Displaying publications 1 - 20 of 604 in total

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  1. Optimizing neural network algorithms for submerged membrane bioreactor: A comparative study of OVAT and RSM hyperparameter optimization techniques
    Ibrahim S, Abdul Wahab N
    Water Sci Technol, 2024 Apr;89(7):1701-1724.
    PMID: 38619898 DOI: 10.2166/wst.2024.099
    Hyperparameter tuning is an important process to maximize the performance of any neural network model. This present study proposed the factorial design of experiment for screening and response surface methodology to optimize the hyperparameter of two artificial neural network algorithms. Feed-forward neural network (FFNN) and radial basis function neural network (RBFNN) are applied to predict the permeate flux of palm oil mill effluent. Permeate pump and transmembrane pressure of the submerge membrane bioreactor system are the input variables. Six hyperparameters of the FFNN model including four numerical factors (neuron numbers, learning rate, momentum, and epoch numbers) and two categorical factors (training and activation function) are used in hyperparameter optimization. RBFNN includes two numerical factors such as a number of neurons and spreads. The conventional method (one-variable-at-a-time) is compared in terms of optimization processing time and the accuracy of the model. The result indicates that the optimal hyperparameters obtained by the proposed approach produce good accuracy with a smaller generalization error. The simulation results show an improvement of more than 65% of training performance, with less repetition and processing time. This proposed methodology can be utilized for any type of neural network application to find the optimum levels of different parameters.
    Matched MeSH terms: Computer Simulation
  2. QSAR modeling approaches to identify a novel ACE2 inhibitor that selectively bind with the C and N terminals of the ectodomain
    Jawarkar RD, Zaki MEA, Al-Hussain SA, Al-Mutairi AA, Samad A, Mukerjee N, et al.
    J Biomol Struct Dyn, 2024 Mar;42(5):2550-2569.
    PMID: 37144753 DOI: 10.1080/07391102.2023.2205948
    Due to the high rates of drug development failure and the massive expenses associated with drug discovery, repurposing existing drugs has become more popular. As a result, we have used QSAR modelling on a large and varied dataset of 657 compounds in an effort to discover both explicit and subtle structural features requisite for ACE2 inhibitory activity, with the goal of identifying novel hit molecules. The QSAR modelling yielded a statistically robust QSAR model with high predictivity (R2tr=0.84, R2ex=0.79), previously undisclosed features, and novel mechanistic interpretations. The developed QSAR model predicted the ACE2 inhibitory activity (PIC50) of 1615 ZINC FDA compounds. This led to the detection of a PIC50 of 8.604 M for the hit molecule (ZINC000027990463). The hit molecule's docking score is -9.67 kcal/mol (RMSD 1.4). The hit molecule revealed 25 interactions with the residue ASP40, which defines the N and C termini of the ectodomain of ACE2. The HIT molecule conducted more than thirty contacts with water molecules and exhibited polar interaction with the ARG522 residue coupled with the second chloride ion, which is 10.4 nm away from the zinc ion. Both molecular docking and QSAR produced comparable findings. Moreover, MD simulation and MMGBSA studies verified docking analysis. The MD simulation showed that the hit molecule-ACE2 receptor complex is stable for 400 ns, suggesting that repurposed hit molecule 3 is a viable ACE2 inhibitor.
    Matched MeSH terms: Computer Simulation
  3. A simulation study on the radiosensitization properties of gold nanorods
    Taheri A, Khandaker MU, Moradi F, Bradley DA
    Phys Med Biol, 2024 Feb 15;69(4).
