Displaying publications 1 - 20 of 405 in total

  1. Abdul Latif NS, Wake GC, Reglinski T, Elmer PA
    J Theor Biol, 2014 Apr 21;347:144-50.
    PMID: 24398025 DOI: 10.1016/j.jtbi.2013.12.023
    Plant disease control has traditionally relied heavily on the use of agrochemicals despite their potentially negative impact on the environment. An alternative strategy is that of induced resistance (IR). However, while IR has proven effective in controlled environments, it has shown variable field efficacy, thus raising questions about its potential for disease management in a given crop. Mathematical modelling of IR assists researchers with understanding the dynamics of the phenomenon in a given plant cohort against a selected disease-causing pathogen. Here, a prototype mathematical model of IR promoted by a chemical elicitor is proposed and analysed. Standard epidemiological models describe that, under appropriate environmental conditions, Susceptible plants (S) may become Diseased (D) upon exposure to a compatible pathogen or are able to Resist the infection (R) via basal host defence mechanisms. The application of an elicitor enhances the basal defence response thereby affecting the relative proportion of plants in each of the S, R and D compartments. IR is a transient response and is modelled using reversible processes to describe the temporal evolution of the compartments. Over time, plants can move between these compartments. For example, a plant in the R-compartment can move into the S-compartment and can then become diseased. Once in the D-compartment, however, it is assumed that there is no recovery. The terms in the equations are identified using established principles governing disease transmission and this introduces parameters which are determined by matching data to the model using computer-based algorithms. These then give the best match of the model with experimental data. The model predicts the relative proportion of plants in each compartment and quantitatively estimates elicitor effectiveness. An illustrative case study will be given; however, the model is generic and will be applicable for a range of plant-pathogen-elicitor scenarios.
    Matched MeSH terms: Models, Biological*
  2. Tee SH
    PMID: 30594412 DOI: 10.1016/j.shpsc.2018.12.001
    It is commonly held that in vivo biological experimental models are concrete and non-fictional. This belief is primarily supported by the fact that in vivo studies involve biological models which are alive, and what is alive cannot be fictional. However, I argue that this is not always the case. The design of an experimental model could still render an in vivo model fictional because fictional elements and processes can be built into these in vivo experimental models. These fictional elements are essential parts of a credentialed fiction because the designs of in vivo experimental models are constrained by imaginability, conceivability, and credit-worthiness. Therefore, despite its fictionality, it is credible for an in vivo experimental model to stand in for the phenomenon of interest.
    Matched MeSH terms: Models, Biological*
  3. Gaeid KS, Ping HW, Khalid M, Masaoud A
    Sensors (Basel), 2012;12(4):4031-50.
    PMID: 22666016 DOI: 10.3390/s120404031
    Fault Tolerant Control (FTC) systems are crucial in industry to ensure safe and reliable operation, especially of motor drives. This paper proposes the use of multiple controllers for a FTC system of an induction motor drive, selected based on a switching mechanism. The system switches between sensor vector control, sensorless vector control, closed-loop voltage by frequency (V/f) control and open loop V/f control. Vector control offers high performance, while V/f is a simple, low cost strategy with high speed and satisfactory performance. The faults dealt with are speed sensor failures, stator winding open circuits, shorts and minimum voltage faults. In the event of compound faults, a protection unit halts motor operation. The faults are detected using a wavelet index. For the sensorless vector control, a novel Boosted Model Reference Adaptive System (BMRAS) to estimate the motor speed is presented, which reduces tuning time. Both simulation results and experimental results with an induction motor drive show the scheme to be a fast and effective one for fault detection, while the control methods transition smoothly and ensure the effectiveness of the FTC system. The system is also shown to be flexible, reverting rapidly back to the dominant controller if the motor returns to a healthy state.
    Matched MeSH terms: Models, Biological
  4. Mukhamedov F, Izzat Qaralleh, Wan Nur Fairuz Alwani Wan Rozali
    Sains Malaysiana, 2014;43:1275-1281.
