Patient and Disease-Specific Induced Pluripotent Stem Cells for Discovery of Personalized Cardiovascular Drugs and Therapeutics.

Human induced pluripotent stem cells (iPSCs) have emerged as an effective platform for regenerative therapy, disease modeling, and drug discovery. iPSCs allow for the production of limitless supply of patient-specific somatic cells that enable advancement in cardiovascular precision medicine. Over the past decade, researchers have developed protocols to differentiate iPSCs to multiple cardiovascular lineages, as well as to enhance the maturity and functionality of these cells. Despite significant advances, drug therapy and discovery for cardiovascular disease have lagged behind other fields such as oncology. We speculate that this paucity of drug discovery is due to a previous lack of efficient, reproducible, and translational model systems. Notably, existing drug discovery and testing platforms rely on animal studies and clinical trials, but investigations in animal models have inherent limitations due to interspecies differences. Moreover, clinical trials are inherently flawed by assuming that all individuals with a disease will respond identically to a therapy, ignoring the genetic and epigenomic variations that define our individuality. With ever-improving differentiation and phenotyping methods, patient-specific iPSC-derived cardiovascular cells allow unprecedented opportunities to discover new drug targets and screen compounds for cardiovascular disease. Imbued with the genetic information of an individual, iPSCs will vastly improve our ability to test drugs efficiently, as well as tailor and titrate drug therapy for each patient

Quartz crystal microbalance use in biological studies

Design, development, and applications of quartz crystal microbalance are discussed. Two types of crystals are used. One serves as reference and other senses changes in mass. Specific application to study of bacterial spores is described

Giant thoracic schwannoma presenting with abrupt onset of abdominal pain: a case report

<p>Abstract</p> <p>Introduction</p> <p>Giant intradural extramedullary schwannomas of the thoracic spine are not common. Schwannomas, that is, tumors derived from neoplastic Schwann cells, and neurofibromas represent the most common intradural extramedullary spinal lesions. We report the case of a patient with a giant thoracic schwannoma presenting unusually with acute abdominal pain and with delayed neurological impairment.</p> <p>Case presentation</p> <p>A 26-year-old Hispanic man with no previous medical problems presented with acute periumbilical pain. After extensive work-up including an exploratory laparotomy for appendectomy, magnetic resonance imaging scans of the lumbar and thoracic spine revealed a giant intradural extramedullary thoracic schwannoma within the spinal canal posterior to the T9, T10, and T11 vertebral bodies. Magnetic resonance imaging signal prolongation was noted in the spinal cord both rostral and caudal to the schwannoma. The patient underwent an urgent laminectomy from T8 to L1. After sacrificing the T10 root, the tumor was removed en bloc. Postoperatively, the patient improved significantly gaining antigravity strength in both lower extremities.</p> <p>Conclusion</p> <p>The T10 dermatome is represented by the umbilical region. This referred pain may represent a mechanism by which a giant thoracic schwannoma may present as acute abdominal pain. Acute, intense abdominal pain with delayed neurologic deficit is a rare presentation of a thoracic schwannoma but should be considered as a possible cause of abdominal pain presenting without clear etiology. Although these lesions may be delayed in their diagnosis, early diagnosis and treatment may lead to an improved clinical outcome.</p