    PMID: 38286017 DOI: 10.1088/1361-6560/ad2380
    Objective. Gold nanorods (GNRs) have emerged as versatile nanoparticles with unique properties, holding promise in various modalities of cancer treatment through drug delivery and photothermal therapy. In the rapidly evolving field of nanoparticle radiosensitization (NPRS) for cancer therapy, this study assessed the potential of gold nanorods as radiosensitizing agents by quantifying the key features of NPRS, such as secondary electron emission and dose enhancement, using Monte Carlo simulations.Approach. Employing the TOPAS track structure code, we conducted a comprehensive evaluation of the radiosensitization behavior of spherical gold nanoparticles and gold nanorods. We systematically explored the impact of nanorod geometry (in particular size and aspect ratio) and orientation on secondary electron emission and deposited energy ratio, providing validated results against previously published simulations.Main results. Our findings demonstrate that gold nanorods exhibit comparable secondary electron emission to their spherical counterparts. Notably, nanorods with smaller surface-area-to-volume ratios (SA:V) and alignment with the incident photon beam proved to be more efficient radiosensitizing agents, showing superiority in emitted electron fluence. However, in the microscale, the deposited energy ratio (DER) was not markedly influenced by the SA:V of the nanorod. Additionally, our findings revealed that the geometry of gold nanoparticles has a more significant impact on the emission of M-shell Auger electrons (with energies below 3.5 keV) than on higher-energy electrons.Significance. This research investigated the radiosensitization properties of gold nanorods, positioning them as promising alternatives to the more conventionally studied spherical gold nanoparticles in the context of cancer research. With increasing interest in multimodal cancer therapy, our findings have the potential to contribute valuable insights into the perspective of gold nanorods as effective multipurpose agents for synergistic photothermal therapy and radiotherapy. Future directions may involve exploring alternative metallic nanorods as well as further optimizing the geometry and coating materials, opening new possibilities for more effective cancer treatments.
    Matched MeSH terms: Computer Simulation
  4. Understanding the ideal wound healing mechanistic behavior using in silico modelling perspectives: A review
    Abu Bakar N, Mydin RBSMN, Yusop N, Matmin J, Ghazalli NF
    J Tissue Viability, 2024 Feb;33(1):104-115.
    PMID: 38092620 DOI: 10.1016/j.jtv.2023.11.001
    Complexity of the entire body precludes an accurate assessment of the specific contributions of tissues or cells during the healing process, which might be expensive and time consuming. Because of this, controlling the wound's size, depth, and dimensions may be challenging, and there is not yet an efficient and reliable chronic wound model representation. Furthermore, given the inherent challenges associated with conducting non-invasive in vivo investigations, it becomes peremptory to explore alternative methodologies for studying wound healing. In this context, biologically-realistic mathematical and computational models emerge as a valuable framework that can effectively address this need. Therefore, it might improve our approach to understanding the process at its core. This article will examines all facets of wound healing, including the kinds, pathways, and most current developments in wound treatment worldwide, particularly in silico modelling utilizing both mathematical and structure-based modelling techniques. It may be helpful to identify the crucial traits through the feedback loop of computer models and experimental investigations in order to build innovative therapies to cure wounds. Hence the effectiveness of personalised medicine and more targeted therapy in the healing of wounds may be enhanced by this interdisciplinary expertise.
    Matched MeSH terms: Computer Simulation
  5. Fulltext Performance of Google bard and ChatGPT in mass casualty incidents triage
    Gan RK, Ogbodo JC, Wee YZ, Gan AZ, González PA
    Am J Emerg Med, 2024 Jan;75:72-78.
    PMID: 37967485 DOI: 10.1016/j.ajem.2023.10.034
    AIM: The objective of our research is to evaluate and compare the performance of ChatGPT, Google Bard, and medical students in performing START triage during mass casualty situations.

    METHOD: We conducted a cross-sectional analysis to compare ChatGPT, Google Bard, and medical students in mass casualty incident (MCI) triage using the Simple Triage And Rapid Treatment (START) method. A validated questionnaire with 15 diverse MCI scenarios was used to assess triage accuracy and content analysis in four categories: "Walking wounded," "Respiration," "Perfusion," and "Mental Status." Statistical analysis compared the results.

    RESULT: Google Bard demonstrated a notably higher accuracy of 60%, while ChatGPT achieved an accuracy of 26.67% (p = 0.002). Comparatively, medical students performed at an accuracy rate of 64.3% in a previous study. However, there was no significant difference observed between Google Bard and medical students (p = 0.211). Qualitative content analysis of 'walking-wounded', 'respiration', 'perfusion', and 'mental status' indicated that Google Bard outperformed ChatGPT.

    CONCLUSION: Google Bard was found to be superior to ChatGPT in correctly performing mass casualty incident triage. Google Bard achieved an accuracy of 60%, while chatGPT only achieved an accuracy of 26.67%. This difference was statistically significant (p = 0.002).

    Matched MeSH terms: Computer Simulation
  6. Fulltext Molecular docking and dynamics simulation studies uncover the host-pathogen protein-protein interactions in Penaeus vannamei and Vibrio parahaemolyticus
    Rosilan NF, Jamali MAM, Sufira SA, Waiho K, Fazhan H, Ismail N, et al.
