Cancer gene signatures in risk stratification: use in personalized medicine

Cancer is a complex disease which arises due to a series of genetic changes related to cell division and growth control. Cancer remains the second leading cause of death in humans next to heart diseases. As a testimony to our progress in understanding the biology of cancer and developments in cancer diagnosis and treatment methods, the overall median survival time of all cancers has increased six fold one year to six years during the last four decades. However, while the median survival time has increased dramatically for some cancers like breast and colon, there has been only little change for other cancers like pancreas and brain. Further, not all patients having a single type of tumour respond to the standard treatment. The differential response is due to genetic heterogeneity which exists not only between tumours, which is called intertumour heterogeneity, but also within individual tumours, which is called intratumoural heterogeneity. Thus it becomes essential to personalize the cancer treatment based on a specific genetic change in a given tumour. It is also possible to stratify cancer patients into low- and high-risk groups based on expression changes or alterations in a group of genes gene signatures and choose a more suitable mode of therapy. It is now possible that each tumour can be analysed using various high-throughput methods like gene expression profiling and next-generation sequencing to identify its unique fingerprint based on which a personalized or tailor-made therapy can be developed. Here, we review the important progress made in the recent years towards personalizing cancer treatment with the use of gene signatures.

Polyester derived from recycled poly(ethylene terephthalate) waste for regenerative medicine

Despite advances in regenerative medicine, the cost of such therapies is beyond the reach of many patients globally in part due to the use of expensive biomedical polymers. Large volumes of poly(ethylene terephthalate) (PET) in municipal waste is a potential source of low cost polymers. A novel polyester was prepared by a catalyst-free, melt polycondensation reaction of bis(hydroxyethylene) terephthalate derived from PET post-consumer waste with other multi-functional monomers from renewable sources such as citric acid, sebacic acid and D-mannitol. The mechanical properties and degradation rate of the polyester can be tuned by varying the composition and the post-polymerization time. The polyester was found to be elastomeric, showed excellent cytocompatibility in vitro and elicited minimal immune response in vivo. Three-dimensional porous scaffolds facilitated osteogenic differentiation and mineralization. This class of polyester derived from low cost, recycled waste and renewable sources is a promising candidate for use in regenerative medicine.

Industry perceptions of barriers to commercialization of regenerative medicine products in the UK.

AIMS: Regenerative medicine is an emerging field with the potential to provide widespread improvement in healthcare and patient wellbeing via the delivery of therapies that can restore, regenerate or repair damaged tissue. As an industry, it could significantly contribute to economic growth if products are successfully commercialized. However, to date, relatively few products have reached the market owing to a variety of barriers, including a lack of funding and regulatory hurdles. The present study analyzes industry perceptions of the barriers to commercialization that currently impede the success of the regenerative medicine industry in the UK. MATERIALS & METHODS: The analysis is based on 20 interviews with leading industrialists in the field. RESULTS: The study revealed that scientific research in regenerative medicine is thriving in the UK. Unfortunately, lack of access to capital, regulatory hurdles, lack of clinical evidence leading to problems with reimbursement, as well as the culture of the NHS do not provide a good environment for the commercialization of regenerative medicine products. CONCLUSION: Policy interventions, including increased translational government funding, a change in NHS and NICE organization and policies, and regulatory clarity, would likely improve the general outcomes for the regenerative medicine industry in the UK.

Exploiting Chinese character models to improve speech recognition performance

The Chinese language is based on characters which are syllabic in nature. Since languages have syllabotactic rules which govern the construction of syllables and their allowed sequences, Chinese character sequence models can be used as a first level approximation of allowed syllable sequences. N-gram character sequence models were trained on 4.3 billion characters. Characters are used as a first level recognition unit with multiple pronunciations per character. For comparison the CU-HTK Mandarin word based system was used to recognize words which were then converted to character sequences. The character only system error rates for one best recognition were slightly worse than word based character recognition. However combining the two systems using log-linear combination gives better results than either system separately. An equally weighted combination gave consistent CER gains of 0.1-0.2% absolute over the word based standard system. Copyright © 2009 ISCA.

Ancient Chinese algorithm: The Ying Buzu Shu (method of surplus and deficiency) vs Newton iteration method

Air exploratory discussion of an ancient Chinese algorithm, the Ying Buzu Shu, in about 2nd century BC, known as the rule of double false position in the West is given. In addition to pointing out that the rule of double false position is actually a translation version of the ancient Chinese algorithm, a comparison with well-known Newton iteration method is also made. If derivative is introduced, the ancient Chinese algorithm reduces to the Newton method. A modification of the ancient Chinese algorithm is also proposed, and some of applications to nonlinear oscillators are illustrated.