The relationship between wearable-derived sleep features and relapse in Major Depressive Disorder.

BACKGROUND: Changes in sleep and circadian function are leading candidate markers for the detection of relapse in Major Depressive Disorder (MDD). Consumer-grade wearable devices may enable remote and real-time examination of dynamic changes in sleep. Fitbit data from individuals with recurrent MDD were used to describe the longitudinal effects of sleep duration, quality, and regularity on subsequent depression relapse and severity. METHODS: Data were collected as part of a longitudinal observational mobile Health (mHealth) cohort study in people with recurrent MDD. Participants wore a Fitbit device and completed regular outcome assessments via email for a median follow-up of 541 days. We used multivariable regression models to test the effects of sleep features on depression outcomes. We considered respondents with at least one assessment of relapse (n = 218) or at least one assessment of depression severity (n = 393). RESULTS: Increased intra-individual variability in total sleep time, greater sleep fragmentation, lower sleep efficiency, and more variable sleep midpoints were associated with worse depression outcomes. Adjusted Population Attributable Fractions suggested that an intervention to increase sleep consistency in adults with MDD could reduce the population risk for depression relapse by up to 22 %. LIMITATIONS: Limitations include a potentially underpowered primary outcome due to the smaller number of relapses identified than expected. CONCLUSION: Our study demonstrates a role for consumer-grade activity trackers in estimating relapse risk and depression severity in people with recurrent MDD. Variability in sleep duration and midpoint may be useful targets for stratified interventions.

Household carbon monoxide (CO) concentrations in a large African city: An unquantified public health burden?

Carbon monoxide (CO) is a poisonous gas produced by incomplete combustion of carbon-based fuels that is linked to mortality and morbidity. Household air pollution from burning fuels on poorly ventilated stoves can lead to high concentrations of CO in homes. There are few datasets available on household concentrations of CO in urban areas of sub-Saharan African countries. CO was measured every minute over 24 h in a sample of homes in Nairobi, Kenya. Data on household characteristics were gathered by questionnaire. Metrics of exposure were summarised and analysis of temporal changes in concentration was performed. Continuous 24-h data were available from 138 homes. The mean (SD), median (IQR) and maximum 24-h CO concentration was 4.9 (6.4), 2.8 (1.0-6.3) and 44 ppm, respectively. 50% of homes had detectable CO concentrations for 847 min (14h07m) or longer during the 24-h period, and 9% of homes would have activated a CO-alarm operating to European specifications. An association between a metric of total CO exposure and self-reported exposure to vapours >15 h per week was identified, however this were not statistically significant after adjustment for the multiple comparisons performed. Mean concentrations were broadly similar in homes from a more affluent area and an informal settlement. A model of typical exposure suggests that cooking is likely to be responsible for approximately 60% of the CO exposure of Nairobi schoolchildren. Household CO concentrations are substantial in Nairobi, Kenya, despite most homes using gas or liquid fuels. Concentrations tend to be highest during the evening, probably associated with periods of cooking. Household air pollution from cooking is the main source of CO exposure of Nairobi schoolchildren. The public health impacts of long-term CO exposure in cities in sub-Saharan Africa may be considerable and should be studied further.

An example of governance for AI in health services from Aotearoa New Zealand.

Artificial Intelligence (AI) is undergoing rapid development, meaning that potential risks in application are not able to be fully understood. Multiple international principles and guidance documents have been published to guide the implementation of AI tools in various industries, including healthcare practice. In Aotearoa New Zealand (NZ) we recognised that the challenge went beyond simply adapting existing risk frameworks and governance guidance to our specific health service context and population. We also deemed prioritising the voice of Maori (the indigenous people of Aotearoa NZ) a necessary aspect of honouring Te Tiriti (the Treaty of Waitangi), as well as prioritising the needs of healthcare service users and their families. Here we report on the development and establishment of comprehensive and effective governance over the development and implementation of AI tools within a health service in Aotearoa NZ. The implementation of the framework in practice includes testing with real-world proposals and ongoing iteration and refinement of our processes.

The usability of daytime and night-time heart rate dynamics as digital biomarkers of depression severity.

