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Introduction: COVID-19 infection is associated with marked inflammatory response and the patients who are admitted to the hospital are at increased risk of developing venous thromboembolism. sRAGE (soluble receptor for advanced glycation end-products) are acutely elevated in host inflammatory response to infections [1]. Fractal dimension ( df), the biomarker of clot microstructure that measures thrombogenicity has shown to be elevated in acute inflammatory conditions such as sepsis and severe sepsis. The aim of the study was to analyse these biomarkers in COVID-19 infection and whether these biomarkers help to predict mortality. Method(s): 120 suspected COVID-19 patients were recruited from the Emergency Department of a tertiary teaching hospital. One patient was excluded because they were anticoagulated, blood samples were taken to perform fractal dimension ( df) and sRAGE. Result(s): When compared to PCR -ve group, 95 patients in the PCR + ve group had significantly elevated sRAGE (p < 0.001), but not df (p = 0.43). When compared to those who survived, sRAGE was significantly elevated (p = 0.01) in 14 patients who died in PCR + ve group, but not df (p = 0.08). No significant correlation existed between sRAGE levels and df in those patients who survived (p = 0.72) or died (p = 0.92). Logistic regression analysis showed that sRAGE and df in combination acted as highly significant predictors of mortality in COVID-19 (p = 0.009) in PCR + ve group. Conclusion(s): COVID-19 patients had a profound inflammatory response as evidenced by significantly elevated sRAGE levels. This inflammatory process was more profound in those who died. The thrombogenicity in COVID-19 patients and those who died with COVID-19 appears to be not significant as measured by df. sRAGE in combination with df can be utilised as significant predictors of mortality in COVID-19 patients.
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The Outreach Program involves Community -engaged Learning that seeks to involve both the classroom and the Christian Brothers University (CBU) biochemistry and chemistry clubs in our K-12 education focused activities. These activities include a regional science fair for middle and high school students, the Chemistry Olympiad local and national competitions, and a summer outreach program for K-5 children who were recent immigrants from war-torn areas of Africa. Students in a seniorlevel research seminar class had an opportunity to put their skills to use by assisting in the judging of research projects conducted by grades 6, 7, and 8 students in our middle school fair which was affiliated with the Broadcom Masters national competition. Students in both the biochemistry and chemistry clubs and the research class have assisted with both the middle school and high school fairs by setting up tables for the presenting students and serving as information guides for the middle and high school students and their parents and teachers who came to our CBU campus to participate in the fairs prior to the Covid-19 pandemic. The assistance of our students from the research course and the clubs has enabled us to allow area students from public, private, and home-school to compete in the fairs at no charge to them or their parents or schools. Students from the biochemistry and chemistry clubs also helped by serving as information guides at the Chemistry Olympiad competitions and preparing the lab for the practical experimental section of the National Chemistry Olympiad Exam. These programs are resuming this spring with In-person only fairs and Chemistry Olympiad competitions with active participation by our students. Also prior to the COVID-19 pandemic, students from the biochemistry and chemistry clubs participated in the summer enrichment programs outside CBU for children of recent immigrants from Africa. These programs focused on a variety of STEM related activities related to chemistry, weather, geology, and fractals.Copyright © 2023 The American Society for Biochemistry and Molecular Biology, Inc.
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In this paper, we propose to quantitatively compare the loss of human lung health under the influence of the illness with COVID-19, based on the fractal-analysis interpretation of the chest-pulmonary CT pictures, in the case of small datasets, which are usually encountered in medical applications. The fractal analysis characteristics, such as fractal dimension and lacunarity measured values, have been utilized as an effective advisor to interpretation of pulmonary CT picture texture.
