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The psychedelic research renaissance is gaining traction. Preliminary clinical studies of the hallucinogenic fungi, psilocybin, with psychological support, have indicated improvements in mood, anxiety and quality of life. A seminal, open-label study demonstrated marked reductions in depression symptoms in participants with treatment-resistant depression (TRD). The associated neurobiological processes involve alterations in brain connectivity, together with altered amygdala and default mode network activity. At the cellular level, psychedelics promote synaptogenesis and neural plasticity. Prompted by the promising preliminary studies, a randomized, double-blind trial has recently been launched across Europe and North America to investigate the efficacy of psilocybin in TRD. One of these centres is based in Ireland – CHO Area 7 and Tallaght University Hospital. The outcome of this trial will determine whether psilocybin with psychological support will successfully translate into the psychiatric clinic for the benefit of patients.
Clinical Enterobacteriacae isolates with a colistin minimum inhibitory concentration (MIC) ≥4 mg/L from a United States hospital were screened for the mcr-1 gene using real-time polymerase chain reaction (RT-PCR) and confirmed by whole-genome sequencing. Four colistin-resistant Escherichia coli isolates contained mcr-1. Two isolates belonged to the same sequence type (ST-632). All subjects had prior international travel and antimicrobial exposure.
Introduction: High quality Cardiopulmonary Resuscitation (CPR) saves lives, however skill retention after standard Basic Life support (BLS) courses has been shown to be poor. Our goal was to develop a student-run, mentorship based program to allow repetitive practice of BLS skills while minimizing resource commitment and time requirements. Methods: We developed a top down training program that relied on online teaching resources, regular simulation training and near-peer feedback. First year medical students were given the opportunity to participate in the program and baseline CPR quality was documented. They were then divided into intervention and control groups. The intervention group participated in bi-monthly 40-minute small group training sessions directed by senior medical students and monitored by a staff physician. The control group received no further training. At the end of the 8-month study period CPR quality was documented for all participants. Results: We included data from 54 medical students. Overall compression depth and rate were monitored using Laderall SimMan 3G(TM) high-fidelity CPR mannequins. Average rate and depth of compression were significantly improved in the intervention group relative to both the control group that did not receive training, as well as relative to the intervention groups own pre intervention values (both with p values below 0.05 using Mann-Whitney tests and an intention to treat analysis for loss to follow up). Conclusion: Our study demonstrated a significant improvement in CPR quality as a result of our intervention. Survey data also indicated positive feedback from participants in relation to comfort with in-hospital CPR. As such we intend to continue to run this program, identifying participants each year whom can move into training and leadership roles to help foster CPR and basic resuscitation in our medical community.
Traditionally, personalised nutrition was delivered at an individual level. However, the concept of delivering tailored dietary advice at a group level through the identification of metabotypes or groups of metabolically similar individuals has emerged. Although this approach to personalised nutrition looks promising, further work is needed to examine this concept across a wider population group. Therefore, the objectives of this study are to: (1) identify metabotypes in a European population and (2) develop targeted dietary advice solutions for these metabotypes. Using data from the Food4Me study (n 1607), k-means cluster analysis revealed the presence of three metabolically distinct clusters based on twenty-seven metabolic markers including cholesterol, individual fatty acids and carotenoids. Cluster 2 was identified as a metabolically healthy metabotype as these individuals had the highest Omega-3 Index (6·56 (sd 1·29) %), carotenoids (2·15 (sd 0·71) µm) and lowest total saturated fat levels. On the basis of its fatty acid profile, cluster 1 was characterised as a metabolically unhealthy cluster. Targeted dietary advice solutions were developed per cluster using a decision tree approach. Testing of the approach was performed by comparison with the personalised dietary advice, delivered by nutritionists to Food4Me study participants (n 180). Excellent agreement was observed between the targeted and individualised approaches with an average match of 82 % at the level of delivery of the same dietary message. Future work should ascertain whether this proposed method could be utilised in a healthcare setting, for the rapid and efficient delivery of tailored dietary advice solutions.