BACKGROUND: Alterations in heart rate (HR) may provide new information about physiological signatures of depression severity. This 2-year study in individuals with a history of recurrent major depressive disorder (MDD) explored the intra-individual variations in HR parameters and their relationship with depression severity. METHODS: Data from 510 participants (Number of observations of the HR parameters = 6666) were collected from three centres in the Netherlands, Spain, and the UK, as a part of the remote assessment of disease and relapse-MDD study. We analysed the relationship between depression severity, assessed every 2 weeks with the Patient Health Questionnaire-8, with HR parameters in the week before the assessment, such as HR features during all day, resting periods during the day and at night, and activity periods during the day evaluated with a wrist-worn Fitbit device. Linear mixed models were used with random intercepts for participants and countries. Covariates included in the models were age, sex, BMI, smoking and alcohol consumption, antidepressant use and co-morbidities with other medical health conditions. RESULTS: Decreases in HR variation during resting periods during the day were related with an increased severity of depression both in univariate and multivariate analyses. Mean HR during resting at night was higher in participants with more severe depressive symptoms. CONCLUSIONS: Our findings demonstrate that alterations in resting HR during all day and night are associated with depression severity. These findings may provide an early warning of worsening depression symptoms which could allow clinicians to take responsive treatment measures promptly.

Did it hurt? COVID-19 vaccination experience in people with multiple sclerosis.

BACKGROUND: Current guidelines recommend vaccination against SARS-CoV2 for people with multiple sclerosis (pwMS). The long-term review of the safety and effectiveness of COVID-19 vaccines in pwMS is limited. METHODS: Service re-evaluation. PwMS using the MS service at Barts Health National Health Service Trust were sent questionnaires via email to report symptoms following first and second COVID-19 vaccinations (n = 570). A retrospective review of electronic health records was conducted for clinical and safety data post-vaccination(s); cut-off was end of September 2021. Separate logistic regressions were carried out for symptoms experienced at each vaccination. Two sets of regressions were fitted with covariates: (i) Disease-modifying therapy type and (ii) patient characteristics for symptoms experienced. RESULTS: 193/570 pwMS responded. 184 pwMS had both vaccinations. 144 received the AZD1222 and 49 the BNT162b2 vaccine. 87% and 75% of pwMS experienced any symptoms at first and second vaccinations, respectively. The majority of symptoms resolved within a short timeframe. No severe adverse effects were reported. Two pwMS subsequently died; one due to COVID-19 and one due to aspiration pneumonia. Males were at a reduced risk of reporting symptoms at first vaccination. There was evidence that pwMS in certain treatment groups were at reduced risk of reporting symptoms at second vaccination only. CONCLUSIONS: Findings are consistent with our preliminary data. Symptoms post-vaccination were similar to the non-MS population and were mostly temporary. It is important to inform the MS community of vaccine safety data.

Predictors of engagement with remote sensing technologies for symptom measurement in Major Depressive Disorder.

BACKGROUND: Remote sensing for the measurement and management of long-term conditions such as Major Depressive Disorder (MDD) is becoming more prevalent. User-engagement is essential to yield any benefits. We tested three hypotheses examining associations between clinical characteristics, perceptions of remote sensing, and objective user engagement metrics. METHODS: The Remote Assessment of Disease and Relapse - Major Depressive Disorder (RADAR-MDD) study is a multicentre longitudinal observational cohort study in people with recurrent MDD. Participants wore a FitBit and completed app-based assessments every two weeks for a median of 18 months. Multivariable random effects regression models pooling data across timepoints were used to examine associations between variables. RESULTS: A total of 547 participants (87.8% of the total sample) were included in the current analysis. Higher levels of anxiety were associated with lower levels of perceived technology ease of use; increased functional disability was associated with small differences in perceptions of technology usefulness and usability. Participants who reported higher system ease of use, usefulness, and acceptability subsequently completed more app-based questionnaires and tended to wear their FitBit activity tracker for longer. All effect sizes were small and unlikely to be of practical significance. LIMITATIONS: Symptoms of depression, anxiety, functional disability, and perceptions of system usability are measured at the same time. These therefore represent cross-sectional associations rather than predictions of future perceptions. CONCLUSIONS: These findings suggest that perceived usability and actual use of remote measurement technologies in people with MDD are robust across differences in severity of depression, anxiety, and functional impairment.

