Practices of falls risk assessment and prevention in acute hospital settings: a realist investigation.

Background: Falls are the most common safety incident reported by acute hospitals. The National Institute of Health and Care Excellence recommends multifactorial falls risk assessment and tailored interventions, but implementation is variable. Aim: To determine how and in what contexts multifactorial falls risk assessment and tailored interventions are used in acute National Health Service hospitals in England. Design: Realist review and multisite case study. (1) Systematic searches to identify stakeholders' theories, tested using empirical data from primary studies. Review of falls prevention policies of acute Trusts. (2) Theory testing and refinement through observation, staff interviews (n = 50), patient and carer interviews (n = 31) and record review (n = 60). Setting: Three Trusts, one orthopaedic and one older person ward in each. Results: Seventy-eight studies were used for theory construction and 50 for theory testing. Four theories were explored. (1) Leadership: wards had falls link practitioners but authority to allocate resources for falls prevention resided with senior nurses. (2) Shared responsibility: a key falls prevention strategy was patient supervision. This fell to nursing staff, constraining the extent to which responsibility for falls prevention could be shared. (3) Facilitation: assessments were consistently documented but workload pressures could reduce this to a tick-box exercise. Assessment items varied. While individual patient risk factors were identified, patients were categorised as high or low risk to determine who should receive supervision. (4) Patient participation: nursing staff lacked time to explain to patients their falls risks or how to prevent themselves from falling, although other staff could do so. Sensitive communication could prevent patients taking actions that increase their risk of falling. Limitations: Within the realist review, we completed synthesis for only two theories. We could not access patient records before observations, preventing assessment of whether care plans were enacted. Conclusions: (1) Leadership: There should be a clear distinction between senior nurses' roles and falls link practitioners in relation to falls prevention; (2) shared responsibility: Trusts should consider how processes and systems, including the electronic health record, can be revised to better support a multidisciplinary approach, and alternatives to patient supervision should be considered; (3) facilitation: Trusts should consider how to reduce documentation burden and avoid tick-box responses, and ensure items included in the falls risk assessment tools align with guidance. Falls risk assessment tools and falls care plans should be presented as tools to support practice, rather than something to be audited; (4) patient participation: Trusts should consider how they can ensure patients receive individualised information about risks and preventing falls and provide staff with guidance on brief but sensitive ways to talk with patients to reduce the likelihood of actions that increase their risk of falling. Future work: (1) Development and evaluation of interventions to support multidisciplinary teams to undertake, and involve patients in, multifactorial falls risk assessment and selection and delivery of tailored interventions; (2) mixed method and economic evaluations of patient supervision; (3) evaluation of engagement support workers, volunteers and/or carers to support falls prevention. Research should include those with cognitive impairment and patients who do not speak English. Study registration: This study is registered as PROSPERO CRD42020184458. Funding: This award was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme (NIHR award ref: NIHR129488) and is published in full in Health and Social Care Delivery Research; Vol. 12, No. 5. See the NIHR Funding and Awards website for further award information.

Many accidental falls by older people in hospitals could be avoided. There are guidelines to prevent falls, but some hospitals are better at following them than others. This study aimed to find out why. First, we looked at research and hospitals' falls policies for ideas about what stops falls. With advice from service users, we tested these ideas in four hospitals in England, watching how falls were prevented on wards for older people and people who need bone care, and talking to 50 staff, 28 patients and 3 carers. We found the following: Falls leadership: wards had staff called falls link practitioners who supported falls prevention, but senior nurses, not link practitioners, made the most important decisions. Sharing responsibility: patients with falls risks were monitored to try to stop falls. Because only nursing teams were always present to monitor patients, they had most responsibility for preventing falls. This limited sharing responsibility with other staff. Computer tools: nurses used computers to record prevention work, but high workloads could make this a 'tick-box' exercise. Computer tools reminded them to do this, although tools varied. Patients had individual falls plans, but they were also ranked more generally as high or low risk of falling, with 'high-risk' patients being monitored. Patient involvement: nursing staff did not have time to explain to patients how to prevent falls, but other staff could have such conversations. Many patients had problems like dementia and found it difficult to follow safety advice, although some could take steps to keep safe, with sensitive staff support. We need to involve patients, carers and different staff in falls prevention. Hospitals could develop computer systems to support this, think how to involve more ward staff, and provide guidance on helpful ways to talk with patients about falls.

