The spatiotemporal dynamics of cerebral autoregulation in functional magnetic resonance imaging

The thigh-cuff release (TCR) maneuver is a physiological challenge that is widely used to assess dynamic cerebral autoregulation (dCA). It is often applied in conjunction with Transcranial Doppler ultrasound (TCD), which provides temporal information of the global flow response in the brain. This established method can only yield very limited insights into the regional variability of dCA, whereas functional MRI (fMRI) has the ability to reveal the spatial distribution of flow responses in the brain with high spatial resolution. The aim of this study was to use whole-brain blood-oxygenation-level-dependent (BOLD) fMRI to characterize the spatiotemporal dynamics of the flow response to the TCR challenge, and thus pave the way toward mapping dCA in the brain. We used a data driven approach to derive a novel basis set that was then used to provide a voxel-wise estimate of the TCR associated haemodynamic response function (HRFTCR). We found that the HRFTCR evolves with a specific spatiotemporal pattern, with gray and white matter showing an asynchronous response, which likely reflects the anatomical structure of cerebral blood supply. Thus, we propose that TCR challenge fMRI is a promising method for mapping spatial variability in dCA, which will likely prove to be clinically advantageous

A deep learning approach for staging embryonic tissue isolates with small data

Machine learning approaches are becoming increasingly widespread and are now present in most areas of research. Their recent surge can be explained in part due to our ability to generate and store enormous amounts of data with which to train these models. The requirement for large training sets is also responsible for limiting further potential applications of machine learning, particularly in fields where data tend to be scarce such as developmental biology. However, recent research seems to indicate that machine learning and Big Data can sometimes be decoupled to train models with modest amounts of data. In this work we set out to train a CNN-based classifier to stage zebrafish tail buds at four different stages of development using small information-rich data sets. Our results show that two and three dimensional convolutional neural networks can be trained to stage developing zebrafish tail buds based on both morphological and gene expression confocal microscopy images, achieving in each case up to 100% test accuracy scores. Importantly, we show that high accuracy can be achieved with data set sizes of under 100 images, much smaller than the typical training set size for a convolutional neural net. Furthermore, our classifier shows that it is possible to stage isolated embryonic structures without the need to refer to classic developmental landmarks in the whole embryo, which will be particularly useful to stage 3D culture in vitro systems such as organoids. We hope that this work will provide a proof of principle that will help dispel the myth that large data set sizes are always required to train CNNs, and encourage researchers in fields where data are scarce to also apply ML approaches

A deep learning approach for staging embryonic tissue isolates with small data

Machine learning approaches are becoming increasingly widespread and are now present in most areas of research. Their recent surge can be explained in part due to our ability to generate and store enormous amounts of data with which to train these models. The requirement for large training sets is also responsible for limiting further potential applications of machine learning, particularly in fields where data tend to be scarce such as developmental biology. However, recent research seems to indicate that machine learning and Big Data can sometimes be decoupled to train models with modest amounts of data. In this work we set out to train a CNN-based classifier to stage zebrafish tail buds at four different stages of development using small information-rich data sets. Our results show that two and three dimensional convolutional neural networks can be trained to stage developing zebrafish tail buds based on both morphological and gene expression confocal microscopy images, achieving in each case up to 100% test accuracy scores. Importantly, we show that high accuracy can be achieved with data set sizes of under 100 images, much smaller than the typical training set size for a convolutional neural net. Furthermore, our classifier shows that it is possible to stage isolated embryonic structures without the need to refer to classic developmental landmarks in the whole embryo, which will be particularly useful to stage 3D culture in vitro systems such as organoids. We hope that this work will provide a proof of principle that will help dispel the myth that large data set sizes are always required to train CNNs, and encourage researchers in fields where data are scarce to also apply ML approaches

Cerebrovascular Function in the Large Arteries Is Maintained Following Moderate Intensity Exercise

Exercise has been shown to induce cerebrovascular adaptations. However, the underlying temporal dynamics are poorly understood, and regional variation in the vascular response to exercise has been observed in the large cerebral arteries. Here, we sought to measure the cerebrovascular effects of a single 20-min session of moderate-intensity exercise in the one hour period immediately following exercise cessation. We employed transcranial Doppler (TCD) ultrasonography to measure cerebral blood flow velocity (CBFV) in the middle cerebral artery (MCAv) and posterior cerebral artery (PCAv) before, during, and following exercise. Additionally, we simultaneously measured cerebral blood flow (CBF) in the internal carotid artery (ICA) and vertebral artery (VA) before and up to one hour following exercise cessation using Duplex ultrasound. A hypercapnia challenge was used before and after exercise to examine exercise-induced changes in cerebrovascular reactivity (CVR). We found that MCAv and PCAv were significantly elevated during exercise (p = 4.81 × 10-5 and 2.40 × 10-4, respectively). A general linear model revealed that these changes were largely explained by the partial pressure of end-tidal CO2 and not a direct vascular effect of exercise. After exercise cessation, there was no effect of exercise on CBFV or CVR in the intracranial or extracranial arteries (all p > 0.05). Taken together, these data confirm that CBF is rapidly and uniformly regulated following exercise cessation in healthy young males