    A quadratic stochastic operator (Qso) is usually used to present the time evolution of differing species in biology. Some quadratic stochastic operators have been studied by Lotka and Volterra. The general problem in the nonlinear operator theory is to study the behavior of operators. This problem was not fully finished even for quadratic stochastic operators which are the simplest nonlinear operators. To study this problem, several classes of QSO were investigated. In this paper, we study the fri)-Qso defined on 2D simplex. We first classify 4-(a)-QS0 into 2 non-conjugate classes. Further, we investigate the dynamics of these classes of such operators.
    Matched MeSH terms: Models, Biological
  5. Law KB, Peariasamy KM, Gill BS, Singh S, Sundram BM, Rajendran K, et al.
    Sci Rep, 2020 12 10;10(1):21721.
    PMID: 33303925 DOI: 10.1038/s41598-020-78739-8
    The susceptible-infectious-removed (SIR) model offers the simplest framework to study transmission dynamics of COVID-19, however, it does not factor in its early depleting trend observed during a lockdown. We modified the SIR model to specifically simulate the early depleting transmission dynamics of COVID-19 to better predict its temporal trend in Malaysia. The classical SIR model was fitted to observed total (I total), active (I) and removed (R) cases of COVID-19 before lockdown to estimate the basic reproduction number. Next, the model was modified with a partial time-varying force of infection, given by a proportionally depleting transmission coefficient, [Formula: see text] and a fractional term, z. The modified SIR model was then fitted to observed data over 6 weeks during the lockdown. Model fitting and projection were validated using the mean absolute percent error (MAPE). The transmission dynamics of COVID-19 was interrupted immediately by the lockdown. The modified SIR model projected the depleting temporal trends with lowest MAPE for I total, followed by I, I daily and R. During lockdown, the dynamics of COVID-19 depleted at a rate of 4.7% each day with a decreased capacity of 40%. For 7-day and 14-day projections, the modified SIR model accurately predicted I total, I and R. The depleting transmission dynamics for COVID-19 during lockdown can be accurately captured by time-varying SIR model. Projection generated based on observed data is useful for future planning and control of COVID-19.
    Matched MeSH terms: Models, Biological*
  6. Khan AQ, Ahmad I, Alayachi HS, M Noorani MS, Khaliq A
    Math Biosci Eng, 2020 09 09;17(5):5944-5960.
    PMID: 33120584 DOI: 10.3934/mbe.2020317
    We explore the local dynamics, flip bifurcation, chaos control and existence of periodic point of the predator-prey model with Allee effect on the prey population in the interior of $\mathbb{R}^*{_+^2}$. Nu-merical simulations not only exhibit our results with the theoretical analysis but also show the complex dynamical behaviors, such as the period-2, 8, 11, 17, 20 and 22 orbits. Further, maximum Lyapunov exponents as well as fractal dimensions are also computed numerically to show the presence of chaotic behavior in the model under consideration.
    Matched MeSH terms: Models, Biological*
  7. Chan JY, Ooi EH
    Cryobiology, 2016 12;73(3):304-315.
    PMID: 27789380 DOI: 10.1016/j.cryobiol.2016.10.006
    Advancement in biomedical simulation and imaging modality have catalysed the development of in silico predictive models for cryoablation. However, one of the main challenges in ensuring the accuracy of the model prediction is the use of proper thermal and biophysical properties of the patient. These properties are difficult to measure clinically and thus, represent significant uncertainty that can affect the model prediction. Motivated by this, a sensitivity analysis is carried out to identify the model parameters that have the most significant impact on the lesion size during cryoablation. The study is initially carried out using the Morris method to screen for the most dominant parameters. Once determined, analysis of variance (ANOVA) is performed to quantitatively rank the order of importance of each parameter and their interactions. Results from the sensitivity analysis revealed that blood perfusion, water transport and ice nucleation parameters are critical in predicting the lesion size, suggesting that the acquisition of these parameters should be prioritised to ensure the accuracy of the model prediction.
    Matched MeSH terms: Models, Biological*
  8. Mak WY, Ooi QX, Cruz CV, Looi I, Yuen KH, Standing JF
    Br J Clin Pharmacol, 2023 Jan;89(1):330-339.
    PMID: 35976674 DOI: 10.1111/bcp.15496
    AIM: nlmixr offers first-order conditional estimation (FOCE), FOCE with interaction (FOCEi) and stochastic approximation estimation-maximisation (SAEM) to fit nonlinear mixed-effect models (NLMEM). We modelled metformin's pharmacokinetic data using nlmixr and investigated SAEM and FOCEi's performance with respect to bias and precision of parameter estimates, and robustness to initial estimates.