Experience with the COVID-19 AstraZeneca vaccination in people with multiple sclerosis.

BACKGROUND: Some people with multiple sclerosis (pwMS) are at increased risk of severe Coronavirus disease 19 (COVID-19) and should be rapidly vaccinated. However, vaccine supplies are limited, and there are concerns about side-effects, particularly with the ChAdOx1nCoV-19 (AstraZeneca) vaccine. OBJECTIVES: To report our first experience of pwMS receiving the AstraZeneca vaccine. METHODS: Service evaluation. pwMS using the MS service at Barts Health NHS Trust were sent questionnaires to report symptoms following vaccination. RESULTS: Thirty-three responses were returned, 29/33 pwMS received a first dose of AstraZeneca vaccine, the remaining four received a first dose of BioNTech/Pfizer vaccine. All but two patients (94%) reported any symptoms including a sore arm (70%), flu-like symptoms (64%), fever (21%), fatigue (27%), and headache (21%). In more than 2/3 patients, symptoms lasted up to 48 hours, and with the exception of two pwMS reporting symptom duration of 10 and 12 days, respectively, symptoms in the remainder resolved within seven days. No severe adverse effects occurred. CONCLUSIONS: pwMS report transient symptoms following AstraZeneca vaccination, characteristics of which were similar to those reported in the non-MS population. Symptoms may be more pronounced in pwMS due to the temperature-dependent delay in impulse propagation (Uhthoff's phenomenon) due to demyelination.

Particulate matter in aerosols produced by two last generation electronic cigarettes: a comparison in a real-world environment.

The design of e-cigarettes (e-cigs) is constantly evolving and the latest models can aerosolize using high-power sub-ohm resistance and hence may produce specific particle concentrations. The aim of this study was to evaluate the aerosol characteristics generated by two different types of electronic cigarette in real-world conditions, such as a sitting room or a small office, in number of particles (particles/cm3). We compared the real time and time-integrated measurements of the aerosol generated by the e-cigarette types Just Fog and JUUL. Real time (10s average) number of particles (particles/cm3) in 8 different aerodynamic sizes was measured using an optical particle counter (OPC) model Profiler 212-2. Tests were conducted with and without a Heating, Ventilating Air Conditioning System (HVACS) in operation, in order to evaluate the efficiency of air filtration. During the vaping sessions the OPC recorded quite significant increases in number of particles/cm3. The JUUL e-cig produced significantly lower emissions than Just Fog with and without the HVACS in operation. The study demonstrates the rapid volatility or change from liquid or semi-liquid to gaseous status of the e-cig aerosols, with half-life in the order of a few seconds (min. 4.6, max 23.9), even without the HVACS in operation. The e-cig aerosol generated by the JUUL proved significantly lower than that generated by the Just Fog, but this reduction may not be sufficient to eliminate or consistently reduce the health risk for vulnerable non e-cig users exposed to it.

Diurnal variability of fine-particulate pollution concentrations: data from 14 low- and middle-income countries.

BACKGROUND: Scientific understanding of indoor air pollution is predominately based on research carried out in cities in high-income countries (HICs). Less is known about how pollutant concentrations change over the course of a typical day in cities in low- and middle-income countries (LMICs).OBJECTIVE: To understand how concentrations of fine particulate matter smaller than 2.5 microns in diameter (PM2.5) change over the course of the day outdoors (across a range of countries) and indoors (using measurements from Dhaka, Bangladesh).DESIGN: Data on PM2.5 concentrations were gathered from 779 households in Dhaka as part of the MCLASS II (Muslim Communities Learning About Second-hand Smoke in Bangladesh) project, and compared to outdoor PM2.5 concentrations to determine the temporal variation in exposure to air pollution. Hourly PM2.5 data from 23 cities in 14 LMICs, as well as London (UK), Paris (France) and New York (NY, USA), were extracted from publicly available sources for comparison.RESULTS: PM2.5 in homes in Dhaka demonstrated a similar temporal pattern to outdoor measurements, with greater concentrations at night than in the afternoon. This pattern was also evident in 19 of 23 LMIC cities.CONCLUSION: PM2.5 concentrations are greater at night than during the afternoon in homes in Dhaka. Diurnal variations in PM2.5 in LMICs is substantial and greater than in London, Paris or New York. This has implications for public health community approaches to health effects of air pollution in LMICs.

