The ongoing COVID‐19 pandemic has had a profound worldwide impact on the laboratory hematology community. Nevertheless, the pace of COVID‐19 hematology‐related research has continued to accelerate ...and has established the role of laboratory hematology data for many purposes including disease prognosis and outcome. The purpose of this scoping review was to assess the current state of COVID‐19 laboratory hematology research. A comprehensive search of the literature published between December 1, 2019, and July 3, 2020, was performed, and we analyzed the sources, publication dates, study types, and topics of the retrieved studies. Overall, 402 studies were included in this scoping review. Approximately half of these studies (n = 202, 50.37%) originated in China. Retrospective cohort studies comprised the largest study type (n = 176, 43.89%). Prognosis/ risk factors, epidemiology, and coagulation were the most common topics. The number of studies published per day has increased through the end of May. The studies were heavily biased in favor of papers originating in China and on retrospective clinical studies with limited use of and reporting of laboratory data. Despite the major improvements in our understanding of the role of coagulation, automated hematology, and cell morphology in COVID‐19, there are gaps in the literature, including biosafety and the laboratory role in screening and prevention of COVID‐19. There is a gap in the publication of papers focused on guidelines for the laboratory. Our findings suggest that, despite the large number of publications related to laboratory data and their use in COVID‐19 disease, many areas remain unexplored or under‐reported.
Coronavirus Disease-19 (COVID-19) has been in a global pandemic currently and relating symptoms were reported variously around the world. We reported a previously healthy man of COVID-19 presenting ...with anosmia as the obvious symptom with relevant radiological findings on brain magnetic resonance imaging.
Since frequent communication between applications takes place in high speed networks, deep packet inspection (DPI) plays an important role in the network application awareness. The signature-based ...network intrusion detection system (NIDS) contains a DPI technique that examines the incoming packet payloads by employing a pattern matching algorithm that dominates the overall inspection performance. Existing studies focused on implementing efficient pattern matching algorithms by parallel programming on software platforms because of the advantages of lower cost and higher scalability. Either the central processing unit (CPU) or the graphic processing unit (GPU) were involved. Our studies focused on designing a pattern matching algorithm based on the cooperation between both CPU and GPU. In this paper, we present an enhanced design for our previous work, a length-bounded hybrid CPU/GPU pattern matching algorithm (LHPMA). In the preliminary experiment, the performance and comparison with the previous work are displayed, and the experimental results show that the LHPMA can achieve not only effective CPU/GPU cooperation but also higher throughput than the previous method.
The large quantities of data now being transferred via high-speed networks have made deep packet inspection indispensable for security purposes. Scalable and low-cost signature-based network ...intrusion detection systems have been developed for deep packet inspection for various software platforms. Traditional approaches that only involve central processing units (CPUs) are now considered inadequate in terms of inspection speed. Graphic processing units (GPUs) have superior parallel processing power, but transmission bottlenecks can reduce optimal GPU efficiency. In this paper we describe our proposal for a hybrid CPU/GPU pattern-matching algorithm (HPMA) that divides and distributes the packet-inspecting workload between a CPU and GPU. All packets are initially inspected by the CPU and filtered using a simple pre-filtering algorithm, and packets that might contain malicious content are sent to the GPU for further inspection. Test results indicate that in terms of random payload traffic, the matching speed of our proposed algorithm was 3.4 times and 2.7 times faster than those of the AC-CPU and AC-GPU algorithms, respectively. Further, HPMA achieved higher energy efficiency than the other tested algorithms.
An equiatomic, face-centered-cubic, CoCrFeMnNi high-entropy alloy was tensile tested at 800 K and 1,000 K with a nominal strain rate of 6.7 × 10−6 s−1. In addition to the macroscopic (bulk) behavior, ...mesoscopic (lattice) phenomena during the pseudo-creep deformation were investigated simultaneously using in-situ neutron diffraction. The evolutions of lattice strains, peak widths, and intensities of several hkl reflections suggest that the dominant deformation mode is the dislocation glide at 800 K and diffusion-controlled dislocation creep at 1,000 K.
•We investigated an equiatomic, face-centered-cubic, CoCrFeMnNi high entropy alloy.•We applied in-situ neutron diffraction measurements.•The lattice-strain, diffraction peak-width, and intensity evolutions are studied.•Creep is activated at 1,000 K and the associated diffraction patterns are reported.•TEM results support the factual and descriptive characters of neutron patterns.
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•Pd@Pt CINPs are successfully synthesised through a seed-growth method.•Pd@Pt CINPs are successfully as electrocatalysts for GOR in PB solutions.•The mechanism and kinetics of GOR are ...monitored and studied using EQCM.•12.9 nm Pd@Pt CINPs provide greater activity than 9 nm Pt nanoparticles.