    METHOD: Compartmental models were fitted. The final model was determined based on the objective function value and inspection of goodness-of-fit plots. The bias and precision of parameter estimates were compared between SAEM and FOCEi using stochastic simulations and estimations. For robustness, parameters were re-estimated as the initial estimates were perturbed 100 times and resultant changes evaluated.

    RESULTS: The absorption kinetics of metformin depend significantly on food status. Under the fasted state, the first-order absorption into the central compartment was preceded by zero-order infusion into the depot compartment, whereas for the fed state, the absorption into the depot was instantaneous followed by first-order absorption from depot into the central compartment. The means of relative mean estimation error (rMEE) ( ME E SAEM ME E FOCEi ) and rRMSE ( RMS E SAEM RMS E FOCEi ) were 0.48 and 0.35, respectively. All parameter estimates given by SAEM appeared to be narrowly distributed and were close to the true value used for simulation. In contrast, the distribution of estimates from FOCEi were skewed and more biased. When initial estimates were perturbed, FOCEi estimates were more biased and imprecise.

    DISCUSSION: nlmixr is reliable for NLMEM. SAEM was superior to FOCEi in terms of bias and precision, and more robust against initial estimate perturbations.

    Matched MeSH terms: Models, Biological*
  9. Jabaraj DJ
    Ann Biomed Eng, 2020 Jan;48(1):393-402.
    PMID: 31531790 DOI: 10.1007/s10439-019-02356-4
    We examine the low-frequency limit of hearing of the mammalian ear through the analytical modelling of the natural frequency of the tympanic membrane. The resulting equation of the natural frequency of the modelled tympanic membrane is numerically verified against previous theoretical studies, and is statistically validated against the experimental data on the low-frequency limit of hearing. By utilizing the Wilcoxon signed-rank test; W-values of 29 (p value = 0.25014) and 23 (p value = 0.11642) are respectively obtained for the 0.2% and 0.3% prestrain (at 5% significance level for sample size of 13). We fail to reject the null hypothesis as the W-values are within the critical values of the test statistics, and therefore conclude that the tympanic membrane acts as a low-frequency limiter of acoustic stimulus. Based on our study, we can predict the low-frequency limit of hearing in mammals (e.g., for the whale as 3.6 Hz and for the zebra as 44.0 Hz).
    Matched MeSH terms: Models, Biological*
  10. Daud KM, Mohamad MS, Zakaria Z, Hassan R, Shah ZA, Deris S, et al.
    Comput Biol Med, 2019 10;113:103390.
    PMID: 31450056 DOI: 10.1016/j.compbiomed.2019.103390
    Metabolic engineering is defined as improving the cellular activities of an organism by manipulating the metabolic, signal or regulatory network. In silico reaction knockout simulation is one of the techniques applied to analyse the effects of genetic perturbations on metabolite production. Many methods consider growth coupling as the objective function, whereby it searches for mutants that maximise the growth and production rate. However, the final goal is to increase the production rate. Furthermore, they produce one single solution, though in reality, cells do not focus on one objective and they need to consider various different competing objectives. In this work, a method, termed ndsDSAFBA (non-dominated sorting Differential Search Algorithm and Flux Balance Analysis), has been developed to find the reaction knockouts involved in maximising the production rate and growth rate of the mutant, by incorporating Pareto dominance concepts. The proposed ndsDSAFBA method was validated using three genome-scale metabolic models. We obtained a set of non-dominated solutions, with each solution representing a different mutant strain. The results obtained were compared with the single objective optimisation (SOO) and multi-objective optimisation (MOO) methods. The results demonstrate that ndsDSAFBA is better than the other methods in terms of production rate and growth rate.
    Matched MeSH terms: Models, Biological*
  11. Lee JJJ, Loh WP
    Comput Biol Med, 2019 05;108:213-222.
    PMID: 31005013 DOI: 10.1016/j.compbiomed.2019.04.003
    Good badminton lunge skills have been quantitatively described using biomechanical attributes at both static and dynamic phases. The measurement of badminton lunge attributes has often been complicated by various experimental protocols used. No review article has considered or critically reviewed the attributes that align with badminton lunge performance. This paper, hence, presents a review of badminton lunge postures governed by various determinant attributes. This review was performed by involving a number of relevant search engines. A total of 21 articles that fulfilled the predefined inclusion criteria were analysed. The lunge determinant attributes, such as time, lunge distance, plantar, ground reaction force, joint, dynamic balance and muscle attributes, had been examined. Contradictory findings in the dynamic balance attributes, specifically the relative displacement between the centre of mass and the centre of pressure, are presented in this paper. The findings showed that time, lunge distance and ground reaction force determined lunge performance. On the other hand, plantar, joint, dynamic balance and muscle attributes appeared useful in minimising injuries to ensure efficient lunge performance.
    Matched MeSH terms: Models, Biological*
  12. Lee MK, Mohamad MS, Choon YW, Mohd Daud K, Nasarudin NA, Ismail MA, et al.
    J Integr Bioinform, 2020 May 06;17(1).
    PMID: 32374287 DOI: 10.1515/jib-2019-0073
    The metabolic network is the reconstruction of the metabolic pathway of an organism that is used to represent the interaction between enzymes and metabolites in genome level. Meanwhile, metabolic engineering is a process that modifies the metabolic network of a cell to increase the production of metabolites. However, the metabolic networks are too complex that cause problem in identifying near-optimal knockout genes/reactions for maximizing the metabolite's production. Therefore, through constraint-based modelling, various metaheuristic algorithms have been improvised to optimize the desired phenotypes. In this paper, PSOMOMA was compared with CSMOMA and ABCMOMA for maximizing the production of succinic acid in E. coli. Furthermore, the results obtained from PSOMOMA were validated with results from the wet lab experiment.
    Matched MeSH terms: Models, Biological*
  13. Lee JL, Mohd Saffian S, Makmor-Bakry M, Islahudin F, Alias H, Noh LM, et al.
    Br J Clin Pharmacol, 2021 07;87(7):2956-2966.
    PMID: 33377197 DOI: 10.1111/bcp.14712
    AIMS: There is considerable interpatient variability in the pharmacokinetics (PK) of intravenous immunoglobulin G (IVIG), causing difficulty in optimizing individual dosage regimen. This study aims to estimate the population PK parameters of IVIG and to investigate the impact of genetic polymorphism of the FcRn gene and clinical variability on the PK of IVIG in patients with predominantly antibody deficiencies.