    PLoS One, 2024;19(1):e0297759.
    PMID: 38266027 DOI: 10.1371/journal.pone.0297759
    Shrimp aquaculture contributes significantly to global economic growth, and the whiteleg shrimp, Penaeus vannamei, is a leading species in this industry. However, Vibrio parahaemolyticus infection poses a major challenge in ensuring the success of P. vannamei aquaculture. Despite its significance in this industry, the biological knowledge of its pathogenesis remains unclear. Hence, this study was conducted to identify the interaction sites and binding affinity between several immune-related proteins of P. vannamei with V. parahaemolyticus proteins associated with virulence factors. Potential interaction sites and the binding affinity between host and pathogen proteins were identified using molecular docking and dynamics (MD) simulation. The P. vannamei-V. parahaemolyticus protein-protein interaction of Complex 1 (Ferritin-HrpE/YscL family type III secretion apparatus protein), Complex 2 (Protein kinase domain-containing protein-Chemotaxis CheY protein), and Complex 3 (GPCR-Chemotaxis CheY protein) was found to interact with -4319.76, -5271.39, and -4725.57 of the docked score and the formation of intermolecular bonds at several interacting residues. The docked scores of Complex 1, Complex 2, and Complex 3 were validated using MD simulation analysis, which revealed these complexes greatly contribute to the interactions between P. vannamei and V. parahaemolyticus proteins, with binding free energies of -22.50 kJ/mol, -30.20 kJ/mol, and -26.27 kJ/mol, respectively. This finding illustrates the capability of computational approaches to search for molecular binding sites between host and pathogen, which could increase the knowledge of Vibrio spp. infection on shrimps, which then can be used to assist in the development of effective treatment.
    Matched MeSH terms: Computer Simulation
  7. Fulltext Comparative analysis and FPGA realization of different control synchronization approaches for chaos-based secured communication systems
    Bonny T, Al Nassan W, Sambas A
    PLoS One, 2024;19(1):e0291714.
    PMID: 38261569 DOI: 10.1371/journal.pone.0291714
    Synchronization of the chaotic systems has attracted much attention in recent years due to its vital applications in secured communication systems. In this paper, an implementation and comparative analysis of two different control approaches for synchronization between two identical four-dimensional hyperchaotic systems is presented. The two control approaches are the Adaptive nonlinear controller and the linear optimal quadratic regulator LQR. To demonstrate the effectiveness of each controller, the numerical simulation is presented using Matlab/Simulink and the control law is derived. The performance of the proposed controllers is compared based on four factors; response time, squared error integration, energy applied from the controller, and cost function. To measure the robustness of the control approaches, the performance factors are compared when there is a change in system parameters and a variation in the initial conditions. Then the proposed synchronization methods are implemented on the FPGA platform to demonstrate the utilized resources on Field Programmable Gate Array (FPGA) hardware platform and the operation speed. Finally, to generalize the results of the comparison, the study is implemented for the synchronization of another secured communication system consisting of two identical three-dimensional chaotic. The experimental results show that the LQR method is more effective than the Adaptive controller based on the performance factors we propose. Moreover, the LQR is much simpler to implement on hardware and requires fewer resources on the FPGA.
    Matched MeSH terms: Computer Simulation
  8. Fulltext A renewable approach to electric vehicle charging through solar energy storage
    Umair M, Hidayat NM, Sukri Ahmad A, Nik Ali NH, Mawardi MIM, Abdullah E
    PLoS One, 2024;19(2):e0297376.
    PMID: 38422065 DOI: 10.1371/journal.pone.0297376
    Developing novel EV chargers is crucial for accelerating Electric Vehicle (EV) adoption, mitigating range anxiety, and fostering technological advancements that enhance charging efficiency and grid integration. These advancements address current challenges and contribute to a more sustainable and convenient future of electric mobility. This paper explores the performance dynamics of a solar-integrated charging system. It outlines a simulation study on harnessing solar energy as the primary Direct Current (DC) EV charging source. The approach incorporates an Energy Storage System (ESS) to address solar intermittencies and mitigate photovoltaic (PV) mismatch losses. Executed through MATLAB, the system integrates key components, including solar PV panels, the ESS, a DC charger, and an EV battery. The study finds that a change in solar irradiance from 400 W/m2 to 1000 W/m2 resulted in a substantial 47% increase in the output power of the solar PV system. Simultaneously, the ESS shows a 38% boost in output power under similar conditions, with the assessments conducted at a room temperature of 25°C. The results emphasize that optimal solar panel placement with higher irradiance levels is essential to leverage integrated solar energy EV chargers. The research also illuminates the positive correlation between elevated irradiance levels and the EV battery's State of Charge (SOC). This correlation underscores the efficiency gains achievable through enhanced solar power absorption, facilitating more effective and expedited EV charging.