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The volatility and cross-correlations of the energy market and the stock market during the COVID-19 pandemic have been paid close attention by scholars and investors. In this paper, we use the asymmetric multifractal analysis methods to study the fluctuation characteristics, market risks and cross-correlations of the Chinese energy futures market (EFM) and two energy stock markets before and after the COVID-19 outbreak, while the return series of Shanghai fuel oil futures, CSI Energy Index and CSI Mainland New Energy Theme Index are considered. The empirical evidences indicate that the auto- and cross-correlations of the three markets have the asymmetric multifractality, and that the multifractality of the cross-correlations is mainly caused by the fat-tailed distribution of the original series. After the COVID-19 outbreak, the risks of both the traditional energy stock market in the uptrend and the entire new energy stock market become larger, while those of the entire EFM become smaller. In addition, the COVID-19 pandemic has increased the multifractality of the cross-correlations between the energy futures and energy stock markets when the EFM is in downward trend.
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Purpose/Objectives: Nature efficiently self-organizes cells and tissues into complex fractal forms. Whether fractal patterning contributes functionally to maturation, and how cells sense and interpret such shape cues, is not well understood. Methodology: Using kidney podocytes as a model system, bioinspired templating of glomerular histology was leveraged to design controlled fractal 21/2 -D surfaces for cell culture. Results: Microcurvature was associated with charge density gradients in space, found to direct extracellular matrix protein organization resulting in hierarchical assembly of cell structures and fractally-branching podocyte morphology in vitro, that was delineated clearly in vitro with a novel highresolution fluorescent assaying technique. Shape stimulation was uniquely associated with development of mature-like foot processes and organized ECM. In applications of drug testing, coronavirus infection, and a cells-as-sensors approach to patient serum diagnostics, fractally stimulated cells were more responsive than flat cultures. Conclusion/Significance: Fractal frameworks may thus provide a functional role in podocyte maturation and could serve to advance other bioengineered systems.
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Purpose : To measure the innervation of the corneal subbasal nerve plexus of Covid-19 patients and compare the results with values of healthy patients. Methods : A prospective, observational study was conducted analyzing 39 eyes of patients who had overcome Covid -19 and 46 eyes of healthy volunteers included as a control group (verified by antibody analysis and negative qPCR result) which underwent in vivo confocal microscopy with Rodstock Cornea Module© attached to Heildelberg HRT3© . Ocular surgery procedures, previous ocular infections or systemic diseases that could cause alteration in corneal innervation were exclusion criteria. At least 5 non overlapping images of each eye were selected and only one eye of each patient was included in the study. Following sub basal nervous plexus parameters were measured with ACC Metrics © software: Corneal Nerve Fiber Density (CNFD), Corneal Nerve Branch Density (CNBD), Corneal Nerve Fiber Length (CNFL), Corneal Total Branch Density (CTBD), Corneal Nerve Fiber Area (CNFA), Corneal Nerve Fractal Dimension (CNFrD). Data analysis was performed with SPSS® software for Windows 22.0 (SPSS® Inc, Chicago, IL.). The differences of age and sex between groups were checked with T-test and chi-square tests. The normality of the sample was checked with the Shapiro-Wilk test and the results were compared with the T test or the Man-Whitney U test based on the distribution of the data. The differences were considered statistically significant for p<0.05. Results : There was no difference in the sex distribution between the groups (p= 0.248). The average age (± standard error) was 46.61±17.55 years for Covid-19 patients and 43.11±16.95 years for healthy control group (p=0.353) The mean of the analyzed variables (± standard error) from Covid-19 patients versus control group were CNFD: 16.09±6.92 and 23.03±8.31 fibers/mm2 (p=0,00008), CNBD: 21.93±15.37 and 28.93±17.84 branches/mm2 (p=0.064), CNFL: 11.61±3.61 and 14.05±3.71 mm/mm2 (p=0.002), CTBD: 38.48±20.02 and 43.29 ± 23.94 (p=0.41), CNFA: 0.0057±0.0017 and 0.006±0.0023 mm2 /mm2 (p=0.853), CNFrD: 1.46±0.041 and 1.47±0.037 (p=0.007). Conclusions : According to the data obtained, corneal subbasal nerve plexus is decreased in Covid-19 patients compared to the healthy control group, statistically significant for density, length, and fractal dimension. The results show the presence of possible small fiber neuropathy induced by Covid-19 disease.
