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PubMed Journals Articles About "Global MicroInjection Molding Machine Market Status Outlook 20182025" RSS

06:31 EDT 21st March 2019 | BioPortfolio

Global MicroInjection Molding Machine Market Status Outlook 20182025 PubMed articles on BioPortfolio. Our PubMed references draw on over 21 million records from the medical literature. Here you can see the latest Global MicroInjection Molding Machine Market Status Outlook 20182025 articles that have been published worldwide.

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Showing "Global MicroInjection Molding Machine Market Status Outlook 20182025" PubMed Articles 1–25 of 11,000+

Back from the Dead. ACA Exchange Market Looking Healthy, Hale, and Competitive.

The brighter outlook for 2019 is what happens when the learning curve starts to flatten out. Remember, this market has only existed for five years. Margins over the past six months have exceeded even pre-ACA levels, and loss ratios reached new lows in the second quarter of 2018.


ISBER's Global Outlook: A Summary of Recent International Activities.

A Comparative Cephalometric Study of Nasoalveolar Molding- and Non-Nasoalveolar Molding-Treated Bilateral Cleft Patients at Early Mixed Dentition Period.

To evaluate and compare early maxillary growth in 2 groups of patients with repaired bilateral cleft lip and palate (BCLP) who had and had not received nasoalveolar molding (NAM) therapy in infancy.


Building a Global Culture of Science - The Vietnam Experience.

We detail the lessons learned, challenges, achievements, and outlook in building a chemistry research center in Vietnam. Through the principles of 'global science', we provide specific insight into the process behind establishing an internationally-competitive research program - a model that is scalable and adaptable to countries beyond Vietnam. Furthermore, we highlight the prospects for success in advancing global science education, research capacity building, and mentorship.

The struggle for existence in the world market ecosystem.

The global trade system can be viewed as a dynamic ecosystem in which exporters struggle for resources: the markets in which they export. We can think that the aim of an exporter is to gain the entirety of a market share (say, car imports from the United States). This is similar to the objective of an organism in its attempt to monopolize a given subset of resources in an ecosystem. In this paper, we adopt a multilayer network approach to describe this struggle. We use longitudinal, multiplex data on trade ...

Prediction of IDH1 Mutation Status in the Glioblastoma Using the Machine Learning Technique Based on the Quantitative Radiomic Data.

Isocitrate dehydrogenase 1 (IDH1) mutation status is an independent favourable prognostic factor for glioblastoma (GBM) and is usually determined by sequencing or immunohistochemistry. An accurate prediction of IDH1 mutation status via noninvasive methods helps establish the appropriate treatment strategy. We aimed to predict IDH1 mutation status using quantitative radiomic data in patients with GBM.

Machine learning reveals multimodal MRI patterns predictive of isocitrate dehydrogenase and 1p/19q status in diffuse low- and high-grade gliomas.

Isocitrate dehydrogenase (IDH) and 1p19q codeletion status are importantin providing prognostic information as well as prediction of treatment response in gliomas. Accurate determination of the IDH mutation status and 1p19q co-deletion prior to surgery may complement invasive tissue sampling and guide treatment decisions.

Normothermic machine perfusion may prevent regulated cell death following renal ischemia-reperfusion injury.

We congratulate DiRito et al (1) on their excellent review, giving an outlook on possible effects of normothermic machine perfusion (NMP) to prevent renal ischemia-reperfusion injury (IRI). Concerning the pathophysiology behind IRI there was not a discussion of the different regulated (nonapoptotic) cell death pathways in this context, which have been very well investigated over the last few years in murine models (2, 3). This article is protected by copyright. All rights reserved.

Effect of Cell Inner Pressure on Deposition Volume in Microinjection.

Microinjection is a widely used technique for introducing exogenous materials into cells. Many applications of microinjection, such as gene editing and drug testing, rely on the accurate control of the deposition volume. However, the deposition volume in microinjection is presently calibrated in open medium without considering the cell inner pressure effect, which we experimentally show in this paper that can induce an error between the actual deposition volume and set volume to be as large as 30%. In this ...

Sociodemographic Indicators of Health Status Using a Machine Learning Approach and Data from the English Longitudinal Study of Aging (ELSA).

BACKGROUND Studies on the effects of sociodemographic factors on health in aging now include the use of statistical models and machine learning. The aim of this study was to evaluate the determinants of health in aging using machine learning methods and to compare the accuracy with traditional methods. MATERIAL AND METHODS The health status of 6,209 adults, age 80 years (n=1,357) were measured using an established health metric (0-100) that incorporated physical function and activities of daily living (ADL)...

Dynamic Advisor-Based Ensemble (dynABE): Case study in stock trend prediction of critical metal companies.

Stock trend prediction is a challenging task due to the market's noise, and machine learning techniques have recently been successful in coping with this challenge. In this research, we create a novel framework for stock prediction, Dynamic Advisor-Based Ensemble (dynABE). dynABE explores domain-specific areas based on the companies of interest, diversifies the feature set by creating different "advisors" that each handles a different area, follows an effective model ensemble procedure for each advisor, and...

Why We Needn't Fear the Machines: Opportunities for Medicine in a Machine Learning World.

