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  1. α-Mangostin and its derivatives against estrogen receptor alpha
    Mardianingrum R, Yusuf M, Hariono M, Mohd Gazzali A, Muchtaridi M
    J Biomol Struct Dyn, 2020 Nov 06.
    PMID: 33155528 DOI: 10.1080/07391102.2020.1841031
    Estrogen receptor alpha (ERα) acts as the transcription factor and the main therapeutic target against breast cancer. One of the compounds that has been shown to act as an ERα is α-mangostin. However, it still has weaknesses due to its low solubility and low potent activity. In this study, α-mangostin was modified by substituting -OH group at C6 using benzoyl derivatives through a step by step in silico study, namely pharmacokinetic prediction (https://preadmet.bmdrc.kr/adme/), pharmacophore modeling (LigandScout 4.1), molecular docking simulation (AutoDock 4.2), molecular dynamics simulation (AMBER 16) and a binding free energy analysis using MM-PBSA method. From the computational studies, three compounds which are derived from α-mangostin (AMB-1 (-9.84 kcal/mol), AMB-2 (-6.80 kcal/mol) and AMB-10 (-12.42 kcal/mol)) have lower binding free energy than α-mangostin (-1.77 kcal/mol), as evidenced by the binding free energy calculation using the MM-PBSA method. They can then be predicted to have potent activities as ERα antagonists.Communicated by Ramaswamy H. Sarma.
    Matched MeSH terms: Entropy
  2. Fulltext Seagrass habitat suitability model for Redang Marine Park using multibeam echosounder data: Testing different spatial resolutions and analysis window sizes
    Muhamad MAH, Che Hasan R, Md Said N, Ooi JL
    PLoS One, 2021;16(9):e0257761.
    PMID: 34555110 DOI: 10.1371/journal.pone.0257761
    Integrating Multibeam Echosounder (MBES) data (bathymetry and backscatter) and underwater video technology allows scientists to study marine habitats. However, use of such data in modeling suitable seagrass habitats in Malaysian coastal waters is still limited. This study tested multiple spatial resolutions (1 and 50 m) and analysis window sizes (3 × 3, 9 × 9, and 21 × 21 cells) probably suitable for seagrass-habitat relationships in Redang Marine Park, Terengganu, Malaysia. A maximum entropy algorithm was applied, using 12 bathymetric and backscatter predictors to develop a total of 6 seagrass habitat suitability models. The results indicated that both fine and coarse spatial resolution datasets could produce models with high accuracy (>90%). However, the models derived from the coarser resolution dataset displayed inconsistent habitat suitability maps for different analysis window sizes. In contrast, habitat models derived from the fine resolution dataset exhibited similar habitat distribution patterns for three different analysis window sizes. Bathymetry was found to be the most influential predictor in all the models. The backscatter predictors, such as angular range analysis inversion parameters (characterization and grain size), gray-level co-occurrence texture predictors, and backscatter intensity levels, were more important for coarse resolution models. Areas of highest habitat suitability for seagrass were predicted to be in shallower (<20 m) waters and scattered between fringing reefs (east to south). Some fragmented, highly suitable habitats were also identified in the shallower (<20 m) areas in the northwest of the prediction models and scattered between fringing reefs. This study highlighted the importance of investigating the suitable spatial resolution and analysis window size of predictors from MBES for modeling suitable seagrass habitats. The findings provide important insight on the use of remote acoustic sonar data to study and map seagrass distribution in Malaysia coastal water.
    Matched MeSH terms: Entropy
  3. Robustness evaluations of pathway activity inference methods on gene expression data
    Hui TX, Kasim S, Aziz IA, Fudzee MFM, Haron NS, Sutikno T, et al.
    BMC Bioinformatics, 2024 Jan 12;25(1):23.
    PMID: 38216898 DOI: 10.1186/s12859-024-05632-w
    BACKGROUND: With the exponential growth of high-throughput technologies, multiple pathway analysis methods have been proposed to estimate pathway activities from gene expression profiles. These pathway activity inference methods can be divided into two main categories: non-Topology-Based (non-TB) and Pathway Topology-Based (PTB) methods. Although some review and survey articles discussed the topic from different aspects, there is a lack of systematic assessment and comparisons on the robustness of these approaches.