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The proceedings contain 79 papers. The topics discussed include: vertebral fractures increase the risk of subsequent vertebral fractures: results from a large German health insurance dataset;analysis of bone architecture using fractal-based TX-Analyzer in adult patients with osteogenesis imperfecta;bone health in nursing home residents in Germany - do we care enough?;types of therapeutic errors in the management of osteoporosis: results of an experimental study;cysteine-rich angiogenic inducer 61 as a tool to efficiently enrich myeloid angiogenic cells from peripheral blood;response of bone tissue to ostarine treatment and/or treadmill exercise in a healthy adult rat model;influence of the cell aggregation technique on the differentiation of human articular chondrocytes in microtissues;changes in dispensing of anti-osteoporotic drugs during COVID-19 pandemic;and loss of adipogenic dickkopf-1 increases trabecular and cortical bone mass by promoting bone formation in male mice.
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The CoViD-19 pandemic has demonstrated the need for future developments in anti-viral immunology. We propose that artificial intelligence (AI) and machine learning, and in particular fractal analysis could play a crucial role in that context. Fractals - never-ending repeats of self-similar shapes whose composite tend to resemble the whole - are found in most natural biological structures including immunoglobulin and antigenic epitopes. Increased knowledge of the fractalomic properties of the idiotype/anti-idiotypic paradigm should help develop a novel and improved simplified artificial model of the immune system. Case in point, the regulation and dampening of antibodies as well as the synergetic recognition of an antigen by multiple idiotypes are both immune mechanisms that require further analysis. An enhanced understanding of these complexities could lead to better data analysis for novel vaccines to improve their sensitivity and specificity as well as open other new doors in the field of immunology.
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In this paper, we explore the impact of COVID-19 on auto-correlations and cross-correlations among NASDAQ stock index of the USA, China iron ore price index (CIOPI), and West Texas Intermediate Crude Oil price (WTI). To find out the effect of COVID-19 on financial markets, we divide the investigated data series into two sub-periods, i.e., pre-COVID19 period and post-COVID19 period. First, multifractal detrended fluctuation analysis (MF-DFA) of those series shows a general trend of strong multifractality after COVID-19, indicating lower market efficiency after the pandemic shock. Second, multifractal detrended cross-correlation analysis (MF-DCCA) method is employed to examine cross-correlations among NASDAQ, CIOPI, and WTI. The three cross-correlations all increase in post-COVID19. The correlation between NASDAQ and CIOPI increases the most, becoming the strongest correlation among the three cross-correlations in post-COVID19. The surrogate procedure shows that the post-COVID19 cross-correlation multifractalities are mostly due to fat-tail distribution. Third, we use multi-scale multifractal analysis (MMA) to visualize the dynamic behaviors of correlations among the series. The Hurst surfaces of the three cross-correlations have more fluctuation, both at small and large scale in post-COVID19 than that of pre-COVID19. Particularly, the Hurst surface of cross-correlation between NASDAQ and CIOPI exhibits stronger multifractality during the outbreak of COVID-19 than that in both pre-COVID19 and post-COVID19. The above investigations provide helpful insights of relevant market trends.
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Network biology is an important finding that uncovers the significant elements in viral infection control. Since viruses use the proteins on their surfaces to attach and enter into the host cell, the establishment of virus-host protein interactions is a potent regulator of the global organization of the viral life cycle after virus entry into host cells. In this instance, a topological study on the SARS-CoV-2/Human Protein-Protein Interaction Network (PPIN) evacuates much information about the protein-protein interactions. By making some interruptions to the interaction between proteins and hosts, we can quickly reduce the spread of the disease and get an insight into the target protein for drug development. This paper mainly focused on the graphical and structural complexity of the SARS-CoV-2/Human PPIN. For this purpose, the various primary (distance, radius, diameter, etc ) and advanced levels of graph measures (density, modularity, clustering coefficient, etc ) as well as a few fractal (box dimension, multifractal analysis) and entropy measures have been used. In addition, several graph descriptions and distribution graphs of PPIN offered to gain a thorough understanding of the SARS-CoV-2/Human PPIN. Conclusively, based on our work, we have discovered that PPIN is moderately complex and identified that hiring Nsp8 as a target node will positively affect the PPIN and has pointed out that mathematically found target proteins are matched with already suggested target proteins in the previous survey.Communicated by Ramaswamy H. Sarma.
