Understanding the diversity and dynamics of living with diabetes : a feasibility study focusing on the case

Despite growing evidence about treatments, many people living with diabetes have poor diabetes control even when healthcare is available. One difficult issue is how to apply medical evidence to individuals. This feasibility study explores change over time and the diversity of pathways to similar health outcomes, to understand how evidence can be tailored to the individual. Six people living with diabetes (two with type 1, and four with type 2) agreed to a series of interviews and diary-keeping. Reading the dataset for each individual reveals a person changing over time through interactions with people and their context. Identifying time as a theme is difficult, as it is ubiquitous. Outcome means little to those living with diabetes: they are living on through time. We developed attributes for each participant relevant to diabetes outcome, describing how they related to others and their environment, capturing emergent properties rather than detail. A similar health outcome could be achieved very differently. Comparison of patterns of attributes may be useful. However, the dynamic, relational nature of the attributes is easily lost from view. How people function in terms of time, change and interaction may be most important for tailoring interventions for improved health outcome

Artificial intelligence for biomarker discovery in Alzheimer's disease and dementia.

Funder: Alzheimer’s Research UK; doi: http://dx.doi.org/10.13039/501100002283Funder: ARUK Junior FellowshipFunder: Fonds de recherche du Québec Santé—Chercheur boursiers Junior 1Funder: Fonds de soutien à la recherche pour les neurosciences du vieillissementFunder: ALS Association Milton Safenowitz Research FellowshipFunder: The NIHR Maudsley Biomedical Research CentreFunder: OPTOS Plc and Hoffman La‐RocheFunder: National Institute for Health Research (NIHR) Applied Research Collaboration South West PeninsulaFunder: National Health and Medical Research Council; doi: http://dx.doi.org/10.13039/501100000925Funder: ARUK Senior FellowshipWith the increase in large multimodal cohorts and high-throughput technologies, the potential for discovering novel biomarkers is no longer limited by data set size. Artificial intelligence (AI) and machine learning approaches have been developed to detect novel biomarkers and interactions in complex data sets. We discuss exemplar uses and evaluate current applications and limitations of AI to discover novel biomarkers. Remaining challenges include a lack of diversity in the data sets available, the sheer complexity of investigating interactions, the invasiveness and cost of some biomarkers, and poor reporting in some studies. Overcoming these challenges will involve collecting data from underrepresented populations, developing more powerful AI approaches, validating the use of noninvasive biomarkers, and adhering to reporting guidelines. By harnessing rich multimodal data through AI approaches and international collaborative innovation, we are well positioned to identify clinically useful biomarkers that are accurate, generalizable, unbiased, and acceptable in clinical practice. HIGHLIGHTS: Artificial intelligence and machine learning approaches may accelerate dementia biomarker discovery. Remaining challenges include data set suitability due to size and bias in cohort selection. Multimodal data, diverse data sets, improved machine learning approaches, real-world validation, and interdisciplinary collaboration are required.This paper was the product of a DEMON Network state of the science symposium entitled “Harnessing Data Science and AI in Dementia Research” funded by Alzheimer’s Research UK. LW is supported by an ARUK Junior Fellowship. ELH is supported by the Cambridge British Heart Foundation Centre of Research Excellence (RE/18/1/34212). AB is supported by Fonds de recherche du Québec Santé – Chercheur boursiers Junior 1 and the Fonds de soutien à la recherche pour les neurosciences du vieillissement from the Fondation Courtois. AAK is funded by ALS Association Milton Safenowitz Research Fellowship, The Motor Neurone Disease Association (MNDA) Fellowship (Al Khleifat/Oct21/975-799) and The NIHR Maudsley Biomedical Research Centre. ILe receives unrestricted research funding from OPTOS Plc and Hoffman La-Roche and a grant from Medical Research Council (MR/N029941/1) and Alzheimer’s Society UK (Grant No: 6245). JMR and DJL are supported by Alzheimer’s Research UK and the Alan Turing Institute/Engineering and Physical Sciences Research Council (EP/N510129/1). DJL also receives funding from the Medical Research Council (MR/X005674/1), National Institute for Health Research (NIHR) Applied Research Collaboration South West Peninsula, National Health and Medical Research Council (NHMRC), and National Institute on Aging/National Institutes of Health (RF1AG055654). PP is supported by an ARUK Senior Fellowship

Artificial intelligence for biomarker discovery in Alzheimer's disease and dementia