Obesity during the COVID-19 pandemic: both cause of high risk and potential effect of lockdown? A population-based electronic health record study.

OBJECTIVES: Obesity is a modifiable risk factor for coronavirus disease 2019 (COVID-19)-related mortality. We estimated excess mortality in obesity, both 'direct', through infection, and 'indirect', through changes in health care, and also due to potential increasing obesity during lockdown. STUDY DESIGN: The study design of this study is a retrospective cohort study and causal inference methods. METHODS: In population-based electronic health records for 1,958,638 individuals in England, we estimated 1-year mortality risk ('direct' and 'indirect' effects) for obese individuals, incorporating (i) pre-COVID-19 risk by age, sex and comorbidities, (ii) population infection rate and (iii) relative impact on mortality (relative risk [RR]: 1.2, 1.5, 2.0 and 3.0). Using causal inference models, we estimated impact of change in body mass index (BMI) and physical activity during 3-month lockdown on 1-year incidence for high-risk conditions (cardiovascular diseases, diabetes, chronic obstructive pulmonary disease and chronic kidney disease), accounting for confounders. RESULTS: For severely obese individuals (3.5% at baseline), at 10% population infection rate, we estimated direct impact of 240 and 479 excess deaths in England at RR 1.5 and 2.0, respectively, and indirect effect of 383-767 excess deaths, assuming 40% and 80% will be affected at RR = 1.2. Owing to BMI change during the lockdown, we estimated that 97,755 (5.4%: normal weight to overweight, 5.0%: overweight to obese and 1.3%: obese to severely obese) to 434,104 individuals (15%: normal weight to overweight, 15%: overweight to obese and 6%: obese to severely obese) would be at higher risk for COVID-19 over one year. CONCLUSIONS: Prevention of obesity and promotion of physical activity are at least as important as physical isolation of severely obese individuals during the pandemic.

Long-term follow-up of a randomized controlled trial of a text-message diabetes self-management support programme, SMS4BG.

AIMS: To determine the long-term effectiveness of an individually tailored text-message diabetes self-management support programme, SMS4BG, on glycaemic control at 2 years in adults with diabetes with an HbA1c concentration > 64 mmol/mol (8%). METHODS: We conducted a 2-year follow-up of a two-arm, parallel, randomized controlled trial across health services in New Zealand. Participants were English-speaking adults with type 1 or 2 diabetes and with an HbA1c >64 mmol/mol (8%). In the main trial participants randomized to the intervention group (N=183) received up to 9 months of an automated tailored text-message programme in addition to usual care. Participants in the control group (N=183) received usual care for 9 months. In this follow-up study, 293 (80%) of 366 randomized participants in the main trial were included. The primary outcome measure was change in glycaemic control (HbA1c ) from baseline to 2 years. Mixed-effect models were used to compare the group differences at 3, 6, 9 and 24 months, adjusted for baseline HbA1c and stratification factors (health district category, diabetes type and ethnicity). RESULTS: The decrease in HbA1c at 2 years was significantly greater in the intervention group [mean (sd) -10 (18) mmol/mol or -0.9 (1.6)%] compared with the control group [mean (sd) -1 (20) mmol/mol or -0.1 (1.8)%], with an adjusted mean difference of -9 mmol/mol (95% CI -14, -5) or -0.8% (95% CI -1.2, -0.4; P<0.0001). CONCLUSIONS: Improvements in glycaemic control resulting from a text-message diabetes self-management support programme were sustained at 2 years after randomization. These findings support the implementation of SMS4BG in current practice.

Cross-sectional study of the prevalence, causes and management of hospital-onset diarrhoea.