Concave Pd core/ island Pt shell nanoparticles (Pd@Pt CINPs) were successfully synthesised and used as catalysts for the glucose oxidation reaction (GOR) and to directly sense D-glucose in phosphate-buffered solutions. The results measured by an electrochemical quartz crystal microbalance (EQCM) indicate that the GOR accelerates from -0.3 V (vs. Ag/AgCl) to 0.2 V and at least 1.0067 μg of glucose reacted with the Pd@Pt CINPs for each catalysis. A favourable comparison shows that 12.9 nm Pd@Pt CINPs have high sensitivity (15.14 μAmM−1 cm-2), which is four times greater than the 3.7 μAmM−1 cm-2 of 9 nm Pt NPs. The greater sensitivity within a linear range of 1–8.5 mM and high stability during repeated GORs indicate that Pd@Pt CINPs are potential non-enzymatic glucose sensors.
An equal-molar CoCrFeMnNi, face-centered-cubic high-entropy alloy system and a face-centered-cubic stainless steel described as a medium-entropy system, are measured by in situ neutron-diffraction ...experiments subjected to continuous tension at room and several elevated temperatures, respectively. With spallation neutron, the evolution of multiple diffraction peaks is collected simultaneously for lattice-elasticity study. Temperature variation of elastic stiffness of a single face-centered-cubic-phase Ni and a single face-centered-cubic-phase Fe are compared as low-entropy metals. The CoCrFeMnNi high-entropy alloy shows distinct lattice anisotropy.
Histone deacetylase inhibitors (HDACI) are promising antitumor agents. Although transcriptional deregulation is thought to be the main mechanism underlying their therapeutic effects, the exact ...mechanism and targets by which HDACIs achieve their antitumor effects remain poorly understood. It is not known whether any of the HDAC members support robust tumor growth. In this report, we show that HDAC6, a cytoplasmic-localized and cytoskeleton-associated deacetylase, is required for efficient oncogenic transformation and tumor formation. We found that HDAC6 expression is induced upon oncogenic Ras transformation. Fibroblasts deficient in HDAC6 are more resistant to both oncogenic Ras and ErbB2-dependent transformation, indicating a critical role for HDAC6 in oncogene-induced transformation. Supporting this hypothesis, inactivation of HDAC6 in several cancer cell lines reduces anchorage-independent growth and the ability to form tumors in mice. The loss of anchorage-independent growth is associated with increased anoikis and defects in AKT and extracellular signal-regulated kinase activation upon loss of adhesion. Lastly, HDAC6-null mice are more resistant to chemical carcinogen-induced skin tumors. Our results provide the first experimental evidence that a specific HDAC member is required for efficient oncogenic transformation and indicate that HDAC6 is an important component underlying the antitumor effects of HDACIs.
Objectives
This study aimed to evaluate the feasibility of automatic Stanford classification of classic aortic dissection (AD) using a 2-step hierarchical neural network.
Methods
Between 2015 and ...2019, 130 arterial phase series (57 type A, 43 type B, and 30 negative cases) in aortic CTA were collected for the training and validation. A 2-step hierarchical model was built including the first step detecting AD and the second step predicting the probability (0–1) of Stanford types. The model’s performance was evaluated with an off-line prospective test in 2020. The sensitivity and specificity for Stanford type A, type B, and no AD (Sens
A, B, N
and Spec
A, B, N
, respectively) and Cohen’s kappa were reported.
Results
Of 298 cases (22 with type A, 29 with type B, and 247 without AD) in the off-line prospective test, the Sens
A
, Sens
B
, and Sens
N
were 95.45% (95% confidence interval CI, 77.16–99.88%), 79.31% (95% CI, 60.28–92.01%), and 93.52% (95% CI, 89.69–96.25%), respectively. The Spec
A
, Spec
B
, and Spec
N
were 98.55% (95% CI, 96.33–99.60%), 94.05% (95% CI, 90.52–96.56%), and 94.12% (95% CI, 83.76–98.77%), respectively. The classification rate achieved 92.28% (95% CI, 88.64–95.04%). The Cohen’s kappa was 0.766 (95% CI, 0.68–0.85;
p
< 0.001).
Conclusions
Stanford classification of classic AD can be determined by a 2-step hierarchical neural network with high sensitivity and specificity of type A and high specificity in type B and no AD.
Key Points
•
The Stanford classification for aortic dissection is widely adopted and divides it into Stanford type A and type B based on the ascending thoracic aorta dissected or not
.
•
The 2-step hierarchical neural network for Stanford classification of classic aortic dissection achieved high sensitivity (95.45%) and specificity (98.55%) of type A and high specificity in type B and no aortic dissection (94.05% and 94.12%, respectively) in 298 test cases
.
•
The 2-step hierarchical neural network demonstrated moderate agreement (Cohen’s kappa: 0.766, p < 0.001) with cardiovascular radiologists in detection and Stanford classification of classic aortic dissection in 298 test cases
.
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