Increasing incidence of dementia in the oldest old: evidence and implications

The oldest old are the fastest growing segment of the US population but accurate estimates of the incidence of dementia in this age group have been elusive. Corrada and colleagues present data on the 5-year age-specific rates of dementia incidence in persons 90 years and older from The 90+ Study. Their findings show a continued exponential increase in dementia incidence after age 90 that mirrors the increase observed in persons aged 65 to 90, with a doubling every 5.5 years. This contrasts with previous smaller studies reporting a slowing of the increase in incidence after age 90. If confirmed, the continued increase, rather than a plateau, in the incidence of dementia in the oldest old has implications for proper healthcare planning. Strategies for prevention and treatment will require more information regarding risk factors and the etiopathogenesis of dementia in the oldest old

Visually guided vergence in a new stereo camera system

People move their eyes several times each second, to selectivelyanalyze visual information from specific locations. This is impor-tant, because analyzing the whole scene in foveal detail would re-quire a beachball-sized brain and thousands of additional caloriesper day. As artificial vision becomes more sophisticated, it mayface analogous constraints. Anticipating this, we previously devel-oped a robotic head with biologically realistic oculomotor capabil-ities. Here we present a system for accurately orienting the cam-eras toward a three-dimensional point. The robot’s cameras con-verge when looking at something nearby, so each camera shouldideally centre the same visual feature. At the end of a saccade,we combine priors with cross-correlation of the images from eachcamera to iteratively fine-tune their alignment, and we use the ori-entations to set focus distance. This system allows the robot toaccurately view a visual target with both eyes

Anti-VEGF therapy as adjuvant therapy: clouds on the horizon?

Anti-angiogenic therapies have demonstrated their value in the setting of advanced cancer, and are being explored for use in micrometastatic disease. Recent preclinical studies suggest that adjuvant anti-vascular endothelial growth factor (VEGF) therapies may increase the risk of metastasis. How concerning are these preclinical studies, and should they affect our willingness to explore anti-VEGF therapy in the adjuvant setting

A bioinformatics approach for precision medicine off-label drug drug selection among triple negative breast cancer patients

Cancer has been extensively characterized on the basis of genomics. The integration of genetic information about cancers with data on how the cancers respond to target based therapy to help to optimum cancer treatment. OBJECTIVE: The increasing usage of sequencing technology in cancer research and clinical practice has enormously advanced our understanding of cancer mechanisms. The cancer precision medicine is becoming a reality. Although off-label drug usage is a common practice in treating cancer, it suffers from the lack of knowledge base for proper cancer drug selections. This eminent need has become even more apparent considering the upcoming genomics data. METHODS: In this paper, a personalized medicine knowledge base is constructed by integrating various cancer drugs, drug-target database, and knowledge sources for the proper cancer drugs and their target selections. Based on the knowledge base, a bioinformatics approach for cancer drugs selection in precision medicine is developed. It integrates personal molecular profile data, including copy number variation, mutation, and gene expression. RESULTS: By analyzing the 85 triple negative breast cancer (TNBC) patient data in the Cancer Genome Altar, we have shown that 71.7% of the TNBC patients have FDA approved drug targets, and 51.7% of the patients have more than one drug target. Sixty-five drug targets are identified as TNBC treatment targets and 85 candidate drugs are recommended. Many existing TNBC candidate targets, such as Poly (ADP-Ribose) Polymerase 1 (PARP1), Cell division protein kinase 6 (CDK6), epidermal growth factor receptor, etc., were identified. On the other hand, we found some additional targets that are not yet fully investigated in the TNBC, such as Gamma-Glutamyl Hydrolase (GGH), Thymidylate Synthetase (TYMS), Protein Tyrosine Kinase 6 (PTK6), Topoisomerase (DNA) I, Mitochondrial (TOP1MT), Smoothened, Frizzled Class Receptor (SMO), etc. Our additional analysis of target and drug selection strategy is also fully supported by the drug screening data on TNBC cell lines in the Cancer Cell Line Encyclopedia. CONCLUSIONS: The proposed bioinformatics approach lays a foundation for cancer precision medicine. It supplies much needed knowledge base for the off-label cancer drug usage in clinics

Massive Science with VO and Grids

There is a growing need for massive computational resources for the analysis of new astronomical datasets. To tackle this problem, we present here our first steps towards marrying two new and emerging technologies; the Virtual Observatory (e.g, AstroGrid) and the computational grid (e.g. TeraGrid, COSMOS etc.). We discuss the construction of VOTechBroker, which is a modular software tool designed to abstract the tasks of submission and management of a large number of computational jobs to a distributed computer system. The broker will also interact with the AstroGrid workflow and MySpace environments. We discuss our planned usages of the VOTechBroker in computing a huge number of n-point correlation functions from the SDSS data and massive model-fitting of millions of CMBfast models to WMAP data. We also discuss other applications including the determination of the XMM Cluster Survey selection function and the construction of new WMAP maps.Comment: Invited talk at ADASSXV conference published as ASP Conference Series, Vol. XXX, 2005 C. Gabriel, C. Arviset, D. Ponz and E. Solano, eds. 9 page

Efficacy of Different Leuprolide Administration Schedules in Premenopausal Breast Cancer: A Retrospective Review

Background Leuprolide is a safe and effective treatment of estrogen receptor–positive premenopausal breast cancer. Data from the SOFT/TEXT trials solidified leuprolide in combination with an aromatase inhibitor as an effective hormonal treatment for premenopausal breast cancer. However, the efficacy of monthly leuprolide depot compared to leuprolide depot every 3 months in combination with an aromatase inhibitor in this patient population is unclear. Patients and Methods In this single center retrospective study, 201 patients were enrolled between January 1, 2015, and October 1, 2016; 100 were included in the 7.5 mg leuprolide monthly injection plus aromatase inhibitor group and 101 in the 22.5 mg leuprolide injection every 3 months plus aromatase inhibitor group. The primary end point was the proportion of patients who experienced ovarian ablation, defined as an estradiol concentration less than 40 pg/mL and a follicle-stimulating hormone concentration of 23 to 116 mU/mL after 3 months of treatment. Significance threshold was P < .05 (2 sided). Secondary end points included disease-free survival and overall survival at 1-year follow-up, as well as adverse events reported during treatment. Results All patients in the monthly leuprolide arm experienced ovarian ablation compared to 100 (99%) of 101 patients in the arm treated every 3 months ( P = 1). The disease-free survival rate at 1 year was 95% in the monthly leuprolide arm and 97% in the arm treated every 3 months ( P = .75). The overall survival rate at 1 year was 100% in the monthly leuprolide arm and 99% in the arm treated every 3 months ( P = 1). The most common treatment-related adverse events between the 2 groups were musculoskeletal pain, hot flashes, fatigue, and insomnia. Conclusion Leuprolide acetate depot administered every 3 months is as efficacious and tolerable as a monthly injection in combination with an aromatase inhibitor for premenopausal patients with hormone receptor–positive breast cancer