Objectives: Careful characterization of how functional decline co-evolves with cognitive decline in older adults has yet to be well described. Most models of neurodegenerative disease postulate that cognitive decline predates and potentially leads to declines in everyday functional abilities; however, there is mounting evidence that subtle decline in instrumental activities of daily living (IADLs) may be detectable in older individuals who are still cognitively normal. Methods: The present study examines how the relationship between change in cognition and change in IADLs are best characterized among older adults who participated in the ACTIVE trial. Neuropsychological and IADL data were analyzed for 2802 older adults who were cognitively normal at study baseline and followed for up to 10 years. Results: Findings demonstrate that subtle, self-perceived difficulties in performing IADLs preceded and predicted subsequent declines on cognitive tests of memory, reasoning, and speed of processing. Conclusions: Findings are consistent with a growing body of literature suggesting that subjective changes in everyday abilities can be associated with more precipitous decline on objective cognitive measures and the development of mild cognitive impairment and dementia. (JINS, 2018, 24, 104–112)
Research shows that cognitive rehabilitation (CR) has the potential to improve goal performance and enhance well-being for people with early stage Alzheimer’s disease (AD). This single subject, multiple baseline design (MBD) research investigated the clinical efficacy of an 8-week individualised CR intervention for individuals with early stage AD.
Three participants with early stage AD were recruited to take part in the study. The intervention consisted of eight sessions of 60–90 minutes of CR. Outcomes included goal performance and satisfaction, quality of life, cognitive and everyday functioning, mood, and memory self-efficacy for participants with AD; and carer burden, general mental health, quality of life, and mood of carers.
Visual analysis of MBD data demonstrated a functional relationship between CR and improvements in participants’ goal performance. Subjective ratings of goal performance and satisfaction increased from baseline to post-test for three participants and were maintained at follow-up for two. Baseline to post-test quality of life scores improved for three participants, whereas cognitive function and memory self-efficacy scores improved for two.
Our findings demonstrate that CR can improve goal performance, and is a socially acceptable intervention that can be implemented by practitioners with assistance from carers between sessions. This study represents one of the promising first step towards filling a practice gap in this area. Further research and randomised-controlled trials are required.
It is postulated that knowledge of genotype may be more powerful than other types of personalised information in terms of motivating behaviour change. However, there is also a danger that disclosure of genetic risk may promote a fatalistic attitude and demotivate individuals. The original concept of personalised nutrition (PN) focused on genotype-based tailored dietary advice; however, PN can also be delivered based on assessment of dietary intake and phenotypic measures. Whilst dietitians currently provide PN advice based on diet and phenotype, genotype-based PN advice is not so readily available. The aim of this review is to examine the evidence for genotype-based personalised information on motivating behaviour change, and factors which may affect the impact of genotype-based personalised advice. Recent findings in PN will also be discussed, with respect to a large European study, Food4Me, which investigated the impact of varying levels of PN advice on motivating behaviour change. The researchers reported that PN advice resulted in greater dietary changes compared with general healthy eating advice, but no additional benefit was observed for PN advice based on phenotype and genotype information. Within Food4Me, work from our group revealed that knowledge of MTHFR genotype did not significantly improve intakes of dietary folate. In general, evidence is weak with regard to genotype-based PN advice. For future work, studies should test the impact of PN advice developed on a strong nutrigenetic evidence base, ensure an appropriate study design for the research question asked, and incorporate behaviour change techniques into the intervention.