Designing assisted living technologies 'in the wild' : preliminary experiences with cultural probe methodology

Background There is growing interest in assisted living technologies to support independence at home. Such technologies should ideally be designed ‘in the wild’ i.e. taking account of how real people live in real homes and communities. The ATHENE (Assistive Technologies for Healthy Living in Elders: Needs Assessment by Ethnography) project seeks to illuminate the living needs of older people and facilitate the co-production with older people of technologies and services. This paper describes the development of a cultural probe tool produced as part of the ATHENE project and how it was used to support home visit interviews with elders with a range of ethnic and social backgrounds, family circumstances, health conditions and assisted living needs. Method Thirty one people aged 60 to 98 were visited in their homes on three occasions. Following an initial interview, participants were given a set of cultural probe materials, including a digital camera and the ‘Home and Life Scrapbook’ to complete in their own time for one week. Activities within the Home and Life Scrapbook included maps (indicating their relationships to people, places and objects), lists (e.g. likes, dislikes, things they were concerned about, things they were comfortable with), wishes (things they wanted to change or improve), body outline (indicating symptoms or impairments), home plan (room layouts of their homes to indicate spaces and objects used) and a diary. After one week, the researcher and participant reviewed any digital photos taken and the content of the Home and Life Scrapbook as part of the home visit interview. Findings The cultural probe facilitated collection of visual, narrative and material data by older people, and appeared to generate high levels of engagement from some participants. However, others used the probe minimally or not at all for various reasons including limited literacy, physical problems (e.g. holding a pen), lack of time or energy, limited emotional or psychological resources, life events, and acute illness. Discussions between researchers and participants about the materials collected (and sometimes about what had prevented them completing the tasks) helped elicit further information relevant to assisted living technology design. The probe materials were particularly helpful when having conversations with non-English speaking participants through an interpreter. Conclusions Cultural probe methods can help build a rich picture of the lives and experiences of older people to facilitate the co-production of assisted living technologies. But their application may be constrained by the participant’s physical, mental and emotional capacity. They are most effective when used as a tool to facilitate communication and development of a deeper understanding of older people’s needs

Clonal differences in Staphylococcus aureus bacteraemia-associated mortality.

The bacterium Staphylococcus aureus is a major human pathogen for which the emergence of antibiotic resistance is a global public health concern. Infection severity, and in particular bacteraemia-associated mortality, has been attributed to several host-related factors, such as age and the presence of comorbidities. The role of the bacterium in infection severity is less well understood, as it is complicated by the multifaceted nature of bacterial virulence, which has so far prevented a robust mapping between genotype, phenotype and infection outcome. To investigate the role of bacterial factors in contributing to bacteraemia-associated mortality, we phenotyped a collection of sequenced clinical S. aureus isolates from patients with bloodstream infections, representing two globally important clonal types, CC22 and CC30. By adopting a genome-wide association study approach we identified and functionally verified several genetic loci that affect the expression of cytolytic toxicity and biofilm formation. By analysing the pooled data comprising bacterial genotype and phenotype together with clinical metadata within a machine-learning framework, we found significant clonal differences in the determinants most predictive of poor infection outcome. Whereas elevated cytolytic toxicity in combination with low levels of biofilm formation was predictive of an increased risk of mortality in infections by strains of a CC22 background, these virulence-specific factors had little influence on mortality rates associated with CC30 infections. Our results therefore suggest that different clones may have adopted different strategies to overcome host responses and cause severe pathology. Our study further demonstrates the use of a combined genomics and data analytic approach to enhance our understanding of bacterial pathogenesis at the individual level, which will be an important step towards personalized medicine and infectious disease management

A pilot randomised controlled trial of a Telehealth intervention in patients with chronic obstructive pulmonary disease: challenges of clinician-led data collection

