Development of a Rice Plant Disease Classification Model in Big Data Environment.

More than the half of the global population consume rice as their primary energy source. Therefore, this work focused on the development of a prediction model to minimize agricultural loss in the paddy field. Initially, rice plant diseases, along with their images, were captured. Then, a big data framework was used to encounter a large dataset. In this work, at first, feature extraction process is applied on the data and after that feature selection is also applied to obtain the reduced data with important features which is used as the input to the classification model. For the rice disease datasets, features based on color, shape, position, and texture are extracted from the infected rice plant images and a rough set theory-based feature selection method is used for the feature selection job. For the classification task, ensemble classification methods have been implemented in a map reduce framework for the development of the efficient disease prediction model. The results on the collected disease data show the efficiency of the proposed model.

Optimization of intraperitoneal aerosolized drug delivery using computational fluid dynamics (CFD) modeling.

Intraperitoneal (IP) aerosolized anticancer drug delivery was recently introduced in the treatment of patients with peritoneal metastases. However, little is known on the effect of treatment parameters on the spatial distribution of the aerosol droplets in the peritoneal cavity. Here, computational fluid dynamics (CFD) modeling was used in conjunction with experimental validation in order to investigate the effect of droplet size, liquid flow rate and viscosity, and the addition of an electrostatic field on the homogeneity of IP aerosol. We found that spatial distribution is optimal with small droplet sizes (1-5 µm). Using the current clinically used technology (droplet size of 30 µm), the optimal spatial distribution of aerosol is obtained with a liquid flow rate of 0.6 mL s-1. Compared to saline, nebulization of higher viscosity liquids results in less homogeneous aerosol distribution. The addition of electrostatic precipitation significantly improves homogeneity of aerosol distribution, but no further improvement is obtained with voltages higher than 6.5 kV. The results of the current study will allow to choose treatment parameters and settings in order to optimize spatial distribution of IP aerosolized drug, with a potential to enhance its anticancer effect.

Exploring high pressure nebulization of Pluronic F127 hydrogels for intraperitoneal drug delivery.

Peritoneal metastasis is an advanced cancer type which can be treated with pressurized intraperitoneal aerosol chemotherapy (PIPAC). Here, chemotherapeutics are nebulized under high pressure in the intraperitoneal (IP) cavity to obtain a better biodistribution and tumor penetration. To prevent the fast leakage of chemotherapeutics from the IP cavity, however, nebulization of controlled release formulations is of interest. In this study, the potential of the thermosensitive hydrogel Pluronic F127 to be applied by high pressure nebulization is evaluated. Therefore, aerosol formation is experimentally examined by laser diffraction and theoretically simulated by computational fluid dynamics (CFD) modelling. Furthermore, Pluronic F127 hydrogels are subjected to rheological characterization after which the release of fluorescent model nanoparticles from the hydrogels is determined. A delicate equilibrium is observed between controlled release properties and suitability for aerosolization, where denser hydrogels (20% and 25% w/v Pluronic F127) are able to sustain nanoparticle release up to 30 h, but cannot effectively be nebulized and vice versa. This is demonstrated by a growing aerosol droplet size and exponentially decreasing aerosol cone angle when Pluronic F127 concentration and viscosity increase. Novel nozzle designs or alternative controlled release formulations could move intraperitoneal drug delivery by high pressure nebulization forward.

Polydisperse Aerosol Transport and Deposition in Upper Airways of Age-Specific Lung.