    METHODS: Patients were recruited from four hospitals. Clinical data were recorded and blood samples were taken for PK and genetic studies. Population PK parameters were estimated by nonlinear mixed-effects modelling in Monolix®. Models were evaluated using the difference in objective function value, goodness-of-fit plots, visual predictive check and bootstrap analysis. Monte Carlo simulation was conducted to evaluate different dosing regimens for IVIG.

    RESULTS: A total of 30 blood samples were analysed from 10 patients. The immunoglobulin G concentration data were best described by a one-compartment model with linear elimination. The final model included both volume of distribution (Vd) and clearance (CL) based on patient's individual weight. Goodness-of-fit plots indicated that the model fit the data adequately, with minor model mis-specification. Genetic polymorphism of the FcRn gene and the presence of bronchiectasis did not affect the PK of IVIG. Simulation showed that 3-4-weekly dosing intervals were sufficient to maintain IgG levels of 5 g L-1 , with more frequent intervals needed to achieve higher trough levels.

    CONCLUSIONS: Body weight significantly affects the PK parameters of IVIG. Genetic and other clinical factors investigated did not affect the disposition of IVIG.

    Matched MeSH terms: Models, Biological*
  14. Thompson MS, Bajuri MN, Khayyeri H, Isaksson H
    Proc Inst Mech Eng H, 2017 May;231(5):369-377.
    PMID: 28427319 DOI: 10.1177/0954411917692010
    Tendons are adapted to carry large, repeated loads and are clinically important for the maintenance of musculoskeletal health in an increasing, actively ageing population, as well as in elite athletes. Tendons are known to adapt to mechanical loading. Also, their healing and disease processes are highly sensitive to mechanical load. Computational modelling approaches developed to capture this mechanobiological adaptation in tendons and other tissues have successfully addressed many important scientific and clinical issues. The aim of this review is to identify techniques and approaches that could be further developed to address tendon-related problems. Biomechanical models are identified that capture the multi-level aspects of tendon mechanics. Continuum whole tendon models, both phenomenological and microstructurally motivated, are important to estimate forces during locomotion activities. Fibril-level microstructural models are documented that can use these estimated forces to detail local mechanical parameters relevant to cell mechanotransduction. Cell-level models able to predict the response to such parameters are also described. A selection of updatable mechanobiological models is presented. These use mechanical signals, often continuum tissue level, along with rules for tissue change and have been applied successfully in many tissues to predict in vivo and in vitro outcomes. Signals may include scalars derived from the stress or strain tensors, or in poroelasticity also fluid velocity, while adaptation may be represented by changes to elastic modulus, permeability, fibril density or orientation. So far, only simple analytical approaches have been applied to tendon mechanobiology. With the development of sophisticated computational mechanobiological models in parallel with reporting more quantitative data from in vivo or clinical mechanobiological studies, for example, appropriate imaging, biochemical and histological data, this field offers huge potential for future development towards clinical applications.
    Matched MeSH terms: Models, Biological*
  15. Nasser AB, Zamli KZ, Alsewari AA, Ahmed BS
    PLoS One, 2018;13(5):e0195187.
    PMID: 29718918 DOI: 10.1371/journal.pone.0195187
