Is The Burden of Medical Education Affecting Ethics of Medical Treatment?

A substantial number of medical students in India have to bear an enormous financial burden for earning a bachelor's degree in medicine referred to as MBBS (bachelor of medicine and bachelor of surgery). This degree program lasts for four and one-half years followed by one year of internship. A postgraduate degree, such as MD, has to be pursued separately on completion of a MBBS. Every medical college in India is part of a hospital where the medical students get clinical exposure during the course of their study. All or at least a number of medical colleges in a given state are affiliated to a university that mainly plays a role of an overseeing authority. The medical colleges usually have no official interaction with other disciplines of education such as science and engineering, perhaps because of their independent location and absence of emphasis on medical research. However, many of the medical colleges are adept in imparting high-quality and sound training in medical practices including diagnostics and treatment. The medical colleges in India are generally of two types, i.e., government owned and private. Since only a limited number of seats are available across India in the former category of colleges, only a small fraction of aspiring candidates can find admission in these colleges after performing competitively in the relevant entrance tests. A major advantage of studying in these colleges is the nominal tuition fees that have to be paid. On the other hand, a large majority of would-be medical graduates have to seek admission in the privately run medical institutes in which the tuition and other related fees can be mind boggling when compared to their public counterparts. Except for candidates of exceptionally affluent background, the only alternative for fulfilling the dream of becoming a doctor is by financing one's study through hefty bank loans that may take years to pay back. It is often heard from patients that they are asked by doctors to undergo a plethora of diagnostic tests for apparently minor illnesses, which may financially benefit those prescribing the tests. The present paper attempts to throw light on the extent of disparity in cost of a medical education between state-funded and privately managed medical colleges in India; the average salary of a new medical graduate, which is often ridiculously low when compared to what is offered in entry-level engineering and business jobs; and the possible repercussions of this apparently unjust economic situation regarding the exploitation of patients.

A MatLAB Based Virtual Phantom for 2D Electrical Impedance Tomography (MatVP2DEIT): Studying the Medical Electrical Impedance Tomography Reconstruction in Computer

Electrical Impedance Tomography (EIT) is a computerized medical imaging technique which reconstructs the electrical impedance images of a domain under test from the boundary voltage-current data measured by an EIT electronic instrumentation using an image reconstruction algorithm. Being a computed tomography technique, EIT injects a constant current to the patient's body through the surface electrodes surrounding the domain to be imaged (Omega) and tries to calculate the spatial distribution of electrical conductivity or resistivity of the closed conducting domain using the potentials developed at the domain boundary (partial derivative Omega). Practical phantoms are essentially required to study, test and calibrate a medical EIT system for certifying the system before applying it on patients for diagnostic imaging. Therefore, the EIT phantoms are essentially required to generate boundary data for studying and assessing the instrumentation and inverse solvers a in EIT. For proper assessment of an inverse solver of a 2D EIT system, a perfect 2D practical phantom is required. As the practical phantoms are the assemblies of the objects with 3D geometries, the developing of a practical 2D-phantom is a great challenge and therefore, the boundary data generated from the practical phantoms with 3D geometry are found inappropriate for assessing a 2D inverse solver. Furthermore, the boundary data errors contributed by the instrumentation are also difficult to separate from the errors developed by the 3D phantoms. Hence, the errorless boundary data are found essential to assess the inverse solver in 2D EIT. In this direction, a MatLAB-based Virtual Phantom for 2D EIT (MatVP2DEIT) is developed to generate accurate boundary data for assessing the 2D-EIT inverse solvers and the image reconstruction accuracy. MatVP2DEIT is a MatLAB-based computer program which simulates a phantom in computer and generates the boundary potential data as the outputs by using the combinations of different phantom parameters as the inputs to the program. Phantom diameter, inhomogeneity geometry (shape, size and position), number of inhomogeneities, applied current magnitude, background resistivity, inhomogeneity resistivity all are set as the phantom variables which are provided as the input parameters to the MatVP2DEIT for simulating different phantom configurations. A constant current injection is simulated at the phantom boundary with different current injection protocols and boundary potential data are calculated. Boundary data sets are generated with different phantom configurations obtained with the different combinations of the phantom variables and the resistivity images are reconstructed using EIDORS. Boundary data of the virtual phantoms, containing inhomogeneities with complex geometries, are also generated for different current injection patterns using MatVP2DEIT and the resistivity imaging is studied. The effect of regularization method on the image reconstruction is also studied with the data generated by MatVP2DEIT. Resistivity images are evaluated by studying the resistivity parameters and contrast parameters estimated from the elemental resistivity profiles of the reconstructed phantom domain. Results show that the MatVP2DEIT generates accurate boundary data for different types of single or multiple objects which are efficient and accurate enough to reconstruct the resistivity images in EIDORS. The spatial resolution studies show that, the resistivity imaging conducted with the boundary data generated by MatVP2DEIT with 2048 elements, can reconstruct two circular inhomogeneities placed with a minimum distance (boundary to boundary) of 2 mm. It is also observed that, in MatVP2DEIT with 2048 elements, the boundary data generated for a phantom with a circular inhomogeneity of a diameter less than 7% of that of the phantom domain can produce resistivity images in EIDORS with a 1968 element mesh. Results also show that the MatVP2DEIT accurately generates the boundary data for neighbouring, opposite reference and trigonometric current patterns which are very suitable for resistivity reconstruction studies. MatVP2DEIT generated data are also found suitable for studying the effect of the different regularization methods on reconstruction process. Comparing the reconstructed image with an original geometry made in MatVP2DEIT, it would be easier to study the resistivity imaging procedures as well as the inverse solver performance. Using the proposed MatVP2DEIT software with modified domains, the cross sectional anatomy of a number of body parts can be simulated in PC and the impedance image reconstruction of human anatomy can be studied.

A Brief Overview of the Recent Bio-Medical Applications of Fiber Bragg Grating Sensors

Fiber Bragg Grating (FBG) sensors have become one of the most widely used sensors in the recent times for a variety of applications in the fields of aerospace, civil, automotive, etc. It has been recently realized that FBGs and etched FBGs can play an important role in biomedical applications. This article provides a brief overview of the recent advancements in the application of FBG sensors in bio-mechanical, bio-sensing and bio-medical fields.

Understanding Medical Device Patenting and Clinical Trials for Product Leadership

Unmet clinical needs remain the primary driving force for innovations in medical devices. While appropriate mechanisms to protect these innovative outcomes are essential, the performance of clinical trials to ensure safety is also mandated before the invention is ready for public use. Literature explaining the relationship between patenting activities and clinical trials of medical devices is scarce. Linking patent ownership to clinical trials may imply product leadership and value chain control. In this paper, we use patent data from Indian Patent Office (IPO), PCT, and data from Clinical Trials Registry of India (CTRI) to identify whether patent assignees have any role in leading as primary sponsors of clinical trials. A total of 42 primary sponsors are identified from the CTRI database in India. Number of patents awarded to these primary sponsors in the particular medical device, total number of patents awarded to the primary sponsor in all technologies, total number of patents in the specific medical device technology provides an indication of leadership and control in the value chain.