Prediction of prognosis in oral squamous cell carcinoma using infrared microspectroscopy.

BACKGROUND: Estimation of prognosis of oral squamous cell carcinoma (OSCC) is inaccurate prior to surgery, only being effected following subsequent pathological analysis of the primary tumour and excised lymph nodes. Consequently, a proportion of patients are overtreated, with an increase in morbidity, or undertreated, with inadequate margins and risk of recurrence. We hypothesise that it is possible to accurately characterise clinical outcomes from infrared spectra arising from diagnostic biopsies. In this first step, we correlate survival with IR spectra derived from the primary tumour. METHODS: Infrared spectra were collected from tumour tissue from 29 patients with OSCC and subject to classification modelling. RESULTS: The model had a median AUROC of 0.89 with regard to prognosis, a median specificity of 0.83, and a hazard ratio of 6.29 in univariate Cox proportional hazard modelling. CONCLUSION: The data suggest that FTIR spectra may be a useful early biomarker of prognosis in OSCC.

Tissue discrimination in head and neck cancer using image fusion of IR and optical microscopy.

A regression-based fusion algorithm has been used to merge hyperspectral Fourier transform infrared (FTIR) data with an H&E image of oral squamous cell carcinoma metastases in cervical lymphoid nodal tissue. This provides insight into the success of the ratio of FTIR absorbances at 1252 cm-1 and 1285 cm-1 in discriminating between these tissue types. The success is due to absorbances at these two wavenumbers being dominated by contributions from DNA and collagen, respectively. A pixel-by-pixel fit of the fused spectra to the FTIR spectra of collagen, DNA and cytokeratin reveals the contributions of these molecules to the tissue at high spatial resolution.

Weakly supervised anomaly detection coupled with Fourier transform infrared (FT-IR) spectroscopy for the identification of non-normal tissue.

The detection and classification of histopathological abnormal tissue constituents using machine learning (ML) techniques generally requires example data for each tissue or cell type of interest. This creates problems for studies on tissue that will have few regions of interest, or for those looking to identify and classify diseases of rarity, resulting in inadequate sample sizes from which to build multivariate and ML models. Regarding the impact on vibrational spectroscopy, specifically infrared (IR) spectroscopy, low numbers of samples may result in ineffective modelling of the chemical composition of sample groups, resulting in detection and classification errors. Anomaly detection may be a solution to this problem, enabling users to effectively model tissue constituents considered to represent normal tissue to capture any abnormal tissue and identify instances of non-normal tissue, be it disease or spectral artefacts. This work illustrates how a novel approach using a weakly supervised anomaly detection algorithm paired with IR microscopy can detect non-normal tissue spectra. In addition to incidental interferents such as hair, dust, and tissue scratches, the algorithm can also detect regions of diseased tissue. The model is never introduced to instances of these groups, training solely on healthy control data using only the IR spectral fingerprint region. This approach is demonstrated using liver tissue data from an agrochemical exposure mouse study.

The extent of anticholinergic burden across an older Welsh population living with frailty: cross-sectional analysis of general practice records.

BACKGROUND: Anticholinergic medicines are associated with adverse outcomes for older people. However, little is known about their use in frailty. The objectives were to (i) investigate the prevalence of anticholinergic prescribing for older patients, and (ii) examine anticholinergic burden according to frailty status. METHODS: Cross-sectional analysis of Welsh primary care data from the Secure Anonymised Information Linkage databank including patients aged ≥65 at their first GP consultation between 1 January and 31 December 2018. Frailty was identified using the electronic Frailty Index and anticholinergic burden using the Anticholinergic Cognitive Burden (ACB) scale. Descriptive analysis and logistic regression were conducted to (i) describe the type and frequency of anticholinergics prescribed; and (ii) to estimate the association between frailty and cumulative ACB score (ACB-Sum). RESULTS: In this study of 529,095 patients, 47.4% of patients receiving any prescription medications were prescribed at least one anticholinergic medicine. Adjusted regression analysis showed that patients with increasing frailty had higher odds of having an ACB-Sum of >3 compared with patients who were fit (mild frailty, adj OR 1.062 (95%CI 1.061-1.064), moderate frailty, adj OR 1.134 (95%CI 1.131-1.136), severe frailty, adj OR 1.208 (95%CI 1.203-1.213)). CONCLUSIONS: Anticholinergic prescribing was high in this older population. Older people with advancing frailty are exposed to the highest anticholinergic burden despite being the most vulnerable to the associated adverse effects. Older people with advancing frailty should be considered for medicines review to prevent overaccumulation of anticholinergic medications, given the risks of functional and cognitive decline that frailty presents.

