Prasoon Kumar

Mathematical model of mechanical behavior of micro/nanofibrous materials designed for extracellular matrix substitutes

Electrospun micro/nanofibrous biomaterials are widely used as extracellular matrix substitutes in tissue engineering applications because of their structural and mechanical properties. To explore the influence of microstructure on the mechanical behavior of fibrous material, a mathematical model of the fiber system was developed. The model describes the microstructural properties of a fibrous matrix using a probability density function, and enables study of their mechanical properties. The results from the mathematical model were validated by qualitative comparison with the experimental results of mechanical testing of polystyrene electrospun nanofibrous materials. The analyses show a trend of three-phase load-displacement behavior. Initially, as an increasing number of fibers are recruited for load bearing, the load-displacement curve has a J-shaped toe region, which is followed by a nearly linear load-displacement curve, in which the number of load-bearing fibers remains nearly steady. Finally, there is a phase when the load-displacement curve descends, indicating failure of the material. The increase in flexibility of the fibrous material makes it stronger, but the randomness of fiber orientation makes the fibrous structure more flexible at the cost of lower strength. The measured mechanical properties of a fibrous matrix were also observed to be dependent on sample size. Therefore, the analyses establish a clear link between the structure and strength of fibrous materials for optimized design and fabrication of fibrous biomaterials with targeted use in tissue engineering, regenerative medicine and drug delivery. The model also establishes a need for standardization of experimental protocols for mechanical characterization of fibrous materials for consistency.

Design and fabrication of thin microvascularised polymer matrices inspired from secondary lamellae of fish gills

Gills are one of the most primitive gas, solute exchange organs available in fishes. They facilitate exchange of gases, solutes and ions with a surrounding water medium through their functional unit called secondary lamella. These lamellae through their extraordinary morphometric features and peculiar arrangement in gills, achieve remarkable mass transport properties. Therefore, in the current study, modeling and simulation of convection-diffusion transport through a two dimensional model of secondary lamella and theoretical analysis of morphometric features of fish gills were carried out. Such study suggested an evolutionary conservation of parametric ratios across fishes of different weights. Further, we have also fabricated a thin microvascularised PDMS matrices mimicking secondary lamella by use of micro-technologies like electrospinning. In addition, we have also demonstrated the fluid flow by capillary action through these thin microvascularised PDMS matrices. Eventually, we also illustrated the application of these thin microvascularied PDMS matrices in solute exchange process under capillary flow conditions. Thus, our study suggested that fish gills have optimized parameteric ratios, at multiple length scale, throughout an evolution to achieve an organ with enhanced mass transport capabilities. Thus, these defined parametric ratios could be exploited to design and develop efficient, scaled-up gas/solute exchange microdevices. We also proposed an inexpensive and scalable method of fabrication of thin microvascularised polymer matrices and demonstrated its solute exchange capabilities under capillary flow conditions. Thus, mimicking the microstructures of secondary lamella will enable fabrication of microvascularised thin polymer systems through micro manufacturing technologies for potential applications in filtration, self-healing/cooling materials and bioengineering.

Image registration: A pre-step in patient's position verification in radiation therapy

Radiation therapy (RT) is one of the modalities employed for treatment of cancer patients by irradiating the region to be treated using ionizing radiation. In radiation therapy, accurate patient positioning is an important requirement for precise dosage delivery. This paper presents a methodology for image registration employed in verification of the patients position during radiation therapy for the treatment of cancer patients. Before treatment, portal image of the patient is acquired using Electronic Portal Imaging Device (EPID) and compared with the Digitally Reconstructed Radiograph (DRR) to evaluate patient set-up error. Our approach involves the use of image registration techniques based on anatomical landmarks prior to the estimation of positional errors between the reference i.e. DRR and portal image. In proposed approach, three pairs of visually recognizable corresponding landmarks were located on DRR and the portal image to extract the transformational parameters required for accurate image registration. The proposed approach was applied on model images to verify the algorithm and results suggested that the proposed approach is promising for image registration before evaluating positional error for radiation therapy.

