In recent years, a number of large-scale genome-wide association studies have been published for human traits adjusted for other correlated traits with a genetic basis. In most studies, the ...motivation for such an adjustment is to discover genetic variants associated with the primary outcome independently of the correlated trait. In this report, we contend that this objective is fulfilled when the tested variants have no effect on the covariate or when the correlation between the covariate and the outcome is fully explained by a direct effect of the covariate on the outcome. For all other scenarios, an unintended bias is introduced with respect to the primary outcome as a result of the adjustment, and this bias might lead to false positives. Here, we illustrate this point by providing examples from published genome-wide association studies, including large meta-analysis of waist-to-hip ratio and waist circumference adjusted for body mass index (BMI), where genetic effects might be biased as a result of adjustment for body mass index. Using both theory and simulations, we explore this phenomenon in detail and discuss the ramifications for future genome-wide association studies of correlated traits and diseases.
The authors conduct a meta-analysis on the effect of electronic word of mouth on sales by examining 51 studies (involving 339 volume and 271 valence elasticities) and primary data collected on ...product characteristics (durability, trialability, and usage condition), industry characteristics (industry growth and competition), and platform characteristics (expertise and trustworthiness). Their analysis reveals that electronic word-of-mouth volume (valence) elasticity is .236 (.417). More importantly, the findings show that volume and valence elasticities are higher for privately consumed, low-trialability products that operate in less competitive industries and whose reviews are carried on independent review sites. Furthermore, volume elasticities are higher for durable goods and for reviews on specialized review sites, whereas valence elasticities are greater for community-based sites. Drawing on the results, they discuss several implications for managers and researchers and explain why valence elasticities are often found to be insignificant. Finally, they propose numerous directions for future research in the area on the basis of their findings.
In neurodegenerative diseases, debris of dead neurons are thought to trigger glia-mediated neuroinflammation, thus increasing neuronal death. Here we show that the expression of neurotoxic proteins ...associated with these diseases in microglia alone is sufficient to directly trigger death of naive neurons and to propagate neuronal death through activation of naive astrocytes to the A1 state. Injury propagation is mediated, in great part, by the release of fragmented and dysfunctional microglial mitochondria into the neuronal milieu. The amount of damaged mitochondria released from microglia relative to functional mitochondria and the consequent neuronal injury are determined by Fis1-mediated mitochondrial fragmentation within the glial cells. The propagation of the inflammatory response and neuronal cell death by extracellular dysfunctional mitochondria suggests a potential new intervention for neurodegeneration-one that inhibits mitochondrial fragmentation in microglia, thus inhibiting the release of dysfunctional mitochondria into the extracellular milieu of the brain, without affecting the release of healthy neuroprotective mitochondria.
The Pfizer-BioNTech (BNT162b2) and the Oxford-AstraZeneca (ChAdOx1 nCoV-19) COVID-19 vaccines have shown excellent safety and efficacy in phase 3 trials. We aimed to investigate the safety and ...effectiveness of these vaccines in a UK community setting.
In this prospective observational study, we examined the proportion and probability of self-reported systemic and local side-effects within 8 days of vaccination in individuals using the COVID Symptom Study app who received one or two doses of the BNT162b2 vaccine or one dose of the ChAdOx1 nCoV-19 vaccine. We also compared infection rates in a subset of vaccinated individuals subsequently tested for SARS-CoV-2 with PCR or lateral flow tests with infection rates in unvaccinated controls. All analyses were adjusted by age (≤55 years vs >55 years), sex, health-care worker status (binary variable), obesity (BMI <30 kg/m2vs ≥30 kg/m2), and comorbidities (binary variable, with or without comorbidities).
Between Dec 8, and March 10, 2021, 627 383 individuals reported being vaccinated with 655 590 doses: 282 103 received one dose of BNT162b2, of whom 28 207 received a second dose, and 345 280 received one dose of ChAdOx1 nCoV-19. Systemic side-effects were reported by 13·5% (38 155 of 282 103) of individuals after the first dose of BNT162b2, by 22·0% (6216 of 28 207) after the second dose of BNT162b2, and by 33·7% (116 473 of 345 280) after the first dose of ChAdOx1 nCoV-19. Local side-effects were reported by 71·9% (150 023 of 208 767) of individuals after the first dose of BNT162b2, by 68·5% (9025 of 13 179) after the second dose of BNT162b2, and by 58·7% (104 282 of 177 655) after the first dose of ChAdOx1 nCoV-19. Systemic side-effects were more common (1·6 times after the first dose of ChAdOx1 nCoV-19 and 2·9 times after the first dose of BNT162b2) among individuals with previous SARS-CoV-2 infection than among those without known past infection. Local effects were similarly higher in individuals previously infected than in those without known past infection (1·4 times after the first dose of ChAdOx1 nCoV-19 and 1·2 times after the first dose of BNT162b2). 3106 of 103 622 vaccinated individuals and 50 340 of 464 356 unvaccinated controls tested positive for SARS-CoV-2 infection. Significant reductions in infection risk were seen starting at 12 days after the first dose, reaching 60% (95% CI 49–68) for ChAdOx1 nCoV-19 and 69% (66–72) for BNT162b2 at 21–44 days and 72% (63–79) for BNT162b2 after 45–59 days.
Systemic and local side-effects after BNT162b2 and ChAdOx1 nCoV-19 vaccination occur at frequencies lower than reported in phase 3 trials. Both vaccines decrease the risk of SARS-CoV-2 infection after 12 days.
ZOE Global, National Institute for Health Research, Chronic Disease Research Foundation, National Institutes of Health, UK Medical Research Council, Wellcome Trust, UK Research and Innovation, American Gastroenterological Association.