A comprehensive understanding of airflow characteristics and particle transport in the human lung can be useful in modelling to inform clinical diagnosis, treatment, and management, including prescription medication and risk assessment for rehabilitation. One of the difficulties in clinical treatment of lung disorders lies in the patients' variable physical lung characteristics caused by age, amongst other factors, such as different lung sizes. A precise understanding of the comparison between different age groups with various flow rates is missing in the literature, and this study aims to analyse the airflow and aerosol transport within the age-specific lung. ANSYS Fluent solver and the large-eddy simulation (LES) model were employed for the numerical simulation. The numerical model was validated with the available literature and the computational results showed airway size-reduction significantly affected airflow and particle transport in the upper airways. This study reports higher deposition at the mouth-throat region for larger diameter particles. The overall deposition efficiency (DE) increased with airway size reduction and flow rate. Lung aging effected the pressure distribution and a higher pressure drop was reported for the aged lung as compared to the younger lung. These findings could inform medical management through individualised simulation of drug-aerosol delivery processes for the patient-specific lung.

Estimation of tumor parameters using neural networks for inverse bioheat problem.

BACKGROUND AND OBJECTIVE: Some types of cancer cause rapid cell growth, while others cause cells to grow and divide at a slower rate. Certain forms of cancer result in visible growths called tumors. This work proposes an inverse estimation of the size and location of the tumor using a feedforward Neural Network (FFNN) model. METHODS: The forward model is a 3D model of the breast induced with a tumor of various sizes at different locations within the breast, and it is solved using the Pennes equation. The data obtained from the simulation of the bioheat transfer is used for training the neural network. In order to optimize the neural network architecture, the work proposes varying the number of neurons in the hidden layer and thus finding the best fit to create a relationship between the temperature profile and tumor parameters which can be used to estimate the tumor parameters given the temperature profile. RESULTS: These simulations resulted in a temperature distribution profile that could thus be used to locate and determine the parameters of the cancerous tumor within the breast. The prediction accuracy showed the capacity of the trained Feed Forward Neural Network to estimate the unknown parameters within an acceptable range of error. The model validations use the Root Mean Square Error method to quantify and minimize the prediction error. CONCLUSIONS: In this work, a non-intrusive method for the diagnosis of breast cancer was modelled, which yields conclusive results for the estimation of the tumor parameters.

Analysis of non-Newtonian magnetic Casson blood flow in an inclined stenosed artery using Caputo-Fabrizio fractional derivatives.

BACKGROUND AND OBJECTIVE: Arterial diseases would lead to several serious disorders in the cardiovascular system such as atherosclerosis. These disorders are mainly caused by the presence of fatty deposits, cholesterol and lipoproteins inside blood vessel. This paper deals with the analysis of non-Newtonian magnetic blood flow in an inclined stenosed artery. METHODS: The Casson fluid was used to model the blood that flows under the influences of uniformly distributed magnetic field and oscillating pressure gradient. The governing fractional differential equations were expressed using the Caputo Fabrizio fractional derivative without singular kernel. RESULTS: The analytical solutions of velocities for non-Newtonian model were then calculated by means of Laplace and finite Hankel transforms. These velocities were then presented graphically. The result shows that the velocity increases with respect to Reynolds number and Casson parameter, while decreases when Hartmann number increases. CONCLUSIONS: Casson blood was treated as the non-Newtonian fluid. The MHD blood flow was accelerated by pressure gradient. These findings are beneficial for studying atherosclerosis therapy, the diagnosis and therapeutic treatment of some medical problems.

Evaluation of silk-based bioink during pre and post 3D bioprinting: A review.

During past few decades, the demand for the replacement of damaged organs is increasing consistently. This is due to the advancement in tissue engineering, which opens the possibility of regeneration of damaged organs or tissues into functional parts with the help of 3D bioprinting. Bioprinting technology presents an excellent potential to develop complex structures with precise control over cell suspension and structure. A brief description of different types of 3D bioprinting techniques, including inkjet-based, laser-based, and extrusion-based bioprinting is presented here. Due to innate advantageous features like tunable biodegradability, biocompatibility, elasticity and mechanical robustness, silk has carved a niche in the realm of tissue engineering. In this review article, the focus is to highlight the possible approach of exploring silk as bioink for fabrication of bioprinted implants using 3D bioprinting. This review discusses different type of degumming, dissolution techniques for extraction of proteins from different sources of silk. Different recently reported 3D bioprinting techniques suitable for silk-based bioink are further elaborated. Postprinting characterization of resultant scaffolds are also describe here. However, there is an astounding progress in 3D bioprinting technology, still there is a need to develop further the current bioprinting technology to make it suitable for generation of heterogeneous tissue construct. The possibility of utilizing the adhesive property of sericin to consider it as bioink is elaborated.

