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data modeling and analysis software matlab r2023a  (MathWorks Inc)


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    MathWorks Inc data modeling and analysis software matlab r2023a
    Data Modeling And Analysis Software Matlab R2023a, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/modeling+software+matlab/pm40151037-124-25-20
    Average 90 stars, based on 1 article reviews
    data modeling and analysis software matlab r2023a - by Bioz Stars, 2026-09
    90/100 stars

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    Software:

    Article Title: Ocular Motor System Control Models and the Cerebellum: Hypothetical Mechanisms.
    Article Snippet: To review our studies and Btop-down^ models of saccadic intrusions and infantile nystagmus syndrome with the aim of hypothesizing areas of cerebellar connections controlling parts of the ocular motor subsystems involved in both types of function and dysfunction.. The methods of eye-movement recording and modeling are described in detail in the cited references.. Saccadic intrusions, such as square-wave jerks and square-wave oscillations, can be simulated by a single malfunction, whereas staircase saccadic intrusions required two independent malfunctions.

    Article Title: Validation of remifentanil propofol response surfaces for sedation, surrogates of surgical stimulus, and laryngoscopy in patients undergoing surgery
    Article Snippet: .. Using modeling software (Matlab, Mathworks, Natick, MA), this binary data of OAA/S was fit to a Greco model adjusted for categorical data[ 10 ] to estimate model parameters. ..

    Article Title: Response Surface Model Predictions of Wake-Up Time During Scoliosis Surgery
    Article Snippet: .. We used a wide range of remifentanil and sevoflurane effect-site concentrations and OAA/S scores to create 3 new models of sedation.10 We used modeling software (Matlab, The Mathworks, Natick, MA) and a naïve pooled technique to develop a model to predict the drug concentrations at which patients would be unresponsive to moderate prodding or shaking (OAA/S<2), a model to predict when patients would respond to moderate prodding or shaking that we defined as moderate sedation (OAA/S<3) and a model to predict when patients would respond to their name being called loudly that we defined as minimal sedation (OAA/S<4).16 Model parameters were determined by using an iterative approach with Matlab routine “fminsearch” to find the minimum value for −2LL in equation 2: − = −+ −( ) −( ) =∑2 2 1 11LL Ri Ln P Ri Ln Pi N * * , (2) N is the number of observations made for all volunteers combined, Ri is the observed response, and P is the corresponding probability of loss of response. ..

    Article Title: Using accelerometers to identify a high risk of catastrophic musculoskeletal injury in three racing Thoroughbreds.
    Article Snippet: .. A risk factor regression algorithm was derived with commercially available modeling software (MATLAB; The Mathworks Inc). ..

    Article Title: A Microfluidic Perfusion Platform for In Vitro Analysis of Drug Pharmacokinetic-Pharmacodynamic (PK-PD) Relationships.
    Article Snippet: Static in vitro cell culture studies cannot capture the dynamic concentration profiles of drugs, nutrients, and other factors that cells experience in physiological systems.. This limits the confidence in the translational relevance of in vitro experiments and increases the reliance on empirical testing of exposure-response relationships and dose optimization in animal models during preclinical drug development, introducing additional challenges owing to species-specific differences in drug pharmacokinetics (PK) and pharmacodynamics (PD).. Here, we describe the development of a microfluidic cell culture device that enables perfusion of cells under 2D or 3D culture conditions with temporally programmable concentration profiles.

    Article Title: A Novel Draft Genome-Scale Reconstruction Model of <i>Isochrysis</i> sp: Exploring Metabolic Pathways for Sustainable Aquaculture Innovations
    Article Snippet: Abhishek Sengupta1, Tushar Gupta1, Aman Chakraborty1, Sudeepti Kulshrestha1, Ritu Redhu1, Raya Bhattacharjya2, Archana Tiwari2, and Priyanka Narad1*,# 1Systems Biology and Data Analytics Research Lab, Amity Institute of Biotechnology, Amity University, Noida, Uttar Pradesh-201301, India 2Diatom Research Laboratory, Amity Institute of Biotechnology, Amity University, Noida, Uttar Pradesh-201301, India #Current Affiliation: Indian Council of Medical Research, New Delhi

    Derivative Assay:

    Article Title: Using accelerometers to identify a high risk of catastrophic musculoskeletal injury in three racing Thoroughbreds.
    Article Snippet: .. A risk factor regression algorithm was derived with commercially available modeling software (MATLAB; The Mathworks Inc). ..

    other:

    Article Title: Fault mode operation strategies for dual H-bridge current flow controller in meshed HVDC grid
    Article Snippet: Then, using the linearized model, the controller (including the PI, the second order compensator and the low pass filter) is tuned using modeling software namely MATLAB Simulink to achieve a first order system response.

    Concentration Assay:

    Article Title: A Microfluidic Perfusion Platform for In Vitro Analysis of Drug Pharmacokinetic-Pharmacodynamic (PK-PD) Relationships.
    Article Snippet: Static in vitro cell culture studies cannot capture the dynamic concentration profiles of drugs, nutrients, and other factors that cells experience in physiological systems.. This limits the confidence in the translational relevance of in vitro experiments and increases the reliance on empirical testing of exposure-response relationships and dose optimization in animal models during preclinical drug development, introducing additional challenges owing to species-specific differences in drug pharmacokinetics (PK) and pharmacodynamics (PD).. Here, we describe the development of a microfluidic cell culture device that enables perfusion of cells under 2D or 3D culture conditions with temporally programmable concentration profiles.

