Predictors of onset of depression and anxiety in the year after diagnosis of breast cancer

Background Depression and anxiety are common after diagnosis of breast cancer. We examined to what extent these are recurrences of previous disorder and, controlling for this, whether shame, self-blame and low social support after diagnosis predicted onset of depression and anxiety subsequently. Method Women with primary breast cancer who had been treated surgically self-reported shame, self-blame, social support and emotional distress post-operatively. Psychiatric interview 12 months later identified those with adult lifetime episodes of major depression (MD) or generalized anxiety disorder (GAD) before diagnosis and onset over the subsequent year. Statistical analysis examined predictors of each disorder in that year. Results Of the patients, two-thirds with episodes of MD and 40% with episodes of GAD during the year after diagnosis were experiencing recurrence of previous disorder. Although low social support, self-blame and shame were each associated with both MD and GAD after diagnosis, they did not mediate the relationship of disorder after diagnosis with previous disorder. Low social support, but not shame or self-blame, predicted recurrence after controlling for previous disorder. Conclusions Anxiety and depression during the first year after diagnosis of breast cancer are often the recurrence of previous disorder. In predicting disorder following diagnosis, self-blame and shame are merely markers of previous disorder. Low social support is an independent predictor and therefore may have a causal role.

The perception of support received from breast care nurses by depressed patients following a diagnosis of breast cancer

INTRODUCTION Due to their specialist training, breast care nurses (BCNs) should be able to detect emotional distress and offer support to breast cancer patients. However, patients who are most distressed after diagnosis generally experience least support from care staff. To test whether BCNs overcome this potential barrier, we compared the support experienced by depressed and non-depressed patients from their BCNs and the other main professionals involved in their care: surgeons and ward nurses. PATIENTS AND METHODS Women with primary breast cancer (n = 355) 2–4 days after mastectomy or wide local excision, self-reported perceived professional support and current depression. Analysis of variance compared support ratings of depressed and non-depressed patients across staff types. RESULTS There was evidence of depression in 31 (9%) patients. Depressed patients recorded less surgeon and ward nurse support than those who were not depressed but the support received by patients from the BCN was high, whether or not patients were depressed. CONCLUSIONS BCNs were able to provide as much support to depressed patients as to non-depressed patients, whereas depressed patients felt less supported by surgeons and ward nurses than did non-depressed patients. Future research should examine the basis of BCNs' ability to overcome barriers to support in depressed patients. Our findings confirm the importance of maintaining the special role of the BCN.

An LDA and probability-based classifier for the diagnosis of Alzheimer's Disease from structural MRI

In this paper a custom classification algorithm based on linear discriminant analysis and probability-based weights is implemented and applied to the hippocampus measurements of structural magnetic resonance images from healthy subjects and Alzheimer’s Disease sufferers; and then attempts to diagnose them as accurately as possible. The classifier works by classifying each measurement of a hippocampal volume as healthy controlsized or Alzheimer’s Disease-sized, these new features are then weighted and used to classify the subject as a healthy control or suffering from Alzheimer’s Disease. The preliminary results obtained reach an accuracy of 85.8% and this is a similar accuracy to state-of-the-art methods such as a Naive Bayes classifier and a Support Vector Machine. An advantage of the method proposed in this paper over the aforementioned state of the art classifiers is the descriptive ability of the classifications it produces. The descriptive model can be of great help to aid a doctor in the diagnosis of Alzheimer’s Disease, or even further the understand of how Alzheimer’s Disease affects the hippocampus.

Students’ perception of the safety and value of a herbal vs. a conventional medicine for treating insomnia

University students suffer from variable sleep patterns including insomnia;[1] furthermore, the highest incidence of herbal use appears to be among college graduates.[2] Our objective was to test the perception of safety and value of herbal against conventional medicine for the treatment of insomnia in a non-pharmacy student population. We used an experimental design and bespoke vignettes that relayed the same effectiveness information to test our hypothesis that students would give higher ratings of safety and value to herbal product compared to conventional medicine. We tested another hypothesis that the addition of side-effect information would lower people’s perception of the safety and value of the herbal product to a greater extent than it would with the conventional medicine.