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Quantitative Electroencephalography as a Biomarker for Cognitive Dysfunction in Parkinson’s Disease
Get PDFBackground: Quantitative electroencephalography (qEEG) has been suggested as a biomarker for cognitive decline in Parkinson\u27s disease (PD). Objective: Determine if applying a wavelet-based qEEG algorithm to 21-electrode, resting-state EEG recordings obtained in a routine clinical setting has utility for predicting cognitive impairment in PD. Methods: PD subjects, evaluated by disease stage and motor score, were compared to healthy controls (N = 20 each). PD subjects with normal (PDN, MoCA 26–30, N = 6) and impaired (PDD, MoCA ≤ 25, N = 14) cognition were compared. The wavelet-transform based time-frequency algorithm assessed the instantaneous predominant frequency (IPF) at 60 ms intervals throughout entire recordings. We then determined the relative time spent by the IPF in the four standard EEG frequency bands (RTF) at each scalp location. The resting occipital rhythm (ROR) was assessed using standard power spectral analysis. Results: Comparing PD subjects to healthy controls, mean values are decreased for ROR and RTF-Beta, greater for RTF-Theta and similar for RTF-Delta and RTF-Alpha. In logistic regression models, arithmetic combinations of RTF values [e.g., (RTF-Alpha) + (RTF-Beta)/(RTF-Delta + RTF-Theta)] and RTF-Alpha values at occipital or parietal locations are most able to discriminate between PD and controls. A principal component (PC) from principal component analysis (PCA) using RTF-band values in all subjects is associated with PD status (p = 0.004, β = 0.31, AUC = 0.780). Its loadings show positive contribution from RTF-Theta at all scalp locations, and negative contributions from RTF-Beta at occipital, parietal, central, and temporal locations. Compared to cognitively normal PD subjects, cognitively impaired PD subjects have lower median RTF-Alpha and RTF-Beta values, greater RTF-Theta values and similar RTF-Delta values. A PC from PCA using RTF-band values in PD subjects is associated with cognitive status (p = 0.002, β = 0.922, AUC = 0.89). Its loadings show positive contributions from RTF-Theta at all scalp locations, negative contributions from RTF-Beta at central locations, and negative contributions from RTF-Delta at central, frontal and temporal locations. Age, disease duration and/or sex are not significant covariates. No PC was associated with motor score or disease stage. Significance: Analyzing standard EEG recordings obtained in a community practice setting using a wavelet-based qEEG algorithm shows promise as a PD biomarker and for predicting cognitive impairment in PD
The Role of Nickel and Brønsted Sites on Ethylene Oligomerization with Ni-H-Beta Catalysts
No full textThe present work studies the adsorption of ethylene on Ni-H-Beta particles to unravel the roles of nickel and Brønsted sites in the catalytic oligomerization of ethylene. Three models (i.e., two based on the Cossee-Arlman mechanism and one based on the metallacycle mechanism) are examined in terms of the nature of the active sites and the adsorption mechanism involved in the ethylene coordination step. The results are consistent with the participation of two active sites in the formation of [Ni(II)-H]+ Cossee-Arlman centers and also suggest that ethylene dissociates upon adsorption on [Ni(II)-H]+ sites. Further characterization of Ni-H-Beta catalysts prepared at different nickel loadings and silica-to-alumina ratios reveals that highly dispersed Ni2+ exists on the catalyst surface and interacts with the catalyst’s lattice oxygen and free NiO crystals. At the same time, the kinetic results indicate that Brønsted sites may form isolated nickel-hydride ([Ni(II)-H]+) centers on the catalyst surface. In addition, the presence of residual, noncoordinated Ni2+ and Brønsted sites (not involved in the formation of [Ni(II)-H]+ sites) shows a reduced probability of the formation of nickel-hydride sites, hindering the conversion rate of ethylene. A mechanism for forming [Ni(II)-H]+ centers is proposed, involving ethylene adsorption over Ni2+ and a Brønsted site. This research has important implications for improving ethylene oligomerization processes over nickel-based heterogeneous catalysts
In Search of a Value Proposition for COVID-19 Testing in the Work Environment: A Social Marketing Analysis
Get PDFBackground: This study examined employer experience with SARS-CoV-2 (COVID-19) asymptomatic testing through a social marketing lens. Social marketing uses commercial marketing principles to achieve socially beneficial ends including improved health and safety behavior. Method: Twenty employers across 11 occupational sectors were interviewed about implementation of COVID-19 testing from January through April 2021. Recorded transcripts were coded and analyzed using marketing\u27s Four P\u27s : product, price, place, promotion. Results: COVID-19 tests (product) were uncomfortable, were easily confused, and didn\u27t solve problems articulated by employers. Testing was not widely available or didn\u27t line up with shifts or locations (place). The perceived price, which included direct and associated costs (e.g., laboratory fees, productivity loss, logistical challenges) was high. Most crucially, the time to receive (PCR) results negated the major benefit of less time spent in quarantine and challenged employer trust. A potential audience segmentation strategy based on perceptions of exposure risk also emerged. Conclusions: This social marketing analysis suggests ways to improve the value proposition for asymptomatic testing through changes in product, price, and placement features in line with employers\u27 expressed needs. Study findings can also inform creation of employee communication materials that balance perceived rewards of testing against perceived risks of exposure
