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Coagulation Profile and Clot Stiffness Analysis from COVID-19-Positive Patients Using a Contactless Viscoelasticity Testing Technology

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Biomedical Materials and Devices 2025年第1期3卷 675-681页

作者： Camasao, Dimitria B. Munguia-Lopez, Jose G. Perez, Anna Correa, José A. Schmitt, Cedric Henni, Anis Hadj Séguin, Chantal Vinh, Donald C. Nazhat, Showan N. Department of Mining and Materials Engineering McGill University Montreal QC Canada Division of Infectious Diseases Department of Experimental Medicine McGill University Health Centre McGill University Montreal QC Canada Department of Mathematics and Statistics McGill University Montreal QC Canada Rheolution Inc. Montreal QC Canada Division of Hematology Department of Medicine McGill University Health Centre McGill University Montreal QC Canada

Coronavirus disease (COVID-19)-associated coagulopathy represents a serious complication of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection that can lead to thromboembolic events. Given the large diversity of patient cohorts, personalized patient-oriented treatment is sought with the analysis of different biomarkers and coagulation profiles. The viscoelasticity testing of bilayered materials (VeTBiM) technology has recently been introduced as a contactless, non-destructive method to measure the real-time viscoelasticity of coagulating blood. In this work, this technology was used to characterize the coagulation profile and the clot stiffness (shear storage modulus, G′) of plasma samples from hospitalized patients in the general COVID-19 ward (non-ICU) and in the intensive care unit (ICU). Analyses revealed that non-ICU patients exhibited a more rapid decrease in G′ with blood sampling time than their ICU counterparts. Additionally, the data suggested that coagulation time decreased while the maximum coagulation rate increased as the disease progressed, underscoring the dynamic nature of coagulation in COVID-19 patients. In sum, the VeTBiM technology has demonstrated the potential to provide precise information on the coagulation profile and stiffness of human plasma samples, which may be applied in fundamental studies of coagulopathies, as well as in clinics, to help establish personalized and more effective treatments in life-threatening complications. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blood biomechanics COVID-19 Long COVID Plasma viscoelasticity VeTBiM

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Phytochemical profiling using FTIR- and LC-HRMS-based metabolomics, mineral content, and free radical scavenging activity of Sonchus arvensis extracts from different growth locations

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Vegetos 2025年 1-12页

作者： Rafi, Mohamad Karomah, Alfi Hudatul Annisa, Dini Faiqoh Maharani Mulyati, Ade Heri Septaningsih, Dewi Anggraini Nurcholis, Waras Syafitri, Utami Dyah Lim, Lee Wah Achmadi, Suminar Setiati Rohaeti, Eti Department of Chemistry Faculty of Mathematics and Natural Sciences IPB University Jalan Tanjung IPB Dramaga Campus Bogor 16880 Indonesia Tropical Biopharmaca Research Center Institute of Research and Community Services IPB University Jalan Taman Kencana No. 3 IPB Taman Kencana Campus Bogor 16128 Indonesia Advance Research Laboratory IPB University Jalan Palem IPB Dramaga Campus Bogor 16680 Indonesia Department of Chemistry Faculty of Mathematics and Natural Sciences Pakuan University Bogor 16143 Indonesia Department of Biochemistry Faculty of Mathematics and Natural Sciences IPB University Jalan Tanjung IPB Dramaga Campus Jawa Barat Bogor 16880 Indonesia Department of Statistics and Data Science Faculty of Mathematics and Natural Sciences IPB University Jalan Meranti IPB Dramaga Campus Bogor 16680 Indonesia Department of Chemistry and Biomolecular Science Faculty of Engineering Gifu University 1-1 Yanagido Gifu 501-1193 Japan

