Mitral annulus velocity determined by DTI is a relatively preload-independent variable in evaluating diastolic function.

Roughness of nanoscopic dimensions can be used to selectively enhance the faradaic current of a sluggish reaction. Using this principle, we constructed mesoporous structures on the surfaces of pure platinum electrodes responding even more sensitively to glucose than to common interfering species, such as L-ascorbic acid and 4-acetamidophenol. Good sensitivities, as high as 9.6 microA cm(-2) mM(-1), were reproducibly observed in the presence of high concentration of chloride ion. The selectivities, sensitivities, and stabilities determined experimentally have demonstrated the potential of mesoporous platinum as a novel candidate for nonenzymatic glucose sensors.

A rapid antibiotic susceptibility test (AST) is desperately needed in clinical settings for fast and appropriate antibiotic administration. Traditional ASTs, which rely on cell culture, are not suitable for urgent cases of bacterial infection and antibiotic resistance owing to their relatively long test times. We describe a novel AST called single-cell morphological analysis (SCMA) that can determine antimicrobial susceptibility by automatically analyzing and categorizing morphological changes in single bacterial cells under various antimicrobial conditions. The SCMA was tested with four Clinical and Laboratory Standards Institute standard bacterial strains and 189 clinical samples, including extended-spectrum β-lactamase-positive Escherichia coli and Klebsiella pneumoniae, imipenem-resistant Pseudomonas aeruginosa, methicillin-resistant Staphylococcus aureus, and vancomycin-resistant Enterococci from hospitals. The results were compared with the gold standard broth microdilution test. The SCMA results were obtained in less than 4 hours, with 91.5% categorical agreement and 6.51% minor, 2.56% major, and 1.49% very major discrepancies. Thus, SCMA provides rapid and accurate antimicrobial susceptibility data that satisfy the recommended performance of the U.S. Food and Drug Administration.

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Objective Studying the biology of the human placenta represents a major experimental challenge. Although conventional cell culture techniques have been used to study different types of placenta-derived cells, current in vitro models have limitations in recapitulating organ-specific structure and key physiological functions of the placenta. Here we demonstrate that it is possible to leverage microfluidic and microfabrication technologies to develop a microengineered biomimetic model that replicates the architecture and function of the placenta. Materials and methods A “Placenta-on-a-Chip” microdevice was created by using a set of soft elastomer-based microfabrication techniques known as soft lithography. This microsystem consisted of two polydimethylsiloxane (PDMS) microfluidic channels separated by a thin extracellular matrix (ECM) membrane. To reproduce the placental barrier in this model, human trophoblasts (JEG-3) and human umbilical vein endothelial cells (HUVECs) were seeded onto the opposite sides of the ECM membrane and cultured under dynamic flow conditions to form confluent epithelial and endothelial layers in close apposition. We tested the physiological function of the microengineered placental barrier by measuring glucose transport across the trophoblast-endothelial interface over time. The permeability of the barrier study was analyzed and compared to that obtained from acellular devices and additional control groups that contained epithelial or endothelial layers alone. Results Our microfluidic cell culture system provided a tightly controlled fluidic environment conducive to the proliferation and maintenance of JEG-3 trophoblasts and HUVECs on the ECM scaffold. Prolonged culture in this model produced confluent cellular monolayers on the intervening membrane that together formed the placental barrier. This in vivo-like microarchitecture was also critical for creating a physiologically relevant effective barrier to glucose transport. Quantitative investigation of barrier function was conducted by calculating permeability coefficients and metabolic rates in varying conditions of barrier structure. The rates of glucose transport and metabolism were consistent with previously reported in vivo observations. Conclusion The “Placenta-on-a-Chip” microdevice described herein provides new opportunities to simulate and analyze critical physiological responses of the placental barrier. This system may be used to address the major limitations of existing placenta model systems and serve to enable research platforms for reproductive biology and medicine.

A mounting body of evidence in cancer research suggests that the local microenvironment of tumor cells has a profound influence on cancer progression and metastasis. In vitro studies on the tumor microenvironment and its pharmacological modulation, however, are often hampered by the technical challenges associated with creating physiological cell culture environments that integrate cancer cells with the key components of their native niche such as neighboring cells and extracellular matrix (ECM) to mimic complex microarchitecture of cancerous tissue. Using early-stage breast cancer as a model disease, here we describe a biomimetic microengineering strategy to reconstitute three-dimensional (3D) structural organization and microenvironment of breast tumors in human cell-based in vitro models. Specifically, we developed a microsystem that enabled co-culture of breast tumor spheroids with human mammary ductal epithelial cells and mammary fibroblasts in a compartmentalized 3D microfluidic device to replicate microarchitecture of breast ductal carcinoma in situ (DCIS). We also explored the potential of this breast cancer-on-a-chip system as a drug screening platform by evaluating the efficacy and toxicity of an anticancer drug (paclitaxel). Our microengineered disease model represents the first critical step towards recapitulating pathophysiological complexity of breast cancer, and may serve as an enabling tool to systematically examine the contribution of the breast cancer microenvironment to the progression of DCIS to an invasive form of the disease.

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A computer-aided diagnosis (CAD) algorithm identifying breast nodule malignancy using multiple ultrasonography (US) features and artificial neural network (ANN) classifier was developed from a database of 584 histologically confirmed cases containing 300 benign and 284 malignant breast nodules. The features determining whether a breast nodule is benign or malignant were extracted from US images through digital image processing with a relatively simple segmentation algorithm applied to the manually preselected region of interest. An ANN then distinguished malignant nodules in US images based on five morphological features representing the shape, edge characteristics, and darkness of a nodule. The structure of ANN was selected using k-fold cross-validation method with k = 10. The ANN trained with randomly selected half of breast nodule images showed the normalized area under the receiver operating characteristic curve of 0.95. With the trained ANN, 53.3% of biopsies on benign nodules can be avoided with 99.3% sensitivity. Performance of the developed classifier was reexamined with new US mass images in the generalized patient population of total 266 (167 benign and 99 malignant) cases. The developed CAD algorithm has the potential to increase the specificity of US for characterization of breast lesions.