-
Berger Abel posted an update 6 months, 2 weeks ago
ct variability of breathing patterns and amplitudes, requiring further consideration.Test-retest reliability is essential for using resting-state functional magnetic resonance imaging (rs-fMRI) as a potential biomarker for Alzheimer’s disease (AD), especially when monitoring longitudinal changes and treatment effects. In addition, test-retest variability itself might represent a feature of AD. Using 3.0 T rs-fMRI data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, we examined the long-term (1-year) test-retest reliability of resting-state networks (RSNs) in 31 healthy elderly subjects, 63 patients with mild cognitive impairment (MCI), and 17 patients with AD by applying temporal concatenation group independent component analysis and dual regression. The intraclass correlation coefficient estimates of RSN amplitudes ranged from 0.44 to 0.77 in healthy elderly subjects, from 0.31 to 0.62 in patients with MCI, and from -0.06 to 0.44 in patients with AD. The overall test-retest reliability of RSNs was lower in patients with MCI than in healthy elderly subjects, and was lower in patients with AD than in patients with MCI. The differences in the test-retest reliabilities were due to the RSN amplitudes rather than the RSN shapes. Head motion was not significantly different among the three groups of subjects. The results indicate that the test-retest stability of RSNs generally declines with progression to MCI and AD, mainly due to the RSN amplitudes rather than the RSN shapes. The test-retest instability in MCI and AD may reflect progressive neurofunctional alterations related to the pathology of AD.Glaucoma is a chronic eye disease, which causes gradual vision loss and eventually blindness. Accurate glaucoma screening at early stage is critical to mitigate its aggravation. Extracting high-quality features are critical in training of classification models. In this paper, we propose a deep ensemble network with attention mechanism that detects glaucoma using optic nerve head stereo images. The network consists of two main sub-components, a deep Convolutional Neural Network that obtains global information and an Attention-Guided Network that localizes optic disc while maintaining beneficial information from other image regions. Both images in a stereo pair are fed into these sub-components, the outputs are fused together to generate the final prediction result. Abundant image features from different views and regions are being extracted, providing compensation when one of the stereo images is of poor quality. The attention-based localization method is trained in a weakly-supervised manner and only image-level annotation is required, which avoids expensive segmentation labelling. Results from real patient images show that our approach increases recall (sensitivity) from the state-of-the-art 88.89% to 95.48%, while maintaining precision and performance stability. The marked reduction in false-negative rate can significantly enhance the chance of successful early diagnosis of glaucoma.
Studies abound regarding the views of faculty anatomists and medical students on the importance of anatomy and the dissection of human bodies, but very little is known about the views of practicing physicians.
A survey was distributed among physicians and surgeons practicing in Spain in order to find out their views on the practice and consequences of human dissection by undergraduate students of medicine. The most relevant definition to qualify faculty anatomists of medical schools was also requested. Responses were repeatedly clustered into characteristic subgroups for analysis.
In total, 536 physicians and surgeons belonging to 36 different specialties in seven Spanish hospitals responded to the survey. The results highlighted two main facts. Firstly, faculty anatomists were perceived as teachers, above any other professional identity (namely physician, biologist or scientist); nonetheless, the ascription of identities varied between specialties (p=0.009); and it also depended on whether the respondents had dissected in their undergraduate degree (p=0.03) and on the respondent’s gender (p=0.03). Secondly, physicians and surgeons confirmed that dissecting human cadavers serves the undergraduate student not only for acquiring anatomical knowledge, but also essential skills and attitudes, including professionalism.
The results strongly suggest that dissection practice should be reinforced and enriched in undergraduate medical school. As this is important in itself, the results of the study could also help with the development of strategies to alleviate the current shortage of adequately trained anatomists for medical degrees.
The results strongly suggest that dissection practice should be reinforced and enriched in undergraduate medical school. As this is important in itself, the results of the study could also help with the development of strategies to alleviate the current shortage of adequately trained anatomists for medical degrees.Gold nanoparticles (AuNPS) represent one of the most studied classes of nanomaterials for biomedical applications, especially in the field of cancer research. In fact, due to their unique properties and high versatility, they can be exploited under all aspects connected to cancer management, from early detection to diagnosis and treatment. AuNPs have thus been tested with amazing results as biosensors, contrast agents, therapeutics. Their importance as potent theranostics is undoubted, but the translation to clinical practice has been hampered by a series of aspects, such as the unclear toxicity in humans and the lack of thorough studies on reliable animal models. https://www.selleckchem.com/products/Vandetanib.html Still, their potential action is so appealing and the results so impressive that an outstanding number of papers is being published every year, with the consequence that any review on this topic becomes obsolete within a few months. Here we would like to report the latest findings on AuNPs research addressing all their functions as theranostic agents.The rapid evolution of the COVID-19 pandemic has underscored the need to quickly disseminate the latest clinical knowledge during a public-health emergency. One surprisingly effective platform for healthcare professionals (HCPs) to share knowledge and experiences from the front lines has been social media (for example, the “#medtwitter” community on Twitter). However, identifying clinically-relevant content in social media without manual labeling is a challenge because of the sheer volume of irrelevant data. We present an unsupervised, iterative approach to mine clinically relevant information from social media data, which begins by heuristically filtering for HCP-authored texts and incorporates topic modeling and concept extraction with MetaMap. This approach identifies granular topics and tweets with high clinical relevance from a set of about 52 million COVID-19-related tweets from January to mid-June 2020. We also show that because the technique does not require manual labeling, it can be used to identify emerging topics on a week-to-week basis.
Please follow & like us :)
All content contained on CatsWannaBeCats.Com, unless otherwise acknowledged,is the property of CatsWannaBeCats.Com and subject to copyright.