Laparoscopic colorectal resections with and without routine mechanical bowel preparation: a comparative study

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Optimal simulation of full binary trees on faulty hypercubes

The problem of operating full binary tree based algorithms on a hypercube with faulty nodes was investigated. Developing a method for embedding a full binary tree into the faulty hypercube is the solution to this problem. Two outcomes for embedding an (n-1)-tree into an n-cube with unit dilation and load, that were based on a new embedding technique, were presented. For the problem where the root can be mapped to any nonfaulty hypercube node, the optimum toleration of faults was shown. Moreover, it was demonstrated that the algorithm for the variable root embedding problem is maximal within a class algorithms called recursive embedding algorithms as far as the number of tolerable faults is concerned. Lastly, it was demonstrated that when an O(1/√n) fraction of nodes in the hypercube are faulty, a O(1)-load variable root embedding is not always possible regardless of the significance of the dilation.published_or_final_versio

Yttrium-90 radioembolization for advanced inoperable hepatocellular carcinoma

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Anti-cadherin-17 antibody modulates Beta-catenin signaling and tumorigenicity of hepatocellular carcinoma

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Predictive performance of the competing risk model in screening for preeclampsia.

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.BACKGROUND: The established method of screening for preeclampsia (PE) is to identify risk factors from maternal demographic characteristics and medical history; in the presence of such factors the patient is classified as high-risk and in their absence as low-risk. However, the performance of such approach is poor. We developed a competing risks model which allows combination of maternal factors (age, weight, height, race, parity, personal and family history of PE, chronic hypertension, diabetes mellitus, systemic lupus erythematosus or antiphospholipid syndrome, method of conception and interpregnancy interval), with biomarkers to estimate the individual patient-specific risks of PE requiring delivery before any specified gestation. The performance of this approach is by far superior to that of the risk scoring systems. OBJECTIVE: To examine the predictive performance of the competing risks model in screening for PE by a combination of maternal factors, mean arterial pressure (MAP), uterine artery pulsatility index (PI), and serum placental growth factor (PLGF), referred to as the triple test, in a training dataset for development of the model and two validation studies. STUDY DESIGN: The data for this study were derived from three previously reported prospective non-intervention multicenter screening studies for PE in singleton pregnancies at 11+0 - 13+6 weeks' gestation. In all three studies, there was recording of maternal factors and biomarkers and ascertainment of outcome by appropriately trained personnel. The first study of 35,948 women, which was carried out between February 2010 and July 2014, was used to develop the competing risks model for prediction of PE and is therefore considered to be the training set. The two validation studies comprised of 8,775 and 16,451 women, respectively and they were carried out between February and September 2015 and between April and December 2016, respectively. Patient-specific risks of delivery with PE at 0.95, >0.90 and >0.80, respectively, demonstrating a very high discrimination between affected and unaffected pregnancies. Similarly, the calibration slopes were very close to 1.0 demonstrating a good agreement between the predicted risks and observed incidence of PE. In the prediction of early-PE and preterm-PE the observed incidence in the training set and one of the validation datasets was consistent with the predicted one. In the other validation dataset, which was specifically designed for evaluation of the model, the incidence was higher than predicted presumably because of better ascertainment of outcome. The incidence of all-PE was lower than predicted in all three datasets because at term many pregnancies deliver for reasons other than PE and therefore pregnancies considered to be at high-risk for PE that deliver for other reasons before they develop PE can be wrongly considered to be false positives. CONCLUSIONS: The competing risks model provides an effective and reproducible method for first-trimester prediction of early-PE and preterm-PE, as long as the various components of screening are carried out by appropriately trained and audited practitioners. Early prediction of preterm-PE is beneficial because treatment of the high-risk group with aspirin is highly effective in the prevention of the disease.Fetal Medicine Foundatio

NeuroQuantify -- An Image Analysis Software for Detection and Quantification of Neurons and Neurites using Deep Learning

The segmentation of cells and neurites in microscopy images of neuronal networks provides valuable quantitative information about neuron growth and neuronal differentiation, including the number of cells, neurites, neurite length and neurite orientation. This information is essential for assessing the development of neuronal networks in response to extracellular stimuli, which is useful for studying neuronal structures, for example, the study of neurodegenerative diseases and pharmaceuticals. However, automatic and accurate analysis of neuronal structures from phase contrast images has remained challenging. To address this, we have developed NeuroQuantify, an open-source software that uses deep learning to efficiently and quickly segment cells and neurites in phase contrast microscopy images. NeuroQuantify offers several key features: (i) automatic detection of cells and neurites; (ii) post-processing of the images for the quantitative neurite length measurement based on segmentation of phase contrast microscopy images, and (iii) identification of neurite orientations. The user-friendly NeuroQuantify software can be installed and freely downloaded from GitHub https://github.com/StanleyZ0528/neural-image-segmentation

2D versus 3D human induced pluripotent stem cell-derived cultures for neurodegenerative disease modelling

Neurodegenerative diseases, such as Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD) and amyotrophic lateral sclerosis (ALS), affect millions of people every year and so far, there are no therapeutic cures available. Even though animal and histological models have been of great aid in understanding disease mechanisms and identifying possible therapeutic strategies, in order to find disease-modifying solutions there is still a critical need for systems that can provide more predictive and physiologically relevant results. One possible avenue is the development of patient-derived models, e.g. by reprogramming patient somatic cells into human induced pluripotent stem cells (hiPSCs), which can then be differentiated into any cell type for modelling. These systems contain key genetic information from the donors, and therefore have enormous potential as tools in the investigation of pathological mechanisms underlying disease phenotype, and progression, as well as in drug testing platforms. hiPSCs have been widely cultured in 2D systems, but in order to mimic human brain complexity, 3D models have been proposed as a more advanced alternative. This review will focus on the use of patient-derived hiPSCs to model AD, PD, HD and ALS. In brief, we will cover the available stem cells, types of 2D and 3D culture systems, existing models for neurodegenerative diseases, obstacles to model these diseases in vitro, and current perspectives in the field

Multi-messenger observations of a binary neutron star merger

On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ~1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40+8-8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 Mo. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ~40 Mpc) less than 11 hours after the merger by the One- Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ~10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ~9 and ~16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta

Implantable Photonic Neural Probes with 3D-Printed Microfluidics and Applications to Uncaging

Advances in chip-scale photonic-electronic integration are enabling a new generation of foundry-manufacturable implantable silicon neural probes incorporating nanophotonic waveguides and microelectrodes for optogenetic stimulation and electrophysiological recording in neuroscience research. Further extending neural probe functionalities with integrated microfluidics is a direct approach to achieve neurochemical injection and sampling capabilities. In this work, we use two-photon polymerization 3D printing to integrate microfluidic channels onto photonic neural probes, which include silicon nitride nanophotonic waveguides and grating emitters. The customizability of 3D printing enables a unique geometry of microfluidics that conforms to the shape of each neural probe, enabling integration of microfluidics with a variety of existing neural probes while avoiding the complexities of monolithic microfluidics integration. We demonstrate the photonic and fluidic functionalities of the neural probes via fluorescein injection in agarose gel and photoloysis of caged fluorescein in solution and in flxed brain tissue