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We investigate the dynamic correlation between the Bitcoin price (BTC) and the U.S. economic policy uncertainty index (USEPU) from the perspective of multifractality. Utilizing the multifractal detrended cross-correlation analysis (MF-DCCA), we confirm a long-range cross-correlation between BTC and USEPU. Moreover, the empirical results of MF-DCCA show that the power-law properties and multifractal characteristics between BTC and USEPU are significant. We further examine the long-range dependency of cross-correlation between BTC and USEPU series via the Hurst exponent test and confirm the durable cross-correlation. Finally, we introduce another multifractal indicator and examine the extent of multifractality among time series. The empirical results indicate that the BTC series, USEPU series, and the cross-correlation of BTC-USEPU present apparent multifractality, where BTC shows the strongest degree of multifractality.
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Since the industrial revolution, the geopolitics of energy has been a driver of global prosperity and security, and determines the survival of life on our planet. This study examines the nonlinear structure and multifractal behavior of the cross-correlation between geopolitical risk and energy markets (West Texas Intermediate (WTI), Brent, natural gas and heating oil), using the multifractal detrended cross-correlation analysis. Furthermore, an in-depth analysis reveals different associations of the indices of overall geopolitical risk, geopolitical acts, and geopolitical threats against the four energy products. Based on daily data ranging from 1 January 1985 to 30 August 2021, the findings confirm the presence of nonlinear dependencies, suggesting that geopolitical risk and energy markets are interlinked. Furthermore, significant multifractal characteristics are found and the degree of multifractality is stronger between the overall geopolitical risk and WTI while the lowest degree of multifractality is with Brent. Overall, for the WTI and heating-oil markets, the influence of geopolitical threats is more pronounced rather than their fulfilment. Contrarily, the Brent and natural gas are more correlated to geopolitical acts. Energy products exhibit heterogeneous persistence levels of cross-correlation with all the indicators of geopolitical risk, being more persistent in the case of small fluctuations compared to large fluctuations.
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Nature efficiently self-organizes cells and tissues into complex fractal forms. Whether fractal patterning contributes functionally to maturation, and how cells sense and interpret such shape cues, is not well understood. Using kidney podocytes as a model system, bioinspired templating of glomerular histology was leveraged to design controlled fractal 21/2 -D surfaces for cell culture. Microcurvature was associated with charge density gradients in space, found to direct extracellular matrix protein organization resulting in hierarchical assembly of cell structures and fractally-branching podocyte morphology in vitro, outlined with a novel fluorescent assaying technique. Shape simulation was uniquely associated with mature-like foot processes on an organized ECM. In applications of drug testing, coronavirus infection, and a cells-as-sensors approach to patient serum diagnostics, fractally stimulated cells were more responsive than flat cultures. Fractal frameworks may thus provide a functional role in podocyte maturation and could serve to advance other bioengineered systems.
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Introduction: A significant degree of mortality and morbidity in COVID-19 is due to thromboembolic disease. We use viscoelastic testing to investigate changes to coagulation profile over the progression of COVID-19 infection. Methods: Patients presenting to a single large University Teaching Hospital ED were recruited at presentation. Those with positive COVID- 19 PCR test were included for analysis. Whole blood samples were taken for viscoelastic tests. Fractal Dimension ( Df) and Time to Gel Point ( TGP) are biomarkers of thrombogenicity which measure the biomechanical properties of the incipient clot [1]. Patients were followed up throughout their hospital stay, with sampling taken at arrival, after 24 h, 3-5 days and 7 days. Length of stay and patient outcome were recorded and used for subgroup analysis. Once admitted to the hospital all patients received low molecular weight heparin (LMWH) as per standard treatment pathways, if commenced before the first sample was taken, this was recorded and controlled for. Results: Df and TGP showed no changes over time in COVID-19 infection. Subgroup analysis also showed no differences in Df or TGP in different outcome groups. Patients who received LMWH from the clinical team before recruitment to the study demonstrated no significant difference in Df (1.715 ± 0.061 no LMWH vs 1.699 ± 0.068 with LMWH), but TGP was prolonged in those receiving LMWH(445.0 ± 195.2 vs 307.6 ± 91.6). Additionally there was no correlation between Anti-Xa level and Df. Conclusions: The therapeutic efficacy of LMWH appears to be blunted in COVID-19 infection. This may be due to the inflammatory state creating a resistance to the activity of LMWH, and may in part explain why LMWH appears to have less effect in reducing thromboembolic disease in COVID-19 than it does in other disease states.