Recently in medicine, the accuracy of machine learning models in predictive tasks has started to meet or exceed that of board certified specialists. The ability to automate cognitive tasks using software has raised new questions about the future role of human physicians in health care. Emerging technologies can displace people from their jobs, forcing them to learn new skills, so it is clear that this looming challenge needs to be addressed by the medical education system. While current medical education se...

Size effects in plasma-enhanced nano-transfer adhesion.

Plasma bonding and layer-by-layer transfer molding have co-existed for decades, and here we offer a combination of the two that drives both techniques to the nanoscale. Using fluorinated elastomeric stamps, lines of plasma-treated poly(dimethylsiloxane) (PDMS) were stacked into multi-layer woodpile structures via transfer molding, and we observe a pronounced size effect wherein nanoscale lines (≤280 nm period) require ultra-low plasma dose (

Metabolic Syndrome Prediction Using Machine Learning Models with Genetic and Clinical Information from a Nonobese Healthy Population.

The prevalence of metabolic syndrome (MS) in the nonobese population is not low. However, the identification and risk mitigation of MS are not easy in this population. We aimed to develop an MS prediction model using genetic and clinical factors of nonobese Koreans through machine learning methods. A prediction model for MS was designed for a nonobese population using clinical and genetic polymorphism information with five machine learning algorithms, including naïve Bayes classification (NB). The analysis...

World health status 1950-2015: Converging or diverging.

To advance the goal of "Grand Convergence" in global health by 2035, this study tested the convergence hypothesis in the progress of the health status of individuals from 193 countries, using both standard and cutting-edge convergence metrics.

Market and Patent Analyses of Wearables in Medicine.

Wearable medical devices (WMDs) will advance point-of-care diagnostics and therapeutics. This article analyses the market and patents for wearable devices. Activity monitors have the largest market share, and the intellectual property landscape is dominated by electronics corporations. However, the majority of these patents have not been realized in commercial products.

Ensemble machine learning prediction of posttraumatic stress disorder screening status after emergency room hospitalization.

Posttraumatic stress disorder (PTSD) develops in a substantial minority of emergency room admits. Inexpensive and accurate person-level assessment of PTSD risk after trauma exposure is a critical precursor to large-scale deployment of early interventions that may reduce individual suffering and societal costs. Toward this aim, we applied ensemble machine learning to predict PTSD screening status three months after severe injury using cost-effective and minimally invasive data. Participants (N = 271) were re...

A chicken bioreactor for efficient production of functional cytokines.

The global market for protein drugs has the highest compound annual growth rate of any pharmaceutical class but their availability, especially outside of the US market, is compromised by the high cost of manufacture and validation compared to traditional chemical drugs. Improvements in transgenic technologies allow valuable proteins to be produced by genetically-modified animals; several therapeutic proteins from such animal bioreactors are already on the market after successful clinical trials and regulato...

Is It Ethical to Use Prognostic Estimates from Machine Learning to Treat Psychosis?

Machine learning is a method for predicting clinically relevant variables, such as opportunities for early intervention, potential treatment response, prognosis, and health outcomes. This commentary examines the following ethical questions about machine learning in a case of a patient with new onset psychosis: (1) When is clinical innovation ethically acceptable? (2) How should clinicians communicate with patients about the ethical issues raised by a machine learning predictive model?

The science of molding faces.

A model to predict and optimise machine guarding operator's compliance activities in a bottling process plant: A developing country experience.

The accurate tracking, elimination and control of hazards are fundamentals in accident avoidance at the operational machine guarding stations. This paper develops a machine guard usage compliance (MGUC) model. Nonetheless, very few studies account for operator's compliance to the usage of machine guards in workplaces.

Can Machine-learning Techniques Be Used for 5-year Survival Prediction of Patients With Chondrosarcoma?

Several studies have identified prognostic factors for patients with chondrosarcoma, but there are few studies investigating the accuracy of computationally intensive methods such as machine learning. Machine learning is a type of artificial intelligence that enables computers to learn from data. Studies using machine learning are potentially appealing, because of its possibility to explore complex patterns in data and to improve its models over time.

Identifying a neuroanatomical signature of schizophrenia, reproducible across sites and stages, using machine learning with structured sparsity.

Structural MRI (sMRI) increasingly offers insight into abnormalities inherent to schizophrenia. Previous machine learning applications suggest that individual classification is feasible and reliable and, however, is focused on the predictive performance of the clinical status in cross-sectional designs, which has limited biological perspectives. Moreover, most studies depend on relatively small cohorts or single recruiting site. Finally, no study controlled for disease stage or medication's effect. These el...

Health system measurement: Harnessing machine learning to advance global health.

Further improvements in population health in low- and middle-income countries demand high-quality care to address an increasingly complex burden of disease. Health facility surveys provide an important but costly source of information on readiness to provide care. To improve the efficiency of health system measurement, we applied unsupervised machine learning methods to assess the performance of the service readiness index (SRI) defined by the World Health Organization and compared it to empirically derived...

Gender inequality in global burden of uncorrected refractive error.

To explore gender inequality in global burden of Uncorrected refractive error (URE) by year, age, and socioeconomic status using disability-adjusted life years (DALYs).


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