    The application of meta-heuristic algorithms for t-way testing has recently become prevalent. Consequently, many useful meta-heuristic algorithms have been developed on the basis of the implementation of t-way strategies (where t indicates the interaction strength). Mixed results have been reported in the literature to highlight the fact that no single strategy appears to be superior compared with other configurations. The hybridization of two or more algorithms can enhance the overall search capabilities, that is, by compensating the limitation of one algorithm with the strength of others. Thus, hybrid variants of the flower pollination algorithm (FPA) are proposed in the current work. Four hybrid variants of FPA are considered by combining FPA with other algorithmic components. The experimental results demonstrate that FPA hybrids overcome the problems of slow convergence in the original FPA and offers statistically superior performance compared with existing t-way strategies in terms of test suite size.
    Matched MeSH terms: Models, Biological*
  16. Chew WX, Kaizu K, Watabe M, Muniandy SV, Takahashi K, Arjunan SNV
    Phys Rev E, 2019 Apr;99(4-1):042411.
    PMID: 31108654 DOI: 10.1103/PhysRevE.99.042411
    Microscopic models of reaction-diffusion processes on the cell membrane can link local spatiotemporal effects to macroscopic self-organized patterns often observed on the membrane. Simulation schemes based on the microscopic lattice method (MLM) can model these processes at the microscopic scale by tracking individual molecules, represented as hard spheres, on fine lattice voxels. Although MLM is simple to implement and is generally less computationally demanding than off-lattice approaches, its accuracy and consistency in modeling surface reactions have not been fully verified. Using the Spatiocyte scheme, we study the accuracy of MLM in diffusion-influenced surface reactions. We derive the lattice-based bimolecular association rates for two-dimensional (2D) surface-surface reaction and one-dimensional (1D) volume-surface adsorption according to the Smoluchowski-Collins-Kimball model and random walk theory. We match the time-dependent rates on lattice with off-lattice counterparts to obtain the correct expressions for MLM parameters in terms of physical constants. The expressions indicate that the voxel size needs to be at least 0.6% larger than the molecule to accurately simulate surface reactions on triangular lattice. On square lattice, the minimum voxel size should be even larger, at 5%. We also demonstrate the ability of MLM-based schemes such as Spatiocyte to simulate a reaction-diffusion model that involves all dimensions: three-dimensional (3D) diffusion in the cytoplasm, 2D diffusion on the cell membrane, and 1D cytoplasm-membrane adsorption. With the model, we examine the contribution of the 2D reaction pathway to the overall reaction rate at different reactant diffusivity, reactivity, and concentrations.
    Matched MeSH terms: Models, Biological*
  17. Tang BH, Guan Z, Allegaert K, Wu YE, Manolis E, Leroux S, et al.
    Clin Pharmacokinet, 2021 11;60(11):1435-1448.
    PMID: 34041714 DOI: 10.1007/s40262-021-01033-x
    BACKGROUND: Population pharmacokinetic evaluations have been widely used in neonatal pharmacokinetic studies, while machine learning has become a popular approach to solving complex problems in the current era of big data.

    OBJECTIVE: The aim of this proof-of-concept study was to evaluate whether combining population pharmacokinetic and machine learning approaches could provide a more accurate prediction of the clearance of renally eliminated drugs in individual neonates.

    METHODS: Six drugs that are primarily eliminated by the kidneys were selected (vancomycin, latamoxef, cefepime, azlocillin, ceftazidime, and amoxicillin) as 'proof of concept' compounds. Individual estimates of clearance obtained from population pharmacokinetic models were used as reference clearances, and diverse machine learning methods and nested cross-validation were adopted and evaluated against these reference clearances. The predictive performance of these combined methods was compared with the performance of two other predictive methods: a covariate-based maturation model and a postmenstrual age and body weight scaling model. Relative error was used to evaluate the different methods.

