Enhancing legacy in palliative care: study protocol for a randomized controlled trial of Dignity Therapy focused on positive outcomes.

BackgroundDignity Therapy is a brief psychotherapy that can enhance a sense of legacy while addressing the emotional and existential needs of patients receiving hospice or palliative care. In Dignity Therapy, patients create a formalized "legacy" document that records their most cherished memories, their lessons learned in life, as well as their hopes and dreams for loved ones in the future. To date, this treatment has been studied for its impact on mitigating distress within hospice and palliative care populations and has provided mixed results. This study will instead focus on whether Dignity Therapy enhances positive outcomes in this population.Methods/designIn this study, 90 patients with cancer receiving hospice or palliative care will complete a mixed-methods randomized controlled trial of Dignity Therapy (n = 45) versus Supportive Attention (n = 45). The patients will be enrolled in the study for 3 weeks, receiving a total of six study visits. The primary outcomes examine whether the treatment will quantitatively increase levels of positive affect and a sense of life closure. Secondary outcomes focus on gratitude, hope, life satisfaction, meaning in life, resilience, and self-efficacy. Using a fixed, embedded dataset design, this study will additionally use qualitative interviews to explore patients' perceptions regarding the use of positive outcome measures and whether these outcomes are appropriately matched to their experiences in therapy.DiscussionDignity Therapy has shown mixed results when evaluating its impact on distress, although no other study to date has solely focused on the potential positive aspects of this treatment. This study is novel in its use of mixed methods assessments to focus on positive outcomes, and will provide valuable information about patients' direct experiences in this area.Trial registrationISRCTN91389194

Social closeness increases salivary progesterone in humans

We examined whether interpersonal closeness increases salivary progesterone. One hundred and sixty female college students (80 dyads) were randomly assigned to participate in either a closeness task with a partner versus a neutral task with a partner. Those exposed to the closeness induction had higher levels of progesterone relative to those exposed to the neutral task. Across conditions, progesterone increase one week later predicted the willingness to sacrifice for the partner. These results are discussed in terms of the links between social contact, stress, and health

Snowballs in Euclid and WFIRST Detectors

Snowballs are transient events observed in HgCdTe detectors with a sudden increase of charge in a few pixels. They appear between consecutive reads of the detector, after which the affected pixels return to their normal behavior. The origin of the snowballs is unknown, but it was speculated that they could be the result of alpha decay of naturally radioactive contaminants in the detectors, but a cosmic ray origin cannot be ruled out. Even though previous studies predicted a low rate of occurrence of these events, and consequently, a minimal impact on science, it is interesting to investigate the cause or causes that may generate snowballs and their impact in detectors designed for future missions. We searched for the presence of snowballs in the dark current data in Euclid and Wide Field Infrared Survey Telescope (WFIRST) detectors tested in the Detector Characterization Laboratory at Goddard Space Flight Center. Our investigation shows that for Euclid and WFIRST detectors, there are snowballs that appear only one time, and others that repeat in the same spatial localization. For Euclid detectors, there is a correlation between the snowballs that repeat and bad pixels in the operational masks (pixels that do not fulfill the requirements to pass spectroscopy noise, photometry noise, quantum efficiency, and/or linearity). The rate of occurrence for a snowball event is about 0.9 snowballs/hr. in Euclid detectors (for the ones that do not have associated bad pixels in the mask), and about 0.7 snowballs/hr. in PV3 Full Array Lot WFIRST detectors

Enhancing legacy in palliative care: study protocol for a randomized controlled trial of Dignity Therapy focused on positive outcomes

BACKGROUND: Dignity Therapy is a brief psychotherapy that can enhance a sense of legacy while addressing the emotional and existential needs of patients receiving hospice or palliative care. In Dignity Therapy, patients create a formalized “legacy” document that records their most cherished memories, their lessons learned in life, as well as their hopes and dreams for loved ones in the future. To date, this treatment has been studied for its impact on mitigating distress within hospice and palliative care populations and has provided mixed results. This study will instead focus on whether Dignity Therapy enhances positive outcomes in this population. METHODS/DESIGN: In this study, 90 patients with cancer receiving hospice or palliative care will complete a mixed-methods randomized controlled trial of Dignity Therapy (n = 45) versus Supportive Attention (n = 45). The patients will be enrolled in the study for 3 weeks, receiving a total of six study visits. The primary outcomes examine whether the treatment will quantitatively increase levels of positive affect and a sense of life closure. Secondary outcomes focus on gratitude, hope, life satisfaction, meaning in life, resilience, and self-efficacy. Using a fixed, embedded dataset design, this study will additionally use qualitative interviews to explore patients’ perceptions regarding the use of positive outcome measures and whether these outcomes are appropriately matched to their experiences in therapy. DISCUSSION: Dignity Therapy has shown mixed results when evaluating its impact on distress, although no other study to date has solely focused on the potential positive aspects of this treatment. This study is novel in its use of mixed methods assessments to focus on positive outcomes, and will provide valuable information about patients’ direct experiences in this area. TRIAL REGISTRATION: ISRCTN9138919