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Mitochondria are best known for their role in ATP generation. However, studies over the past two decades have shown that mitochondria do much more than that. Mitochondria regulate ...both necrotic and apoptotic cell death pathways, they store and therefore coordinate cellular Ca2+ signaling, they generate and metabolize important building blocks, by-products and signaling molecules, and they also generate and are targets of free radical species that modulate many aspects of cell physiology and pathology. Most estimates suggest that although the brain makes up only 2 percent of body weight, utilizes about 20 percent of the body’s total ATP. Thus, mitochondrial dysfunction greatly impacts brain functions and is indeed associated with numerous neurodegenerative diseases. Furthermore, a number of abnormal disease-associated proteins have been shown to interact directly with mitochondria, leading to mitochondrial dysfunction and subsequent neuronal cell death. Here, we discuss the role of mitochondrial dynamics impairment in the pathological processes associated with neurodegeneration and suggest that a therapy targeting mitochondrialdysfunction holds a great promise.
Electromyography (EMG) signal is gaining popularity to developn intelligent bionics and prosthetic devices using machine learning techniques. Feature extraction is essential step for the EMG pattern ...recognition based application. In this article, a fused wavelet packet transform based feature extraction approach is proposed for EMG pattern classification. Total nine subjects (six intact and three amputees) are recruited for the data acquisition. Data acquisition is performed by an ADS1298-based system with eight bipolar electrodes. Further 11 activities are performed by each subject at the time of EMG signal recording including lateral grasp, cylindrical grasp, spherical grasp, and grasp with force. The visual feedback system is utilized for EMG signal acquisition of amputees. The comparison of commonly used wavelet transform based features and proposed fused wavelet transform based features is also presented with respect to classification accuracy and time complexity. The proposed method exhibits highest classification accuracy up to 98.32% for the amputees using discriminant analysis classification with marginal variation in time complexity. Similar trends in results are observed when standard dataset (NinaPro) has been utilized. The results validate the enhanced performance of the proposed technique over conventional counterparts.
Kar Seva Joshi, Amit R.T.
Journal of surgical education,
7/2024
Journal Article
Recenzirano
Odprti dostop
•Collaboration amongst surgical educators and organizations has yielded durable products that have substantively improved the way in which residents and fellows are prepared for independent ...practice.•Data-driven quality improvement will be critical to adapting accreditation and certification standards to meet the needs of training programs.•The APDS should continue to build on international relationships with sister societies across the world.
Understanding organelle biogenesis is a central focus of cell biology. Whereas some are generated from existing organelles, others can be generated de novo. Most de novo organelle biogenesis occurs ...in the endoplasmic reticulum (ER). Here, we review the role of the ER in the generation of peroxisomes, lipid droplets, and omegasomes, which are platforms for autophagosome production, and discuss how ER subdomains with specific protein and lipid composition form and promote organelle biogenesis.
People around the globe rely on their blood samples for their glucose level measurement. There is a demand for non-invasive, precise and cost-effective solutions to monitor blood glucose level and ...control of diabetes. Serum glucose is an accurate blood glucose measurement method in comparison to capillary glucose measurement. Presently, the serum glucose is measured through laboratory setup with an invasive approach. The invasive method is painful and is not suitable for continuous glucose measurement. In this paper, we propose a novel wearable non-invasive consumer device (called iGLU 2.0) which can be used by consumers for accurate continuous blood glucose monitoring. This device uses a novel short near infrared (NIR) spectroscopy developed by us. It is incorporated with Internet-of-Medical-Things (IoMT) for smart healthcare where the healthcare data is stored on the cloud and is accessible to the users and caregivers. Analysis of the optimized regression model is performed and the system is calibrated and validated through healthy, prediabetic and diabetic patients. The robust regression models of serum glucose level is then deployed as the mechanism for precise measurement in iGLU 2.0. The performance of iGLU 2.0 is validated with the prediction of capillary blood glucose using Average Error (AvgE) and Mean Absolute Relative Difference (mARD) which are calculated as 6.09% and 6.07%, respectively, whereas for serum glucose, AvgE and mARD are estimated as 4.88% and 4.86%, respectively.
In the field of neuroscience, brain activity measurement and analysis are considered crucial areas. Schizophrenia (Sz) is a brain disorder that severely affects the thinking, behavior, and feelings ...of people worldwide. Thus, an accurate and rapid detection method is needed for proper care and quality treatment of the patients. Electroencephalography (EEG) is proved to be an efficient biomarker in Sz detection as it records brain activities. This article aims to improve the performance of EEG-based Sz detection using a deep-learning approach in remote applications. A hybrid deep-learning model identified as schizophrenia hybrid neural network (SzHNN), which is a combination of convolutional neural networks (CNNs) and long short-term memory (LSTM), has been proposed wherein the CNN for local feature extraction and LSTM for classification is utilized. In this article, the proposed model has been compared with several deep-learning and machine-learning-based models. All the models have been evaluated on two different datasets wherein dataset 1 consists of 19 subjects and dataset 2 consists of 16 subjects. The proposed model is also implemented with the Internet-of-Medical-Things (IoMT) framework for smart healthcare and remote-based applications. Several experiments have been conducted using various parametric settings on different frequency bands and different sets of electrodes on the scalp. Based on all the experiments, it is evident that the proposed hybrid model (SzHNN) provides the highest classification accuracy of 99.9% compared to other implemented models and existing models of previous papers. The proposed model overcomes the influence of different frequency bands and shows a better accuracy of 96.10% (dataset 1) and 91.00% (dataset 2) with only five electrodes. Subject-wise testing is also done for SzHNN, which proposes an accuracy of 90.11% and 89.60% for datasets 1 and 2, respectively.
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