: With the increase in large multimodal cohorts and high-throughput technologies, the potential for discovering novel biomarkers is no longer limited by data set size. Artificial intelligence (AI) and machine learning approaches have been developed to detect novel biomarkers and interactions in complex data sets. We discuss exemplar uses and evaluate current applications and limitations of AI to discover novel biomarkers. Remaining challenges include a lack of diversity in the data sets available, the sheer complexity of investigating interactions, the invasiveness and cost of some biomarkers, and poor reporting in some studies. Overcoming these challenges will involve collecting data from underrepresented populations, developing more powerful AI approaches, validating the use of noninvasive biomarkers, and adhering to reporting guidelines. By harnessing rich multimodal data through AI approaches and international collaborative innovation, we are well positioned to identify clinically useful biomarkers that are accurate, generalizable, unbiased, and acceptable in clinical practice. HIGHLIGHTS: Artificial intelligence and machine learning approaches may accelerate dementia biomarker discovery. Remaining challenges include data set suitability due to size and bias in cohort selection. Multimodal data, diverse data sets, improved machine learning approaches, real-world validation, and interdisciplinary collaboration are required

Artificial intelligence for biomarker discovery in Alzheimer’s disease and dementia

INTRODUCTION: With the increase in large multimodal cohorts and high throughput technologies, the potential for discovering novel biomarkers is no longer limited by dataset size. METHODS: Artificial intelligence (AI) and machine learning approaches have been developed to detect novel biomarkers and interactions in complex datasets. We discuss exemplar uses and evaluate current applications and limitations of AI to discover novel biomarkers. RESULTS: Remaining challenges include a lack of diversity in the datasets available, the sheer complexity of investigating interactions, the invasiveness and cost of some biomarkers, and poor reporting in some studies. Overcoming these challenges will involve collecting data from underrepresented populations, developing more powerful AI approaches, validating the use of noninvasive biomarkers, and adhering to reporting guidelines. DISCUSSION: By harnessing rich multimodal data through AI approaches and international collaborative innovation, we are well positioned to identify clinically useful biomarkers that are accurate, generalizable, unbiased, and acceptable in clinical practice.This paper was the product of a DEMON Network state of the science symposium entitled “Harnessing Data Science and AI in Dementia Research” funded by Alzheimer’s Research UK. LW is supported by an ARUK Junior Fellowship. ELH is supported by the Cambridge British Heart Foundation Centre of Research Excellence (RE/18/1/34212). AB is supported by Fonds de recherche du Québec Santé – Chercheur boursiers Junior 1 and the Fonds de soutien à la recherche pour les neurosciences du vieillissement from the Fondation Courtois. AAK is funded by ALS Association Milton Safenowitz Research Fellowship, The Motor Neurone Disease Association (MNDA) Fellowship (Al Khleifat/Oct21/975-799) and The NIHR Maudsley Biomedical Research Centre. ILe receives unrestricted research funding from OPTOS Plc and Hoffman La-Roche and a grant from Medical Research Council (MR/N029941/1) and Alzheimer’s Society UK (Grant No: 6245). JMR and DJL are supported by Alzheimer’s Research UK and the Alan Turing Institute/Engineering and Physical Sciences Research Council (EP/N510129/1). DJL also receives funding from the Medical Research Council (MR/X005674/1), National Institute for Health Research (NIHR) Applied Research Collaboration South West Peninsula, National Health and Medical Research Council (NHMRC), and National Institute on Aging/National Institutes of Health (RF1AG055654). PP is supported by an ARUK Senior Fellowship

Drug Derived Fluorescent Probes for the Specific Visualization of Cannabinoid Type 2 Receptor - A Toolbox Approach

Cannabinoid type 2 receptor (CB2R) is a fundamental part of the endocannabinoid signaling system (eCB system), and is known to play an important role in tissue injury, inflammation, cancer and pain. In stark contrast to its significance, the underlying signaling mechanisms and tissue expression profiles are poorly understood. Due to its low expression in healthy tissue and lack of reliable chemical tools, CB2R visualization in live cells remains uncharted. Here we report the development of a drug derived toolbox of highly potent, CB2R-selective fluorescent probes based on reverse design. Extensive validation in several applications such as CB2R detection in flow cytometry and time-resolved imaging, and the development of a novel fluorescent-based TR-FRET assay to generate kinetic and equilibrium binding data demonstrate the high versatility of our toolbox. These probes are the first to preserve affinity and efficacy in both human and mouse CB2R, a crucial aspect for preclinical translatability, and to enable imaging of CB2R internalization in living cells using confocal microscopy

Highly Specific, Fluorescent Cannabinoid Type 2 Receptor Probes Enable Applications in Microscopy, Flow Cytometry and FRET-based Binding Assays

Pharmacological modulation of cannabinoid type 2 receptor (CB2R) holds promise for the treatment of numerous conditions including inflammatory diseases, autoimmune disorders, pain, and cancer. Despite its significance, researchers lack reliable tools to address questions concerning the complex mechanism of CB2R signaling and its downstream consequences, especially in cell-type and tissue-dependent contexts. Herein, we report highly specific CB2R fluorescent probes and their use in a variety of applications: flow cytometry with overexpressing as well as endogenously expressing cells, real-time confocal microscopy of living cells, and a novel FRET-based, CB2R binding assay amenable to high throughput screening.<br /