Intraperitoneal aerosolized drug delivery: Technology, recent developments, and future outlook.

Current therapies for patients with peritoneal metastases (PM) are only moderately effective. Recently, a novel locoregional treatment method for PM was introduced, consisting of a combination of laparoscopy with intraperitoneal (IP) delivery of anticancer agents as an aerosol. This 'pressurized intraperitoneal aerosol chemotherapy' (PIPAC) may enhance tissue drug penetration by the elevated IP pressure during CO2 capnoperitoneum. Also, repeated PIPAC cycles allow to accurately stage peritoneal disease and verify histological response to treatment. This review provides an overview of the rationale, indications, and currently used technology for therapeutic IP nebulization, and discusses the basic mechanisms governing aerosol particle transport and peritoneal deposition. We discuss early clinical results in patients with advanced, irresectable PM and highlight the potential of electrostatic aerosol precipitation. Finally, we discuss promising novel approaches, including nebulization of nanoparticles and prolonged release formulations.

Electrostatic Intraperitoneal Aerosol Delivery of Nanoparticles: Proof of Concept and Preclinical Validation.

There is an increasing interest in intraperitoneal delivery of chemotherapy as an aerosol in patients with peritoneal metastasis. The currently used technology is hampered by inhomogenous drug delivery throughout the peritoneal cavity because of gravity, drag, and inertial impaction. Addition of an electrical force to aerosol particles, exerted by an electrostatic field, can improve spatial aerosol homogeneity and enhance tissue penetration. A computational fluid dynamics model shows that electrostatic precipitation (EP) results in a significantly improved aerosol distribution. Fluorescent nanoparticles (NPs) remain stable after nebulization in vitro, while EP significantly improves spatial homogeneity of NP distribution. Next, pressurized intraperitoneal chemotherapy with and without EP using NP albumin bound paclitaxel (Nab-PTX) in a novel rat model is examined. EP does not worsen the effects of CO2 insufflation and intraperitoneal Nab-PTX on mesothelial structural integrity or the severity of peritoneal inflammation. Importantly, EP significantly enhances tissue penetration of Nab-PTX in the anatomical regions not facing the nozzle of the nebulizer. Also, the addition of EP leads to more homogenous peritoneal tissue concentrations of Nab-PTX, in parallel with higher plasma concentrations. In conclusion, EP enhances spatial homogeneity and tissue uptake after intraperitoneal nebulization of anticancer NPs.

Prediction of stenosis behaviour in artery by neural network and multiple linear regressions.

Blood flow analysis in the artery is a paramount study in the field of arterial stenosis evaluation. Studies conducted so far have reported the analysis of blood flow parameters using different techniques, but the regression analysis is not adequately used. Artificial neural network is a nonlinear and nonparametric approach. It uses back-propagation algorithm for regression analysis, which is effective as compared to statistical model that requires a higher domain of statistics for prediction. In our manuscript, twofold analyses of data are done. First phase involves the determination of blood flow parameters using physiological flow pulse generator. The second phase includes regression modelling. The inputs to the model were axial length from stenosis, radial distance, inlet velocity, mean pressure, density, viscosity, time, and degree of blockage. Output included dependent variables in the form of output as mean velocity, root-mean-square (RMS) velocity, turbulent intensity, mean frequency, RMS frequency, frequency of turbulent intensity, gate time mean, gate time RMS. The temperature, density, and viscosity conditions were kept constant for various degrees of blockages. It was followed by regression analysis of variables using conventional statistical and neural network approach. The result shows that the neural network model is more appropriate, because value of percentage of response variation of dependent variable is almost approaching unity as compared to statistical analysis.

