Chemotherapy is a treatment of disease conditions with chemical compounds that was an obvious end point of most of the biological research until a separate branch launched as ‘gene therapy’ where genetic information could be manipulated for treatment of diseases. However, much promising ‘gene therapy’ field has achieved little success due to various reasons. But the traditional chemotherapy field continues to grow and, until now the most prominent approach for new drug development. The modern chemotherapy started with the production of recombinant molecules by genetic engineering to use as drugs was pioneered by Boyer (1971) for insulin. After that several hundreds of recombinant molecules such as interleukin, interferon etc were successfully produced and being used as drugs. In the next approach, disease specific genetic and genomic information were used to develop gene specific drugs. Although not many, but the first and most successful rational drug design is Gleevac, developed by Novartis for some leukemia patients against tyrosine kinase using the information of BCR-ABL translocation specific hyperactivation. In the post genomic era vast explosion of disease specific genetic and genomic information lead to emergence of numerous companies and involvement of academia for developing drugs using patient specific genetic signatures. These also led to the formulation of many directions of the field as pharmacogenomics, pharmacogenetics etc.

But the basic problems of chemotherapies remain elusive. Why a particular drug does not respond to everybody or why different individuals respond differently for a same treatment or why apparently similar individuals experience different complications for a same treatment? Most intriguing problem is that after initial response, some individuals get resistance to the drug and the symptoms relapse. The recent evidences suggest that individual genetic signature might play important role for some of these problems. Identification of genetic variation and its association with drug metabolism could indicate the outcome of drug treatment. When lung cancer patients with EGFR mutation were treated with Gifatinib with an EGFR mutation directed drug, some of the patients were resistant. Subsequent genetic analysis showed that these resistant patients tumor have a second mutation in the same EGFR gene that confers resistant to Gifatinib. Similarly, genetic association studies reveal that HLA-DRB genotype is a major determinants for flucloxacillin induced liver injury. Thus, genetic association studies could be incorporated for treatment of drug to treat the particular genotype carrying patients. But the most challenging part is to implement the genetic informations in drug design and chemotherapy. High density SNP microchip genotyping or whole genome sequencing data would complement the chemotherapeutic response for a specific drug to understand why the same drug behaves differently in patients with similar symptoms or a specific group of patients develop other complications.

Again, in many cases genetic signatures are mostly ethnic population specific. i.e a particular polymorphism (SNP) that is associated with a specific phenotypes vary from one population to another. In one population SNP could be monomorphic means only one form exists but could be polymorphic to other population. Thus a drug developed against the product of the risk allele carrying genes only would work in that population where this risk allele existed. This drug could be useless to other population where risk allele did not present. It is evident that carbamazepin increases hypersensitivity to only european patients due to interaction with particular genotype carrying patients that is present only in european population. Thus, ethnic population specific genetic informations from all parts of the world are important prerequisite for next generation drug design and efficcacy.

Drug resistance seems to be more towards alteration of the physiology of the cell like changes in MDR (multiple drug resistance) activation or ROS (reactive oxygen species) generation in the cell. However, genetic component for ROS management or MDR are also emerging as a influencial factor for chemotherapeutic drug resistance. FDA approved approximately 200 drug metabolism and transporter genes are already in considerations for many studies involving drug resistance. However, large scale association studies with these genes and drug metabolisms seem to be extremely important for specifying drug induced resistance or development of other complications and could be initiated for successful chemotherapy.

written by Chemotherapy

The analysis of periodic and quasiperiodic waveforms typically involves some form of automatic cycle detection. There are powerful algorithms available to isolate specific frequencies of interest in a given waveform; many of these provide readily available measurements. In the case of physiological behavior, rate measurements are often used as summary statistics of the waveforms representing that behavior (e.g. number of breaths per minute). Unfortunately, cycle averaging techniques are often inadequate when it comes to describing complex behavior, particularly when that behavior changes over time. In the case of respiratory behavior, at least two factors preclude the use of simple cycle averaging: (a) the detection of individual and physiologically informative variation in chest wall kinematics, and (b) the discrimination of interpretable and uninterpretable cycles. Both of these factors can be mitigated by the expertise of an experienced coder who can evaluate each cycle of a given waveform to determine which ones are acceptable, and then measure them accordingly. The downside to human visual coding and measurement is the time required to complete it. The twofold purpose of this inquiry is to design an algorithm that automatically detects tidal breath cycles across a variety of human subjects and to compare the algorithm’s performance to that achieved by an experienced human coder.

The proposed Automatic Cycle Identification Algorithm (ACIA) was used for tidal breath signals. The algorithm was designed in four steps using filtering, derivation, and other signal processing techniques via MATLAB programming. To facilitate further analysis for tidal breath signals, the algorithm produced the exact start time for each cycle and isolated the distorted cycles due to artifacts. Simulations results have shown that, despite the inter- and intra-participant variability of the tidal breath signals, the proposed algorithm can identify tidal breath cycles correctly and accurately. It can also isolate those portions of the signals negatively impacted by motion artifact.

The algorithm was designed in four steps using filtering, derivation, and other signal processing techniques. To verify the accuracy of the proposed algorithm, its results were compared with those of cycles identified manually by a human coder. Simulations results showed that despite the complexity of respiratory signals, the proposed algorithm could identify cycles more accurately than the human coder. This algorithm could serve as an important first step toward timely identification and coding for more complex respiratory signals, such as those underlying speech productions.

written by Journal of Bioengineering and Biomedical Sciences

Chemical batteries drive a plethora of mobile electronic devices, but frequent repeated cycles of charging and discharging degrade the battery over the life of the device. An alternative energy storage device known as an ultracapacitor can be recharged hundreds of thousands of times without degrading. A research group from the University of West Florida (UWF) published a paper this week in the American Institute of Physics’ Journal of Renewable and Sustainable Energy describing an ultracapacitor that was fabricated and tested which maintains a near constant voltage. This highly innovative constant-voltage design has the potential to enable the application of ultracapacitors in low-voltage electric vehicle circuits and mobile electronic devices.

Standard capacitors store energy in an electric field created when opposite electrical charges congregate on two plates separated by a thin insulator material. In contrast, for ultracapacitors, the surface area of the plates is increased with a coating of porous carbon, which is filled with tiny holes and cracks that can capture charged particles. The space between the plates is filled with an electrolyte solution containing positive and negative ions. As charge accumulates on the plates, they attract ions, creating an additional layer of stored energy. In both standard capacitors and ultracapacitors, the voltage drops as the stored charge is released, but the majority of electronic devices require constant voltage to function properly. An electronic circuit called a DC-DC converter can alter the falling voltage of the capacitor into a constant voltage output, but the converters on the market are typically unstable below one volt.

herefore, the UWF team designed an ultracapacitor that maintains a near-constant voltage without a DC-DC converter. This device is connected to an electromechanical system that can slowly lift the core of the device out of the electrolyte solution as the stored charged is released. As the electrolyte drains away, the device holds less charge; thus, lowers its capacitance. Since the voltage of the capacitor is related to the ratio of the stored charge to the capacitance, the system maintains a steady voltage as charge leaks away. Testing by the UWF group showed that the constant-voltage mechanism operates with a 99 percent efficiency or higher. The lifetime of the electromechanical motor is expected to be about the same as the lifetime of the ultracapacitor’s core, which would dramatically improve the robustness and performance of next-generation power storage devices.

written by Journal of Electrical and Electronics