Plant-like substitutions in the large-subunit carboxy terminus of Chlamydomonas Rubisco increase CO2/O2 Specificity

<p>Abstract</p> <p>Background</p> <p>Ribulose-1,5-bisphosphate is the rate-limiting enzyme in photosynthesis. The catalytic large subunit of the green-algal enzyme from <it>Chlamydomonas reinhardtii </it>is ~90% identical to the flowering-plant sequences, although they confer diverse kinetic properties. To identify the regions that may account for species variation in kinetic properties, directed mutagenesis and chloroplast transformation were used to create four amino-acid substitutions in the carboxy terminus of the <it>Chlamydomonas </it>large subunit to mimic the sequence of higher-specificity plant enzymes.</p> <p>Results</p> <p>The quadruple-mutant enzyme has a 10% increase in CO₂/O₂specificity and a lower carboxylation catalytic efficiency. The mutations do not seem to influence the protein expression, structural stability or the function in vivo.</p> <p>Conclusion</p> <p>Owing to the decreased carboxylation catalytic efficiency, the quadruple-mutant is not a "better" enzyme. Nonetheless, because of its positive influence on specificity, the carboxy terminus, relatively far from the active site, may serve as a target for enzyme improvement via combinatorial approaches.</p

Genetic screening for gynecological cancer: where are we heading?

The landscape of cancer genetics in gynecological oncology is rapidly changing. The traditional family history-based approach has limitations and misses >50% mutation carriers. This is now being replaced by population-based approaches. The need for changing the clinical paradigm from family history-based to population-based BRCA1/BRCA2 testing in Ashkenazi Jews is supported by data that demonstrate population-based BRCA1/BRCA2 testing does not cause psychological harm and is cost effective. This article covers various genetic testing strategies for gynecological cancers, including population-based approaches, panel and direct-to-consumer testing as well as the need for innovative approaches to genetic counseling. Advances in genetic testing technology and computational analytics have facilitated an integrated systems medicine approach, providing increasing potential for population-based genetic testing, risk stratification, and cancer prevention. Genomic information along-with biological/computational tools will be used to deliver predictive, preventive, personalized and participatory (P4) and precision medicine in the future

Siamese Survival Analysis with Competing Risks

Survival analysis in the presence of multiple possible adverse events, i.e., competing risks, is a pervasive problem in many industries (healthcare, finance, etc.). Since only one event is typically observed, the incidence of an event of interest is often obscured by other related competing events. This nonidentifiability, or inability to estimate true cause-specific survival curves from empirical data, further complicates competing risk survival analysis. We introduce Siamese Survival Prognosis Network (SSPN), a novel deep learning architecture for estimating personalized risk scores in the presence of competing risks. SSPN circumvents the nonidentifiability problem by avoiding the estimation of cause-specific survival curves and instead determines pairwise concordant time-dependent risks, where longer event times are assigned lower risks. Furthermore, SSPN is able to directly optimize an approximation to the C-discrimination index, rather than relying on well-known metrics which are unable to capture the unique requirements of survival analysis with competing risks

Herbicide, fumigant, and fungicide use and breast cancer risk among farmers' wives

Background: Evidence from epidemiologic and laboratory studies relating pesticides to breast cancer risk is inconsistent. Women engaging in agricultural work or living in agricultural areas may experience appreciable exposures to a wide range of pesticides, including herbicides, fumigants, and fungicides. Methods: We examined exposure to herbicides, fumigants, and fungicides in relation to breast cancer risk among farmers' wives with no prior history of breast cancer in the Agricultural Health Study. Women provided information on pesticide use, demographics, and reproductive history at enrollment (1993-1997) and at a 5-year follow-up interview. We used Cox proportional hazards regression to estimate associations (hazard ratios [HRs] and 95% confidence intervals [CIs]) between the women's and their husbands' self-reported use of individual pesticides and incident breast cancer risk. Results: Out of 30,594 women, 38% reported using herbicides, fumigants, or fungicides and 1,081 were diagnosed with breast cancer during a median 15.3 years of follow-up. We found elevated risk in relation to women's ever use of the fungicide benomyl (HR = 1.6; 95% CI = 0.9, 2.7) and the herbicide 2,4,5-trichlorophenoxyacetic acid (2,4,5-T) (HR = 1.6; 95% CI = 0.8, 3.1) and to their husbands' use of the herbicide 2-(2,4,5-trichlorophenoxy) propionic acid (2,4,5-TP) (HR = 1.5; 95% CI = 0.9, 2.7). We observed few other chemical associations and little evidence of differential risk by tumor estrogen receptor status or linear exposure-response relationships. Conclusion: We did not observe clear excesses between use of specific pesticides and breast cancer risk across exposure metrics, although we did observe elevated risk associated with women's use of benomyl and 2,4,5-T and husbands' use of 2,4,5-TP

