On-Tissue Chemical Derivatization in Mass Spectrometry Imaging

Mass spectrometry imaging (MSI) combines molecular and spatial information in a valuable tool for a wide range of applications. Matrix‐assisted laser desorption/ionization (MALDI) is at the forefront of MSI ionization due to its wide availability and increasing improvement in spatial resolution and analysis speed. However, ionization suppression, low concentrations, and endogenous and methodological interferences cause visualization problems for certain molecules. Chemical derivatization (CD) has proven a viable solution to these issues when applied in mass spectrometry platforms. Chemical tagging of target analytes with larger, precharged moieties aids ionization efficiency and removes analytes from areas of potential isobaric interferences. Here, we address the application of CD on tissue samples for MSI analysis, termed on‐tissue chemical derivatization (OTCD). MALDI MSI will remain the focus platform due to its popularity, however, alternative ionization techniques such as liquid extraction surface analysis and desorption electrospray ionization will also be recognized. OTCD reagent selection, application, and optimization methods will be discussed in detail. MSI with OTCD is a powerful tool to study the spatial distribution of poorly ionizable molecules within tissues. Most importantly, the use of OTCD−MSI facilitates the analysis of previously inaccessible biologically relevant molecules through the adaptation of existing CD methods. Though further experimental optimization steps are necessary, the benefits of this technique are extensive

CLEAR-AI: empowering people living with dementia and their carers to understand and reduce distress

People living with dementia sometimes present with behaviours that carers find difficult to understand and manage. These include aggression, pacing, vocalising, exit-seeking and sexually inappropriate behaviour. They can be present in up to 70% of people living with dementia and often present because of misunderstanding or because of the distress the person experiences trying to cope with the daily challenges of living with their illness. These behaviours increase the risk that a person will move from their home to a care home. CLEAR Dementia Care© helps carers to understand behaviour in the context of the person and their environment, identify unmet needs and respond in ways to reduce distress. We present the pilot of “CLEAR-AI”, an artificial intelligence (AI) powered platform that interprets data from a range of connected smart sensors, apps and devices to model the person with dementia’s daily routines. Analysis of the data and training the AI model enables the platform to identify the triggers that precede distress episodes and to recognise when episodes occur in the context of previous activities in the day. Using these models, and with CLEAR’s assessment as baseline, we can initiate interventions into daily schedules that reduce or mitigate distress where it is likely to arise. The goal is to reduce carer burden and enable the person to live at home with as much independence as possible for as long as possible. Our consortium brings together people living with dementia and their carers, commissioners of digital social care, specialists in dementia care, AI and digital solutions. The co-design approach ensures that we are led by stakeholders’ needs to improve quality of life

Analysis of reactive aldehydes in urine and plasma of type-2 diabetes mellitus patients through liquid chromatography-mass spectrometry: Reactive aldehydes as potential markers of diabetic nephropathy

IntroductionDiabetes is a major public health issue that is approaching epidemic proportions globally. Diabetes mortality is increasing in all ethnic groups, irrespective of socio-economic class. Obesity is often seen as the main contributor to an increasing prevalence of diabetes. Oxidative stress has been shown to trigger obesity by stimulating the deposition of white adipose tissue. In this study, we measured reactive aldehydes by liquid chromatography-mass spectrometry (LC-MS), in the urine and plasma of type-2 diabetic mellitus (T2DM) patients, as potential surrogates of oxidative stress. Our hypothesis was that reactive aldehydes play a significant role in the pathophysiology of diabetes, and these reactive species, may present potential drug targets for patient treatment.Materials and methodsStudy participants [N = 86; control n = 26; T2DM n = 32, and diabetic nephropathy (DN) n = 28] were recruited between 2019 and 2020. Urine and blood samples were collected from all participants, including a detailed clinical history, to include patient behaviours, medications, and co-morbidities. Reactive aldehyde concentrations in urine and plasma were measured using pre-column derivatisation and LC-MS, for control, T2DM and DN patients.ResultsReactive aldehydes were measured in the urine and plasma of control subjects and patients with T2DM and DN. In all cases, the reactive aldehydes under investigation; 4-HNE, 4-ONE, 4-HHE, pentanal, methylglyoxal, and glyoxal, were significantly elevated in the urine and serum of the patients with T2DM and DN, compared to controls (p < 0.001) (Kruskal–Wallis). Urine and serum reactive aldehydes were significantly correlated (≥0.7) (p < 0.001) (Spearman rho). The concentrations of the reactive aldehydes were significantly higher in plasma samples, when compared to urine, suggesting that plasma is the optimal matrix for screening T2DM and DN patients for oxidative stress.ConclusionReactive aldehydes are elevated in the urine and plasma of T2DM and DN patients. Reactive aldehydes have been implicated in the pathobiology of T2DM. Therefore, if reactive aldehydes are surrogates of oxidative stress, these reactive aldehyde species could be therapeutic targets for potential drug development