Classification of formalin-fixed bladder cancer cells with laser tweezer Raman spectroscopy.

Bladder cancer is a common cancer that is relatively hard to detect at an early stage because of its non-obvious symptoms. It is known that bladder cells can be found in urine samples which potentially could be used for early detection of bladder cancer. Raman spectroscopy is a powerful non-invasive tool for accessing biochemical information of cells. Combined with laser tweezers, to allow isolation of single cells, Raman spectroscopy has been used to characterise a number of bladder cells that might be found in a urine sample. Using principal component-canonical variates analysis (PC-CVA) and k-fold validation, the results shows that the invasive bladder cancer cells can be identified with accuracy greater than 87%. This demonstrates the potential of developing an early detection method that identifies the invasive bladder cancer cells in urine samples.

Exploring variation in implementation of multifactorial falls risk assessment and tailored interventions: a realist review.

BACKGROUND: Falls are the most common safety incident reported by acute hospitals. In England national guidance recommends delivery of a multifactorial falls risk assessment (MFRA) and interventions tailored to address individual falls risk factors. However, there is variation in how these practices are implemented. This study aimed to explore the variation by examining what supports or constrains delivery of MFRAs and tailored interventions in acute hospitals. METHODS: A realist review of literature was conducted with searches completed in three stages: (1) to construct hypotheses in the form of Context, Mechanism, Outcome configurations (CMOc) about how MFRAs and interventions are delivered, (2) to scope the breadth and depth of evidence available in Embase to test the CMOcs, and (3) following prioritisation of CMOcs, to refine search strategies for use in multiple databases. Citations were managed in EndNote; titles, abstracts, and full texts were screened, with 10% independently screened by two reviewers. RESULTS: Two CMOcs were prioritised for testing labelled: Facilitation via MFRA tools, and Patient Participation in interventions. Analysis indicated that MFRA tools can prompt action, but the number and type of falls risk factors included in tools differ across organisations leading to variation in practice. Furthermore, the extent to which tools work as prompts is influenced by complex ward conditions such as changes in patient condition, bed swaps, and availability of falls prevention interventions. Patient participation in falls prevention interventions is more likely where patient directed messaging takes individual circumstances into account, e.g., not wanting to disturb nurses by using the call bell. However, interactions that elicit individual circumstances can be resource intensive and patients with cognitive impairment may not be able to participate despite appropriately directed messaging. CONCLUSIONS: Organisations should consider how tools can be developed in ways that better support consistent and comprehensive identification of patients' individual falls risk factors and the complex ward conditions that can disrupt how tools work as facilitators. Ward staff should be supported to deliver patient directed messaging that is informed by their individual circumstances to encourage participation in falls prevention interventions, where appropriate. TRIAL REGISTRATION: PROSPERO: CRD42020184458.

Metric-based analysis of FTIR data to discriminate tissue types in oral cancer.

A machine learning algorithm (MLA) has predicted the prognosis of oral potentially malignant lesions and discriminated between lymph node tissue and metastatic oral squamous cell carcinoma (OSCC). The MLA analyses metrics, which are ratios of Fourier transform infrared absorbances, and identifies key wavenumbers that can be associated with molecular biomarkers. The wider efficacy of the MLA is now shown in the more complex primary OSCC tumour setting, where it is able to identify seven types of tissue. Three epithelial and four non-epithelial tissue types were discriminated from each other with sensitivities between 82% and 96% and specificities between 90% and 99%. The wavenumbers involved in the five best discriminating metrics for each tissue type were tightly grouped, indicating that small changes in the spectral profiles of the different tissue types are important. The number of samples used in this study was small, but the information will provide a basis for further, larger investigations.

