64 research outputs found
Understanding and Improving Life
No full textCreation of knowledge through understanding is a perpetual process for humanity. Our understanding grows
through application of logic and scientific methodology which form the basis of research. The endowment of
intellectual ability has empowered humans to adopt this course to keep on generating better and deeper
understanding of our existential situation. Advancements in technology keep on providing better and better
means to facilitate our understanding even more
COVID 19 and The New Normal
No full textThe world is waking up to a new reality where an invisible enemy has made human contact contagious and exposed the long-hidden fault line of verisimilitude, blurring the lines between reality and paranoia. The global reaction is unprecedented with world economy virtually at a standstill. The economic fallout of the outbreak could trigger a recession of unparalleled scale. But is economics the only challenge? The pandemic is attacking societies at their core. The author of Sapiens, Yuval Noah Harari puts it well when he writes “The biggest danger is not the virus itself. Humanity has all the scientific knowledge and technological tools to overcome the virus. The really big problem is our own inner demons, our own hatred, greed and ignorance
Health in Context: COVID-19 Pandemic
No full textAs the corona virus infection rates soar around the world, it remains to be seen whether the resurgent second wave will have the same fatality rate. The 1918-20 Spanish flu came in three waves, during which it killed at least 30 million people across the globe, with some historians quoting the figure at 100 million, making it more deadly than the total number of military and civilian deaths that resulted from World War I.1,2 The increase in lethality was assumed to be due to natural selection or random antigenic drift, accumulated by the virus in its initial first wave, that allowed the virus to evade existing immunity from previous infections.3 Korber et al. in their study has shown an amino acid change in the severe acute respiratory syndrome coronavirus 2 (SARS-CoV- 2) spike protein, D614G. This variant of SARS-CoV-2, containing G614, is now dominant in many places around the world.4,5 Based on the evidence collected from thousand COVID-19 cases in the United Kingdom, the authors have generated a hypothesis that reason for rapid spread of G614 is that it is more infectious than D614.4,5 Patients infected with viruses containing G614 had higher levels of virus RNA.5 In vitro experiments yielded high titers for G614 in pseudo viruses.6,7,8 However, implications of this preliminary data on the transmission patterns, disease presentation, vaccine and therapeutic development remain to be seen. Another aspect of this pandemic is the global focus upon breaking the chain of transmission since the cause of this crisis is viewed primarily as an infectious disease. But the story of COVID-19 is not so simple. Two categories of disease are interacting, infection with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and Non-Communicable Diseases (NCDs).9 The clustering of these diseases within existing health inequalities among socially disadvantaged and low-income groups has amplified the adverse effects of each separate disease. Thus, COVID -19 is not just an epidemic10 but syndemic, a term coined by Merrill Singer, an American medical anthropologist. It is a synergistic epidemic characterized by aggregation of two or more concurrent disease clusters that adversely interact and affect each disease trajectory, resulting in an exacerbation of the prognosis and burden of disease.11 It appears that SARS CoV-2 patients in older age group, with chronic comorbidities like diabetes mellitus and hypertension and belonging to less advantage social strata racial and ethnic minorities, tend to suffer with more severe multisystem inflammatory syndrome. Therefore, successful containment of SARS-CoV-2 requires an urgent attention to NCDs and socioeconomic inequities. On a positive note, this pandemic has initiated a great human pause. The introspection, experienced during the lockdown, has made us review the very basics of the way we perceive and practice healthcare. It has made us wiser to execute our social contract by practicing a more socially conscious medicine. Today, in a post-COVID world,humanitystandsatcrossroads. InwordsofRobertFrost:“Tworoadsdivergedinawood,andItookthe ones less travelled by, and that has made all the difference.”12
Editor-in-Chie
Covid-19: Navigating Scientific Uncertainty
No full textAlvin Toffler once wrote: "The illiterate of the 21st century will not be those who cannot read and write, but those who cannot learn, unlearn, and relearn." This pandemic has proven his statement correct. The global academic community has learned a completely new culture of research, with torrents of data being released daily on preprint servers1,2 and dissected on platforms such as Slack and Twitter before formally peer reviewed. Fifty-five thousand viral genomes sequences of hCoV-19 shared on GISAID platforms to date3 that have been analyzed instantaneously, by a phalanx of evolutionary biologists who share their phylogenetic trees in preprints. Such advances have allowed scientists to trace and monitor the COVID-19 pandemic faster than any previous outbreak.