Development of High-Specificity Fluorescent Probes to Enable Cannabinoid Type 2 Receptor Studies in Living Cells

Pharmacological modulation of cannabinoid type 2 receptor (CB2R) holds promise for the treatment of numerous conditions, including inflammatory diseases, autoimmune disorders, pain, and cancer. Despite the significance of this receptor, researchers lack reliable tools to address questions concerning the expression and complex mechanism of CB2R signaling, especially in cell-type and tissue-dependent context. Herein, we report for the first time a versatile ligand platform for the modular design of a collection of highly specific CB2R fluorescent probes, used successfully across applications, species and cell types. These include flow cytometry of endogenously expressing cells, real-time confocal microscopy of mouse splenocytes and human macrophages, as well as FRET-based kinetic and equilibrium binding assays. High CB2R specificity was demonstrated by competition experiments in living cells expressing CB2R at native levels. The probes were effectively applied to FACS analysis of microglial cells derived from a mouse model relevant to Alzheimer’s disease and to the detection of CB2R in human breast cancer cells

Health-status outcomes with invasive or conservative care in coronary disease

BACKGROUND In the ISCHEMIA trial, an invasive strategy with angiographic assessment and revascularization did not reduce clinical events among patients with stable ischemic heart disease and moderate or severe ischemia. A secondary objective of the trial was to assess angina-related health status among these patients. METHODS We assessed angina-related symptoms, function, and quality of life with the Seattle Angina Questionnaire (SAQ) at randomization, at months 1.5, 3, and 6, and every 6 months thereafter in participants who had been randomly assigned to an invasive treatment strategy (2295 participants) or a conservative strategy (2322). Mixed-effects cumulative probability models within a Bayesian framework were used to estimate differences between the treatment groups. The primary outcome of this health-status analysis was the SAQ summary score (scores range from 0 to 100, with higher scores indicating better health status). All analyses were performed in the overall population and according to baseline angina frequency. RESULTS At baseline, 35% of patients reported having no angina in the previous month. SAQ summary scores increased in both treatment groups, with increases at 3, 12, and 36 months that were 4.1 points (95% credible interval, 3.2 to 5.0), 4.2 points (95% credible interval, 3.3 to 5.1), and 2.9 points (95% credible interval, 2.2 to 3.7) higher with the invasive strategy than with the conservative strategy. Differences were larger among participants who had more frequent angina at baseline (8.5 vs. 0.1 points at 3 months and 5.3 vs. 1.2 points at 36 months among participants with daily or weekly angina as compared with no angina). CONCLUSIONS In the overall trial population with moderate or severe ischemia, which included 35% of participants without angina at baseline, patients randomly assigned to the invasive strategy had greater improvement in angina-related health status than those assigned to the conservative strategy. The modest mean differences favoring the invasive strategy in the overall group reflected minimal differences among asymptomatic patients and larger differences among patients who had had angina at baseline

Initial invasive or conservative strategy for stable coronary disease

BACKGROUND Among patients with stable coronary disease and moderate or severe ischemia, whether clinical outcomes are better in those who receive an invasive intervention plus medical therapy than in those who receive medical therapy alone is uncertain. METHODS We randomly assigned 5179 patients with moderate or severe ischemia to an initial invasive strategy (angiography and revascularization when feasible) and medical therapy or to an initial conservative strategy of medical therapy alone and angiography if medical therapy failed. The primary outcome was a composite of death from cardiovascular causes, myocardial infarction, or hospitalization for unstable angina, heart failure, or resuscitated cardiac arrest. A key secondary outcome was death from cardiovascular causes or myocardial infarction. RESULTS Over a median of 3.2 years, 318 primary outcome events occurred in the invasive-strategy group and 352 occurred in the conservative-strategy group. At 6 months, the cumulative event rate was 5.3% in the invasive-strategy group and 3.4% in the conservative-strategy group (difference, 1.9 percentage points; 95% confidence interval [CI], 0.8 to 3.0); at 5 years, the cumulative event rate was 16.4% and 18.2%, respectively (difference, 121.8 percentage points; 95% CI, 124.7 to 1.0). Results were similar with respect to the key secondary outcome. The incidence of the primary outcome was sensitive to the definition of myocardial infarction; a secondary analysis yielded more procedural myocardial infarctions of uncertain clinical importance. There were 145 deaths in the invasive-strategy group and 144 deaths in the conservative-strategy group (hazard ratio, 1.05; 95% CI, 0.83 to 1.32). CONCLUSIONS Among patients with stable coronary disease and moderate or severe ischemia, we did not find evidence that an initial invasive strategy, as compared with an initial conservative strategy, reduced the risk of ischemic cardiovascular events or death from any cause over a median of 3.2 years. The trial findings were sensitive to the definition of myocardial infarction that was used