Sonchus arvensis is a medicinal plant widely used to treat hypertension, kidney stones, and gout. This work aims to profile the metabolites, minerals content, and free radical scavenging activity of S. arvensis from different growth locations. We analyzed total phenolics content (Folin-Ciocalteu method), free radical scavenging activity (DPPH method), levels of eight flavonoid compounds (HPLC), and minerals content (ICP-OES). We also performed FTIR fingerprint analysis and UHPLC-Q-Orbitrap HRMS metabolite profiling. S. arvensis from Malang gave the highest extraction yield, total phenolic content, levels of eight flavonoid compounds, free radical scavenging activity, and the highest metabolite content compared with S. arvensis from other regions used in this study. K and Ca are minerals with the highest content in S. arvensis extract, while B, Cu, and Zn have concentrations < 100 mg/kg. The FTIR spectra of all S. arvensis had a similar pattern, indicating that the extracted metabolites were not significantly different except for the levels of the compounds. Metabolite profiling using UHPLC-Q-Orbitrap HRMS successfully identified 36 compounds, most belonging to the phenolic acid and flavonoid groups. Distinguishing S. arvensis from different growth locations was performed using principal component analysis (PCA) with the FTIR spectra’ absorbance and hierarchical cluster analysis (HCA) of the identified compounds’ peak areas as the variables. PCA was able to classify each sample based on the growth location. Differences in the metabolite profiles were observed in S. arvensis from different growth locations, which affected its free radical scavenging activities. © The Author(s) under exclusive licence to Society for Plant Research 2025.

关键词： Antioxidants Metabolite profiling Metabolomics Minerals Sonchus arvensis

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A simple-to-implement nonlinear preconditioning of Newton’s method for solving the steady Navier-Stokes equations

arXiv

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arXiv 2025年

作者： Mohebujjaman, Muhammad Xiao, Mengying Zhang, Cheng Department of Mathematics University of Alabama BirminghamAL35294 United States Department of Mathematics and Statistics University of West Florida PensacolaFL32514 United States Department of Mechanical Engineering University of West Florida PensacolaFL32514 United States

The Newton’s method for solving stationary Navier-Stokes equations (NSE) is known convergent fast, however, may fail due to a bad initial guess. This work presents a simple-to-implement nonlinear preconditioning of the Newton’s iteration, that remains the quadratic convergence and enlarges the domain of convergence. The proposed AAPicard-Newton method adds the Anderson accelerated Picard step at each iteration of Newton’s method for solving NSE, which has been shown globally stable for the relaxation parameter βk+1 ≡ 1 in the Anderson acceleration optimization step, convergent quadratically, and converges faster with a smaller convergence rate for large Reynolds number. Several benchmark numerical tests have been tested and are well-aligned with the theoretical results. © 2025, CC BY.

关键词： Navier Stokes equations

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Advances in Type II Diabetes Prediction: A Comprehensive Review of Machine Learning Techniques

Advances in Type II Diabetes Prediction: A Comprehensive Rev...

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Kushwah, Virendra Singh Krishnan, Sivaneasan Bala Upreti, Kamal Kumar, Manoj Kshirsagar, Pravin R. Singh, Vinai K. Singapore Institute of Technology Singapore School of Sciences Christ University Department of Computer Science DelhiNCR Ghaziabad India Gurukula Kangri University Department of Mathematics and Statistics Haridwar India J D College of Engineering & Management Department of Electronics & Telecommunication Engineering Nagpur India Gl Bajaj Group of Institutions Department of Computer Science & Engineering Mathura India

ISBN: (纸本)9798331527495

Type II diabetes mellitus, on the other hand has been regarded as one of the growing concerns globally and thus clearly raises the need for making accurate forecasts of diabetes. The risk for Type II diabetes can be predicted using Ma-chine Learning as well as any other form to make the predictions much more enhanced than the traditional methods. This paper aims to give a broad overview of literature that has so far been available on the ML algorithms used in the management of Type II diabetes including such supervised algorithms as logistic regression, alphabet regression, random forest, support vector regression along with other methods such as, ensemble learning, deep learning, and hybrid. Analysis of the main aspects for the performance model such as parameter selection, the way to face and cope with imbalance parameters, interpretability and generalizability across different populations, another aspect that was regarded is the possibility of using real-time data collected with wearable devices and applying tissue and other biomarkers for better prediction. Finally, the key obstacles and future directions towards developing ML algorithms and models explainable and clinically relevant have been introduced to help researchers and practitioners toward effective, personalized, and scalable interventions. © 2025 IEEE.