Infrared micro-spectroscopy coupled with multivariate and machine learning techniques for cancer classification in tissue: a comparison of classification method, performance, and pre-processing technique.

The visual detection, classification, and differentiation of cancers within tissues of clinical patients is an extremely difficult and time-consuming process with severe diagnosis implications. To this end, many computational approaches have been developed to analyse tissue samples to supplement histological cancer diagnoses. One approach is the interrogation of the chemical composition of the actual tissue samples through the utilisation of vibrational spectroscopy, specifically Infrared (IR) spectroscopy. Cancerous tissue can be detected by analysing the molecular vibration patterns of tissues undergoing IR irradiation, and even graded, with multivariate and Machine Learning (ML) techniques. This publication serves to review and highlight the potential for the application of infrared microscopy techniques such as Fourier Transform Infrared Spectroscopy (FTIR) and Quantum Cascade Laser Infrared Spectroscopy (QCL), as a means to improve diagnostic accuracy and allow earlier detection of human neoplastic disease. This review provides an overview of the detection and classification of different cancerous tissues using FTIR spectroscopy paired with multivariate and ML techniques, using the F1-Score as a quantitative metric for direct comparison of model performances. Comparisons also extend to data handling techniques, with a provision of a suggested pre-processing protocol for future studies alongside suggestions as to reporting standards for future publication.

Using routine healthcare data to evaluate the impact of the Medicines at Transitions Intervention (MaTI) on clinical outcomes of patients hospitalised with heart failure: protocol for the Improving the Safety and Continuity Of Medicines management at Transitions of care (ISCOMAT) cluster randomised controlled trial with embedded process evaluation, health economics evaluation and internal pilot.

INTRODUCTION: Heart failure affects 26 million people globally, approximately 900 thousand people in the UK, and is increasing in incidence. Appropriate management of medicines for heart failure at the time of hospital discharge reduces readmissions, improves quality of life and increases survival. The Improving the Safety and Continuity Of Medicines management at Transitions (ISCOMAT) trial tests the effectiveness of the Medicines at Transition Intervention (MaTI), which aims to enhance self-care and increase community pharmacy involvement in the medicines management of heart failure patients. METHODS AND ANALYSIS: ISCOMAT is a parallel-group cluster randomised controlled trial, randomising 42 National Health Service trusts with cardiology wards in England on a 1:1 basis to implement the MaTI or treatment as usual. Around 2100 patients over the age of 18 admitted to hospital with heart failure with at least moderate left ventricular systolic dysfunction within the last 5 years, and planned discharge to the geographical area of the cluster will be recruited. The MaTI consists of training for staff, a toolkit for participants, transfer of discharge information to community pharmacies and a medicines reconciliation/review. Treatment as usual is determined by local policy and practices. The primary outcome is a composite of all-cause mortality and heart failure-related hospitalisation at 12 months postregistration obtained from national electronic health records. The key secondary outcome is continued prescription of guideline-indicated therapies at 12 months measured via patient-reported data and Hospital Episode Statistics. The trial contains a parallel mixed-methods process evaluation and an embedded health economics study. ETHICS AND DISSEMINATION: The study obtained approval from the Yorkshire and the Humber-Bradford Leeds Research Ethics Committee; REC reference 18/YH/0017. Findings will be disseminated via academic and policy conferences, peer-reviewed publications and social media. Amendments to the protocol are disseminated to all relevant parties as required. TRIAL REGISTRATION NUMBER: ISRCTN66212970; Pre-results.

Prediction of malignant transformation in oral epithelial dysplasia using infrared absorbance spectra.