Financing Benefits and Barriers to Routine HIV Screening in Clinical Settings in the United States: A Scoping Review
Get PDFThe Centers for Disease Control and Prevention recommends everyone between 13--64 years be tested for HIV at least once as a routine procedure. Routine HIV screening is reimbursable by Medicare, Medicaid, expanded Medicaid, and most commercial insurance plans. Yet, scaling-up HIV routine screening remains a challenge. We conducted a scoping review for studies on financial benefits and barriers associated with HIV screening in clinical settings in the U.S. to inform an evidence-based strategy to scale-up routine HIV screening. We searched Ovid MEDLINE®, Cochrane, and Scopus for studies published between 2006-2020 in English. The search identified 383 Citations; we screened 220 and excluded 163 (outside the time limit, irrelevant, or outside the U.S.). Of the 220 screened articles, we included 35 and disqualified 155 (did not meet the eligibility criteria). We organized eligible articles under two themes: financial benefits/barriers of routine HIV screening in healthcare settings (9 articles); and Cost-effectiveness of routine screening in healthcare settings (26 articles). The review concluded drawing recommendations in three areas: (1) Finance: Incentivize healthcare providers/systems for implementing HIV routine screening and/or separate its reimbursement from bundle payments; (2) Personnel: Encourage nurse-initiated HIV screening programs in primary care settings and educate providers on CDC recommendations; and (3) Approach: Use opt-out approach
A Deep Learning Framework for Earlier Prediction of Diabetic Retinopathy from Fundus Photographs
Get PDFDiabetic patients can also be identified immediately utilizing retinopathy photos, but it is a challenging task. The blood veins visible in fundus photographs are used in several disease diagnosis approaches. We sought to replicate the findings published in implementation and verification of a deep learning approach for diabetic retinopathy identification in retinal fundus pictures. To address this issue, the suggested investigative study uses recurrent neural networks (RNN) to retrieve characteristics from deep networks. As a result, using computational approaches to identify certain disorders automatically might be a fantastic solution. We developed and tested several iterations of a deep learning framework to forecast the progression of diabetic retinopathy in diabetic individuals who have undergone teleretinal diabetic retinopathy assessment in a basic healthcare environment. A collection of one-field or three-field colour fundus pictures served as the input for both iterations. Utilizing the proposed DRNN methodology, advanced identification of the diabetic state was performed utilizing HE detected in an eye\u27s blood vessel. This research demonstrates the difficulties in duplicating deep learning approach findings, as well as the necessity for more reproduction and replication research to verify deep learning techniques, particularly in the field of healthcare picture processing. This development investigates the utilization of several other Deep Neural Network Frameworks on photographs from the dataset after they have been treated to suitable image computation methods such as local average colour subtraction to assist in highlighting the germane characteristics from a fundoscopy, thus, also enhancing the identification and assessment procedure of diabetic retinopathy and serving as a skilled guidelines framework for practitioners all over the globe
Machine Learning Approach for Cardiovascular Risk and Coronary Artery Calcification Score
Get PDFCoronary artery calcification (CAC) could assist in the discovery of new risk elements for coronary artery disorder. CAC evaluation, on the other hand, is difficult due to the wide range of CAC in the populations. As a reason, evaluating and analysing data among research have become complicated. In the Research of Inherited Risk Factors for Coronary Atherosclerosis, we used CAC information to test the effects of different analytical methodologies on the correlation with recognized cardiovascular risk elements in asymptomatic patients. Cardiac computed tomography (CT) is also seeing an increase in examinations, and machine learning (ML) could assist with the growing amount of extracted data. Furthermore, there are other sectors in cardiac CT where machine learning could be crucial, including coronary calcium scoring, perfusion, and CT angiography. The establishment of risk evaluation algorithms based on information from CAC utilizing machine learning could assist in the categorization of patients undergoing