There is still more to learn. The scientist from the fields of epidemiology, virology and biomedical science are struggling to keep this outbreak under control. Estimation of R0, which have been an important part of characterizing pandemics, including the 2003 SARS pandemic, the 2009 H1N1 influenza pandemic and the 2014 Ebola epidemic in West Africa, is something epidemiologists raced to nail down about SARS-CoV-2. There's uncertainty,foranumberofreasons,aboutmanyofthefactorsthatgointoestimatingR0. First,theincubation period of this viral pathogen is uncertain with an average 5-6 days and can be up to 14 days.4 Researchers cannot predict, without sentinel surveillance, the number of mild or asymptomatic cases that have been missed but nevertheless are spreading the disease.5 Secondly, majority of people who get infected, do recover and are likely to be immune. This alters population susceptibility and affects future trajectory of infection spread. Finally, susceptibility to disease in different communities varies based on their demographics, health conditions and different social structures. And hence, mathematical model accuracy, be it Institute for Health Metrics and Evaluation (IHME)6, Ferguson et.al7, Aleta et.al8, Hellewell et.al9 and Kessler et al models10, is constrained by our knowledge of the virus dynamics since many biologic features of transmission are hard to measure and remain unknown.
Another aspect of the Covid-19, which is reshaping the world of bioscience publishing, is the tension between rapid speed of research publication verses scientific rigor. This has raised serious issues regarding data integrity. The Lancet and NEMJ had had to retract some publication on this account for example, Hydroxychloroquine or chloroquine with or without a macrolide for treatment of COVID-19: a multinational registry analysis11 and Cardiovascular Disease, Drug Therapy, and Mortality in Covid-1912, because independent auditors were unable to validate the primary data sources. This is of concern in the middle of a global health emergency.13
Finally, this crisis has also altered our perspective. An important feature of our ongoing experience is what anthropologist Jane Guyer termed “enforced presentism”, a feeling of being stuck in the present, combined with an inability to plan ahead.14 The question is how do we reclaim our future? The past has provided us a prologue for discussion, whether it is the biological origins of a potential pandemic or its social repercussions, it is up to us to reorder the society in dramatic ways, for better or worse.
Editor-in-Chief
doi: http://doi.org/10.37185/LnS.1.1.12
SPECTRAL ANALYSIS OF HEART RATE VARIABILITY IN PATIENTS WITH CORONARY ARTERY DISEASE
No full textObjective: To carryout frequency domain analysis ofheart rate variability in patients with coronary artery
disease.
Methods: Forty coronary artery disease patients with coronary artery stenosis greater than 70% of at least one
vessel lumen were included. Patients with diabetes mellitus, atrial fibrillation, structural heart diseases and
bundle branch block were excluded. DMS 300 4A Holter monitors were used to obtain long-term 12 lead
digital ECG recordings. Cardio Scan premium luxury software was used for analysis of heart rate variability.
Results: The mean values of heart rate variability in patients were TP (2171.70 ± 1028.7), VLF (1661.41 ±
807.88), LF (392.71 ± 227.92), HF (112.03 ± 77.90) and LF/HF ratio (4.03 ± 1.75). On comparison with normal
reference values there was a significant decrease (p-value < 0.05) in all parameters except VLF (p-value =
0.351). TP was reduced in all the patients (100%), VLF in 26 (65%), LF in 36 (90%), HF in 36 (90%) and LF/HF
ratio in 29 (72.5%) patients. The difference between the frequency of patients with decreased heart rate
variability was statistically significant (p-value < 0.05) except VLF (p-value = 0.082).