Mapping quantitative trait loci in line cross with repeat records

<p>Abstract</p> <p>Background</p> <p>Phenotypes with repeat records from one individual or multiple individuals were often encountered in practices of mapping QTL in linecross. The current genetic mapping method for a trait with repeat records is adopted by simply replacing the phenotype by the average value of the repeat records. This simple treatment has not sufficiently utilized the information from the replication and ignored the impacts of the permanent environmental effects on the accuracy of the estimated QTL.</p> <p>Results</p> <p>We propose to map QTL by using the repeatability model to directly analyze the repeat records rather than simply analyze the mean phenotype, improving the efficiency of QTL detecting because of adequately utilizing the information from data and allowing for the permanent environmental effects. A maximum likelihood method implemented via the expectation-maximization (EM) algorithm is applied to perform the parameter estimation of the repeatability model. The superiority of the mapping method based on the repeatability model over simple analysis using the mean phenotype was demonstrated by a series of simulations.</p> <p>Conclusion</p> <p>Our results suggest that the proposed method can serve as a powerful alternative to existing methods. By mean of the repeatability model, utilizing the repeat records on individual may improve the efficiency of QTL detecting in line cross.</p

Insecticide use and breast cancer risk among farmers’ wives in the agricultural health study

BACKGROUND: Some epidemiologic and laboratory studies suggest that insecticides are related to increased breast cancer risk, but the evidence is inconsistent. Women engaged in agricultural work or who reside in agricultural areas may experience appreciable exposures to a wide range of insecticides. OBJECTIVE: We examined associations between insecticide use and breast cancer incidence among wives of pesticide applicators (farmers) in the prospective Agricultural Health Study. METHODS: Farmers and their wives provided information on insecticide use, demographics, and reproductive history at enrollment in 1993–1997 and in 5-y follow-up interviews. Cancer incidence was determined via cancer registries. Among 30,594 wives with no history of breast cancer before enrollment, we examined breast cancer risk in relation to the women’s and their husbands’ insecticide use using Cox proportional hazards regression to estimate adjusted hazard ratios (HRs) and 95% confidence intervals (CIs). RESULTS: During an average 14.7-y follow-up, 39% of the women reported ever using insecticides, and 1,081 were diagnosed with breast cancer. Although ever use of insecticides overall was not associated with breast cancer risk, risk was elevated among women who had ever used the organophosphates chlorpyrifos [HR = 1:4 (95% CI: 1.0, 2.0)] or terbufos [HR = 1:5 (95% CI: 1.0, 2.1)], with nonsignificantly increased risks for coumaphos [HR = 1:5 (95% CI: 0.9, 2.5)] and heptachlor [HR = 1:5 (95% CI: 0.7, 2.9)]. Risk in relation to the wives’ use was associated primarily with premenopausal breast cancer. We found little evidence of differential risk by tumor estrogen receptor status. Among women who did not apply pesticides, the husband’s use of fonofos was associated with elevated risk, although no exposure–response trend was observed. CONCLUSION: Use of several organophosphate insecticides was associated with elevated breast cancer risk. However, associations for the women’s and husbands’ use of these insecticides showed limited concordance. Ongoing cohort follow-up may help clarify the relationship, if any, between individual insecticide exposures and breast cancer risk

Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers

<p>Abstract</p> <p>Background</p> <p>Decision curve analysis is a novel method for evaluating diagnostic tests, prediction models and molecular markers. It combines the mathematical simplicity of accuracy measures, such as sensitivity and specificity, with the clinical applicability of decision analytic approaches. Most critically, decision curve analysis can be applied directly to a data set, and does not require the sort of external data on costs, benefits and preferences typically required by traditional decision analytic techniques.</p> <p>Methods</p> <p>In this paper we present several extensions to decision curve analysis including correction for overfit, confidence intervals, application to censored data (including competing risk) and calculation of decision curves directly from predicted probabilities. All of these extensions are based on straightforward methods that have previously been described in the literature for application to analogous statistical techniques.</p> <p>Results</p> <p>Simulation studies showed that repeated 10-fold crossvalidation provided the best method for correcting a decision curve for overfit. The method for applying decision curves to censored data had little bias and coverage was excellent; for competing risk, decision curves were appropriately affected by the incidence of the competing risk and the association between the competing risk and the predictor of interest. Calculation of decision curves directly from predicted probabilities led to a smoothing of the decision curve.</p> <p>Conclusion</p> <p>Decision curve analysis can be easily extended to many of the applications common to performance measures for prediction models. Software to implement decision curve analysis is provided.</p

The in- or exclusion of non-breast cancer related death and contralateral breast cancer significantly affects estimated outcome probability in early breast cancer

A wide variation of definitions of recurrent disease and survival are used in the analyses of outcome of patients with early breast cancer. Explicit definitions with details both on endpoints and censoring are provided in less than half of published studies. We evaluated the effects of various definitions of survival and recurrent disease on estimated outcome in a prospectively determined cohort of 463 patients with primary breast cancer. Outcome estimates were determined both by the Kaplan–Meier and a competing risk method. In- or exclusion of contralateral breast cancer or non-disease related death in the definition of recurrent disease or survival significantly affects estimated outcome probability. The magnitude of this finding was dependent on patient-, tumour-, and treatment characteristics. Knowledge of the contribution of non-disease related death or contralateral breast cancer to estimated recurrent disease rate and overall death rate is indispensable for a correct interpretation and comparison of outcome analyses

Paradoxical tuberculosis immune reconstitution inflammatory syndrome (TB-IRIS) in HIV patients with culture confirmed pulmonary tuberculosis in India and the potential role of IL-6 in prediction

Background: The incidence, manifestations, outcome and clinical predictors of paradoxical TB-IRIS in patients with HIV and culture confirmed pulmonary tuberculosis (PTB) in India have not been studied prospectively. Methods: HIV+ patients with culture confirmed PTB started on anti-tuberculosis therapy (ATT) were followed prospectively after anti-retroviral therapy (ART) initiation. Established criteria for IRIS diagnosis were used including decline in plasma HIV RNA at IRIS event. Pre-ART plasma levels of interleukin (IL)-6 and C-reactive protein (CRP) were measured. Univariate and multivariate logistic regression models were used to evaluate associations between baseline variables and IRIS. Results: Of 57 patients enrolled, 48 had complete follow up data. Median ATT-ART interval was 28 days (interquartile range, IQR 14–47). IRIS events occurred in 26 patients (54.2%) at a median of 11 days (IQR: 7–16) after ART initiation. Corticosteroids were required for treatment of most IRIS events that resolved within a median of 13 days (IQR: 9–23). Two patients died due to CNS TB-IRIS. Lower CD4+ T-cell counts, higher plasma HIV RNA levels, lower CD4/CD8 ratio, lower hemoglobin, shorter ATT to ART interval, extra-pulmonary or miliary TB and higher plasma IL-6 and CRP levels at baseline were associated with paradoxical TB-IRIS in the univariate analysis. Shorter ATT to ART interval, lower hemoglobin and higher IL-6 and CRP levels remained significant in the multivariate analysis. Conclusion: Paradoxical TB–IRIS frequently complicates HIV-TB therapy in India. IL-6 and CRP may assist in predicting IRIS events and serve as potential targets for immune interventions