Individual response to dietary interventions can be highly variable. The phenotypic characteristics of those who will respond positively to personalised dietary advice are largely unknown. The objective of this study was to compare the phenotypic profiles of differential responders to personalised dietary intervention, with a focus on total circulating cholesterol. Subjects from the Food4Me multi-centre study were classified as responders or non-responders to dietary advice on the basis of the change in cholesterol level from baseline to month 6, with lower and upper quartiles defined as responder and non-responder groups, respectively. There were no significant differences between demographic and anthropometric profiles of the groups. Furthermore, with the exception of alcohol, there was no significant difference in reported dietary intake, at baseline. However, there were marked differences in baseline fatty acid profiles. The responder group had significantly higher levels of stearic acid (18 : 0, P=0·034) and lower levels of palmitic acid (16 : 0, P=0·009). Total MUFA (P=0·016) and total PUFA (P=0·008) also differed between the groups. In a step-wise logistic regression model, age, baseline total cholesterol, glucose, five fatty acids and alcohol intakes were selected as factors that successfully discriminated responders from non-responders, with sensitivity of 82 % and specificity of 83 %. The successful delivery of personalised dietary advice may depend on our ability to identify phenotypes that are responsive. The results demonstrate the potential use of metabolic profiles in identifying response to an intervention and could play an important role in the development of precision nutrition.
To characterise clusters of individuals based on adherence to dietary recommendations and to determine whether changes in Healthy Eating Index (HEI) scores in response to a personalised nutrition (PN) intervention varied between clusters.
Food4Me study participants were clustered according to whether their baseline dietary intakes met European dietary recommendations. Changes in HEI scores between baseline and month 6 were compared between clusters and stratified by whether individuals received generalised or PN advice.
Individuals in cluster 1 (C1) met all recommended intakes except for red meat, those in cluster 2 (C2) met two recommendations, and those in cluster 3 (C3) and cluster 4 (C4) met one recommendation each. C1 had higher intakes of white fish, beans and lentils and low-fat dairy products and lower percentage energy intake from SFA (P<0·05). C2 consumed less chips and pizza and fried foods than C3 and C4 (P<0·05). C1 were lighter, had lower BMI and waist circumference than C3 and were more physically active than C4 (P<0·05). More individuals in C4 were smokers and wanted to lose weight than in C1 (P<0·05). Individuals who received PN advice in C4 reported greater improvements in HEI compared with C3 and C1 (P<0·05).
The cluster where the fewest recommendations were met (C4) reported greater improvements in HEI following a 6-month trial of PN whereas there was no difference between clusters for those randomised to the Control, non-personalised dietary intervention.
To characterise participants who dropped out of the Food4Me Proof-of-Principle study.
The Food4Me study was an Internet-based, 6-month, four-arm, randomised controlled trial. The control group received generalised dietary and lifestyle recommendations, whereas participants randomised to three different levels of personalised nutrition (PN) received advice based on dietary, phenotypic and/or genotypic data, respectively (with either more or less frequent feedback).
Seven recruitment sites: UK, Ireland, The Netherlands, Germany, Spain, Poland and Greece.
Adults aged 18–79 years (n 1607).
A total of 337 (21 %) participants dropped out during the intervention. At baseline, dropouts had higher BMI (0·5 kg/m2; P<0·001). Attrition did not differ significantly between individuals receiving generalised dietary guidelines (Control) and those randomised to PN. Participants were more likely to drop out (OR; 95 % CI) if they received more frequent feedback (1·81; 1·36, 2·41; P<0·001), were female (1·38; 1·06, 1·78; P=0·015), less than 45 years old (2·57; 1·95, 3·39; P<0·001) and obese (2·25; 1·47, 3·43; P<0·001). Attrition was more likely in participants who reported an interest in losing weight (1·53; 1·19, 1·97; P<0·001) or skipping meals (1·75; 1·16, 2·65; P=0·008), and less likely if participants claimed to eat healthily frequently (0·62; 0·45, 0·86; P=0·003).
Attrition did not differ between participants receiving generalised or PN advice but more frequent feedback was related to attrition for those randomised to PN interventions. Better strategies are required to minimise dropouts among younger and obese individuals participating in PN interventions and more frequent feedback may be an unnecessary burden.