    Matched MeSH terms: Computer Simulation
  9. Fulltext Optimizing energy efficiency in heterogeneous networks: An integrated stochastic geometry approach with novel sleep mode strategies and QoS framework
    Shabbir A, Rizvi S, Alam MM, Shirazi F, Su'ud MM
    PLoS One, 2024;19(2):e0296392.
    PMID: 38408070 DOI: 10.1371/journal.pone.0296392
    The quest for energy efficiency (EE) in multi-tier Heterogeneous Networks (HetNets) is observed within the context of surging high-speed data demands and the rapid proliferation of wireless devices. The analysis of existing literature underscores the need for more comprehensive strategies to realize genuinely energy-efficient HetNets. This research work contributes significantly by employing a systematic methodology, utilizing This model facilitates the assessment of network performance by considering the spatial distribution of network elements. The stochastic nature of the PPP allows for a realistic representation of the random spatial deployment of base stations and users in multi-tier HetNets. Additionally, an analytical framework for Quality of Service (QoS) provision based on D-DOSS simplifies the understanding of user-base station relationships and offers essential performance metrics. Moreover, an optimization problem formulation, considering coverage, energy maximization, and delay minimization constraints, aims to strike a balance between key network attributes. This research not only addresses crucial challenges in creating EE HetNets but also lays a foundation for future advancements in wireless network design, operation, and management, ultimately benefiting network operators and end-users alike amidst the growing demand for high-speed data and the increasing prevalence of wireless devices. The proposed D-DOSS approach not only offers insights for the systematic design and analysis of EE HetNets but also systematically outperforms other state-of-the-art techniques presented. The improvement in energy efficiency systematically ranges from 67% (min side) to 98% (max side), systematically demonstrating the effectiveness of the proposed strategy in achieving higher energy efficiency compared to existing strategies. This systematic research work establishes a strong foundation for the systematic evolution of energy-efficient HetNets. The systematic methodology employed ensures a comprehensive understanding of the complex interplay of network dynamics and user requirements in a multi-tiered environment.
    Matched MeSH terms: Computer Simulation
  10. Fulltext Stabilization and tracking control of underactuated ball and beam system using metaheuristic optimization based TID-F and PIDD2-PI control schemes
    Zafar F, Malik SA, Ali T, Daraz A, Afzal AR, Bhatti F, et al.
    PLoS One, 2024;19(2):e0298624.
    PMID: 38354203 DOI: 10.1371/journal.pone.0298624
    In this paper, we propose two different control strategies for the position control of the ball of the ball and beam system (BBS). The first control strategy uses the proportional integral derivative-second derivative with a proportional integrator PIDD2-PI. The second control strategy uses the tilt integral derivative with filter (TID-F). The designed controllers employ two distinct metaheuristic computation techniques: grey wolf optimization (GWO) and whale optimization algorithm (WOA) for the parameter tuning. We evaluated the dynamic and steady-state performance of the proposed control strategies using four performance indices. In addition, to analyze the robustness of proposed control strategies, a comprehensive comparison has been performed with a variety of controllers, including tilt integral-derivative (TID), fractional order proportional integral derivative (FOPID), integral-proportional derivative (I-PD), proportional integral-derivative (PI-D), and proportional integral proportional derivative (PI-PD). By comparing different test cases, including the variation in the parameters of the BBS with disturbance, we examine step response, set point tracking, disturbance rejection analysis, and robustness of proposed control strategies. The comprehensive comparison of results shows that WOA-PIDD2-PI-ISE and GWO-TID-F- ISE perform superior. Moreover, the proposed control strategies yield oscillation-free, stable, and quick response, which confirms the robustness of the proposed control strategies to the disturbance, parameter variation of BBS, and tracking performance. The practical implementation of the proposed controllers can be in the field of under actuated mechanical systems (UMS), robotics and industrial automation. The proposed control strategies are successfully tested in MATLAB simulation.