Oral epithelial dysplasia (OED) is a histopathologically-defined, potentially premalignant condition of the oral cavity. The rate of transformation to frank carcinoma is relatively low (12% within 2 years) and prediction based on histopathological grade is unreliable, leading to both over- and under-treatment. Alternative approaches include infrared (IR) spectroscopy, which is able to classify cancerous and non-cancerous tissue in a number of cancers, including oral. The aim of this study was to explore the capability of FTIR (Fourier-transform IR) microscopy and machine learning as a means of predicting malignant transformation of OED. Supervised, retrospective analysis of longitudinally-collected OED biopsy samples from 17 patients with high risk OED lesions: 10 lesions transformed and 7 did not over a follow-up period of more than 3 years. FTIR spectra were collected from routine, unstained histopathological sections and machine learning used to predict malignant transformation, irrespective of OED classification. PCA-LDA (principal component analysis followed by linear discriminant analysis) provided evidence that the subsequent transforming status of these 17 lesions could be predicted from FTIR data with a sensitivity of 79 ± 5% and a specificity of 76 ± 5%. Six key wavenumbers were identified as most important in this classification. Although this pilot study used a small cohort, the strict inclusion criteria and classification based on known outcome, rather than OED grade, make this a novel study in the field of FTIR in oral cancer and support the clinical potential of this technology in the surveillance of OED.

Designing health IT to support falls prevention in hospitals: Findings from a realist review.

Inpatient falls are an international patient safety concern, accounting for 30-40% of reported safety incidents in acute hospitals. They can cause both physical (e.g. hip fractures) and non-physical harm (e.g. reduced confidence) to patients. We used an approach known as a realist review to identify theories about what interventions might work for whom in what contexts, focusing on what supports and constrains effective use of multifactorial falls risk assessment and falls prevention interventions. One of these theories suggested that staff will integrate recommended practices into their work routines if falls risk assessment tools, including health IT, are quick and easy to use and facilitate existing work routines. Synthesis of empirical studies undertaken in the process of testing and refining this theory has implications for the design of health IT, suggesting that while health IT can support falls prevention through automation, such tools should also allow for incorporation of clinical judgement.

Mindfulness-Based Approaches for Managing Stress, Anxiety and Depression for Health Students in Tertiary Education: a Scoping Review.

OBJECTIVES: High rates of depression, anxiety and stress are reported in tertiary health students. Mindfulness-based programs have been included in the training of health students to help them manage depression, anxiety and stress; however, to date, there has been no review of best practice implementation of mindfulness for health students. The aim of this review was to evaluate the outcomes of mindfulness-based practice for health students to inform best practice with this population. METHODS: A comprehensive search was conducted of three electronic databases (PsychINFO, Medline and Embase) guided by the five-step systematic process for conducting scoping reviews to investigate mindfulness-based intervention programs for students enrolled in a tertiary institution in a health-related course. RESULTS: Twenty-four papers met the eligibility criteria and were reviewed in detail. Findings suggested that mindfulness-based intervention approaches are useful in decreasing depression, anxiety and stress in health students; however, challenges exist in student engagement and retention. Generalization of results was limited by the heterogeneous population, intervention designs and delivery methods, as well as a lack of standardized outcome measures. CONCLUSION: The inclusion of mindfulness-based programs within tertiary curricula can be an effective approach to assist with managing depression, stress and anxiety in health students. Providing academic credit to students, improving translation of skills to working with future clients, and embedding mindfulness-based programs within the curriculum could improve engagement and retention. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12671-021-01740-3.

Compliance of the fish outflow tract is altered by thermal acclimation through connective tissue remodelling.

To protect the gill capillaries from high systolic pulse pressure, the fish heart contains a compliant non-contractile chamber called the bulbus arteriosus which is part of the outflow tract (OFT) which extends from the ventricle to the ventral aorta. Thermal acclimation alters the form and function of the fish atria and ventricle to ensure appropriate cardiac output at different temperatures, but its impact on the OFT is unknown. Here we used ex vivo pressure-volume curves to demonstrate remodelling of passive stiffness in the rainbow trout (Oncorhynchus mykiss) bulbus arteriosus following more than eight weeks of thermal acclimation to 5, 10 and 18°C. We then combined novel, non-biased Fourier transform infrared spectroscopy with classic histological staining to show that changes in compliance were achieved by changes in tissue collagen-to-elastin ratio. In situ gelatin zymography and SDS-PAGE zymography revealed that collagen remodelling was underpinned, at least in part, by changes in activity and abundance of collagen degrading matrix metalloproteinases. Collectively, we provide the first indication of bulbus arteriosus thermal remodelling in a fish and suggest this remodelling ensures optimal blood flow and blood pressure in the OFT during temperature change.