关键词： Support vector regression

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Comparative Analysis of Silver and Titanium Oxide Hybrid Nanofluid in MHD Unsteady Flow Past an Accelerated Vertical Plate with Radiation Effects

Hybrid Advances

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Hybrid Advances 2025年 10卷

作者： Lutera, Joseph Nicholaus Goud, B. Shankar Shekar, M.N. Raja Department of Mathematics JNTUH University College of Engineering Science and Technology Hyderabad Kukatpally Hyderabad Telangana 500085 India Department of Mathematics and Statistics (CoSTE) Mbeya University of Science and Technology Mbeya Tanzania

The problem of MHD unsteady movement of the nanofluid past an accelerated perpendicular surface is investigated. Thermal radiation and electromagnetic field properties are taken into explanation of the hybrid nanofluid composed of Silver and Titanium dioxide nanoparticles. The nanoparticles offer an enhanced thermal conductivity property, a factor important in technological advancement including cooling, separation, nuclear reactors, solar energy and electronic devices. The study aims to analyze the characteristics of different nanofluids on the influence of dimensionless parameters for mass and heat transmission. The resultant nondimensionalized boundary problem is solved numerically by the finite difference technique employed to obtain the mathematical results of the partial differential equations. The results demonstrate a comparison of velocity and thermal energy profiles of Silver-water and Titanium dioxide-water nanofluids graphically. Silver particles portray greater impacts on the nanofluid flow rate than the Titanium dioxide particles. The temperature of silver water is greater than the temperature of Titanium dioxide water. The improvement of thermal radiation strength was observed to enhance the temperature and velocity profiles of the nanofluid. The study is crucial in decision-making, especially for the choice of efficient nanofluid in mass and heat transmission applications. © 2025 The Authors

关键词： FDM Hybrid nanofluid MHD Thermal radiation Vertical plate

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Epileptic seizure detection in EEG signals via an enhanced hybrid CNN with an integrated attention mechanism

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Mathematical Biosciences and engineering 2025年第1期22卷 73-105页

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao56000 Thailand Department of Applied Statistics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research Institute King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand

Epileptic seizures, a prevalent neurological condition, necessitate precise and prompt identification for optimal care. Nevertheless, the intricate characteristics of electroencephalography (EEG) signals, noise, and the want for real-time analysis require enhancement in the creation of dependable detection approaches. Despite advances in machine learning and deep learning, capturing the intricate spatial and temporal patterns in EEG data remains challenging. This study introduced a novel deep learning framework combining a convolutional neural network (CNN), bidirectional gated recurrent unit (BiGRU), and convolutional block attention module (CBAM). The CNN extracts spatial features, the BiGRU captures long-term temporal dependencies, and the CBAM emphasizes critical spatial and temporal regions, creating a hybrid architecture optimized for EEG pattern recognition. Evaluation of a public EEG dataset revealed superior performance compared to existing methods. The model achieved 99.00% accuracy in binary classification, 96.20% in three-class tasks, 92.00% in four-class scenarios, and 89.00% in five-class classification. High sensitivity (89.00–99.00%) and specificity (89.63–99.00%) across all tasks highlighted the model’s robust ability to identify diverse EEG patterns. This approach supports healthcare professionals in diagnosing epileptic seizures accurately and promptly, improving patient outcomes and quality of life. © 2025 American Institute of Mathematical Sciences. All rights reserved.