    Matched MeSH terms: Computer Simulation
  11. Synthesis, spectral characterisation, biocidal investigation, in-silico and molecular docking studies of 4-[(2-chloro-4-methylphenyl)carbamoyl]butanoic acid derived triorganotin(IV) compounds
    Hanifa B, Bibi N, Sirajuddin M, Tiekink ERT, Kubicki M, Khan I, et al.
    J Biomol Struct Dyn, 2024;42(4):1826-1845.
    PMID: 37114651 DOI: 10.1080/07391102.2023.2204160
    Three triorganotin(IV) compounds, R3Sn(L), with R = CH3 (1), n-C4H9 (2) and C6H5 (3), and LH = 4-[(2-chloro-4-methylphenyl)carbamoyl]butanoic acid, were prepared and confirmed by various techniques. A five-coordinate, distorted trigonal-bipyramidal geometry was elucidated for tin(IV) centres both in solution and solid states. An intercalation mode was confirmed for the compound SS-DNA interaction by UV-visible, viscometric techniques and molecular docking. MD simulation revealed stable binding of LH with SS-DNA. Anti-bacterial investigation revealed 2 to be generally the most potent, especially against Sa and Ab, i.e. having the lowest MIC values (≤0.25 μg/mL) compared to the standard anti-biotics vancomycin-HCl (MIC = 1 μg/mL) and colistin-sulphate (MIC = 0.25 μg/mL). Similarly, the anti-fungal profile shows 2 exhibits 100% inhibition against Ca and Cn fungal strains and has MIC values (≤0.25 μg/mL) comparatively lower than standard drug fluconazole (0.125 and 8 μg/mL for Ca and Cn, respectively). Compound 2 has the greatest activity with CC50 ≤ 25 μg/mL and HC50 > 32 μg/mL performed against HEC239 and RBC cell lines. The anti-cancer potential was assessed against the MG-U87 cell line, using cisplatin as the standard (133 µM), indicates 2 displays the greatest activity (IC50: 5.521 µM) at a 5 µM dose. The greatest anti-leishmanial potential was observed for 2 (87.75 at 1000 μg/mL) in comparison to amphotericin B (90.67). The biological assay correlates with the observed maximum of 89% scavenging activity exhibited by 2. The Swiss-ADME data publicised the screened compounds generally follow the rule of 5 of drug-likeness and have good bioavailability potential.
    Matched MeSH terms: Computer Simulation
  12. Fulltext Simulation of passive exotendon assistive device for agricultural harvesting task
    Chan YS, Teo YX, Gouwanda D, Nurzaman SG, Gopalai AA
    Phys Eng Sci Med, 2023 Dec;46(4):1375-1386.
    PMID: 37493930 DOI: 10.1007/s13246-023-01305-9
    This study proposes and investigates the feasibility of the passive assistive device to assist agricultural harvesting task and reduce the Musculoskeletal Disorder (MSD) risk of harvesters using computational musculoskeletal modelling and simulations. Several passive assistive devices comprised of elastic exotendon, which acts in parallel with different back muscles (rectus abdominis, longissimus, and iliocostalis), were designed and modelled. These passive assistive devices were integrated individually into the musculoskeletal model to provide passive support for the harvesting task. The muscle activation, muscle force, and joint moment were computed with biomechanical simulations for unassisted and assisted motions. The simulation results demonstrated that passive assistive devices reduced muscle activation, muscle force, and joint moment, particularly when the devices were attached to the iliocostalis and rectus abdominis. It was also discovered that assisting the longissimus muscle can alleviate the workload by distributing a portion of it to the rectus abdominis. The findings in this study support the feasibility of adopting passive assistive devices to reduce the MSD risk of the harvesters during agricultural harvesting. These findings can provide valuable insights to the engineers and designers of physical assistive devices on which muscle(s) to assist during agricultural harvesting.
    Matched MeSH terms: Computer Simulation
  13. Controller Design and Stability Analysis of Intensification Process using Analytical Exact Gain-Phase Margin approach
    Lee CS, Abd Shukor SR
    Environ Sci Pollut Res Int, 2023 Dec;30(60):124790-124805.
