Breastfeeding welcome here: good for mums, babies and your business

This booklet explains how the 'Breastfeeding welcome here' scheme works and includes the membership agreement for businesses to sign.

The pocket guide to good mental health

This leaflet outlines the signs of poor mental health and suggests steps that people can take to promote good mental health�

Robust Factor Analysis for Compositional Data

Factor analysis as frequent technique for multivariate data inspection is widely used also for compositional data analysis. The usual way is to use a centered logratio (clr)transformation to obtain the random vector y of dimension D. The factor model istheny = Λf + e (1)with the factors f of dimension k & D, the error term e, and the loadings matrix Λ.Using the usual model assumptions (see, e.g., Basilevsky, 1994), the factor analysismodel (1) can be written asCov(y) = ΛΛT + ψ (2)where ψ = Cov(e) has a diagonal form. The diagonal elements of ψ as well as theloadings matrix Λ are estimated from an estimation of Cov(y).Given observed clr transformed data Y as realizations of the random vectory. Outliers or deviations from the idealized model assumptions of factor analysiscan severely effect the parameter estimation. As a way out, robust estimation ofthe covariance matrix of Y will lead to robust estimates of Λ and ψ in (2), seePison et al. (2003). Well known robust covariance estimators with good statisticalproperties, like the MCD or the S-estimators (see, e.g. Maronna et al., 2006), relyon a full-rank data matrix Y which is not the case for clr transformed data (see,e.g., Aitchison, 1986).The isometric logratio (ilr) transformation (Egozcue et al., 2003) solves thissingularity problem. The data matrix Y is transformed to a matrix Z by usingan orthonormal basis of lower dimension. Using the ilr transformed data, a robustcovariance matrix C(Z) can be estimated. The result can be back-transformed tothe clr space byC(Y ) = V C(Z)V Twhere the matrix V with orthonormal columns comes from the relation betweenthe clr and the ilr transformation. Now the parameters in the model (2) can beestimated (Basilevsky, 1994) and the results have a direct interpretation since thelinks to the original variables are still preserved.The above procedure will be applied to data from geochemistry. Our specialinterest is on comparing the results with those of Reimann et al. (2002) for the Kolaproject data

Good clinical and cost outcomes using dexrazoxane to treat accidental epirubicin extravasation

A 75-year-old man diagnosed with lower esophageal adenocarcinoma suffered from epirubicin extravasation during the second cycle of neoadjuvant chemotherapy with epirubicin and oxaliplatin. A full recovery was achieved after treatment with dexrazoxane (Cardioxane® ). This is the first time in our hospital that extravasation of an anthracycline has been treated with dexrazoxane. We used Cardioxane® , approved for the prevention of anthracycline-induced cardiotoxicity, while Savene® is indicated for the treatment of anthracycline extravasation. The treatment was effective, and the selection of Cardioxane® (seven-fold cheaper than Savene® ) yielded a cost saving. Consequently, Cardioxane® has been included in our guidelines for anthracycline extravasation.

Why Public Health Agencies Cannot Depend on Good Laboratory Practices as a Criterion for Selecting Data: The Case of Bisphenol A

In their safety evaluations of bisphenol A (BPA), the U.S. Food and Drug Administration (FDA) and a counterpart in Europe, the European Food Safety Authority (EFSA), have given special prominence to two industry-funded studies that adhered to standards defined by Good Laboratory Practices (GLP). These same agencies have given much less weight in risk assessments to a large number of independently replicated non-GLP studies conducted with government funding by the leading experts in various fields of science from around the world. OBJECTIVES: We reviewed differences between industry-funded GLP studies of BPA conducted by commercial laboratories for regulatory purposes and non-GLP studies conducted in academic and government laboratories to identify hazards and molecular mechanisms mediating adverse effects. We examined the methods and results in the GLP studies that were pivotal in the draft decision of the U.S. FDA declaring BPA safe in relation to findings from studies that were competitive for U.S. National Institutes of Health (NIH) funding, peer-reviewed for publication in leading journals, subject to independent replication, but rejected by the U.S. FDA for regulatory purposes. DISCUSSION: Although the U.S. FDA and EFSA have deemed two industry-funded GLP studies of BPA to be superior to hundreds of studies funded by the U.S. NIH and NIH counterparts in other countries, the GLP studies on which the agencies based their decisions have serious conceptual and methodologic flaws. In addition, the U.S. FDA and EFSA have mistakenly assumed that GLP yields valid and reliable scientific findings (i.e., "good science"). Their rationale for favoring GLP studies over hundreds of publically funded studies ignores the central factor in determining the reliability and validity of scientific findings, namely, independent replication, and use of the most appropriate and sensitive state-of-the-art assays, neither of which is an expectation of industry-funded GLP research. CONCLUSIONS: Public health decisions should be based on studies using appropriate protocols with appropriate controls and the most sensitive assays, not GLP. Relevant NIH-funded research using state-of-the-art techniques should play a prominent role in safety evaluations of chemicals.

Magistri Petri Rogerii, Prioris sancti Baudilii, qui fuit postea Clemens Papa VI.lectura super Decretalem Joannis Papae XXII. Quia quorumdam mentes.

Baluzianus

Good agreements between self and clinician-collected specimens for the detection of human papillomavirus in Brazilian patients

Women infected with human papillomavirus (HPV) are at a higher risk of developing cervical lesions. In the current study, self and clinician-collected vaginal and cervical samples from women were processed to detect HPV DNA using polymerase chain reaction (PCR) with PGMY09/11 primers. HPV genotypes were determined using type-specific PCR. HPV DNA detection showed good concordance between self and clinician-collected samples (84.6%; kappa = 0.72). HPV infection was found in 30% women and genotyping was more concordant among high-risk HPV (HR-HPV) than low-risk HPV (HR-HPV). HPV16 was the most frequently detected among the HR-HPV types. LR-HPV was detected at a higher frequency in self-collected; however, HR-HPV types were more frequently identified in clinician-collected samples than in self-collected samples. HPV infections of multiple types were detected in 20.5% of clinician-collected samples and 15.5% of self-collected samples. In this study, we demonstrated that the HPV DNA detection rate in self-collected samples has good agreement with that of clinician-collected samples. Self-collected sampling, as a primary prevention strategy in countries with few resources, could be effective for identifying cases of HR-HPV, being more acceptable. The use of this method would enhance the coverage of screening programs for cervical cancer.