Subspace Detectors: Efficient Implementation

The optimum detector for a known signal in white Gaussian background noise is the matched filter, also known as a correlation detector [Van Trees, 1968]. Correlation detectors offer exquisite sensitivity (high probability of detection at a fixed false alarm rate), but require perfect knowledge of the signal. The sensitivity of correlation detectors is increased by the availability of multichannel data, something common in seismic applications due to the prevalence of three-component stations and arrays. When the signal is imperfectly known, an extension of the correlation detector, the subspace detector, may be able to capture much of the performance of a matched filter [Harris, 2006]. In order to apply a subspace detector, the signal to be detected must be known to lie in a signal subspace of dimension d {ge} 1, which is defined by a set of d linearly-independent basis waveforms. The basis is constructed to span the range of signals anticipated to be emitted by a source of interest. Correlation detectors operate by computing a running correlation coefficient between a template waveform (the signal to be detected) and the data from a window sliding continuously along a data stream. The template waveform and the continuous data stream may be multichannel, as would be true for a three-component seismic station or an array. In such cases, the appropriate correlation operation computes the individual correlations channel-for-channel and sums the result (Figure 1). Both the waveform matching that occurs when a target signal is present and the cross-channel stacking provide processing gain. For a three-component station processing gain occurs from matching the time-history of the signals and their polarization structure. The projection operation that is at the heart of the subspace detector can be expensive to compute if implemented in a straightforward manner, i.e. with direct-form convolutions. The purpose of this report is to indicate how the projection can be computed efficiently for continuous multichannel seismic data. The speed of the calculation is significant as it may become desirable to deploy subspace detectors numbering in the thousands. One application contemplated for these detectors is as screens against signals from repeating sources such as mines or aftershocks of large earthquakes. With many tens of stations and potentially hundreds of sources to screen, efficient implementations are desirable. Speed, of course, can be achieved by procuring faster computers or special-purpose hardware. The approach we examine here is the development of two efficient algorithms that can make the calculations run faster on any machine. In the first section, we describe the subspace detector as we use it for the detection of repeating seismic events, defining terms and the parameterization used in succeeding sections. This section also reviews how the correlation computations central to the matched filter and subspace detectors can be implemented as a collection of convolution operations. Convolution algorithms using fast Fourier transforms, such as the overlap-add and overlap-save methods, have long been known as efficient implementations of discrete-time finite-impulse-response filters [e.g. Oppenheim and Schafer, 1975]. These may be extended in a straightforward manner to implement multichannel correlation detectors. In the second section, we describe how multichannel data can be multiplexed to compute the required convolutions with a single pair of FFT operations instead of a pair for each channel. This approach increases speed approximately twofold. Seismic data, almost invariably, are oversampled. This characteristic provides an opportunity for increased efficiency by decimating the data prior to performing the correlation calculations. In the third section, we describe a bandpass transformation of the data that allows a more aggressive decimation of the data without significant loss of fidelity in the correlation calculation. The transformation computes a complex-analytic representation for the template waveforms and the multichannel data, followed by a demodulation for both to base-band (i.e. a single band around zero frequency). This approach provides a factor of two to four increase in speed depending on the details of data sampling rate and the desired pass band of the correlation calculation. The approaches described in the last two sections can be used simultaneously to compound efficiencies

Strong-field effects in the Rabi oscillations of the superconducting phase qubit

Rabi oscillations have been observed in many superconducting devices, and represent prototypical logic operations for quantum bits (qubits) in a quantum computer. We use a three-level multiphoton analysis to understand the behavior of the superconducting phase qubit (current-biased Josephson junction) at high microwave drive power. Analytical and numerical results for the ac Stark shift, single-photon Rabi frequency, and two-photon Rabi frequency are compared to measurements made on a dc SQUID phase qubit with Nb/AlOx/Nb tunnel junctions. Good agreement is found between theory and experiment.Comment: 4 pages, 4 figures, accepted for publication in IEEE Trans. Appl. Supercon

Comparison of coherence times in three dc SQUID phase qubits

We report measurements of spectroscopic linewidth and Rabi oscillations in three thin-film dc SQUID phase qubits. One device had a single-turn Al loop, the second had a 6-turn Nb loop, and the third was a first order gradiometer formed from 6-turn wound and counter-wound Nb coils to provide isolation from spatially uniform flux noise. In the 6 - 7.2 GHz range, the spectroscopic coherence times for the gradiometer varied from 4 ns to 8 ns, about the same as for the other devices (4 to 10 ns). The time constant for decay of Rabi oscillations was significantly longer in the single-turn Al device (20 to 30 ns) than either of the Nb devices (10 to 15 ns). These results imply that spatially uniform flux noise is not the main source of decoherence or inhomogenous broadening in these devices.Comment: 4 pages, 5 figures, accepted for publication in IEEE Trans. Appl. Supercon