    PMID: 36961637 DOI: 10.1007/s11356-023-26358-x
    The controllable intensified process has received immense attention from researchers in order to deliver the benefit of process intensification to be operated in a desired way to provide a more sustainable process toward reduction of environmental impact and improvement of intrinsic safety and process efficiency. Despite numerous studies on gain and phase margin approach on conventional process systems, it is yet to be tested on intensified systems as evidenced by the lack of available literature, to improve the controller performance and robustness. Thus, this paper proposed the exact gain and phase margin (EGPM) through analytical method to develop suitable controller design for intensified system using Proportional-Integral-Derivative (PID) controller formulation, and it was compared to conventional Direct Synthesis methods (DS), Internal Model Control (IMC), and Industrial IMC method in terms of the performance and stability analysis. Simulation results showed that EGPM method provides good setpoint tracking and disturbance rejection as compared to DS, IMC, and Industrial IMC while retaining overall performance stability as time delay increases. The Bode Stability Criterion was used to determine the stability of the open-loop transfer function of each method and the result demonstrated decrease in stability as time delay increases for controllers designed using DS, IMC, and Industrial IMC, and hence control performance degrades. However, the proposed EGPM controller maintains the overall robustness and control performance throughout the increase of time delay and outperform other controller design methods at higher time delay with [Formula: see text] uncertainty test. Additionally, the proposed EGPM controller design method provides overall superior control performance with lower overshoot and shorter rise time compared to other controllers when process time constant is smaller in magnitude ([Formula: see text]) than the instrumentation element, which is one of the major concerns during the design of intensified controllers, resulting overall system with a higher order. The desired selection of gain margin and phase margin were suggested at 2.5 to 4 and 60 °-70 [Formula: see text], respectively, for a wide range of control conditions for intensified processes where higher instrumentation dynamic would be possible to achieve robust control as well. The proposed EGPM method controller is thought to be a more reliable design strategy for maintaining the overall robustness and performance of higher order and complex systems that are highly affected by time delay and high dynamic response of intensified processes.
    Matched MeSH terms: Computer Simulation
  14. Fulltext ChatGPT's performance before and after teaching in mass casualty incident triage
    Gan RK, Uddin H, Gan AZ, Yew YY, González PA
    Sci Rep, 2023 Nov 21;13(1):20350.
    PMID: 37989755 DOI: 10.1038/s41598-023-46986-0
    Since its initial launching, ChatGPT has gained significant attention from the media, with many claiming that ChatGPT's arrival is a transformative milestone in the advancement of the AI revolution. Our aim was to assess the performance of ChatGPT before and after teaching the triage of mass casualty incidents by utilizing a validated questionnaire specifically designed for such scenarios. In addition, we compared the triage performance between ChatGPT and medical students. Our cross-sectional study employed a mixed-methods analysis to assess the performance of ChatGPT in mass casualty incident triage, pre- and post-teaching of Simple Triage And Rapid Treatment (START) triage. After teaching the START triage algorithm, ChatGPT scored an overall triage accuracy of 80%, with only 20% of cases being over-triaged. The mean accuracy of medical students on the same questionnaire yielded 64.3%. Qualitative analysis on pre-determined themes on 'walking-wounded', 'respiration', 'perfusion', and 'mental status' on ChatGPT showed similar performance in pre- and post-teaching of START triage. Additional themes on 'disclaimer', 'prediction', 'management plan', and 'assumption' were identified during the thematic analysis. ChatGPT exhibited promising results in effectively responding to mass casualty incident questionnaires. Nevertheless, additional research is necessary to ensure its safety and efficacy before clinical implementation.
    Matched MeSH terms: Computer Simulation
  15. Virtual patient with temporal evolution for mechanical ventilation trial studies: A stochastic model approach
    Ang CYS, Chiew YS, Wang X, Ooi EH, Nor MBM, Cove ME, et al.
    Comput Methods Programs Biomed, 2023 Oct;240:107728.
    PMID: 37531693 DOI: 10.1016/j.cmpb.2023.107728
    BACKGROUND AND OBJECTIVE: Healthcare datasets are plagued by issues of data scarcity and class imbalance. Clinically validated virtual patient (VP) models can provide accurate in-silico representations of real patients and thus a means for synthetic data generation in hospital critical care settings. This research presents a realistic, time-varying mechanically ventilated respiratory failure VP profile synthesised using a stochastic model.