    RESULTS: The extra tree regressor was selected as the best-fit machine learning method. Using the combined method, more than 95% of predictions for all six drugs had a relative error of < 50% and the mean relative error was reduced by an average of 44.3% and 71.3% compared with the other two predictive methods.

    CONCLUSION: A combined population pharmacokinetic and machine learning approach provided improved predictions of individual clearances of renally cleared drugs in neonates. For a new patient treated in clinical practice, individual clearance can be predicted a priori using our model code combined with demographic data.

    Matched MeSH terms: Models, Biological*
  18. Dass SC, Kwok WM, Gibson GJ, Gill BS, Sundram BM, Singh S
    PLoS One, 2021;16(5):e0252136.
    PMID: 34043676 DOI: 10.1371/journal.pone.0252136
    The second wave of COVID-19 in Malaysia is largely attributed to a four-day mass gathering held in Sri Petaling from February 27, 2020, which contributed to an exponential rise of COVID-19 cases in the country. Starting from March 18, 2020, the Malaysian government introduced four consecutive phases of a Movement Control Order (MCO) to stem the spread of COVID-19. The MCO was implemented through various non-pharmaceutical interventions (NPIs). The reported number of cases reached its peak by the first week of April and then started to reduce, hence proving the effectiveness of the MCO. To gain a quantitative understanding of the effect of MCO on the dynamics of COVID-19, this paper develops a class of mathematical models to capture the disease spread before and after MCO implementation in Malaysia. A heterogeneous variant of the Susceptible-Exposed-Infected-Recovered (SEIR) model is developed with additional compartments for asymptomatic transmission. Further, a change-point is incorporated to model disease dynamics before and after intervention which is inferred based on data. Related statistical analyses for inference are developed in a Bayesian framework and are able to provide quantitative assessments of (1) the impact of the Sri Petaling gathering, and (2) the extent of decreasing transmission during the MCO period. The analysis here also quantitatively demonstrates how quickly transmission rates fall under effective NPI implementation within a short time period. The models and methodology used provided important insights into the nature of local transmissions to decision makers in the Ministry of Health, Malaysia.
    Matched MeSH terms: Models, Biological*
  19. Alias MA, Buenzli PR
    Biophys J, 2017 Jan 10;112(1):193-204.
    PMID: 28076811 DOI: 10.1016/j.bpj.2016.11.3203
    The growth of several biological tissues is known to be controlled in part by local geometrical features, such as the curvature of the tissue interface. This control leads to changes in tissue shape that in turn can affect the tissue's evolution. Understanding the cellular basis of this control is highly significant for bioscaffold tissue engineering, the evolution of bone microarchitecture, wound healing, and tumor growth. Although previous models have proposed geometrical relationships between tissue growth and curvature, the role of cell density and cell vigor remains poorly understood. We propose a cell-based mathematical model of tissue growth to investigate the systematic influence of curvature on the collective crowding or spreading of tissue-synthesizing cells induced by changes in local tissue surface area during the motion of the interface. Depending on the strength of diffusive damping, the model exhibits complex growth patterns such as undulating motion, efficient smoothing of irregularities, and the generation of cusps. We compare this model with in vitro experiments of tissue deposition in bioscaffolds of different geometries. By including the depletion of active cells, the model is able to capture both smoothing of initial substrate geometry and tissue deposition slowdown as observed experimentally.
    Matched MeSH terms: Models, Biological*
  20. Waran V, Narayanan V, Karuppiah R, Thambynayagam HC, Muthusamy KA, Rahman ZA, et al.
    Simul Healthc, 2015 Feb;10(1):43-8.
    PMID: 25514588 DOI: 10.1097/SIH.0000000000000060
    Training in intraventricular endoscopy is particularly challenging because the volume of cases is relatively small and the techniques involved are unlike those usually used in conventional neurosurgery. Present training models are inadequate for various reasons. Using 3-dimensional (3D) printing techniques, models with pathology can be created using actual patient's imaging data. This technical article introduces a new training model based on a patient with hydrocephalus secondary to a pineal tumour, enabling the models to be used to simulate third ventriculostomies and pineal biopsies.
    Matched MeSH terms: Models, Biological*
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