Pain management in patients with dementia

There are an estimated 35 million people with dementia across the world, of whom 50% experience regular pain. Despite this, current assessment and treatment of pain in this patient group are inadequate. In addition to the discomfort and distress caused by pain, it is frequently the underlying cause of behavioral symptoms, which can lead to inappropriate treatment with antipsychotic medications. Pain also contributes to further complications in treatment and care. This review explores four key perspectives of pain management in dementia and makes recommendations for practice and research. The first perspective discussed is the considerable uncertainty within the literature on the impact of dementia neuropathology on pain perception and processing in Alzheimer’s disease and other dementias, where white matter lesions and brain atrophy appear to influence the neurobiology of pain. The second perspective considers the assessment of pain in dementia. This is challenging, particularly because of the limited capacity of self-report by these individuals, which means that assessment relies in large part on observational methods. A number of tools are available but the psychometric quality and clinical utility of these are uncertain. The evidence for efficient treatment (the third perspective) with analgesics is also limited, with few statistically well-powered trials. The most promising evidence supports the use of stepped treatment approaches, and indicates the benefit of pain and behavioral interventions on both these important symptoms. The fourth perspective debates further difficulties in pain management due to the lack of sufficient training and education for health care professionals at all levels, where evidence-based guidance is urgently needed. To address the current inadequate management of pain in dementia, a comprehensive approach is needed. This would include an accurate, validated assessment tool that is sensitive to different types of pain and therapeutic effects, supported by better training and support for care staff across all settings

Pain management in patients with dementia

There are an estimated 35 million people with dementia across the world, of whom 50% experience regular pain. Despite this, current assessment and treatment of pain in this patient group are inadequate. In addition to the discomfort and distress caused by pain, it is frequently the underlying cause of behavioral symptoms, which can lead to inappropriate treatment with antipsychotic medications. Pain also contributes to further complications in treatment and care. This review explores four key perspectives of pain management in dementia and makes recommendations for practice and research. The first perspective discussed is the considerable uncertainty within the literature on the impact of dementia neuropathology on pain perception and processing in Alzheimer’s disease and other dementias, where white matter lesions and brain atrophy appear to influence the neurobiology of pain. The second perspective considers the assessment of pain in dementia. This is challenging, particularly because of the limited capacity of self-report by these individuals, which means that assessment relies in large part on observational methods. A number of tools are available but the psychometric quality and clinical utility of these are uncertain. The evidence for efficient treatment (the third perspective) with analgesics is also limited, with few statistically well-powered trials. The most promising evidence supports the use of stepped treatment approaches, and indicates the benefit of pain and behavioral interventions on both these important symptoms. The fourth perspective debates further difficulties in pain management due to the lack of sufficient training and education for health care professionals at all levels, where evidence-based guidance is urgently needed. To address the current inadequate management of pain in dementia, a comprehensive approach is needed. This would include an accurate, validated assessment tool that is sensitive to different types of pain and therapeutic effects, supported by better training and support for care staff across all settings.publishedVersio

Adenoviral gene transfer of interleukin 12 into tumors synergizes with adoptive T cell therapy both at the induction and effector level

Tumors infected with a recombinant defective adenovirus expressing interleukin 12 (IL-12) undergo regression, associated with a cytotoxic T lymphocyte (CTL)-mediated antitumor immune response. In the present study we generated anti-CT26 CTLs by short-term coculture of CT26 cells and lymph node cells obtained from mice harboring subcutaneous CT26 tumors injected with an adenoviral vector expressing IL-12 (AdCMVIL-12), control adenovirus (AdCMVlacZ), or saline. Regression of small intrahepatic CT26 tumors in unrelated syngeneic animals was achieved with CTLs derived from mice whose subcutaneous tumors had been injected with AdCMVIL-12 but not with CTLs from the other two control groups. The necessary and sufficient effector cell population for adoptive transfer consisted of CD8+ T cells that showed anti-CT26 specificity partly directed against the AH1 epitope presented by H-2Ld. Interestingly, treatment of a subcutaneous tumor nodule with AdCMVIL-12, combined with intravenous adoptive T cell therapy with short-term CTL cultures, had a marked synergistic effect against large, concomitant live tumors. Expression of IL-12 in the liver in the vicinity of the hepatic tumor nodules, owing to spillover of the vector into the systemic circulation, appeared to be involved in the increased in vivo antitumor activity of injected CTLs. In addition, adoptive T cell therapy improved the outcome of tumor nodules transduced with suboptimal doses of AdCMVIL-12. Our data provide evidence of a strong synergy between gene transfer of IL-12 and adoptive T cell therapy. This synergy operates both at the induction and effector phases of the CTL response, thus providing a rationale for combined therapeutic strategies for human malignancies

Reservoir characterisation of a laminated sediment

Abstract unavailable refer to PD

Automated Classification of Well Test Responses in Naturally Fractured Reservoirs Using Unsupervised Machine Learning

Understanding the impact of fractures on fluid flow is fundamental for developing geoenergy reservoirs. Pressure transient analysis could play a key role for fracture characterization purposes if better links can be established between the pressure derivative responses (p′) and the fracture properties. However, pressure transient analysis is particularly challenging in the presence of fractures because they can manifest themselves in many different p′ curves. In this work, we aim to provide a proof-of-concept machine learning approach that allows us to effectively handle the diversity in fracture-related p′ curves by automatically classifying them and identifying the characteristic fracture patterns. We created a synthetic dataset from numerical simulation that comprised 2560 p′ curves that represent a wide range of fracture network properties. We developed an unsupervised machine learning approach that can distinguish the temporal variations in the p′ curves by combining dynamic time warping with k-medoids clustering. Our results suggest that the approach is effective at recognizing similar shapes in the p′ curves if the second pressure derivatives are used as the classification variable. Our analysis indicated that 12 clusters were appropriate to describe the full collection of p′ curves in this particular dataset. The classification exercise also allowed us to identify the key geological features that influence the p′ curves in this particular dataset, namely (1) the distance from the wellbore to the closest fracture(s), (2) the local/global fracture connectivity, and (3) the local/global fracture intensity. With additional training data to account for a broader range of fracture network properties, the proposed classification method could be expanded to other naturally fractured reservoirs and eventually serve as an interpretation framework for understanding how complex fracture network properties impact pressure transient behaviour