BACKGROUND: The National Health Service in England advises hospitals collect data on hospital-onset diarrhoea (HOD). Contemporaneous data on HOD are lacking. AIM: To investigate prevalence, aetiology and management of HOD on medical, surgical and elderly-care wards. METHODS: A cross-sectional study in a volunteer sample of UK hospitals, which collected data on one winter and one summer day in 2016. Patients admitted ≥72 h were screened for HOD (definition: ≥2 episodes of Bristol Stool Type 5-7 the day before the study, with diarrhoea onset >48 h after admission). Data on HOD aetiology and management were collected prospectively. FINDINGS: Data were collected on 141 wards in 32 hospitals (16 acute, 16 teaching). Point-prevalence of HOD was 4.5% (230/5142 patients; 95% confidence interval (CI) 3.9-5.0%). Teaching hospital HOD prevalence (5.9%, 95% CI 5.1-6.9%) was twice that of acute hospitals (2.8%, 95% CI 2.1-3.5%; odds ratio 2.2, 95% CI 1.7-3.0). At least one potential cause was identified in 222/230 patients (97%): 107 (47%) had a relevant underlying condition, 125 (54%) were taking antimicrobials, and 195 (85%) other medication known to cause diarrhoea. Nine of 75 tested patients were Clostridium difficile toxin positive (4%). Eighty (35%) patients had a documented medical assessment of diarrhoea. Documentation of HOD in medical notes correlated with testing for C. difficile (78% of those tested vs 38% not tested, P<0.001). One-hundred and forty-four (63%) patients were not isolated following diarrhoea onset. CONCLUSION: HOD is a prevalent symptom affecting thousands of patients across the UK health system each day. Most patients had multiple potential causes of HOD, mainly iatrogenic, but only a third had medical assessment. Most were not tested for C. difficile and were not isolated.

DNAscan: personal computer compatible NGS analysis, annotation and visualisation.

BACKGROUND: Next Generation Sequencing (NGS) is a commonly used technology for studying the genetic basis of biological processes and it underpins the aspirations of precision medicine. However, there are significant challenges when dealing with NGS data. Firstly, a huge number of bioinformatics tools for a wide range of uses exist, therefore it is challenging to design an analysis pipeline. Secondly, NGS analysis is computationally intensive, requiring expensive infrastructure, and many medical and research centres do not have adequate high performance computing facilities and cloud computing is not always an option due to privacy and ownership issues. Finally, the interpretation of the results is not trivial and most available pipelines lack the utilities to favour this crucial step. RESULTS: We have therefore developed a fast and efficient bioinformatics pipeline that allows for the analysis of DNA sequencing data, while requiring little computational effort and memory usage. DNAscan can analyse a whole exome sequencing sample in 1 h and a 40x whole genome sequencing sample in 13 h, on a midrange computer. The pipeline can look for single nucleotide variants, small indels, structural variants, repeat expansions and viral genetic material (or any other organism). Its results are annotated using a customisable variety of databases and are available for an on-the-fly visualisation with a local deployment of the gene.iobio platform. DNAscan is implemented in Python. Its code and documentation are available on GitHub: https://github.com/KHP-Informatics/DNAscan . Instructions for an easy and fast deployment with Docker and Singularity are also provided on GitHub. CONCLUSIONS: DNAscan is an extremely fast and computationally efficient pipeline for analysis, visualization and interpretation of NGS data. It is designed to provide a powerful and easy-to-use tool for applications in biomedical research and diagnostic medicine, at minimal computational cost. Its comprehensive approach will maximise the potential audience of users, bringing such analyses within the reach of non-specialist laboratories, and those from centres with limited funding available.

Remote assessment of disease and relapse in major depressive disorder (RADAR-MDD): a multi-centre prospective cohort study protocol.