Scaffold-free: A developing technique in field of tissue engineering.

Scaffold-free tissue engineering can be considered as a rapidly developing technique in the field of tissue engineering. In the areas of regenerative medicine and wound healing, there is a demand of techniques where no scaffolds are used for the development of desired tissue. These techniques will overcome the problems of rejection and tissue failure which are common with scaffolds. Main breakthrough of scaffold free tissue engineering was after invention of 3-D printers which made it possible to print complex tissues which were not possible by conventional methods. Mathematical modeling is a prediction technique is used in tissue engineering for simulation of the model to be constructed. Coming to scaffold-free technique, mathematical modeling is necessary for the processing of the model that has to be bio-printed so as to avoid and changes in the final construct. Tissue construct is developed by use of non-destructive imaging techniques i.e. computed tomography (CT) and magnetic resonance imaging (MRI).In this review, we discussed about various mathematical models and the models which are widely used in bioprinting techniques such as Cellular Potts Model (CPM) and Cellular Particle Dynamic (CPD) model. Later, developed of 3-D tissue construct using micro CT scan images was explained. Finally, we discussed about scaffold free techniques such as 3-D bioprinting and cell sheet technology. In this manuscript, we proposed a cell sheet based bioprinting technique where mesenchymal stem cells (MSCs) on the surface of thermoresponsive polymer were subjected to mechanosensing either by introducing acoustic energies or stress created by polymeric strain energy function. Mechanosensing stimulus will trigger the intracellular signal transduction pathway leading to differentiation of the MSCs into desired cells.

A review study on blood in human coronary artery: Numerical approach.

Computational fluid dynamics (CFD) study of blood flow in human coronary artery is one of the emerging fields of Biomed- ical engineering. In present review paper, Finite Volume Method with governing equations and boundary conditions are briefly discussed for different coronary models. Many researchers have come up with astonishing results related to the various factors (blood viscosity, rate of blood flow, shear stress on the arterial wall, Reynolds number, etc.) affecting the hemodynamic of blood in the right/left coronary artery. The aim of this paper is to present an overview of all those work done by the researchers to justify their work related to factors which hampers proper functioning of heart and lead to Coronary Artery Disease (CAD). Governing equations like Navier-stokes equations, continuity equations etc. are widely used and are solved using CFD solver to get a clearer view of coronary artery blockage. Different boundary conditions and blood properties published in the last ten years are summarized in the tabulated form. This table will help new researchers to work on this area.

Three dimensional numerical analysis of hemodynamic of stenosed artery considering realistic outlet boundary conditions.

BACKGROUND AND OBJECTIVE: Mortality rate increases globally among which one third is due to diseased blood vessels. Due to late diagnoses of the disease in vessels (severe stenoses), qualitative and rapid assessment becomes difficult. Earlier assessment of stenoses can lead to formulation of effective treatment protocol. It is often found that proliferation of secondary stenoses at downstream of a stenosed vessel depends on the degree of severity of primary stenoses. Numerical investigation of flow dynamics of blood in such condition helps in prediction of distributed field of secondarystenoses. This investigation also requires consideration of rigorous boundary conditions at inlet and outlet of defined flow domain. METHODS: Patient-specific geometry of aortic arch with stenoses in descending aorta was considered for numerical estimation of biofluid dynamics. Boundary conditionsat inlet and outlet were extracted from time-resolved pulsed Doppler Ultrasound imaging at appropriate sections of the vessel. Womersley inlet flux was considered. Flow parameters like wall shear stress, oscillatory shear index, etc. were evaluated at upper and lower aortic arch of the vessel at different combinations of boundary conditions at inlet and four outlets respectively. RESULTS: Effect of outlet boundary conditions were acknowledged for the progression of secondary stenoses. Severity of primary stenoses was found influencing the progression of secondary stenoses. It was found that the outlets Left Subclavian Artery and Left Common Carotid Artery greatly influence the flow dynamic structure within the stenosed aortic arch. Simultaneously, lower wall of aortic-arch had shown more affinity for secondary stenoses progression. CONCLUSION: Aortic arch is a vital anatomical region of circulatory system which is vulnerable to progression of secondary stenoses in presence of primary stenoses in ascending or descending aorta. It also drives the author to speculate the influence of anurysm in descending aorta on this landmark of aortic arch.

