Transdiagnostic cognitive behavioural treatment and the impact of co-morbidity:an open trial in a cohort of primary care patients

To access publisher's full text version of this article click on the hyperlink at the bottom of the pageThe development of initiatives to improve access to psychological therapies has been driven by the realization that untreated anxiety and depression are both very common and costly to individuals as well as society. Effective and efficient treatments, mostly in the form of cognitive behavioural therapies (CBT), can be used in ways which enhance their acceptability and accessibility. To date, numbers of group therapies have been developed to improve cost efficiency, but in spite of growing interest in transdiagnostic approaches, group therapies have so far mostly been diagnosis specific.This study is aimed at evaluating a brief transdiagnostic cognitive behavioural group therapy (TCBGT) designed to treat both anxiety and depression among patients in primary care.The participants were 287 adult patients in primary care with diagnoses of depression and/or anxiety disorders. They underwent a 5-week TCBGT. A mixed design ANOVA was used to evaluate differential effects of treatment according to diagnostic groups (anxiety versus depression) and number of diagnoses (co-morbidity).Pre-post differences were significant and the treatment was equally effective for both anxiety disorders and depression. Number of diagnoses did not affect the outcome.The study indicates feasibility of the brief transdiagnostic group therapy for a wide range of mood and anxiety disorders in primary care. The results indicate that low intensity, brief transdiagnostic group therapies may be a feasible way to improve access to psychological therapies for a large number of patients

Bioinformatic identification of proteins with tissue-specific expression for biomarker discovery

<p>Abstract</p> <p>Background</p> <p>There is an important need for the identification of novel serological biomarkers for the early detection of cancer. Current biomarkers suffer from a lack of tissue specificity, rendering them vulnerable to non-disease-specific increases. The present study details a strategy to rapidly identify tissue-specific proteins using bioinformatics.</p> <p>Methods</p> <p>Previous studies have focused on either gene or protein expression databases for the identification of candidates. We developed a strategy that mines six publicly available gene and protein databases for tissue-specific proteins, selects proteins likely to enter the circulation, and integrates proteomic datasets enriched for the cancer secretome to prioritize candidates for further verification and validation studies.</p> <p>Results</p> <p>Using colon, lung, pancreatic and prostate cancer as case examples, we identified 48 candidate tissue-specific biomarkers, of which 14 have been previously studied as biomarkers of cancer or benign disease. Twenty-six candidate biomarkers for these four cancer types are proposed.</p> <p>Conclusions</p> <p>We present a novel strategy using bioinformatics to identify tissue-specific proteins that are potential cancer serum biomarkers. Investigation of the 26 candidates in disease states of the organs is warranted.</p

Prevalence of persistent physical symptoms and association with depression, anxiety and health anxiety in Iceland

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked DownloadINNGANGUR Þrálát líkamleg einkenni sem ekki eiga sér þekktar líkamlegar orsakir geta skert færni til að sinna athöfnum daglegs lífs. Markmið rannsóknarinnar var að meta algengi slíkra einkenna meðal fólks sem sækir heilsugæsluþjónustu á höfuðborgarsvæðinu, tengsl þeirra við færniskerðingu og einkenni þunglyndis, almenns kvíða og heilsukvíða, og meta hlutfall sjúklinga sem líklega hafi gagn af sálfræðimeðferð við þrálátum líkamlegum einkennum. EFNIVIÐUR OG AÐFERÐIR Spurningalistar sem meta þrálát líkamleg einkenni, færniskerðingu og einkenni þunglyndis, almenns kvíða og heilsukvíða voru lagðir fyrir 106 þátttakendur á tveimur heilsugæslustöðvum á höfuðborgarsvæðinu. NIÐURSTÖÐUR Tuttugu og níu (27,4%) þátttakendur reyndust vera með þrálát líkamleg einkenni og voru sterk tengsl á milli þeirra og einkenna geðraskana. Þátttakendur með þrálát líkamleg einkenni voru 8 sinnum líklegri til að vera með einkenni þunglyndis og almenns kvíða en þátttakendur án þeirra, fjórum sinnum líklegri til að vera með einkenni heilsukvíða og 13 sinnum líklegri til að vera með færniskerðingu yfir klínískum viðmiðunarmörkum. Rúmlega helmingur þátttakenda með þrálát líkamleg einkenni voru með tvær eða fleiri gerðir einkenna en þreyta og vöðvavandamál var algengasta gerðin. 65% þátttakenda greindu frá þrálátum líkamlegum einkennum og sálrænum einkennum yfir klínískum viðmiðunarmörkum. ÁLYKTUN Algengi þrálátra líkamlegra einkenna meðal notenda heilsugæsluþjónustu á höfuðborgarsvæðinu samræmist niðurstöðum erlendra rannsókna. Sama má segja um tengsl þeirra við einkenni þunglyndis og kvíða. Líklegt er að tveir þriðju heilsugæslusjúklinga með slík einkenni myndu njóta góðs af sálfræðilegri meðferð. Hugræn atferlismeðferð við þrálátum líkamlegum einkennum gæti gert þessum hópi gagn en í slíkri meðferð er unnið sérstaklega með samspil sálrænna og líkamlegra einkennaINTRODUCTION: Persistent physical symptoms that are medically unexplained can result in significant functional impairment. The aim of this study was to estimate the prevalence of persistent physical symptoms among people seeking primary healthcare in Reykjavík, Iceland, how they relate to functional impairment, symptoms of depression, general anxiety and health anxiety, and estimate the proportion of people with such symptoms who would likely benefit from psychological treatment. MATERIALS AND METHODS: Questionnaires measuring persistent physical symptoms, functional impairment, and symptoms of depression, general anxiety and health anxiety were administered to 106 patients attending two primary healthcare clinics. RESULTS: The prevalence of persistent physical symptoms was 27.4% among the primary care patients and they had a strong relationship to symptoms of mental disorders. Participants with persistent physical symptoms were 8 times more likely to have clinical levels of depression and general anxiety than participants without such symptoms, 4 times more likely to have clinical levels of health anxiety and 13 times more likely to have clinical levels of functional impairment. At least two-thirds of participants with persistent physical symptoms would likely benefit from psychological treatment. CONCLUSION: The prevalence of persistent physical symptoms among health care patients in the capital area of Iceland is in line with previous studies. Similarly, the strong relationship between persistent physical symptoms and symptoms of depression and anxiety corresponds to previous studies. It is likely that at least two out of three patients with persistent physical symptoms would benefit from psychological treatment. Transdiagnostic cognitive behavioural therapy for persistent physical symptoms might be particularly useful as is focuses on the interplay between physical and mental symptoms