    RESULTS: Thus, this study presents comprehensive robustness evaluations of seven widely used pathway activity inference methods using six cancer datasets based on two assessments. The first assessment seeks to investigate the robustness of pathway activity in pathway activity inference methods, while the second assessment aims to assess the robustness of risk-active pathways and genes predicted by these methods. The mean reproducibility power and total number of identified informative pathways and genes were evaluated. Based on the first assessment, the mean reproducibility power of pathway activity inference methods generally decreased as the number of pathway selections increased. Entropy-based Directed Random Walk (e-DRW) distinctly outperformed other methods in exhibiting the greatest reproducibility power across all cancer datasets. On the other hand, the second assessment shows that no methods provide satisfactory results across datasets.

    CONCLUSION: However, PTB methods generally appear to perform better in producing greater reproducibility power and identifying potential cancer markers compared to non-TB methods.

    Matched MeSH terms: Entropy
  4. Fulltext Quantifying 'causality' in complex systems: understanding transfer entropy
    Abdul Razak F, Jensen HJ
    PLoS One, 2014;9(6):e99462.
    PMID: 24955766 DOI: 10.1371/journal.pone.0099462
    'Causal' direction is of great importance when dealing with complex systems. Often big volumes of data in the form of time series are available and it is important to develop methods that can inform about possible causal connections between the different observables. Here we investigate the ability of the Transfer Entropy measure to identify causal relations embedded in emergent coherent correlations. We do this by firstly applying Transfer Entropy to an amended Ising model. In addition we use a simple Random Transition model to test the reliability of Transfer Entropy as a measure of 'causal' direction in the presence of stochastic fluctuations. In particular we systematically study the effect of the finite size of data sets.
    Matched MeSH terms: Entropy*
  5. Purification, characterization and thermal inactivation kinetics of a non-regioselective thermostable lipase from a genotypically identified extremophilic Bacillus subtilis NS 8
    Olusesan AT, Azura LK, Forghani B, Bakar FA, Mohamed AK, Radu S, et al.
    N Biotechnol, 2011 Oct;28(6):738-45.
    PMID: 21238617 DOI: 10.1016/j.nbt.2011.01.002
    Thermostable lipase produced by a genotypically identified extremophilic Bacillus subtilis NS 8 was purified 500-fold to homogeneity with a recovery of 16% by ultrafiltration, DEAE-Toyopearl 650M and Sephadex G-75 column. The purified enzyme showed a prominent single band with a molecular weight of 45 kDa. The optimum pH and temperature for activity of lipase were 7.0 and 60°C, respectively. The enzyme was stable in the pH range between 7.0 and 9.0 and temperature range between 40 and 70°C. It showed high stability with half-lives of 273.38 min at 60°C, 51.04 min at 70°C and 41.58 min at 80°C. The D-values at 60, 70 and 80°C were 788.70, 169.59 and 138.15 min, respectively. The enzyme's enthalpy, entropy and Gibb's free energy were in the range of 70.07-70.40 kJ mol(-1), -83.58 to -77.32 kJ mol(-1)K(-1) and 95.60-98.96 kJ mol(-1), respectively. Lipase activity was slightly enhanced when treated with Mg(2+) but there was no significant enhancement or inhibition of the activity with Ca(2+). However, other metal ions markedly inhibited its activity. Of all the natural vegetable oils tested, it had slightly higher hydrolytic activity on soybean oil compared to other oils. On TLC plate, the enzyme showed non-regioselective activity for triolein hydrolysis.
    Matched MeSH terms: Entropy
  6. Production of bio-hydrogen from dairy wastewater using pretreated landfill leachate sludge as an inoculum
    Wong YM, Show PL, Wu TY, Leong HY, Ibrahim S, Juan JC
    J Biosci Bioeng, 2019 Feb;127(2):150-159.