Conclusion: Heart rate variability decreases significantly in patients with coronary artery disease
Nonlinear Characterization of Heart Rate Variability in Normal Sinus Rhythm, Atrial Fibrillation and Congestive Heart Failure
No full textThis paper highlights the application of methods and techniques from nonlinear analysis to illustrate their far superior capability in revealing complex cardiac dynamics under various physiological and pathological states. The purpose is to augment conventional (time and frequency based) heart rate variability analysis, and to extract significant prognostic and clinically relevant information for risk stratification and improved diagnosis. In this work, several nonlinear indices are estimated for RR intervals based time series data acquired for Healthy Sinus Rhythm (HSR) and Congestive Heart Failure (CHF), as the two groups represent different cases of Normal Sinus Rhythm (NSR). In addition to this, nonlinear algorithms are also applied to investigate the internal dynamics of Atrial Fibrillation (AFib). Application of nonlinear tools in normal and diseased cardiovascular states manifest their strong ability to support clinical decision support systems and highlights the internal complex properties of physiological time series data such as complexity, irregularity, determinism and recurrence trends in cardiovascular regulation mechanisms.</jats:p
Residual Effect of Rock Phosphate, Farmyard Manure and Effective Microorganisms on Nutrient Uptake and Yield of Wheat
No full textNon-Similar Analysis of Boundary Layer Flow and Heat Transfer in Non-Newtonian Hybrid Nanofluid over a Cylinder with Viscous Dissipation Effects
No full textHighlighting the importance of artificial intelligence and machine learning approaches in engineering and fluid mechanics problems, especially in heat transfer applications is main goal of the presented article. With the advancement in Artificial Intelligence (AI) and Machine Learning (ML) techniques, the computational efficiency and accuracy of numerical results are enhanced. The theme of the study is to use machine learning techniques to examine the thermal analysis of MHD boundary layer flow of Eyring-Powell Hybrid Nanofluid (EPHNFs) passing a horizontal cylinder embedded in a porous medium with heat source/sink and viscous dissipation effects. The considered base fluid is water (H2O) and hybrid nanoparticles titanium oxide (TiO2) and Copper oxide (CuO). The governing flow equations are nonlinear PDEs. Non-similar system of PDEs are obtained with efficient conversion variables. The dimensionless PDEs are truncated using a local non-similarity approach up to third level and numerical solution is evaluated using MATLAB built-in-function bvp4c. Artificial Neural Networks (ANNs) simulation approach is used to trained the networks to predict the solution behavior. Thermal boundary layer improves with the enhancement in the value of Rd. The accuracy and reliability of ANNs predicted solution is addressed with computation of correlation index and residual analysis. The RMSE is evaluated [0.04892, 0.0007597, 0.0007596, 0.01546, 0.008871, 0.01686] for various scenarios. It is observed that when concentration of hybrid nanoparticles increases then thermal characteristics of the Eyring-Powell Hybrid Nanofluid (EPHNFs) passing a horizontal cylinder
Deep Learning Approach for Automatic Cardiovascular Disease Prediction Employing ECG Signals
No full textCardiovascular problems have become the predominant cause of death worldwide and a rise in the number of patients has been observed lately. Currently, electrocardiogram (ECG) data is analyzed by medical experts to determine the cardiac abnormality, which is time-consuming. In addition, the diagnosis requires experienced medical experts and is error-prone. However, automated identification of cardiovascular disease using ECGs is a challenging problem and state-of-the-art performance has been attained by complex deep learning architectures. This study proposes a simple multilayer perceptron (MLP) model for heart disease prediction to reduce computational complexity. ECG dataset containing averaged signals with window size 10 is used as an input. Several competing deep learning and machine learning models are used for comparison. K-fold cross-validation is used to validate the results. Experimental outcomes reveal that the MLP-based architecture can produce better outcomes than existing approaches with a 94.40% accuracy score. The findings of this study show that the proposed system achieves high performance indicating that it has the potential for deployment in a real-world, practical medical environment.This research work receives no external funding. Funding is done by author
Abdullah Mohamed.https://www.techscience.com/CMES/v137n2/5335
Correction: Zaman et al. Synthesis and Evaluation of Thiol-Conjugated Poloxamer and Its Pharmaceutical Applications. <em>Pharmaceutics</em> 2021, <em>13</em>, 693
No full textIn the original publication, there was a mistake in one author name, Mohammed Alasmari should be Mohammed S [...
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