Feature Extraction Through Wavelet Decomposition For Automatic Detection Of Landmarks On Cephalogram Images

Manual cephalogram marking has long way from marking on tracing sheet to availability of commercial softwares for cephalometric analysis. With the effort involved in manual marking and time consumption, it becomes imperative for modern science to envisage algorithms which could automatically locate landmarks on the cephalogram images and perform various analysis. In this work, we herby propose a wavelet transform based feature extraction algorithm for detection of landmark on cephalogram images. 15 landmarks were detected on the images using wavelet transform and all landmarks were detected within the acceptable accuracy limits. This algorithm may have a promising approach in detection of further anatomical landmarks automatically and analysis and thus may help orthodontic practioners in better and faster treatment planning.

Feature Engineering Coupled Machine Learning Algorithms For Epileptic Seizure Forecasting From Intracranial EEGs

Epilepsy is one of the major neurological disorders affecting nearly 1 percentage of the global population. The major blunt is born by under developed and developing countries due to expensive treatment of epileptic conditions. Further, the lack of proper forecasting methods for an occurrence of epileptic seizures in epileptic-drug resistant patients or patients not amenable for surgery affects their psychological behaviour and restricts their daily activities. The forecasting is usually performed by human experts that leave a wide gap for human-bias and human error. Therefore, in the current work, we have evaluated the efficiency of several machine learning algorithms to automatically identify the preictal patterns corresponding to epileptic seizures from intracranial EEG signals. The robustness of the machine learning algorithms were tested after the data set was pre-processed using carefully chosen feature engineering strategies viz. denoised Fourier transforms as well as cross-correlation across electrodes in time and frequency domain. Extensive experimentations were carried out to determine the best combination of feature engineering techniques and machine learning algorithms. The best combination of feature engineering techniques and machine learning algorithm resulted in 0.7685 AUC (Area under the Receiver Operating Characteristic curve) on the random test samples. The suggested approach was fairly good at prediction of epilepsy in random samples and therefore, it can be used in epileptic seizure forecasting in patients where medication/surgery is ineffective. Eventually, our strategy reveals a robust method for brain disorders forecasting from EEGs.

Interfacing 3D micro/nanochannels with a branch-shaped reservoir enhances fluid and mass transport

Three-dimensional (3D) micro/nanofluidic devices can accelerate progress in numerous fields such as tissue engineering, drug delivery, self-healing and cooling devices. However, efficient connections between networks of micro/nanochannels and external fluidic ports are key to successful applications of 3D micro/nanofluidic devices. Therefore, in this work, the extent of the role of reservoir geometry in interfacing with vascular (micro/nanochannel) networks, and in the enabling of connections with external fluidic ports while maintaining the compactness of devices, has been experimentally and theoretically investigated. A statistical modelling suggested that a branch-shaped reservoir demonstrates enhanced interfacing with vascular networks when compared to other regular geometries of reservoirs. Time-lapse dye flow experiments by capillary action through fabricated 3D micro/nanofluidic devices confirmed the connectivity of branch-shaped reservoirs with micro/nanochannel networks in fluidic devices. This demonstrated a ~2.2-fold enhancement of the volumetric flow rate in micro/nanofluidic networks when interfaced to branch-shaped reservoirs over rectangular reservoirs. The enhancement is due to a ~2.8-fold increase in the perimeter of the reservoirs. In addition, the mass transfer experiments exhibited a ~1.7-fold enhancement in solute flux across 3D micro/nanofluidic devices that interfaced with branch-shaped reservoirs when compared to rectangular reservoirs. The fabrication of 3D micro/nanofluidic devices and their efficient interfacing through branch-shaped reservoirs to an external fluidic port can potentially enable their use in complex applications, in which enhanced surface-to-volume interactions are desirable.