    PMID: 30224189 DOI: 10.1016/j.jbiosc.2018.07.012
    Bio-hydrogen production from wastewater using sludge as inoculum is a sustainable approach for energy production. This study investigated the influence of initial pH and temperature on bio-hydrogen production from dairy wastewater using pretreated landfill leachate sludge (LLS) as an inoculum. The maximum yield of 113.2 ± 2.9 mmol H2/g chemical oxygen demand (COD) (12.8 ± 0.3 mmol H2/g carbohydrates) was obtained at initial pH 6 and 37 °C. The main products of volatile fatty acids were acetate and butyrate with the ratio of acetate:butyrate was 0.4. At optimum condition, Gibb's free energy was estimated at -40 kJ/mol, whereas the activation enthalpy and entropy were 65 kJ/mol and 0.128 kJ/mol/l, respectively. These thermodynamic quantities suggest that bio-hydrogen production from dairy wastewater using pretreated LLS as inoculum was effective and efficient. In addition, genomic and bioinformatics analyses were performed in this study.
    Matched MeSH terms: Entropy
  7. Fulltext Predictive modeling and mapping of Malayan Sun Bear (Helarctos malayanus) distribution using maximum entropy
    Nazeri M, Jusoff K, Madani N, Mahmud AR, Bahman AR, Kumar L
    PLoS One, 2012;7(10):e48104.
    PMID: 23110182 DOI: 10.1371/journal.pone.0048104
    One of the available tools for mapping the geographical distribution and potential suitable habitats is species distribution models. These techniques are very helpful for finding poorly known distributions of species in poorly sampled areas, such as the tropics. Maximum Entropy (MaxEnt) is a recently developed modeling method that can be successfully calibrated using a relatively small number of records. In this research, the MaxEnt model was applied to describe the distribution and identify the key factors shaping the potential distribution of the vulnerable Malayan Sun Bear (Helarctos malayanus) in one of the main remaining habitats in Peninsular Malaysia. MaxEnt results showed that even though Malaysian sun bear habitat is tied with tropical evergreen forests, it lives in a marginal threshold of bio-climatic variables. On the other hand, current protected area networks within Peninsular Malaysia do not cover most of the sun bears potential suitable habitats. Assuming that the predicted suitability map covers sun bears actual distribution, future climate change, forest degradation and illegal hunting could potentially severely affect the sun bear's population.
    Matched MeSH terms: Entropy
  8. Fulltext Numerical study of entropy generation due to coupled laminar and turbulent mixed convection and thermal radiation in an enclosure filled with a semitransparent medium
    Goodarzi M, Safaei MR, Oztop HF, Karimipour A, Sadeghinezhad E, Dahari M, et al.
    ScientificWorldJournal, 2014;2014:761745.
    PMID: 24778601 DOI: 10.1155/2014/761745
    The effect of radiation on laminar and turbulent mixed convection heat transfer of a semitransparent medium in a square enclosure was studied numerically using the Finite Volume Method. A structured mesh and the SIMPLE algorithm were utilized to model the governing equations. Turbulence and radiation were modeled with the RNG k-ε model and Discrete Ordinates (DO) model, respectively. For Richardson numbers ranging from 0.1 to 10, simulations were performed for Rayleigh numbers in laminar flow (10⁴) and turbulent flow (10⁸). The model predictions were validated against previous numerical studies and good agreement was observed. The simulated results indicate that for laminar and turbulent motion states, computing the radiation heat transfer significantly enhanced the Nusselt number (Nu) as well as the heat transfer coefficient. Higher Richardson numbers did not noticeably affect the average Nusselt number and corresponding heat transfer rate. Besides, as expected, the heat transfer rate for the turbulent flow regime surpassed that in the laminar regime. The simulations additionally demonstrated that for a constant Richardson number, computing the radiation heat transfer majorly affected the heat transfer structure in the enclosure; however, its impact on the fluid flow structure was negligible.
    Matched MeSH terms: Entropy*
  9. Fulltext Multi-Objective Optimization of Friction Stir Welding Process Parameters of AA6061-T6 and AA7075-T6 Using a Biogeography Based Optimization Algorithm
    Tamjidy M, Baharudin BTHT, Paslar S, Matori KA, Sulaiman S, Fadaeifard F
    Materials (Basel), 2017 May 15;10(5).
