The Change Up Project : using social norming theory with young people to address domestic abuse and promote healthy relationships

This paper presents the findings of a secondary analysis of data collected during a pilot project, Change Up, which used a social norming approach (SNA) to address domestic violence and abuse (DVA) with young people aged 13–14. A SNA is based upon a well-articulated theory of behavior and evidence-based methodology for addressing social justice issues. This reflects a paradigm shift focusing upon strengths and positives, rather than pathologizing behaviours. Adopting a SNA, the Change Up project comprised a baseline survey followed by the intervention (workshop and peer-to-peer campaign), ending with a post-intervention survey. It was delivered in two high schools in a UK city between 2015 and 16. A secondary analysis of the survey data collected during the surveys and qualitative data collected at the end of each workshop was undertaken and this is reported here. Change Up data illustrates that most young people in the sample thought that DVA is unacceptable. There was, however, a gender difference in the norms held about the social acceptability of girls using physical violence against boys (and vice versa). The analysis of Change Up data indicates that a social norming approach to DVA programs aimed at young people can be successful in promoting attitude and behaviour change. It also highlights a continuing need for young people’s education about relationships and gender equality

Metabolite Profiling of Alzheimer's Disease Cerebrospinal Fluid

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive loss of cognitive functions. Today the diagnosis of AD relies on clinical evaluations and is only late in the disease. Biomarkers for early detection of the underlying neuropathological changes are still lacking and the biochemical pathways leading to the disease are still not completely understood. The aim of this study was to identify the metabolic changes resulting from the disease phenotype by a thorough and systematic metabolite profiling approach. For this purpose CSF samples from 79 AD patients and 51 healthy controls were analyzed by gas and liquid chromatography-tandem mass spectrometry (GC-MS and LC-MS/MS) in conjunction with univariate and multivariate statistical analyses. In total 343 different analytes have been identified. Significant changes in the metabolite profile of AD patients compared to healthy controls have been identified. Increased cortisol levels seemed to be related to the progression of AD and have been detected in more severe forms of AD. Increased cysteine associated with decreased uridine was the best paired combination to identify light AD (MMSE>22) with specificity and sensitivity above 75%. In this group of patients, sensitivity and specificity above 80% were obtained for several combinations of three to five metabolites, including cortisol and various amino acids, in addition to cysteine and uridine

Laser-induced modification of the patellar ligament tissue: comparative study of structural and optical changes

The effects of non-ablative infrared (IR) laser treatment of collagenous tissue have been commonly interpreted in terms of collagen denaturation spread over the laser-heated tissue area. In this work, the existing model is refined to account for the recently reported laser-treated tissue heterogeneity and complex collagen degradation pattern using comprehensive optical imaging and calorimetry toolkits. Patella ligament (PL) provided a simple model of type I collagen tissue containing its full structural content from triple-helix molecules to gross architecture. PL ex vivo was subjected to IR laser treatments (laser spot, 1.6 mm) of equal dose, where the tissue temperature reached the collagen denaturation temperature of 60 ± 2°C at the laser spot epicenterin the first regime, and was limited to 67 ± 2°C in the second regime. The collagen network was analyzed versus distance from the epicenter. Experimental characterization of the collagenous tissue at all structural levels included cross-polarization optical coherence tomography, nonlinear optical microscopy, light microscopy/histology, and differential scanning calorimetry. Regressive rearrangement of the PL collagen network was found to spread well outside the laser spot epicenter (>2 mm) and was accompanied by multilevel hierarchical reorganization of collagen. Four zones of distinct optical and morphological properties were identified, all elliptical in shape, and elongated in the direction perpendicular to the PL long axis. Although the collagen transformation into a random-coil molecular structure was occasionally observed, it was mechanical integrity of the supramolecular structures that was primarily compromised. We found that the structural rearrangement of the collagen network related primarily to the heat-induced thermo-mechanical effects rather than molecular unfolding. The current body of evidence supports the notion that the supramolecular collagen structure suffered degradation of various degrees, which gave rise to the observed zonal character of the laser-treated lesion

The patriotism of gentlemen with red hair: European Jews and the liberal state, 1789–1939