Entropy generation in the human lung due to effect of psychrometric condition and friction in the respiratory tract.

BACKGROUND AND OBJECTIVE: Entropy generation is associated with the irreversibility of any thermodynamic system. It provides an indication of lost energy and hence the efficiency of a system. In this paper, an attempt has been made to study the effects of specific humidity, relative humidity, ambient temperature change, breathing air friction with the respiratory tract on the entropy generation during the respiration process at different physiological conditions. METHODS: To address the above issues, a human respiratory tract model with realistic length to diameter ratio at different branches has been considered. The analysis examines air flow rates of 6 lpm and 60 lpm during rest and exercise condition respectively; corresponding to breathing rates of 30 and 60 per minute, respectively. The body temperature has been considered at 36°C, and ambient condition of air has been taken at 25°C DBT and 50% RH. The respiratory tract geometry has been modelled on the basis of Weibel's experimental results. RESULTS: It has been noticed that, at a particular Lewis number entropy generation per day decreases with the increase in specific humidity, again at a particular specific humidity entropy generation increases with the decrease in Lewis number. For a particular physical condition and Lewis number entropy generation decreases with the increase in relative humidity. In this work, it has been observed that negentropy increases with the increase in ambient temperature for a constant relative humidity, however the net entropy generation is always positive. This study reports that, maximum resistance of flow occurs where duct aspect ratio (i.e. tract diameter to length) is minimal. For a typical geometry of air passage, velocity of flow increases up to 3rd generation then it is decreases gradually till 23rd generation. Amount of entropy generation goes on reducing as the duct goes on bifurcating except for the third generation where a local peak in entropy generation is observed. This is a consequence of typical geometry of human respiratory duct. This work reveals that, at rest entropy generation due to conditioning of breathing air is higher than its frictional component and during heavy physical activity, entropy generation due to breathing air friction with the respiratory tract is higher than its air conditioning component. CONCLUSIONS: Entropy generation is significantly higher due to conditioning of breathing air than that of frictional effect with the tract. This is a preliminary attempt in quantifying this aspect and the authors believe that, these two components of entropy generation have a probable connection with the bronchial thermoplasty, which helps to treat the asthma.

Details of regional particle deposition and airflow structures in a realistic model of human tracheobronchial airways: two-phase flow simulation.

In the present investigation, detailed two-phase flow modeling of airflow, transport and deposition of micro-particles (1-10µm) in a realistic tracheobronchial airway geometry based on CT scan images under various breathing conditions (i.e. 10-60l/min) was considered. Lagrangian particle tracking has been used to investigate the particle deposition patterns in a model comprising mouth up to generation G6 of tracheobronchial airways. The results demonstrated that during all breathing patterns, the maximum velocity change occurred in the narrow throat region (Larynx). Due to implementing a realistic geometry for simulations, many irregularities and bending deflections exist in the airways model. Thereby, at higher inhalation rates, these areas are prone to vortical effects which tend to entrap the inhaled particles. According to the results, deposition fraction has a direct relationship with particle aerodynamic diameter (for dp=1-10µm). Enhancing inhalation flow rate and particle size will largely increase the inertial force and consequently, more particle deposition is evident suggesting that inertial impaction is the dominant deposition mechanism in tracheobronchial airways.