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This chapter presents a brief introduction to fractal analysis usage areas and their roles in the healthcare system. The definition of fractal, the properties of fractal, what is fractal dimension, most known methods used in fractal analysis and fractal analysis usage areas in healthcare have been examined under the different titles. The comparison of Euclidean geometry and fractal geometry has been explained. Accordingly, the relationship between fractal dimension (D ) and Euclidean topological dimension (DT ) has been expressed. A range of scientific research about fractal analysis in the healthcare system have been reviewed. As a most known fractal analysis methods Box Counting method, Richardson’s method, Dilation (pixel dilation) method and Mass (mass-radius) method have been explained briefly. Moreover, a glance of some usage areas for fractal analysis in healthcare system are COVID-19 disease, oncology, cardiology, brain imaging, neuroscience, dental, osteoporosis, ophthalmology and dermatology have been given. The reviewed studies about fractal analysis in many different areas showed that it can be used to obtain information about the severity and progression of the existing disease or early detection of a potential disease. The main goal of this study can be explained as briefly that giving an opinion to researchers about the usage areas of fractal analysis in healthcare. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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The current COVID-19 pandemic mainly affects the upper respiratory tract. People with COVID-19 report a wide range of symptoms, some of which are similar to those of common flu, such as sore throat and rhinorrhea. Additionally, COVID-19 shares many clinical symptoms with severe pneumonia, including fever, fatigue, dry cough, and respiratory distress. Several diagnostic strategies, such as the real-time polymerase chain reaction technique and computed tomography imaging, which are more costly than chest radiography, are employed as diagnostic tools. The purpose of this paper is to describe the role of the d-summable information dimension of X-ray images in differentiating several lesions and lung illnesses better than both fractal and information dimensions. The statistical analysis shows that the d-summable information dimension model better describes the information obtained from the X-ray images. Therefore, it is a more precise measure of complexity than the information and box-counting dimension. The results also show that the X-ray images of COVID-19 pneumonia reveal greater damage than those of tuberculosis, pneumonia, and various lung lesions, where the damage is minor or much focused. Because the d-summable information dimension increases as the image complexity decreases, it could pave the way to formulate a new measure to quantify the lung damage and assist the clinical diagnosis based on the area under the d-summable information model. In addition, the physical meaning of the nu parameter in the d-summable information dimension is given.
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With the continuous promotion of industrialization and urbanization, China's environmental pollution is becoming increasingly serious, which has caused considerable damage to the natural balance. Air pollution seriously harms people's physical and mental health, the ecological environment, and the social sustainable development of society. In this study, the backward trajectory model and multifractal methods were adopted to analyze air pollution in Zhengzhou. The backward trajectory analysis showed that most clusters of air pollution were from southern Hebei, eastern Shandong, and mid-western Henan, which were then transported to Zhengzhou. For the PSCF and CWT analyses, we selected four representative cities to explore how close the air pollution of Zhengzhou is to other areas on the basis of air polluted concentration. The results of several multifractal methods indicated that multifractality existed in the AQI time series of Zhengzhou and cross-correlations between Zhengzhou and each of the four cities. The widths of multifractal spectra showed that the air pollution in Zhengzhou was closest to that in Jinan, followed by Shijiazhuang, Zibo, and Luoyang. The CDFA analysis showed that carbon monoxide (CO), nitrogen dioxide (NO2), and inhalable particulate matter (PM10) had important influences on air pollution in Zhengzhou. These findings offer a useful reference for air pollution sources and their potential contributions in Zhengzhou, which can support policy makers in environmental governance and in achieving sustainable urban development.