BACKGROUND: There is a growing body of literature highlighting the role that wearable and mobile remote measurement technology (RMT) can play in measuring symptoms of major depressive disorder (MDD). Outcomes assessment typically relies on self-report, which can be biased by dysfunctional perceptions and current symptom severity. Predictors of depressive relapse include disrupted sleep, reduced sociability, physical activity, changes in mood, prosody and cognitive function, which are all amenable to measurement via RMT. This study aims to: 1) determine the usability, feasibility and acceptability of RMT; 2) improve and refine clinical outcome measurement using RMT to identify current clinical state; 3) determine whether RMT can provide information predictive of depressive relapse and other critical outcomes. METHODS: RADAR-MDD is a multi-site prospective cohort study, aiming to recruit 600 participants with a history of depressive disorder across three sites: London, Amsterdam and Barcelona. Participants will be asked to wear a wrist-worn activity tracker and download several apps onto their smartphones. These apps will be used to either collect data passively from existing smartphone sensors, or to deliver questionnaires, cognitive tasks, and speech assessments. The wearable device, smartphone sensors and questionnaires will collect data for up to 2-years about participants' sleep, physical activity, stress, mood, sociability, speech patterns, and cognitive function. The primary outcome of interest is MDD relapse, defined via the Inventory of Depressive Symptomatology- Self-Report questionnaire (IDS-SR) and the World Health Organisation's self-reported Composite International Diagnostic Interview (CIDI-SF). DISCUSSION: This study aims to provide insight into the early predictors of major depressive relapse, measured unobtrusively via RMT. If found to be acceptable to patients and other key stakeholders and able to provide clinically useful information predictive of future deterioration, RMT has potential to change the way in which depression and other long-term conditions are measured and managed.

Multiple sclerosis - a review.

Multiple sclerosis (MS) is the commonest non-traumatic disabling disease to affect young adults. The incidence of MS is increasing worldwide, together with the socioeconomic impact of the disease. The underlying cause of MS and mechanisms behind this increase remain opaque, although complex gene-environment interactions almost certainly play a significant role. The epidemiology of MS indicates that low serum levels of vitamin D, smoking, childhood obesity and infection with the Epstein-Barr virus are likely to play a role in disease development. Changes in diagnostic methods and criteria mean that people with MS can be diagnosed increasingly early in their disease trajectory. Alongside this, treatments for MS have increased exponentially in number, efficacy and risk. There is now the possibility of a diagnosis of 'pre-symptomatic MS' being made; as a result potentially preventive strategies could be studied. In this comprehensive review, MS epidemiology, potential aetiological factors and pathology are discussed, before moving on to clinical aspects of MS diagnosis and management.

Risk Factors, Epidemiology and Treatment Strategies for Metabolic Bone Disease in Patients with Neurological Disease.

Metabolic bone disease is a major public health concern, especially when it manifests as hip fracture which carries significant morbidity and mortality. Individuals with neurological disease are at higher risk of osteopenia, osteoporosis and fragility fracture compared to age-matched controls, yet this is under-appreciated by these patients. Clinician attention to this topic is therefore of importance and should address the bone health of men as well as women, a group in whom it may be an under-recognised problem. Evidence for optimal management of bone health in neurological disease remains to be defined, but a growing literature provides some useful guidance. This review focuses on two conditions, multiple sclerosis and Parkinson's disease, where research has been active over recent years. In neuroinflammation, shared immunological pathways between bone and brain are a current domain of interest and it will be intriguing to interrogate the action of emerging immunotherapies on these dual compartments.

Computer modelling of anthelmintic resistance and worm control outcomes for refugia-based nematode control strategies in Merino ewes in Western Australia.

