criterion scores.
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