    PMID: 28772893 DOI: 10.3390/ma10050533
    The development of Friction Stir Welding (FSW) has provided an alternative approach for producing high-quality welds, in a fast and reliable manner. This study focuses on the mechanical properties of the dissimilar friction stir welding of AA6061-T6 and AA7075-T6 aluminum alloys. The FSW process parameters such as tool rotational speed, tool traverse speed, tilt angle, and tool offset influence the mechanical properties of the friction stir welded joints significantly. A mathematical regression model is developed to determine the empirical relationship between the FSW process parameters and mechanical properties, and the results are validated. In order to obtain the optimal values of process parameters that simultaneously optimize the ultimate tensile strength, elongation, and minimum hardness in the heat affected zone (HAZ), a metaheuristic, multi objective algorithm based on biogeography based optimization is proposed. The Pareto optimal frontiers for triple and dual objective functions are obtained and the best optimal solution is selected through using two different decision making techniques, technique for order of preference by similarity to ideal solution (TOPSIS) and Shannon's entropy.
    Matched MeSH terms: Entropy
  10. Modelling gully-erosion susceptibility in a semi-arid region, Iran: Investigation of applicability of certainty factor and maximum entropy models
    Azareh A, Rahmati O, Rafiei-Sardooi E, Sankey JB, Lee S, Shahabi H, et al.
    Sci Total Environ, 2019 Mar 10;655:684-696.
    PMID: 30476849 DOI: 10.1016/j.scitotenv.2018.11.235
    Gully erosion susceptibility mapping is a fundamental tool for land-use planning aimed at mitigating land degradation. However, the capabilities of some state-of-the-art data-mining models for developing accurate maps of gully erosion susceptibility have not yet been fully investigated. This study assessed and compared the performance of two different types of data-mining models for accurately mapping gully erosion susceptibility at a regional scale in Chavar, Ilam, Iran. The two methods evaluated were: Certainty Factor (CF), a bivariate statistical model; and Maximum Entropy (ME), an advanced machine learning model. Several geographic and environmental factors that can contribute to gully erosion were considered as predictor variables of gully erosion susceptibility. Based on an existing differential GPS survey inventory of gully erosion, a total of 63 eroded gullies were spatially randomly split in a 70:30 ratio for use in model calibration and validation, respectively. Accuracy assessments completed with the receiver operating characteristic curve method showed that the ME-based regional gully susceptibility map has an area under the curve (AUC) value of 88.6% whereas the CF-based map has an AUC of 81.8%. According to jackknife tests that were used to investigate the relative importance of predictor variables, aspect, distance to river, lithology and land use are the most influential factors for the spatial distribution of gully erosion susceptibility in this region of Iran. The gully erosion susceptibility maps produced in this study could be useful tools for land managers and engineers tasked with road development, urbanization and other future development.
    Matched MeSH terms: Entropy
  11. MRI Brain Classification Using the Quantum Entropy LBP and Deep-Learning-Based Features
    Hasan AM, Jalab HA, Ibrahim RW, Meziane F, Al-Shamasneh AR, Obaiys SJ
    Entropy (Basel), 2020 Sep 15;22(9).
    PMID: 33286802 DOI: 10.3390/e22091033
    Brain tumor detection at early stages can increase the chances of the patient's recovery after treatment. In the last decade, we have noticed a substantial development in the medical imaging technologies, and they are now becoming an integral part in the diagnosis and treatment processes. In this study, we generalize the concept of entropy difference defined in terms of Marsaglia formula (usually used to describe two different figures, statues, etc.) by using the quantum calculus. Then we employ the result to extend the local binary patterns (LBP) to get the quantum entropy LBP (QELBP). The proposed study consists of two approaches of features extractions of MRI brain scans, namely, the QELBP and the deep learning DL features. The classification of MRI brain scan is improved by exploiting the excellent performance of the QELBP-DL feature extraction of the brain in MRI brain scans. The combining all of the extracted features increase the classification accuracy of long short-term memory network when using it as the brain tumor classifier. The maximum accuracy achieved for classifying a dataset comprising 154 MRI brain scan is 98.80%. The experimental results demonstrate that combining the extracted features improves the performance of MRI brain tumor classification.
    Matched MeSH terms: Entropy
  12. Iterative variational mode decomposition based automated detection of glaucoma using fundus images
    Maheshwari S, Pachori RB, Kanhangad V, Bhandary SV, Acharya UR
    Comput Biol Med, 2017 Sep 01;88:142-149.