关键词： Electroencephalography

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Machine Learning Techniques for Frequency Comb Optimization 3

Machine Learning Techniques for Frequency Comb Optimization

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Quantum Sensing, Imaging, and Precision Metrology III 2025

作者： Floridia, Maximilian Wynn, Scott Nitzsche, Jayan Placke, Bishop Tyler, Matthew Diab, Jack Shariatdoust, Mirali S. Carbajo, Sergio Narang, Prineha Bertozzi, Andrea L. Department of Chemistry and Biochemistry University of California Los AngelesCA90095 United States Department of Computer Science and Engineering University of Washington SeattleWA98195 United States Department of Mathematics and Statistics Pomona College ClaremontCA91711 United States Department of Mathematics University of Nebraska LincolnNE68588 United States Department of Mathematics University of California Los Angeles 520 Portola Plaza Los AngelesCA90095 United States Department of Electrical and Computer Engineering University of California Los Angeles Los AngelesCA90095 United States California NanoSystems Institute 570 Westwood Plaza Los AngelesCA90095 United States Division of Physical Sciences University of California Los AngelesCA90095 United States Department of Mechanical and Aerospace Engineering University of California Los Angeles Los AngelesCA90095 United States

ISBN: (数字)9781510685338

ISBN: (纸本)9781510685321

We use supervised and unsupervised machine learning techniques to investigate how optical frequency combs may be used to identify gas molecules in the atmosphere. Dual comb heterodyne detection with GHz and THz repetition rates are often used as spectroscopic methods for probing ro-vibrational IR spectra of small molecules, proving to be promising in dispersive atmospheric sensing regimes. We simulate and recover an intensity spectrum from the molecule-specific IR absorption in the region spanned by the comb bandwidth. In practice, conventional frequency combs contain a narrow range of frequencies that may not span the spectral range associated with typical atmospheric target signals, prohibiting convenient molecule identification. We present several inexpensive, efficient machine learning methods to analyze optimal comb frequencies to maximize the ability to determine the molecular composition of a sample, based only on the interactions at those frequencies with respect to various channels of noise. Using a synthetic dataset, these methods are able to accurately identify molecules using simulated frequency combs, comparable to the ones used in practice. Furthermore, these methods withstand added noise (based on the imperfections of the experimental equipment) beyond what is expected in real applications. We also investigate the optimal comb generation parameters that provide the most informational value, and provide an analysis of the change in accuracy based on the number and size of allocated combs. The robustness of these methods suggests the techniques presented in this paper may be integrated into a laboratory setup and even in on-chip comb systems deployed in an atmospheric setting. This approach may be used as a detection framework for future quantum-enhanced systems via squeezed bi-photon comb inputs. © 2025 SPIE.

关键词： Learning algorithms

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Weak Closed-loop Solvability for Discrete-time Stochastic Linear-Quadratic Optimal Control

arXiv

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arXiv 2025年

作者： Sun, Yue Wu, Xianping Li, Xun School of Control Science and Engineering Shandong University Shandong Jinan250061 China School of Mathematics and Statistics Guangdong University of Technology Guangzhou China Department of Applied Mathematics The Hong Kong Polytechnic University Hong Kong

In this paper, the solvability of discrete-time stochastic linear-quadratic (LQ) optimal control problem in finite horizon is considered. Firstly, it shows that the closed-loop solvability for the LQ control problem is optimal if and only if the generalized Riccati equation admits a regular solution by solving the forward and backward difference equations iteratively. To this ends, it finds that the open-loop solvability is strictly weaker than closed-loop solvability, that is, the LQ control problem is always open-loop optimal solvable if it is closed-loop optimal solvable but not vice versa. Secondly, by the perturbation method, it proves that the weak-closed loop strategy which is a feedback form of a state feedback representation is equivalent to the open-loop solvability of the LQ control problem. Finally, an example sheds light on the theoretical results *** Codes org © 2025, CC BY.