    Cell Culture:

    Article Title: A Microfluidic Perfusion Platform for In Vitro Analysis of Drug Pharmacokinetic-Pharmacodynamic (PK-PD) Relationships.
    Article Snippet: Static in vitro cell culture studies cannot capture the dynamic concentration profiles of drugs, nutrients, and other factors that cells experience in physiological systems.. This limits the confidence in the translational relevance of in vitro experiments and increases the reliance on empirical testing of exposure-response relationships and dose optimization in animal models during preclinical drug development, introducing additional challenges owing to species-specific differences in drug pharmacokinetics (PK) and pharmacodynamics (PD).. Here, we describe the development of a microfluidic cell culture device that enables perfusion of cells under 2D or 3D culture conditions with temporally programmable concentration profiles.



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    FIGURE 2 | Schematic diagram of human <t>QSP</t> model. Human QSP model was built by combining the reported model of Wang et al. [33] and a por- tion of oncolytic virus mechanism of action in the preclinical QSP model shown in Figure 1. APC, antigen-presenting cell; Arg-1, arginase 1; aTCD8, activated CD8-positive T cells; CCL-2, chemokine (C-C motif) ligand 2; CTLA-4, cytotoxic T-lymphocyte-associated protein 4; e, rate of tumor-cell kill by differentiated effector T cells; IL-2, interleukin 2; IL-7, interleukin 7; IL-12, interleukin 12; mAPC, MHC-presenting APC; MDSC, myeloid- derived suppressor cells; MHC, major histocompatibility complex; nTCD4, naïve CD4-positive T cells; nTCD8, naïve CD8-positive T cells; NO, nitric oxide; PD-1, programmed cell death protein 1; PD-L1, programmed death-ligand 1; QSP, quantitative systems pharmacology; TCR, T-cell receptor; Teff, effector T cells; Treg, regulatory T cells; Tumi, infected tumor cells; Tumni, noninfected tumor cells; Valpha, viral production size.
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    FIGURE 2 | Schematic diagram of human QSP model. Human QSP model was built by combining the reported model of Wang et al. [33] and a por- tion of oncolytic virus mechanism of action in the preclinical QSP model shown in Figure 1. APC, antigen-presenting cell; Arg-1, arginase 1; aTCD8, activated CD8-positive T cells; CCL-2, chemokine (C-C motif) ligand 2; CTLA-4, cytotoxic T-lymphocyte-associated protein 4; e, rate of tumor-cell kill by differentiated effector T cells; IL-2, interleukin 2; IL-7, interleukin 7; IL-12, interleukin 12; mAPC, MHC-presenting APC; MDSC, myeloid- derived suppressor cells; MHC, major histocompatibility complex; nTCD4, naïve CD4-positive T cells; nTCD8, naïve CD8-positive T cells; NO, nitric oxide; PD-1, programmed cell death protein 1; PD-L1, programmed death-ligand 1; QSP, quantitative systems pharmacology; TCR, T-cell receptor; Teff, effector T cells; Treg, regulatory T cells; Tumi, infected tumor cells; Tumni, noninfected tumor cells; Valpha, viral production size.

    Journal: CPT: pharmacometrics & systems pharmacology

    Article Title: A Multiple-Model-Informed Drug-Development Approach for Optimal Regimen Selection of an Oncolytic Virus in Combination With Pembrolizumab.

    doi: 10.1002/psp4.13297

    Figure Lengend Snippet: FIGURE 2 | Schematic diagram of human QSP model. Human QSP model was built by combining the reported model of Wang et al. [33] and a por- tion of oncolytic virus mechanism of action in the preclinical QSP model shown in Figure 1. APC, antigen-presenting cell; Arg-1, arginase 1; aTCD8, activated CD8-positive T cells; CCL-2, chemokine (C-C motif) ligand 2; CTLA-4, cytotoxic T-lymphocyte-associated protein 4; e, rate of tumor-cell kill by differentiated effector T cells; IL-2, interleukin 2; IL-7, interleukin 7; IL-12, interleukin 12; mAPC, MHC-presenting APC; MDSC, myeloid- derived suppressor cells; MHC, major histocompatibility complex; nTCD4, naïve CD4-positive T cells; nTCD8, naïve CD8-positive T cells; NO, nitric oxide; PD-1, programmed cell death protein 1; PD-L1, programmed death-ligand 1; QSP, quantitative systems pharmacology; TCR, T-cell receptor; Teff, effector T cells; Treg, regulatory T cells; Tumi, infected tumor cells; Tumni, noninfected tumor cells; Valpha, viral production size.

    Article Snippet: Clinical QSP model Clinical ABM Software MATLAB, SimBiology Virtual Tumour (coded in MATLAB) Number of equations 160 67 Number of species 124 34 Number of parameters 185 47 Time to run Approximately 2 h Around 90 s per individual simulation (overall run time depends on the number of individual simulations required) Output No clear difference was observed.

    Techniques: Virus, Derivative Assay, Immunopeptidomics, Infection