    METHODS: A stochastic model was developed using respiratory elastance (Ers) data from two clinical cohorts and averaged over 30-minute time intervals. The stochastic model was used to generate future Ers data based on current Ers values with added normally distributed random noise. Self-validation of the VPs was performed via Monte Carlo simulation and retrospective Ers profile fitting. A stochastic VP cohort of temporal Ers evolution was synthesised and then compared to an independent retrospective patient cohort data in a virtual trial across several measured patient responses, where similarity of profiles validates the realism of stochastic model generated VP profiles.

    RESULTS: A total of 120,000 3-hour VPs for pressure control (PC) and volume control (VC) ventilation modes are generated using stochastic simulation. Optimisation of the stochastic simulation process yields an ideal noise percentage of 5-10% and simulation iteration of 200,000 iterations, allowing the simulation of a realistic and diverse set of Ers profiles. Results of self-validation show the retrospective Ers profiles were able to be recreated accurately with a mean squared error of only 0.099 [0.009-0.790]% for the PC cohort and 0.051 [0.030-0.126]% for the VC cohort. A virtual trial demonstrates the ability of the stochastic VP cohort to capture Ers trends within and beyond the retrospective patient cohort providing cohort-level validation.

    CONCLUSION: VPs capable of temporal evolution demonstrate feasibility for use in designing, developing, and optimising bedside MV guidance protocols through in-silico simulation and validation. Overall, the temporal VPs developed using stochastic simulation alleviate the need for lengthy, resource intensive, high cost clinical trials, while facilitating statistically robust virtual trials, ultimately leading to improved patient care and outcomes in mechanical ventilation.

    Matched MeSH terms: Computer Simulation
  16. Fulltext Classification-based deep neural network vs mixture density network models for insulin sensitivity prediction problem
    Benyó B, Paláncz B, Szlávecz Á, Szabó B, Kovács K, Chase JG
    Comput Methods Programs Biomed, 2023 Oct;240:107633.
    PMID: 37343375 DOI: 10.1016/j.cmpb.2023.107633
    Model-based glycemic control (GC) protocols are used to treat stress-induced hyperglycaemia in intensive care units (ICUs). The STAR (Stochastic-TARgeted) glycemic control protocol - used in clinical practice in several ICUs in New Zealand, Hungary, Belgium, and Malaysia - is a model-based GC protocol using a patient-specific, model-based insulin sensitivity to describe the patient's actual state. Two neural network based methods are defined in this study to predict the patient's insulin sensitivity parameter: a classification deep neural network and a Mixture Density Network based method. Treatment data from three different patient cohorts are used to train the network models. Accuracy of neural network predictions are compared with the current model- based predictions used to guide care. The prediction accuracy was found to be the same or better than the reference. The authors suggest that these methods may be a promising alternative in model-based clinical treatment for patient state prediction. Still, more research is needed to validate these findings, including in-silico simulations and clinical validation trials.
    Matched MeSH terms: Computer Simulation
  17. Fulltext Effect of nanoshell geometries, sizes, and quantum emitter parameters on the sensitivity of plasmon-exciton hybrid nanoshells for sensing application
    Firoozi A, Amphawan A, Khordad R, Mohammadi A, Jalali T, Edet CO, et al.
    Sci Rep, 2023 Jul 13;13(1):11325.
    PMID: 37443203 DOI: 10.1038/s41598-023-38475-1
    A proposed nanosensor based on hybrid nanoshells consisting of a core of metal nanoparticles and a coating of molecules is simulated by plasmon-exciton coupling in semi classical approach. We study the interaction of electromagnetic radiation with multilevel atoms in a way that takes into account both the spatial and the temporal dependence of the local fields. Our approach has a wide range of applications, from the description of pulse propagation in two-level media to the elaborate simulation of optoelectronic devices, including sensors. We have numerically solved the corresponding system of coupled Maxwell-Liouville equations using finite difference time domain (FDTD) method for different geometries. Plasmon-exciton hybrid nanoshells with different geometries are designed and simulated, which shows more sensitive to environment refractive index (RI) than nanosensor based on localized surface plasmon. The effects of nanoshell geometries, sizes, and quantum emitter parameters on the sensitivity of nanosensors to changes in the RI of the environment were investigated. It was found that the cone-like nanoshell with a silver core and quantum emitter shell had the highest sensitivity. The tapered shape of the cone like nanoshell leads to a higher density of plasmonic excitations at the tapered end of the nanoshell. Under specific conditions, two sharp, deep LSPR peaks were evident in the scattering data. These distinguishing features are valuable as signatures in nanosensors requiring fast, noninvasive response.