This study utilised computer simulation modelling (Risk Management Model for Nematodes) to investigate the impact of different parasite refugia scenarios on the development of anthelmintic resistance and worm control effectiveness. The simulations were conducted for adult ewe flocks in a Mediterranean climatic region over a 20 year time period. Factors explored in the simulation exercise were environment (different weather conditions), drug efficacy, the percentage of the flock left untreated, the timing of anthelmintic treatments, the initial worm egg count, and the number of drenches per annum. The model was run with variable proportions of the flock untreated (0, 10, 20, 30, 40 and 50%), with ewes selected at random so that reductions in the mean worm burden or egg count were proportional to the treated section of the flock. Treatments to ewes were given either in summer (December; low refugia potential, hence highly selective) or autumn (March; less selective due to a greater refugia potential), and the use of different anthelmintics was simulated to indicate the difference between active ingredients of different efficacy. Each model scenario was run for two environments, specifically a lower rainfall area (more selective) and a higher rainfall area (less selective) within a Mediterranean climatic zone, characterised by hot, dry summers and cool, wet winters. Univariate general linear models with least square difference post-hoc tests were used to examine differences between means of factors. The results confirmed that leaving a proportion of sheep in a flock untreated was effective in delaying the development of anthelmintic resistance, with as low as 10% of a flock untreated sufficient to significantly delay resistance, although this strategy was associated with a small reduction in worm control. Administering anthelmintics in autumn rather than summer was also effective in delaying the development of anthelmintic resistance in the lower rainfall environment where all sheep were treated, although the effect of treatment timing on worm control effectiveness varied between the environments and the proportion of ewes left untreated. The use of anthelmintics with higher efficacy delayed the development of resistance, but the initial worm egg count or number of annual treatments had no effect on either the time to resistance development or worm control effectiveness. In conclusion, the modelling study suggests that leaving a small proportion of ewes untreated, or changing the time of treatment, can delay the onset of anthelmintic resistance in a highly selective environment.

Blood metabolite markers of neocortical amyloid-ß burden: discovery and enrichment using candidate proteins.

We believe this is the first study to investigate associations between blood metabolites and neocortical amyloid burden (NAB) in the search for a blood-based biomarker for Alzheimer's disease (AD). Further, we present the first multi-modal analysis of blood markers in this field. We used blood plasma samples from 91 subjects enrolled in the University of California, San Francisco Alzheimer's Disease Research Centre. Non-targeted metabolomic analysis was used to look for associations with NAB using both single and multiple metabolic feature models. Five metabolic features identified subjects with high NAB, with 72% accuracy. We were able to putatively identify four metabolites from this panel and improve the model further by adding fibrinogen gamma chain protein measures (accuracy=79%). One of the five metabolic features was studied in the Alzheimer's Disease Neuroimaging Initiative cohort, but results were inconclusive. If replicated in larger, independent studies, these metabolic features and proteins could form the basis of a blood test with potential for enrichment of amyloid pathology in anti-amyloid trials.

The SF3B1 inhibitor spliceostatin A (SSA) elicits apoptosis in chronic lymphocytic leukaemia cells through downregulation of Mcl-1.

The pro-survival Bcl-2 family member Mcl-1 is expressed in chronic lymphocytic leukaemia (CLL), with high expression correlated with progressive disease. The spliceosome inhibitor spliceostatin A (SSA) is known to regulate Mcl-1 and so here we assessed the ability of SSA to elicit apoptosis in CLL. SSA induced apoptosis of CLL cells at low nanomolar concentrations in a dose- and time-dependent manner, but independently of SF3B1 mutational status, IGHV status and CD38 or ZAP70 expression. However, normal B and T cells were less sensitive than CLL cells (P=0.006 and P<0.001, respectively). SSA altered the splicing of anti-apoptotic MCL-1(L) to MCL-1(s) in CLL cells coincident with induction of apoptosis. Overexpression studies in Ramos cells suggested that Mcl-1 was important for SSA-induced killing since its expression inversely correlated with apoptosis (P=0.001). IL4 and CD40L, present in patient lymph nodes, are known to protect tumour cells from apoptosis and significantly inhibited SSA, ABT-263 and ABT-199 induced killing following administration to CLL cells (P=0.008). However, by combining SSA with the Bcl-2/Bcl-x(L) antagonists ABT-263 or ABT-199, we were able to overcome this pro-survival effect. We conclude that SSA combined with Bcl-2/Bcl-x(L) antagonists may have therapeutic utility for CLL.

Atraumatic needles for lumbar puncture: why haven't neurologists changed?

Diagnostic lumbar puncture is a key procedure in neurology; however, it is commonly complicated by post-lumbar puncture headache. Atraumatic needle systems can dramatically reduce the incidence of this iatrogenic complication. However, only a minority of neurologists use such needles. In this paper, we discuss possible reasons why neurologists have not switched to new technology, looking more at diffusion of innovation rather than lack of evidence. We suggest ways to overcome this failure to adopt change, ranging from local interventions to patient empowerment.