Depression is a significant problem for some breast cancer survivors after the end of treatment. This study assessed depression using the CES-D for 84 breast cancer patients at the conclusion of radiation treatment, and at 3 and 6 months post-treatment. Based on the pattern of CES-D scores, patients were divided into five groups: (1) Stay Depressed (scores above clinical cutoff for depression at all timepoints); (2) Recover (above threshold at baseline, but below at follow-up); (3) Become Depressed (below threshold at baseline, but above at follow-up); (4) Never Depressed (below threshold at all times); and (5) Vacillate (none of the above patterns). This study examined the relationships between depression groups and a variety of medical, demographic, and psychological measures, including anxiety and quality of life (QOL). Number of children at home significantly distinguished the groups, with the Become Depressed group having more children and the Vacillate group having fewer children. Anxiety levels were different among the groups, with Recover and Never Depressed groups having consistently lower anxiety scores. QOL scores also distinguished the groups in that Never Depressed patients demonstrated better QOL than all other groups. The findings have implications for understanding resilience in cancer patients.

PTSD and depression occur frequently and are independently associated with enduring impairments after injury hospitalization. Early acute care interventions targeting these disorders have the potential to improve functional recovery after injury.

A novel pH sensitive shape‐memory polymer (SMP) is prepared by cross‐linking the β‐cyclodextrin modified alginate (β‐CD‐Alg) and diethylenetriamine modified alginate (DETA‐Alg): The pH reversible β‐CD‐DETA inclusion complexes serve as a reversible phase, and the cross‐linked alginate chains serve as a fixing phase. It is shown that this material can be processed into temporary shape as we needs at pH 11.5 and recover to its initial shape at pH 7. The recovery ratio and the fixity ratio were 95.7 ± 0.9% and 94.8 ± 1.1%, respectively. Furthermore, this material showed good degradability and biocompatibility. Because the shape transition pH value is quite close to that of our body fluid and this pH triggered shape‐memory effect is convenient and safe to use, this material has a high potential for medical application.

These findings suggest that, in addition to being a risk factor for a depressive episode, persistent insomnia may serve to perpetuate the illness in some elderly patients and especially in those receiving standard care for depression in primary care settings. Enhanced depression care may partially mitigate the perpetuating effects of insomnia on depression.

The propensity score method is widely used in clinical studies to estimate the effect of a treatment with two levels on patient's outcomes. However, due to the complexity of many diseases, an effective treatment often involves multiple components. For example, in the practice of Traditional Chinese Medicine (TCM), an effective treatment may include multiple components, e.g. Chinese herbs, acupuncture, and massage therapy. In clinical trials involving TCM, patients could be randomly assigned to either the treatment or control group, but they or their doctors may make different choices about which treatment component to use. As a result, treatment components are not randomly assigned. Rosenbaum and Rubin proposed the propensity score method for binary treatments, and Imbens extended their work to multiple treatments. These authors defined the generalized propensity score as the conditional probability of receiving a particular level of the treatment given the pre-treatment variables. In the present work, we adopted this approach and developed a statistical methodology based on the generalized propensity score in order to estimate treatment effects in the case of multiple treatments. Two methods were discussed and compared: propensity score regression adjustment and propensity score weighting. We used these methods to assess the relative effectiveness of individual treatments in the multiple-treatment IMPACT clinical trial. The results reveal that both methods perform well when the sample size is moderate or large.