11 research outputs found
Telemedicine Network Telecolposcopy Compared with Computer-Based Telecolposcopy
No full textObjective. To compare computer-based telecolposcopy with telemedicine network telecolposcopy. Materials and Methods. An on-site expert and local clinician at two rural sites conducted colposcopic examinations on 264 women. Colposcopic images were captured and transmitted to two other experts at a remote location using a statewide telemedicine system and a computer and modem-based system. Sensitivity and specificity, agreement of examination adequacy and management, effects of delayed interpretations, and costs were compared for each system. Results. A greater rate of satisfactory colposcopy results was reported by the telemedicine network (66.1%) compared with computer-based (43.6%) telecolposcopy (p < .0001). Greater rates of cervical biopsy (p = .005) and endocervical curettage (p = .03) were required by delayed telecolposcopy compared with immediate telecolposcopic services. There were no significant differences in sensitivity of detecting cervical neoplasia among the types of the telecolposcopy. Computer-based telecolposcopy cost $28 less per patient than telemedicine network telecolposcopy. Conclusions. Computer-based telecolposcopy may be a reasonable, cost-effective adjunct to on-site colposcopy for evaluating women in medically underserved areas. Synchronous telecolposcopic examination minimizes histologic sampling and improves consultation.link_to_subscribed_fulltex
Serum protein profile at remission can accurately assess therapeutic outcomes and survival for serous ovarian cancer.
No full textBACKGROUND: Biomarkers play critical roles in early detection, diagnosis and monitoring of therapeutic outcome and recurrence of cancer. Previous biomarker research on ovarian cancer (OC) has mostly focused on the discovery and validation of diagnostic biomarkers. The primary purpose of this study is to identify serum biomarkers for prognosis and therapeutic outcomes of ovarian cancer. EXPERIMENTAL DESIGN: Forty serum proteins were analyzed in 70 serum samples from healthy controls (HC) and 101 serum samples from serous OC patients at three different disease phases: post diagnosis (PD), remission (RM) and recurrence (RC). The utility of serum proteins as OC biomarkers was evaluated using a variety of statistical methods including survival analysis. RESULTS: Ten serum proteins (PDGF-AB/BB, PDGF-AA, CRP, sFas, CA125, SAA, sTNFRII, sIL-6R, IGFBP6 and MDC) have individually good area-under-the-curve (AUC) values (AUC = 0.69-0.86) and more than 10 three-marker combinations have excellent AUC values (0.91-0.93) in distinguishing active cancer samples (PD & RC) from HC. The mean serum protein levels for RM samples are usually intermediate between HC and OC patients with active cancer (PD & RC). Most importantly, five proteins (sICAM1, RANTES, sgp130, sTNFR-II and sVCAM1) measured at remission can classify, individually and in combination, serous OC patients into two subsets with significantly different overall survival (best HR = 17, p<10(-3)). CONCLUSION: We identified five serum proteins which, when measured at remission, can accurately predict the overall survival of serous OC patients, suggesting that they may be useful for monitoring the therapeutic outcomes for ovarian cancer
Temperature sensitivity of soil carbon decomposition
No full textThe world's soils contain three times as much carbon as the atmosphere. Thus, any changes in this carbon pool may affect atmospheric CO₂ levels with implications for climate change. Anthropogenic contributions to global carbon and nitrogen cycles have increased in the last century. Both temperature and nitrogen influence decomposition processes and are therefore critical in determining CO₂ return to the atmosphere.
Kinetic theory predicts that the chemical composition of soil organic matter
represents a dominant influence on the temperature response of decomposition.
However, empirical observations and modeling indicate that this relationship is
constrained by other factors. We address a number of research questions related to these factors, which are central to a thorough understanding of temperature sensitivity in decomposition. Specifically it offers one of the first empirical observations consistent with modeling in demonstrating increased temperature sensitivity for the uptake of carbon monomers over microbial cell membranes. Using NMR spectroscopy we were able to demonstrate how temperature response is directly related to the chemical composition of the organic material present. The thesis shows how increased soil nitrogen reduces temperature response. The key mechanism behind this observation, we suggest, is the influence of nitrogen on the chemical composition of organic matter, mediating a direct effect on temperature response. Given that nitrogen availability in terrestrial ecosystems has doubled relative to preindustrial levels, this observation may be vital in understanding the net effect of temperature increase on CO₂ return to the atmosphere. The proportion of carbon in plant litter transformed by microorganisms into biomass (carbon use efficiency; CUE) is a central factor determining global land-atmosphere CO₂ exchange. CUE was highly sensitive to
whether carbon monomers or polymers were degraded; yet temperature had no clear effect on CUE. The majority of soil organic matter is comprised of polymers,
highlighting the importance of using these as model substrates in studies of CUE.
This thesis represents a major contribution to our understanding of the intrinsic and external controls acting on temperature sensitivity of decomposition, and thus to regulation of CO₂ return to the atmosphere under a changing climate
Boxplots representing the serum protein levels in patient subgroups and healthy controls.
No full text<p>PD: Post Diagnosis, RC: Recurrence, RM: Remission, HC: Healthy Controls. (*) significant difference as compared to healthy controls.</p
Survival analyses of the samples from the RM stage.
No full text<p><b>A:</b> The survival curves for the top five molecules that can distinguish patient subset with poor overall survival from patients with better survival. The prognostic value of multivariate models (combinations of 4 or 5 proteins) was determined by clustering the patients into two groups based on the expression levels of protein panels and survival differences were then determined between these two clusters using Kaplan-Meier analyses. <b>B:</b> The heatmap of protein expression in the samples from the RM stage. The patients with poor survival have higher expression levels for the five proteins.</p
The ROC curves for the top molecules that can distinguish cancer patients (post diagnosis and recurrence) from healthy controls.
No full text<p>Single proteins (A) and multi-marker models (B) were used for the classification analyses. For multi-marker models, linear discriminate analysis was performed using combinations of 3 proteins. The diagnostic performance of each model was evaluated using leave one out cross validation method. The utility of serum proteins as ovarian cancer biomarkers was evaluated using the area-under-curve (AUC) of the ROC curves for different models.</p
Significant changes in serum protein levels in patients as compared to healthy controls (PD: Post Diagnosis, HC: Healthy Controls, RC: Recurrence, RM: Remission).
No full text*<p>The p-values are adjusted for multiple testing using FDR method.</p
The ROC curves for the top molecules that distinguish samples at remission from samples with active cancer (A, B) or healthy controls (C, D).
No full text<p>Results were shown for single proteins (A, C) and multi-marker models (B, D).</p
Characteristics of the patient population.
No full text<p>A total of 101 serum samples from 75 patients were obtained at 3 different stages of disease progression: post-diagnosis (PD, n = 25), recurrent (RC, n = 43) and remission (RM, n = 33).</p