Exploring AdaBoost and Random Forests machine learning approaches for infrared pathology on unbalanced data sets.

The use of infrared spectroscopy to augment decision-making in histopathology is a promising direction for the diagnosis of many disease types. Hyperspectral images of healthy and diseased tissue, generated by infrared spectroscopy, are used to build chemometric models that can provide objective metrics of disease state. It is important to build robust and stable models to provide confidence to the end user. The data used to develop such models can have a variety of characteristics which can pose problems to many model-building approaches. Here we have compared the performance of two machine learning algorithms - AdaBoost and Random Forests - on a variety of non-uniform data sets. Using samples of breast cancer tissue, we devised a range of training data capable of describing the problem space. Models were constructed from these training sets and their characteristics compared. In terms of separating infrared spectra of cancerous epithelium tissue from normal-associated tissue on the tissue microarray, both AdaBoost and Random Forests algorithms were shown to give excellent classification performance (over 95% accuracy) in this study. AdaBoost models were more robust when datasets with large imbalance were provided. The outcomes of this work are a measure of classification accuracy as a function of training data available, and a clear recommendation for choice of machine learning approach.

What supports and constrains the implementation of multifactorial falls risk assessment and tailored multifactorial falls prevention interventions in acute hospitals? Protocol for a realist review.

INTRODUCTION: Falls are the most common type of safety incident reported by acute hospitals and can cause both physical (eg, hip fractures) and non-physical harm (eg, reduced confidence) to patients. It is recommended that, in order to prevent falls in hospital, patients should receive a multifactorial falls risk assessment and be provided with a multifactorial intervention, tailored to address the patient's identified individual risk factors. It is estimated that such an approach could reduce the incidence of inpatient falls by 25%-30% and reduce the annual cost of falls by up to 25%. However, there is substantial unexplained variation between hospitals in the number and type of assessments undertaken and interventions implemented. METHODS AND ANALYSIS: A realist review will be undertaken to construct and test programme theories regarding (1) what supports and constrains the implementation of multifactorial falls risk assessment and tailored multifactorial falls prevention interventions in acute hospitals; and (2) how, why, in what contexts and for whom tailored multifactorial falls prevention interventions lead to a reduction in patients' falls risk. We will first identify stakeholders' theories concerning these two topics, searching Medline (1946-present) and Medline In-Process & Other Non-Indexed Citations, Health Management Information Consortium (1983-present) and CINAHL (1981-present). We will then test these theories systematically, using primary studies to determine whether empirical evidence supports, refutes or suggests a revision or addition to the identified theories. ETHICS AND DISSEMINATION: The study does not require ethical approval. The review will provide evidence for how to implement multifactorial falls risk assessment and prevention strategies in acute hospital settings. This will be disseminated to academic and clinical audiences and will provide the basis for a future multi-site study through which the theories will be further refined. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42020184458.

Image fusion of IR and optical microscopy for mapping of biomolecules in tissue.

It is shown that a pixel-level image fusion technique can produce images that combine the spatial resolution of optical microscopy images of haematoxylin and eosin (H&E) stained tissue with the chemical information in Fourier transform infrared (FTIR) images. The fused images show minimal distortion and the higher spatial resolution of the H&E images overcomes the diffraction limit on the spatial resolution of the FTIR images. A consideration of the FTIR spectra of nucleic acids and collagen can explain the changes in contrast between non-cancerous oral epithelium and underlying stroma within fused images formed by combining an H&E stain of oral tissue with FTIR images of the tissue obtained at a number of wavenumbers.

Insight into metastatic oral cancer tissue from novel analyses using FTIR spectroscopy and aperture IR-SNOM.