Digital Intervention in Loneliness in Older Adults: Qualitative Analysis of User Studies

Background: Loneliness is a significant well-being issue that affects older adults. Existing, commonly used social connection platforms do not contain facilities to break the cognitive cycle of loneliness, and loneliness interventions implemented without due processes could have detrimental effects on well-being. There is also a lack of digital technology designed with older adults.Objective:We aimed to iteratively design a user-centered smartphone app that can address loneliness in older adults. The aim of this study was to investigate the loneliness-related psychological processes that our conceptual smartphone app promotes. We also identified the emergent needs and concerns that older adults raised regarding the potential benefits and detriments of the app.Methods: We used technology probes to elicit older adults' reflections on the concept of using the app in 2 studies as follows: concept focus groups (n=33) and concept interviews (n=10). We then conducted a prototype trial with 1 week of use and follow-up interviews (n=12).Results: Thematic analysis explored the experiences and emergent challenges of our app through the design process. This led to the development of 4 themes as follows occurring in all 3 qualitative data sets: reflection on a digital social map is reassuring; app features encourage socializing; the risk of compounding loneliness; and individuals feel more control with mutual, socially beneficial activities.Conclusions: Smartphone apps have the potential to increase older adults' awareness of the richness of their social connections, which may support loneliness reduction. Our qualitative approach to app design enabled the inclusion of older adults' experiences in technology design. Thus, we conclude that the older adults in our study most desired functionalities that can support mutual activities and maintain or find new connections rather than enable them to share an emotional state. They were wary of the app replacing their preferred in-person social interaction. Participants also raised concerns about making the user aware of the lack of support in their social network and wanted specific means of addressing their needs. Further user-centered design work could identify how the app can support mutual activities and socializing

Biomarkers for detecting kidney dysfunction in type-2 diabetics (T2D) and diabetic nephropathy (DN) subjects: a case-control study to identify potential biomarkers of DN to stratify risk of progression in T2D patients

INTRODUCTION: Currently there are no biomarkers that are predictive of when patients with type-2 diabetes (T2D) will progress to more serious kidney disease i.e., diabetic nephropathy (DN). Biomarkers that could identify patients at risk of progression would allow earlier, more aggressive treatment intervention and management, reducing patient morbidity and mortality. MATERIALS AND METHODS: Study participants (N=88; control n=26; T2D n=32; DN n=30) were recruited from the renal unit at Antrim Area Hospital, Antrim, UK; Whiteabbey Hospital Diabetic Clinic, Newtownabbey, UK; Ulster University (UU), Belfast, UK; and the University of the Third Age (U3A), Belfast, UK; between 2019 and 2020. Venous blood and urine were collected with a detailed clinical history for each study participant. RESULTS: In total, 13/25 (52.0%) biomarkers measured in urine and 25/34 (73.5%) biomarkers measured in serum were identified as significantly different between control, T2D and DN participants. DN patients, were older, smoked more, had higher systolic blood pressure and higher serum creatinine levels and lower eGFR function. Serum biomarkers significantly inversely correlated with eGFR. CONCLUSION: This pilot-study identified several serum biomarkers that could be used to predict progression of T2D to more serious kidney disease: namely, midkine, sTNFR1 and 2, H-FABP and Cystatin C. Our results warrant confirmation in a longitudinal study using a larger patient cohort