European Jewish history from 1789–1939 supports the view that construction of national identities even in secular liberal states was determined not only by modern considerations alone but also by ancient patterns of thought, behaviour and prejudice. Emancipation stimulated unprecedented patriotism, especially in wartime, as Jews strove to prove loyalty to their countries of citizenship. During World War I, even Zionists split along national lines, as did families and friends. Jewish patriotism was interchangeable with nationalism inasmuch as Jews identified themselves with national cultures. Although emancipation implied acceptance and an end to anti-Jewish prejudice in the modern liberal state, the kaleidoscopic variety of Jewish patriotism throughout Europe inadvertently undermined the idea of national identity and often provoked anti-Semitism. Even as loyal citizens of separate states, the Jews, however scattered, disunited and diverse, were made to feel, often unwillingly, that they were one people in exile

Prevention of depression and sleep disturbances in elderly with memory-problems by activation of the biological clock with light - a randomized clinical trial

<p>Abstract</p> <p>Background</p> <p>Depression frequently occurs in the elderly and in patients suffering from dementia. Its cause is largely unknown, but several studies point to a possible contribution of circadian rhythm disturbances. Post-mortem studies on aging, dementia and depression show impaired functioning of the suprachiasmatic nucleus (SCN) which is thought to be involved in the increased prevalence of day-night rhythm perturbations in these conditions. Bright light enhances neuronal activity in the SCN. Bright light therapy has beneficial effects on rhythms and mood in institutionalized moderate to advanced demented elderly. In spite of the fact that this is a potentially safe and inexpensive treatment option, no previous clinical trial evaluated the use of long-term daily light therapy to prevent worsening of sleep-wake rhythms and depressive symptoms in early to moderately demented home-dwelling elderly.</p> <p>Methods/Design</p> <p>This study investigates whether long-term daily bright light prevents worsening of sleep-wake rhythms and depressive symptoms in elderly people with memory complaints. Patients with early Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI) and Subjective Memory Complaints (SMC), between the ages of 50 and 75, are included in a randomized double-blind placebo-controlled trial. For the duration of two years, patients are exposed to ~10,000 lux in the active condition or ~300 lux in the placebo condition, daily, for two half-hour sessions at fixed times in the morning and evening. Neuropsychological, behavioral, physiological and endocrine measures are assessed at baseline and follow-up every five to six months.</p> <p>Discussion</p> <p>If bright light therapy attenuates the worsening of sleep-wake rhythms and depressive symptoms, it will provide a measure that is easy to implement in the homes of elderly people with memory complaints, to complement treatments with cholinesterase inhibitors, sleep medication or anti-depressants or as a stand-alone treatment.</p> <p>Trial registration</p> <p>ISRCTN29863753</p

Predicting probable Alzheimer's disease using linguistic deficits and biomarkers

BackgroundThe manual diagnosis of neurodegenerative disorders such as Alzheimer’s disease (AD) and related Dementias has been a challenge. Currently, these disorders are diagnosed using specific clinical diagnostic criteria and neuropsychological examinations. The use of several Machine Learning algorithms to build automated diagnostic models using low-level linguistic features resulting from verbal utterances could aid diagnosis of patients with probable AD from a large population. For this purpose, we developed different Machine Learning models on the DementiaBank language transcript clinical dataset, consisting of 99 patients with probable AD and 99 healthy controls.ResultsOur models learned several syntactic, lexical, and n-gram linguistic biomarkers to distinguish the probable AD group from the healthy group. In contrast to the healthy group, we found that the probable AD patients had significantly less usage of syntactic components and significantly higher usage of lexical components in their language. Also, we observed a significant difference in the use of n-grams as the healthy group were able to identify and make sense of more objects in their n-grams than the probable AD group. As such, our best diagnostic model significantly distinguished the probable AD group from the healthy elderly group with a better Area Under the Receiving Operating Characteristics Curve (AUC) using the Support Vector Machines (SVM).ConclusionsExperimental and statistical evaluations suggest that using ML algorithms for learning linguistic biomarkers from the verbal utterances of elderly individuals could help the clinical diagnosis of probable AD. We emphasise that the best ML model for predicting the disease group combines significant syntactic, lexical and top n-gram features. However, there is a need to train the diagnostic models on larger datasets, which could lead to a better AUC and clinical diagnosis of probable AD