    PMID: 28728059 DOI: 10.1016/j.compbiomed.2017.06.017
    Glaucoma is one of the leading causes of permanent vision loss. It is an ocular disorder caused by increased fluid pressure within the eye. The clinical methods available for the diagnosis of glaucoma require skilled supervision. They are manual, time consuming, and out of reach of common people. Hence, there is a need for an automated glaucoma diagnosis system for mass screening. In this paper, we present a novel method for an automated diagnosis of glaucoma using digital fundus images. Variational mode decomposition (VMD) method is used in an iterative manner for image decomposition. Various features namely, Kapoor entropy, Renyi entropy, Yager entropy, and fractal dimensions are extracted from VMD components. ReliefF algorithm is used to select the discriminatory features and these features are then fed to the least squares support vector machine (LS-SVM) for classification. Our proposed method achieved classification accuracies of 95.19% and 94.79% using three-fold and ten-fold cross-validation strategies, respectively. This system can aid the ophthalmologists in confirming their manual reading of classes (glaucoma or normal) using fundus images.
    Matched MeSH terms: Entropy
  13. Investment risk and natural resource potential in "Belt & Road Initiative" countries: A multi-criteria decision-making approach
    Hussain J, Zhou K, Guo S, Khan A
    Sci Total Environ, 2020 Mar 16;723:137981.
    PMID: 32208210 DOI: 10.1016/j.scitotenv.2020.137981
    Chinese enterprises that conduct overseas investment projects encounter diverse challenges that emerge from political, economic, social, and environmental risks in the host countries. To better assess the overseas investment risks faced by Chinese enterprises, this study introduced and assessed novel aspects and an indicator system. Moreover, the "Technique for Order Preference by Similarity to Ideal Solution" (TOPSIS) method based on entropy weight was performed to generate a comprehensive assessment of China's foreign investment risk and natural resource potential in 63 "Belt & Road Initiative" (BRI) countries. This study aims to encourage Chinese enterprises to devise suitable overseas investment decision-making strategies concerning natural resource potential in host countries. A Geographic Information System (GIS) map was also created to assess the potential risks and opportunities for Chinese enterprises when making investment decisions in host countries. The findings indicate that the majority of countries in Central and Eastern Europe and other BRI countries such as Singapore, Malaysia, Nepal, Bhutan, Russia, Armenia, and the United Arab Emirates were the most suitable choices for Chinese enterprises engaging in overseas investment. Based on these results, Chinese enterprises could manage and execute BRI projects more effectively to minimise potential risks and maximise their investment benefits.
    Matched MeSH terms: Entropy
  14. Information-based decoding of the coupling among human brain activity and movement paths
    Kamal SM, Dawi NM, Namazi H
    Technol Health Care, 2021;29(6):1109-1118.
    PMID: 33749623 DOI: 10.3233/THC-202744
    BACKGROUND: Walking like many other actions of a human is controlled by the brain through the nervous system. In fact, if a problem occurs in our brain, we cannot walk correctly. Therefore, the analysis of the coupling of brain activity and walking is very important especially in rehabilitation science. The complexity of movement paths is one of the factors that affect human walking. For instance, if we walk on a path that is more complex, our brain activity increases to adjust our movements.

    OBJECTIVE: This study for the first time analyzed the coupling of walking paths and brain reaction from the information point of view.

    METHODS: We analyzed the Shannon entropy for electroencephalography (EEG) signals versus the walking paths in order to relate their information contents.

    RESULTS: According to the results, walking on a path that contains more information causes more information in EEG signals. A strong correlation (p= 0.9999) was observed between the information contents of EEG signals and walking paths. Our method of analysis can also be used to investigate the relation among other physiological signals of a human and walking paths, which has great benefits in rehabilitation science.

    Matched MeSH terms: Entropy
  15. Information-based analysis of the relationship between brain and facial muscle activities in response to static visual stimuli
    Soundirarajan M, Pakniyat N, Sim S, Nathan V, Namazi H
    Technol Health Care, 2021;29(1):99-109.
    PMID: 32568131 DOI: 10.3233/THC-192085
    BACKGROUND: Human facial muscles react differently to different visual stimuli. It is known that the human brain controls and regulates the activity of the muscles.

    OBJECTIVE: In this research, for the first time, we investigate how facial muscle reaction is related to the reaction of the human brain.

    METHODS: Since both electromyography (EMG) and electroencephalography (EEG) signals, as the features of muscle and brain activities, contain information, we benefited from the information theory and computed the Shannon entropy of EMG and EEG signals when subjects were exposed to different static visual stimuli with different Shannon entropies (information content).

