Baseline characteristics influencing quality of life in women undergoing gynecologic oncology surgery

<p>Abstract</p> <p>Background</p> <p>Quality of life (QoL) measurements are important in evaluating cancer treatment outcomes. Factors other than cancer and its treatment may have significant effects on QoL and affect assessment of treatments. Baseline data from longitudinal studies of women with endometrial or ovarian cancer or adnexal mass determined at surgery to be benign were analyzed to determine the degree to which QoL is affected by baseline differences in demographic variables and health.</p> <p>Methods</p> <p>This study examined the effect of independent variables on domains of the Functional Assessment of Cancer Therapy (FACT-G) pre-operatively in gynecologic oncology patients undergoing surgery for pelvic mass suspected to be malignant or endometrial cancer. Patients also completed the Short Form Medical Outcomes Survey (SF-36) questionnaire (a generic health questionnaire that measures physical and mental health). Independent variables were surgical diagnosis (ovarian or endometrial cancer, benign mass), age, body mass index (BMI), educational level, marital status, smoking status, physical (PCS) and mental (MCS) summary scores of the SF-36. Multiple regression analysis was used to determine the influence of these variables on FACT-G domain scores (physical, functional, social and emotional well-being).</p> <p>Results</p> <p>Data were collected on 157 women at their pre-operative visit (33 ovarian cancer, 45 endometrial cancer, 79 determined at surgery to be benign). Mean scores on the FACT-G subscales and SF-36 summary scores did not differ as a function of surgical diagnosis. PCS, MCS, age, and educational level were positively correlated with physical well-being, while increasing BMI was negatively correlated. Functional well-being was positively correlated with PCS and MCS and negatively correlated with BMI. Social well-being was positively correlated with MCS and negatively correlated with BMI and educational level. PCS, MCS and age were positively correlated with emotional well-being. Models that included PCS and MCS accounted for 30 to 44% of the variability in baseline physical, emotional, and functional well-being on the FACT-G.</p> <p>Conclusion</p> <p>At the time of diagnosis and treatment, patients' QoL is affected by inherent characteristics. Assessment of treatment outcome should take into account the effect of these independent variables. As treatment options become more complex, these variables are likely to be of increasing importance in evaluating treatment effects on QoL.</p

State-Space Analysis of Time-Varying Higher-Order Spike Correlation for Multiple Neural Spike Train Data

Precise spike coordination between the spiking activities of multiple neurons is suggested as an indication of coordinated network activity in active cell assemblies. Spike correlation analysis aims to identify such cooperative network activity by detecting excess spike synchrony in simultaneously recorded multiple neural spike sequences. Cooperative activity is expected to organize dynamically during behavior and cognition; therefore currently available analysis techniques must be extended to enable the estimation of multiple time-varying spike interactions between neurons simultaneously. In particular, new methods must take advantage of the simultaneous observations of multiple neurons by addressing their higher-order dependencies, which cannot be revealed by pairwise analyses alone. In this paper, we develop a method for estimating time-varying spike interactions by means of a state-space analysis. Discretized parallel spike sequences are modeled as multi-variate binary processes using a log-linear model that provides a well-defined measure of higher-order spike correlation in an information geometry framework. We construct a recursive Bayesian filter/smoother for the extraction of spike interaction parameters. This method can simultaneously estimate the dynamic pairwise spike interactions of multiple single neurons, thereby extending the Ising/spin-glass model analysis of multiple neural spike train data to a nonstationary analysis. Furthermore, the method can estimate dynamic higher-order spike interactions. To validate the inclusion of the higher-order terms in the model, we construct an approximation method to assess the goodness-of-fit to spike data. In addition, we formulate a test method for the presence of higher-order spike correlation even in nonstationary spike data, e.g., data from awake behaving animals. The utility of the proposed methods is tested using simulated spike data with known underlying correlation dynamics. Finally, we apply the methods to neural spike data simultaneously recorded from the motor cortex of an awake monkey and demonstrate that the higher-order spike correlation organizes dynamically in relation to a behavioral demand

Identification of Novel Therapeutic Targets in Microdissected Clear Cell Ovarian Cancers

Clear cell ovarian cancer is an epithelial ovarian cancer histotype that is less responsive to chemotherapy and carries poorer prognosis than serous and endometrioid histotypes. Despite this, patients with these tumors are treated in a similar fashion as all other ovarian cancers. Previous genomic analysis has suggested that clear cell cancers represent a unique tumor subtype. Here we generated the first whole genomic expression profiling using epithelial component of clear cell ovarian cancers and normal ovarian surface specimens isolated by laser capture microdissection. All the arrays were analyzed using BRB ArrayTools and PathwayStudio software to identify the signaling pathways. Identified pathways validated using serous, clear cell cancer cell lines and RNAi technology. In vivo validations carried out using an orthotopic mouse model and liposomal encapsulated siRNA. Patient-derived clear cell and serous ovarian tumors were grafted under the renal capsule of NOD-SCID mice to evaluate the therapeutic potential of the identified pathway. We identified major activated pathways in clear cells involving in hypoxic cell growth, angiogenesis, and glucose metabolism not seen in other histotypes. Knockdown of key genes in these pathways sensitized clear cell ovarian cancer cell lines to hypoxia/glucose deprivation. In vivo experiments using patient derived tumors demonstrate that clear cell tumors are exquisitely sensitive to antiangiogenesis therapy (i.e. sunitinib) compared with serous tumors. We generated a histotype specific, gene signature associated with clear cell ovarian cancer which identifies important activated pathways critical for their clinicopathologic characteristics. These results provide a rational basis for a radically different treatment for ovarian clear cell patients

Aptamers for pharmaceuticals and their application in environmental analytics

Aptamers are single-stranded DNA or RNA oligonucleotides, which are able to bind with high affinity and specificity to their target. This property is used for a multitude of applications, for instance as molecular recognition elements in biosensors and other assays. Biosensor application of aptamers offers the possibility for fast and easy detection of environmental relevant substances. Pharmaceutical residues, deriving from human or animal medical treatment, are found in surface, ground, and drinking water. At least the whole range of frequently administered drugs can be detected in noticeable concentrations. Biosensors and assays based on aptamers as specific recognition elements are very convenient for this application because aptamer development is possible for toxic targets. Commonly used biological receptors for biosensors like enzymes or antibodies are mostly unavailable for the detection of pharmaceuticals. This review describes the research activities of aptamer and sensor developments for pharmaceutical detection, with focus on environmental applications