A novel machine learning algorithm is shown to accurately discriminate between oral squamous cell carcinoma (OSCC) nodal metastases and surrounding lymphoid tissue on the basis of a single metric, the ratio of Fourier transform infrared (FTIR) absorption intensities at 1252 cm-1 and 1285 cm-1. The metric yields discriminating sensitivities, specificities and precision of 98.8 ± 0.1%, 99.89 ± 0.01% and 99.78 ± 0.02% respectively, and an area under receiver operator characteristic (AUC) of 0.9935 ± 0.0006. The delineation of the OSCC and lymphoid tissue revealed by the image formed from the metric is in better agreement with an immunohistochemistry (IHC) stained image than are either of the FTIR images obtained at the individual wavenumbers. Scanning near-field optical microscopy (SNOM) images of the tissue obtained at a number of key wavenumbers, with high spatial resolution, show variations in the chemical structure of the tissue with a feature size down to ∼4 µm. The image formed from the ratio of the SNOM images obtained at 1252 cm-1 and 1285 cm-1 shows more contrast than the SNOM images obtained at these or a number of other individual wavenumbers. The discrimination between the two tissue types is dominated by the contribution from the 1252 cm-1 signal, which is representative of nucleic acids, and this shows the OSCC tissue to be accompanied by two wide arcs of tissue which are particularly low in nucleic acids. Haematoxylin and eosin (H&E) staining shows the tumour core in this specimen to be ∼40 µm wide and the SNOM topography shows that the core centre is raised by ∼1 µm compared to the surrounding tissue. Line profiles of the SNOM signal intensity taken through the highly keratinised core show that the increase in height correlates with an increase in the protein signal. SNOM line profiles show that the nucleic acids signal decreases at the centre of the tumour core between two peaks of higher intensity. All these nucleic acid features are ∼25 µm wide, roughly the width of two cancer cells.

Unveiling the composition of historical plastics through non-invasive reflection FT-IR spectroscopy in the extended near- and mid-Infrared spectral range.

The present research exploits the strengths of external reflection FT-IR spectroscopy to non-invasively study heritage plastic objects through inspection, for the first time, of the wide spectral range including the near- and mid-IR (12500-350 cm-1). Unlike most of previous works on historical plastic objects, reflection-mode spectra were not corrected for the unfamiliar surface reflection profiles to the more recognizable absorption-like band shapes. This avoided data misinterpretation due to ill-suited Kramers Krönig correction when volume reflection is also present or when highly absorbing IR compounds generate Reststrahlen bands. The inspection of the enlarged spectral range allowed the detection of fundamental, combination and overtone bands which provided reliable identification and semi-quantitative characterization of different polystyrene-based co-polymers. Furthermore the variation of the plastic optical properties across the explored spectral range allowed us to sample the plastic materials to different depths in the mid- and near-IR regions, so as to probe the chemistry at the surface and in the plastic bulk, respectively, in a non-invasive manner. This proved particularly useful to observe spectral markers of surface degradation occurring in historical ABS-based polymers.

Associations Between Anticholinergic Medication Exposure and Adverse Health Outcomes in Older People with Frailty: A Systematic Review and Meta-analysis.

INTRODUCTION: There are robust associations between use of anticholinergic medicines and adverse effects in older people. However, the nature of these associations for older people living with frailty is yet to be established. OBJECTIVES: The aims were to identify and investigate associations between anticholinergics and adverse outcomes in older people living with frailty and to investigate whether exposure is associated with greater risks according to frailty status. METHODS: MEDLINE, CINAHL, EMBASE, Cochrane Database of Systematic Reviews, Web of Science and PsycINFO were searched to 1 August 2019. Observational studies reporting associations between anticholinergics and outcomes in older adults (average age ≥ 65 years) that reported frailty using validated measures were included. Primary outcomes were physical impairment, cognitive dysfunction, and change in frailty status. Risk of bias was evaluated using the Cochrane Risk of Bias In Non-randomised Studies of Interventions (ROBINS-I) tool. Meta-analysis was undertaken where appropriate. RESULTS: Thirteen studies (21,516 participants) were included (ten community, one residential aged-care facility and two hospital studies). Observed associations included reduced ability for chair standing, slower gait speeds, poorer physical performance, increased risk of falls and mortality. Conflicting results were reported for grip strength, timed up and go test, cognition and activities of daily living. No associations were observed for transitions between frailty states, psychological wellbeing or benzodiazepine-related adverse reactions. There was no clear evidence of differences in risks according to frailty status. CONCLUSIONS: Anticholinergics are associated with adverse outcomes in older people living with frailty; however, the literature has significant methodological limitations. There is insufficient evidence to suggest greater risks based on frailty, and there is an urgent need to evaluate this further in well-designed studies stratifying by frailty.