cardiovascular into distinct risk groups and effectively adapt their treatments to their unique situations. Our findings imply that for forecasting CVD occurrences in asymptomatic people, age-sex segmentation by CAC percentile rank is as effective as absolute CAC scoring. Longitudinal population-based investigations are currently underway and would offer further definitive findings. While machine learning is a strong technology with a lot of possibilities, its implementations in the domain of cardiac CAC are generally in the early stages of development and are not currently commonly accessible in medical practise because of the requirement for substantial verification. Enhanced machine learning will, however, have a significant effect on cardiovascular and coronary artery calcification in the upcoming years
Characterizing the Effect of Adding Boron Nitride Nanotubes on the Mechanical Properties of Electrospun Polymer Nanocomposite Microfibers Mesh
Get PDFElectrospun fibrous meshes have a variety of applications such as filtration, drug delivery, energy storage, and engineered tissues due to their high surface area to mass ratio. Therefore, understanding the mechanical properties of these continuously evolving meshes is critical to expand and improve their performance. In this study, the effect of adding Boron Nitride Nanotube (BNNT) to Polymethylmethacrylate (PMMA) composite meshes on the mechanical properties of the polymer is studied. Electrospinning is used to fabricate microfiber meshes of PMMA and BNNT-PMMA. The fabricated meshes are tested experimentally with a uniaxial tensile tester. In addition, a theoretical model is introduced to investigate the effect of the number of fibers and the diameter of fiber inside the mesh on Young’s Modulus and Tensile Strength of the PMMA mesh. By adding 0.5% BNNT to the PMMA, Young’s Modulus and Tensile Strength of the PMMA mesh improved by 62.4% and 9.3%, respectively. Furthermore, simulated results show enhanced mesh properties when increasing the number of fibers and the single fiber diameter inside the mesh. The findings of this study help in understanding the mechanical properties of the nanocomposite electrospun meshes which expands and improves its utilization in different applications
Towards Multiplexed and Multimodal Biosensor Platforms in Real-Time Monitoring of Metabolic Disorders
Get PDFMetabolic syndrome (MS) is a cluster of conditions that increases the probability of heart disease, stroke, and diabetes, and is very common worldwide. While the exact cause of MS has yet to be understood, there is evidence indicating the relationship between MS and the dysregulation of the immune system. The resultant biomarkers that are expressed in the process are gaining relevance in the early detection of related MS. However, sensing only a single analyte has its limitations because one analyte can be involved with various conditions. Thus, for MS, which generally results from the co-existence of multiple complications, a multi-analyte sensing platform is necessary for precise diagnosis. In this review, we summarize various types of biomarkers related to MS and the non-invasively accessible biofluids that are available for sensing. Then two types of widely used sensing platform, the electrochemical and optical, are discussed in terms of multimodal biosensing, figure-of-merit (FOM), sensitivity, and specificity for early diagnosis of MS. This provides a thorough insight into the current status of the available platforms and how the electrochemical and optical modalities can complement each other for a more reliable sensing platform for MS
The Most Wonderful Time of the Year: Many Retail Stores May Concur
Get PDFIn this issue of the Hibbs Brief, we discuss retail sales during the holiday season in the U.S., and we estimate its economic impact on the Tyler Metropolitan Statistical Area (MSA)
Development of a Smartphone Application as an Asset to Pavement Management Engineers, SMARTP3M
Get PDFAn application specific multi-platform smartphone application can utilize on-board accelerometer, gyroscope, and GPS sensors, along with software derived signals from the same sensors, to sample vibrational and geolocation datasets to capture pavement distresses such as potholes when mounted in a standardized configuration in a vehicle. Several observations were made with regard to the signals obtained from the accelerometer, gyroscope, and GPS sensors, and it was determined that the raw sensor outputs are capable of sampling statistically significant datasets which can be used to distinguish pavement distress from normal driving conditions. Furthermore, an approximate sensor noise margin is established, and a device specific scaling factor is derived. Finally, a MATLAB trained Optimizable Deep Neural Network was trained using training data collected with the custom-built application, and the favorable results are presented in detail. This system, if utilized in scale, will provide relatively inexpensive pavement condition monitoring and pavement distress classifications and locations in real time, while gathering and cataloging invaluable datasets at scale (big data) for future use, all of which occurs automatically, without the need for specialized equipment or relying on the accuracy of user input