    RESULTS: Based on the obtained results, the variations of the information content of the EMG signal are related to the variations of the information content of the EEG signal and the visual stimuli. Statistical analysis also supported the results indicating that the visual stimuli with greater information content have a greater effect on the variation of the information content of both EEG and EMG signals.

    CONCLUSION: This investigation can be further continued to analyze the relationship between facial muscle and brain reactions in case of other types of stimuli.

    Matched MeSH terms: Entropy
  16. Indeks Kemajuan antara Daerah di Semenanjung Malaysia (Development Index among Districts in Peninsular Malaysia)
    Abdul Aziz Jemain
    Sains Malaysiana, 2005;34.
    This paper offers a technique to create a development index among districts. To determine the weight for each criterion in the index entropy theory was used. Two approaches for criteria normalization were also suggested. The data obtained from 1991 census conducted in Peninsular Malaysia were utilized as illustration.
    Kertas ini mencadangkan teknik pembinaan indeks kemajuan daerah. Teori entropy digunakan untuk menentukan pemberat bagi kriteria yang digunakan dalam pembinaan indeks. Dua pendekatan menormalkan data turut dicadangkan. Contoh pembinaan indeks dikemukakan berdasarkan kemudahan asas yang terdapat di daerah-daerah di Semenanjung Malaysia seperti yang diperoleh berdasarkan banci tahun 1991.
    Matched MeSH terms: Entropy
  17. Identification of Distributed Denial of Services Anomalies by Using Combination of Entropy and Sequential Probabilities Ratio Test Methods
    Ali BH, Sulaiman N, Al-Haddad SAR, Atan R, Hassan SLM, Alghrairi M
    Sensors (Basel), 2021 Sep 27;21(19).
    PMID: 34640773 DOI: 10.3390/s21196453
    One of the most dangerous kinds of attacks affecting computers is a distributed denial of services (DDoS) attack. The main goal of this attack is to bring the targeted machine down and make their services unavailable to legal users. This can be accomplished mainly by directing many machines to send a very large number of packets toward the specified machine to consume its resources and stop it from working. We implemented a method using Java based on entropy and sequential probabilities ratio test (ESPRT) methods to identify malicious flows and their switch interfaces that aid them in passing through. Entropy (E) is the first technique, and the sequential probabilities ratio test (SPRT) is the second technique. The entropy method alone compares its results with a certain threshold in order to make a decision. The accuracy and F-scores for entropy results thus changed when the threshold values changed. Using both entropy and SPRT removed the uncertainty associated with the entropy threshold. The false positive rate was also reduced when combining both techniques. Entropy-based detection methods divide incoming traffic into groups of traffic that have the same size. The size of these groups is determined by a parameter called window size. The Defense Advanced Research Projects Agency (DARPA) 1998, DARPA2000, and Canadian Institute for Cybersecurity (CIC-DDoS2019) databases were used to evaluate the implementation of this method. The metric of a confusion matrix was used to compare the ESPRT results with the results of other methods. The accuracy and f-scores for the DARPA 1998 dataset were 0.995 and 0.997, respectively, for the ESPRT method when the window size was set at 50 and 75 packets. The detection rate of ESPRT for the same dataset was 0.995 when the window size was set to 10 packets. The average accuracy for the DARPA 2000 dataset for ESPRT was 0.905, and the detection rate was 0.929. Finally, ESPRT was scalable to a multiple domain topology application.
    Matched MeSH terms: Entropy
  18. Fusion of heart rate variability and salivary cortisol for stress response identification based on adverse childhood experience
    Aimie-Salleh N, Malarvili MB, Whittaker AC
    Med Biol Eng Comput, 2019 Jun;57(6):1229-1245.