Parent-youth agreement on psychiatric diagnoses and symptoms: results from an adolescent outpatient clinical sample.

To access publisher's full text version of this article click on the hyperlink belowObjective: Previous research suggests that agreement, between youths and their parents, regarding assessment of youth psychiatric problems is limited. Due to this discrepancy, a multi-informant, multimethod approach is recommended when gathering psychopathological information. This study examines parent-youth agreement regarding youth psychiatric problems. It does so at a diagnostic level and at a symptom level, as well as studying the influence of age, gender, depressive disorder, anxiety disorder and attention-deficit/hyperactivity disorder (ADHD) as potential moderators of agreement. Methods: The participants in this study were 61 adolescents aged 12-18 years and their parents. The K-SADS-PL DSM-5 was administered in two outpatient units, with adolescents and their parents interviewed separately. Participants also rated symptoms using a broad rating scale (Child Behavior Checklist and the Youth Self-Report) prior to being interviewed. Results: Parent-youth agreement at a diagnostic level ranged from fair to excellent. Agreement at a symptom level was lower than that at a diagnostic level, ranging from poor to fair. These results indicate that parent-youth agreement regarding diagnosis and symptoms is higher than in most previous studies. The results also suggest that some variables, such as age, gender, depressive disorders, and ADHD, potentially influence agreement on symptoms. Conclusion: These findings support the importance of gathering information from both children and parents, and that clinicians should consider moderating factors when integrating data from multiple informants. Keywords: Agreement; assessment; children; parent–youth agreement; schedule for affective disorders and schizophrenia for school-age children Kiddie-SADS-PL (K-SADS).University of Iceland Research Fun

A Texture Based Pattern Recognition Approach to Distinguish Melanoma from Non-Melanoma Cells in Histopathological Tissue Microarray Sections

Aims: Immunohistochemistry is a routine practice in clinical cancer diagnostics and also an established technology for tissue-based research regarding biomarker discovery efforts. Tedious manual assessment of immunohistochemically stained tissue needs to be fully automated to take full advantage of the potential for high throughput analyses enabled by tissue microarrays and digital pathology. Such automated tools also need to be reproducible for different experimental conditions and biomarker targets. In this study we present a novel supervised melanoma specific pattern recognition approach that is fully automated and quantitative. Methods and Results: Melanoma samples were immunostained for the melanocyte specific target, Melan-A. Images representing immunostained melanoma tissue were then digitally processed to segment regions of interest, highlighting Melan-A positive and negative areas. Color deconvolution was applied to each region of interest to separate the channel containing the immunohistochemistry signal from the hematoxylin counterstaining channel. A support vector machine melanoma classification model was learned from a discovery melanoma patient cohort (n = 264) and subsequently validated on an independent cohort of melanoma patient tissue sample images (n = 157). Conclusion: Here we propose a novel method that takes advantage of utilizing an immuhistochemical marker highlighting melanocytes to fully automate the learning of a general melanoma cell classification model. The presented method can be applied on any protein of interest and thus provides a tool for quantification of immunohistochemistry-based protein expression in melanoma

Different groups of the unemployed time perspective and life satisfaction relationship

Aims: Immunohistochemistry is a routine practice in clinical cancer diagnostics and also an established technology for tissue-based research regarding biomarker discovery efforts. Tedious manual assessment of immunohistochemically stained tissue needs to be fully automated to take full advantage of the potential for high throughput analyses enabled by tissue microarrays and digital pathology. Such automated tools also need to be reproducible for different experimental conditions and biomarker targets. In this study we present a novel supervised melanoma specific pattern recognition approach that is fully automated and quantitative. Methods and Results: Melanoma samples were immunostained for the melanocyte specific target, Melan-A. Images representing immunostained melanoma tissue were then digitally processed to segment regions of interest, highlighting Melan-A positive and negative areas. Color deconvolution was applied to each region of interest to separate the channel containing the immunohistochemistry signal from the hematoxylin counterstaining channel. A support vector machine melanoma classification model was learned from a discovery melanoma patient cohort (n = 264) and subsequently validated on an independent cohort of melanoma patient tissue sample images (n = 157). Conclusion: Here we propose a novel method that takes advantage of utilizing an immuhistochemical marker highlighting melanocytes to fully automate the learning of a general melanoma cell classification model. The presented method can be applied on any protein of interest and thus provides a tool for quantification of immunohistochemistry-based protein expression in melanoma