The ENIGMA sports injury working group - an international collaboration to further our understanding of sport-related brain injury

Sport-related brain injury is very common, and the potential long-term effects include a wide range of neurological and psychiatric symptoms, and potentially neurodegeneration. Around the globe, researchers are conducting neuroimaging studies on primarily homogenous samples of athletes. However, neuroimaging studies are expensive and time consuming, and thus current findings from studies of sport-related brain injury are often limited by small sample sizes. Further, current studies apply a variety of neuroimaging techniques and analysis tools which limit comparability among studies. The ENIGMA Sports Injury working group aims to provide a platform for data sharing and collaborative data analysis thereby leveraging existing data and expertise. By harmonizing data from a large number of studies from around the globe, we will work towards reproducibility of previously published findings and towards addressing important research questions with regard to diagnosis, prognosis, and efficacy of treatment for sport-related brain injury. Moreover, the ENIGMA Sports Injury working group is committed to providing recommendations for future prospective data acquisition to enhance data quality and scientific rigor

Linking Symptom Inventories using Semantic Textual Similarity

An extensive library of symptom inventories has been developed over time to measure clinical symptoms, but this variety has led to several long standing issues. Most notably, results drawn from different settings and studies are not comparable, which limits reproducibility. Here, we present an artificial intelligence (AI) approach using semantic textual similarity (STS) to link symptoms and scores across previously incongruous symptom inventories. We tested the ability of four pre-trained STS models to screen thousands of symptom description pairs for related content - a challenging task typically requiring expert panels. Models were tasked to predict symptom severity across four different inventories for 6,607 participants drawn from 16 international data sources. The STS approach achieved 74.8% accuracy across five tasks, outperforming other models tested. This work suggests that incorporating contextual, semantic information can assist expert decision-making processes, yielding gains for both general and disease-specific clinical assessment

The Sariçiçek Howardite Fall in Turkey: Source Crater of HED Meteorites on Vesta and İmpact Risk of Vestoids

The Sariçiçek howardite meteorite shower consisting of 343 documented stones occurred on 2 September 2015 in Turkey and is the first documented howardite fall. Cosmogenic isotopes show that Sariçiçek experienced a complex cosmic ray exposure history, exposed during ~12–14 Ma in a regolith near the surface of a parent asteroid, and that an ~1 m sized meteoroid was launched by an impact 22 ± 2 Ma ago to Earth (as did one third of all HED meteorites). SIMS dating of zircon and baddeleyite yielded 4550.4 ± 2.5 Ma and 4553 ± 8.8 Ma crystallization ages for the basaltic magma clasts. The apatite U-Pb age of 4525 ± 17 Ma, K-Ar age of ~3.9 Ga, and the U,Th-He ages of 1.8 ± 0.7 and 2.6 ± 0.3 Ga are interpreted to represent thermal metamorphic and impact-related resetting ages, respectively. Petrographic, geochemical and O-, Cr- and Tiisotopic studies confirm that Sariçiçek belongs to the normal clan of HED meteorites. Petrographic observations and analysis of organic material indicate a small portion of carbonaceous chondrite material in the Sariçiçek regolith and organic contamination of the meteorite after a few days on soil. Video observations of the fall show an atmospheric entry at 17.3 ± 0.8 kms-1 from NW, fragmentations at 37, 33, 31 and 27 km altitude, and provide a pre-atmospheric orbit that is the first dynamical link between the normal HED meteorite clan and the inner Main Belt. Spectral data indicate the similarity of Sariçiçek with the Vesta asteroid family (V-class) spectra, a group of asteroids stretching to delivery resonances, which includes (4) Vesta. Dynamical modeling of meteoroid delivery to Earth shows that the complete disruption of a ~1 km sized Vesta family asteroid or a ~10 km sized impact crater on Vesta is required to provide sufficient meteoroids ≤4 m in size to account for the influx of meteorites from this HED clan. The 16.7 km diameter Antonia impact crater on Vesta was formed on terrain of the same age as given by the 4He retention age of Sariçiçek. Lunar scaling for crater production to crater counts of its ejecta blanket show it was formed ~22 Ma ago

Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders—ENIGMA study in people with bipolar disorders and obesity

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. Practitioner Points: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.</p

Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders—ENIGMA study in people with bipolar disorders and obesity

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. Practitioner Points: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.</p