    PMID: 30734153 DOI: 10.1007/s11517-019-01958-3
    Adverse childhood experiences have been suggested to cause changes in physiological processes and can determine the magnitude of the stress response which might have a significant impact on health later in life. To detect the stress response, biomarkers that represent both the Autonomic Nervous System (ANS) and Hypothalamic-Pituitary-Adrenal (HPA) axis are proposed. Among the available biomarkers, Heart Rate Variability (HRV) has been proven as a powerful biomarker that represents ANS. Meanwhile, salivary cortisol has been suggested as a biomarker that reflects the HPA axis. Even though many studies used multiple biomarkers to measure the stress response, the results for each biomarker were analyzed separately. Therefore, the objective of this study is to propose a fusion of ANS and HPA axis biomarkers in order to classify the stress response based on adverse childhood experience. Electrocardiograph, blood pressure (BP), pulse rate (PR), and salivary cortisol (SCort) measures were collected from 23 healthy participants; 11 participants had adverse childhood experience while the remaining 12 acted as the no adversity control group. HRV was then computed from the ECG and the HRV features were extracted. Next, the selected HRV features were combined with the other biomarkers using Euclidean distance (ed) and serial fusion, and the performance of the fused features was compared using Support Vector Machine. From the result, HRV-SCort using Euclidean distance achieved the most satisfactory performance with 80.0% accuracy, 83.3% sensitivity, and 78.3% specificity. Furthermore, the performance of the stress response classification of the fused biomarker, HRV-SCort, outperformed that of the single biomarkers: HRV (61% Accuracy), Cort (59.4% Accuracy), BP (78.3% accuracy), and PR (53.3% accuracy). From this study, it was proven that the fused biomarkers that represent both ANS and HPA (HRV-SCort) able to demonstrate a better classification performance in discriminating the stress response. Furthermore, a new approach for classification of stress response using Euclidean distance and SVM named as ed-SVM was proven to be an effective method for the HRV-SCort in classifying the stress response from PASAT. The robustness of this method is crucial in contributing to the effectiveness of the stress response measures and could further be used as an indicator for future health. Graphical abstract ᅟ.
    Matched MeSH terms: Entropy
  19. Exploring the interaction between the antiallergic drug, tranilast and human serum albumin: Insights from calorimetric, spectroscopic and modeling studies
    Tayyab S, Zaroog MS, Feroz SR, Mohamad SB, Malek SN
    Int J Pharm, 2015 Aug 1;491(1-2):352-8.
    PMID: 26142245 DOI: 10.1016/j.ijpharm.2015.06.042
    The interaction of tranilast (TRN), an antiallergic drug with the main drug transporter in human circulation, human serum albumin (HSA) was studied using isothermal titration calorimetry (ITC), fluorescence spectroscopy and in silico docking methods. ITC data revealed the binding constant and stoichiometry of binding as (3.21 ± 0.23) × 10(6)M(-1) and 0.80 ± 0.08, respectively, at 25°C. The values of the standard enthalpy change (ΔH°) and the standard entropy change (ΔS°) for the interaction were found as -25.2 ± 5.1 kJ mol(-1) and 46.9 ± 5.4 J mol(-1)K(-1), respectively. Both thermodynamic data and modeling results suggested the involvement of hydrogen bonding, hydrophobic and van der Waals forces in the complex formation. Three-dimensional fluorescence data of TRN-HSA complex demonstrated significant changes in the microenvironment around the protein fluorophores upon drug binding. Competitive drug displacement results as well as modeling data concluded the preferred binding site of TRN as Sudlow's site I on HSA.
    Matched MeSH terms: Entropy
  20. Fulltext Entropy-based financial asset pricing: Evidence from Pakistan
    Wang S, Khan SA, Munir M, Alhajj R, Khan YA
    PLoS One, 2022;17(12):e0278236.
    PMID: 36548250 DOI: 10.1371/journal.pone.0278236
    Entropy is an alternative measure to calculate the risk, simplify the portfolios and equity risk premium. It has higher explanatory power than capital asset price model (CAPM) beta. The comparison of Entropy and CAPM beta provide in depth analysis about the explanatory power of the model that in turn help investor to make right investment decisions that minimizes risk. In this context, this study aims to compare Shannon and Rennyi Entropies with the CAPM beta for measuring the risk. Ordinary Least square approach has been utilized using a dataset of 67 enterprises registered in Pakistan Stock exchange. The comparative analysis of CAPM beta and entropy has been carried out with the R2 parameters. The result indicates that entropy has more explanatory power as compare to CAPM beta's explanatory power, and this turns out to be the best option to evaluate the risk performances. The result implies that an investor should make the best investment decision by choosing an enterprise that provide with good returns at minimum risk based on entropy technique.
    Matched MeSH terms: Entropy
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