Background The increasing prevalence and associated cost of treating chronic obstructive pulmonary disease (COPD) is unsustainable, and focus is needed on self-management and prevention of hospital admissions. Telehealth monitoring of patients’ vital signs allows clinicians to prioritise their workload and enables patients to take more responsibility for their health. This paper reports the results of a pilot randomised controlled trial (RCT) of Telehealth-supported care within a community-based COPD supported-discharge service. Methods A two-arm pragmatic pilot RCT was conducted comparing the standard service with a Telehealth-supported service and assessed the potential for progressing into a full RCT. The co-primary outcome measures were the proportion of COPD patients readmitted to hospital and changes in patients’ self-reported quality of life. The objectives were to assess the suitability of the methodology, produce a sample size calculation for a full RCT, and to give an indication of cost-effectiveness for both pathways. Results Sixty three participants were recruited (n = 31 Standard; n = 32 Telehealth); 15 participants were excluded from analysis due to inadequate data completion or withdrawal from the Telehealth arm. Recruitment was slow with significant gaps in data collection, due predominantly to an unanticipated 60% reduction of staff capacity within the clinical team. The sample size calculation was guided by estimates of clinically important effects and COPD readmission rates derived from the literature. Descriptive analyses showed that the standard service group had a lower proportion of patients with hospital readmissions and a greater increase in self-reported quality of life compared to the Telehealth-supported group. Telehealth was cost-effective only if hospital admissions data were excluded. Conclusions Slow recruitment rates and service reconfigurations prevented progression to a full RCT. Although there are advantages to conducting an RCT with data collection conducted by a frontline clinical team, in this case, challenges arose when resources within the team were reduced by external events. Gaps in data collection were resolved by recruiting a research nurse. This study reinforces previous findings regarding the difficulty of undertaking evaluation of complex interventions, and provides recommendations for the introduction and evaluation of complex interventions within clinical settings, such as prioritisation of research within the clinical remit

A deep learning approach for staging embryonic tissue isolates with small data.

Machine learning approaches are becoming increasingly widespread and are now present in most areas of research. Their recent surge can be explained in part due to our ability to generate and store enormous amounts of data with which to train these models. The requirement for large training sets is also responsible for limiting further potential applications of machine learning, particularly in fields where data tend to be scarce such as developmental biology. However, recent research seems to indicate that machine learning and Big Data can sometimes be decoupled to train models with modest amounts of data. In this work we set out to train a CNN-based classifier to stage zebrafish tail buds at four different stages of development using small information-rich data sets. Our results show that two and three dimensional convolutional neural networks can be trained to stage developing zebrafish tail buds based on both morphological and gene expression confocal microscopy images, achieving in each case up to 100% test accuracy scores. Importantly, we show that high accuracy can be achieved with data set sizes of under 100 images, much smaller than the typical training set size for a convolutional neural net. Furthermore, our classifier shows that it is possible to stage isolated embryonic structures without the need to refer to classic developmental landmarks in the whole embryo, which will be particularly useful to stage 3D culture in vitro systems such as organoids. We hope that this work will provide a proof of principle that will help dispel the myth that large data set sizes are always required to train CNNs, and encourage researchers in fields where data are scarce to also apply ML approaches

The impact of the COVID-19 pandemic on antimicrobial prescribing at a specialist paediatric hospital: an observational study

BACKGROUND: The COVID-19 pandemic has severely impacted healthcare delivery and there are growing concerns that the pandemic will accelerate antimicrobial resistance. OBJECTIVES: To evaluate the impact of the COVID-19 pandemic on antibiotic prescribing in a tertiary paediatric hospital in London, UK. METHODS: Data on patient characteristics and antimicrobial administration for inpatients treated between 29 April 2019 and Sunday 28 March 2021 were extracted from the electronic health record (EHR). Interrupted time series analysis was used to evaluate antibiotic days of therapy (DOT) and the proportion of prescribed antibiotics from the WHO 'Access' class. RESULTS: A total of 23 292 inpatient admissions were included. Prior to the pandemic there were an average 262 admissions per week compared with 212 during the pandemic period. Patient demographics were similar in the two periods but there was a shift in the specialities that patients had been admitted to. During the pandemic, there was a crude increase in antibiotic DOTs, from 801 weekly DOT before the pandemic to 846. The proportion of Access antibiotics decreased from 44% to 42%. However, after controlling for changes in patient characteristics, there was no evidence for the pandemic having an impact on antibiotic prescribing. CONCLUSIONS: The patient population in a specialist children's hospital was affected by the COVID-19 pandemic, but after adjusting for these changes there was no evidence that antibiotic prescribing was significantly affected by the pandemic. This highlights both the value of routine, high-quality EHR data and importance of appropriate statistical methods that can adjust for underlying changes to populations when evaluating impacts of the pandemic on healthcare