关键词： Riccati equations

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Computable Bounds on the Solution to Poisson’s Equation for General Harris Chains

arXiv

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arXiv 2025年

作者： Glynn, Peter W. Lin, Na Liu, Yuanyuan Department of Management Science and Engineering Institute for Computational and Mathematical Engineering Stanford University United States School of Mathematics and Statistics HNP-LAMA Central South University China

Poisson’s equation is fundamental to the study of Markov chains, and arises in connection with martingale representations and central limit theorems for additive functionals, perturbation theory for stationary distributions, and average reward Markov decision process problems. In this paper, we develop a new probabilistic representation for the solution of Poisson’s equation, and use Lyapunov functions to bound this solution representation explicitly. In contrast to most prior work on this problem, our bounds are computable. Our contribution is closely connected to recent work of Hervé and Ledoux [2025], in which they focus their study on a special class of Harris chains satisfying a particular small set condition. However, our theory covers general Harris chains, and often provides a tighter bound. In addition to the new bound and representation, we also develop a computable uniform bound on marginal expectations for Harris chains, and a computable bound on the potential kernel representation of the solution to Poisson’s equation. Copyright © 2025, The Authors. All rights reserved.

关键词： Poisson equation

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Phytochemical Analysis, Computational Study, and in vitro Assay of Etlingera elatior Inflorescence Extract Towards Inducible Nitric Oxide Synthase

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Journal of Experimental Pharmacology 2025年 17卷 123-141页

作者： Prayoga, Deshanda Kurniawan Pitaloka, Dian Ayu Eka Aulifa, Diah Lia Budiman, Arif Levita, Jutti Jiranusornkul, Supat Nguyen, Binh Phu Program in Pharmacy Faculty of Pharmacy Universitas Padjadjaran Sumedang Indonesia Department of Pharmacology and Clinical Pharmacy Faculty of Pharmacy Universitas Padjadjaran Sumedang Indonesia Department of Pharmaceutical Analysis and Medicinal Chemistry Faculty of Pharmacy Universitas Padjadjaran Sumedang Indonesia Department of Pharmaceutics and Pharmaceutical Technology Faculty of Pharmacy Universitas Padjadjaran Sumedang Indonesia Department of Pharmaceutical Science Faculty of Pharmacy Chiang Mai University Chiang Mai Thailand School of Mathematics and Statistics Victoria University of Wellington Wellington New Zealand

Background: Overproduction of nitric oxide (NO), catalyzed by inducible nitric oxide synthase (iNOS), in the gastric mucosa, contributes to the inflammatory process caused by oxidative stress. Current medications for gastric ulcers, such as proton pump inhibitors and histamine-2 receptor antagonists, have been reported to generate adverse reactions. Purpose: To obtain the phytochemical profile of Etlingera elatior inflorescence extract, computational studies, and in vitro assay of the extract towards iNOS. Methods: Fresh E. elatior inflorescence petals collected from West Java, Indonesia, were extracted using ethanol, and their nutritional composition, anthocyanin content, and levels of vitamin C, C3G, and quercetin in the extract were determined. Drug-likeness and ADMET properties were predicted, and the binding affinity and stability of the phytoconstituents towards iNOS were studied using molecular docking and molecular dynamic simulation, and in vitro assay of the extract towards human iNOS. Results: The extract contains protein 21.81%, fat 0.99%, carbohydrate 38.27%, water 24.56%, and ash 14.37%. The total anthocyanin and vitamin C levels were 47.535 mg/100 g and 985.250 mg/100 g, respectively. The levels of C3G and quercetin were 0.0007% w/w, 0.004% w/w, and 0.0005% w/w, respectively. Drug-likeness and ADMET properties of the constituents showed that most followed Lipinski Rules of Five (Ro5), with few violations. All phytoconstituents occupied the catalytic site by binding to Glu377, and Trp372, similar to S-ethylisothiourea (SEITU) and quinazoline, the iNOS inhibitors. Among these, the flavylium cation of cyanidin, demethoxycurcumin, C3G, cyanidin, and quercetin showed the best binding affinities. Root mean square deviation (RMSD), root mean square fluctuation (RMSF), solvent-accessible surface area (SASA), and radius of gyration (Rg) graphs confirmed the stability of the complexes. E. elatior inflorescence extract inhibited human iNOS with an IC50 value of

关键词： anti-inflammation cyanidin-3-glucoside polyphenols proximate analysis quercetin

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