    Matched MeSH terms: Computer Simulation
  18. Fulltext Polygenic risk prediction: why and when out-of-sample prediction R2 can exceed SNP-based heritability
    Wang X, Walker A, Revez JA, Ni G, Adams MJ, McIntosh AM, et al.
    Am J Hum Genet, 2023 Jul 06;110(7):1207-1215.
    PMID: 37379836 DOI: 10.1016/j.ajhg.2023.06.006
    In polygenic score (PGS) analysis, the coefficient of determination (R2) is a key statistic to evaluate efficacy. R2 is the proportion of phenotypic variance explained by the PGS, calculated in a cohort that is independent of the genome-wide association study (GWAS) that provided estimates of allelic effect sizes. The SNP-based heritability (hSNP2, the proportion of total phenotypic variances attributable to all common SNPs) is the theoretical upper limit of the out-of-sample prediction R2. However, in real data analyses R2 has been reported to exceed hSNP2, which occurs in parallel with the observation that hSNP2 estimates tend to decline as the number of cohorts being meta-analyzed increases. Here, we quantify why and when these observations are expected. Using theory and simulation, we show that if heterogeneities in cohort-specific hSNP2 exist, or if genetic correlations between cohorts are less than one, hSNP2 estimates can decrease as the number of cohorts being meta-analyzed increases. We derive conditions when the out-of-sample prediction R2 will be greater than hSNP2 and show the validity of our derivations with real data from a binary trait (major depression) and a continuous trait (educational attainment). Our research calls for a better approach to integrating information from multiple cohorts to address issues of between-cohort heterogeneity.
    Matched MeSH terms: Computer Simulation
  19. Fulltext Modeling discontinuous growth in reared Panulirus ornatus: A generalized additive model and Cox proportional hazard model approach
    Foo CH
    Math Biosci Eng, 2023 Jul 03;20(8):14487-14501.
    PMID: 37679145 DOI: 10.3934/mbe.2023648
    Crustaceans exhibit discontinuous growth as they shed hard shells periodically. Fundamentally, the growth of crustaceans is typically assessed through two key components, length increase after molting (LI) and time intervals between consecutive molts (TI). In this article, we propose a unified likelihood approach that combines a generalized additive model and a Cox proportional hazard model to estimate the parameters of LI and TI separately in crustaceans. This approach captures the observed discontinuity in individuals, providing a comprehensive understanding of crustacean growth patterns. Our study focuses on 75 ornate rock lobsters (Panulirus ornatus) off the Torres Strait in northeastern Australia. Through a simulation study, we demonstrate the effectiveness of the proposed models in characterizing the discontinuity with a continuous growth curve at the population level.
    Matched MeSH terms: Computer Simulation
  20. A new meta-heuristic optimization-based MPPT control technique for green energy harvesting from photovoltaic systems under different atmospheric conditions
    Kishore DJK, Mohamed MR, Sudhakar K, Peddakapu K
    Environ Sci Pollut Res Int, 2023 Jul;30(35):84167-84182.
    PMID: 37358770 DOI: 10.1007/s11356-023-28248-8
    At present, a photovoltaic (PV) system takes responsibility to reduce the risk of global warming and generate electricity. However, the PV system faces numerous problems to track global maximum peak power (GMPP) owing to the nonlinear nature of the environment especially due to partial shading conditions (PSC). To solve these difficulties, previous researchers have utilized various conventional methods for investigations. Nevertheless, these methods have oscillations around the GMPP. Hence, a new metaheuristic method such as an opposition-based equilibrium optimizer (OBEO) algorithm is used in this work for mitigating the oscillations around GMPP. To find the effectiveness of the proposed method, it can be evaluated with other methods such as SSA, GWO, and P&O. As per the simulation outcome, the proposed OBEO method provides maximum efficiency against all other methods. The efficiency for the proposed method under dynamic PSC is 95.09% in 0.16 s, similarly, 96.17% for uniform PSC and 86.25% for complex PSC.
    Matched MeSH terms: Computer Simulation
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