Identification of histological features to predict MUC2 expression in colon cancer tissues

Colorectal cancer (CRC) is the third-most common form of cancer among Americans. Like normal colon tissue, CRC cells are sustained by a subpopulation of “stem cells” that possess the ability to self-renew and differentiate into more specialized cancer cell types. In normal colon tissue, the enterocytes, goblet cells and other epithelial cells in the mucosa region have distinct morphologies that distinguish them from the other cells in the lamina propria, muscularis mucosa, and submucosa. However, in a tumor, the morphology of the cancer cells varies dramatically. Cancer cells that express genes specific to goblet cells significantly differ in shape and size compared to their normal counterparts. Even though a large number of hematoxylin and eosin (H&E)-stained sections and the corresponding RNA sequencing (RNASeq) data from CRC are available from The Cancer Genome Atlas (TCGA), prediction of gene expression patterns from tissue histological features has not been attempted yet. In this manuscript, we identified histological features that are strongly associated with MUC2 expression patterns in a tumor. Specifically, we show that large nuclear area is associated with MUC2-high tumors (p < 0.001). This discovery provides insight into cancer biology and tumor histology and demonstrates that it may be possible to predict certain gene expressions from histological features

Utilizing Machine Learning Techniques to Rapidly Identify MUC2 Expression in Colon Cancer Tissues

Colorectal cancer is the third-most common form of cancer among American men and women. Like most tumors, colon cancer is sustained by a subpopulation of “stem cells” that possess the ability to self-renew and differentiate into more specialized cell types. It would be useful to detect stem cells in images of colon cancer tissue, but the first step in being able to do so is to know what genes are expressed in the stem cells and how to detect their expression pattern from the tissue images. Machine learning (ML) is a powerful tool that is widely used in biological research as a novel and innovative technique to facilitate rapid diagnosis of cancer. The current study demonstrates the feasibility and effectiveness of using ML techniques to rapidly detect the expression of the gene MUC2 (mucin 2) in colon cancer tissue images. We analyzed histological images of colon cancer and segmented the nuclei to look for features (area, perimeter, eccentricity, compactness, etc.) that correlate with high or low levels of MUC2. Grid search was then run on this data set to tune the hyper-parameters, and the following models were tested as potential classifiers: random forest, gradient boosting, decision trees with AdaBoost, and support vector machines. Of all of the tested models, it was found that the random forest classifier (f1 score of 0.71) and the gradient boosting classifier (f1 score of 0.72) were able to predict the output label most accurately. Under certain conditions, we have identified four features that have predictive capabilities. Predicting individual gene expression with machine learning is the first step in detecting genes that are specific to cancer stem cells in the early stages of cancer, while there is still hope for a cure

Identification of histological features to predict MUC2 expression in colon cancer tissues

Colorectal cancer (CRC) is the third-most common form of cancer among Americans. Like normal colon tissue, CRC cells are sustained by a subpopulation of “stem cells” that possess the ability to self-renew and differentiate into more specialized cancer cell types. In normal colon tissue, the enterocytes, goblet cells and other epithelial cells in the mucosa region have distinct morphologies that distinguish them from the other cells in the lamina propria, muscularis mucosa, and submucosa. However, in a tumor, the morphology of the cancer cells varies dramatically. Cancer cells that express genes specific to goblet cells significantly differ in shape and size compared to their normal counterparts. Even though a large number of hematoxylin and eosin (H&E)-stained sections and the corresponding RNA sequencing (RNASeq) data from CRC are available from The Cancer Genome Atlas (TCGA), prediction of gene expression patterns from tissue histological features has not been attempted yet. In this manuscript, we identified histological features that are strongly associated with MUC2 expression patterns in a tumor. Specifically, we show that large nuclear area is associated with MUC2-high tumors (p < 0.001). This discovery provides insight into cancer biology and tumor histology and demonstrates that it may be possible to predict certain gene expressions from histological features

Factors associated with smoking in low-income persons with and without chronic illness

INTRODUCTION Tobacco disparities persist among low-income smokers who seek care from safety-net clinics. Many of these patients suffer from chronic illnesses (CILs) that are associated with and exacerbated by smoking. The objective of the current study was to examine the differences between safety-net patients with and without CILs in terms of nicotine dependence and related factors (such as depression, anxiety) and self-efficacy regarding ability to abstain from smoking. METHODS Sixty-four low-income smokers who thought about or intended to quit smoking were recruited from the San Francisco Health Network (SFHN) and assessed for CILs, nicotine dependence, depression, anxiety, and smoking abstinence self-efficacy. Four one-way analyses of variance were used to examine the difference between those with and without CIL on the latter four variables. RESULTS The CIL group had significantly higher anxiety (CIL: 8.0 ± 5.35; non-CIL: 4.44 ± 3.48; p=0.02) and tended to have higher nicotine dependence (CIL: 5.40 ± 2.58; non-CIL: 3.88 ± 2.28; p=0.04). In the CIL group, nicotine dependence was positively correlated with anxiety [r(62)=0.39; p\u3c0.01] and negatively correlated with smoking abstinence self-efficacy [r(62)= -0.38; p\u3c0.01]. Both depression (Spearman’s rho=0.39; p\u3c0.01) and anxiety (Spearman’s rho=0.29; p\u3c0.05) were associated with total number of CIL categories. CONCLUSIONS Safety-net patients who smoke and suffer from CILs may be suffering from higher levels of anxiety and have less confidence in their ability to quit smoking. Incorporating mood management and developing interventions that increase a sense of self-efficacy for refraining from smoking may be necessary to help low-income smokers quit smoking

Results From a Survey of American Geriatrics Society Members’ Views on Physician‐Assisted Suicide

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/152741/1/jgs16245_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/152741/2/jgs16245-sup-0001-Supinfo.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/152741/3/jgs16245.pd

Implementing goals of care conversations with veterans in VA long-term care setting: a mixed methods protocol

Abstract Background The program “Implementing Goals of Care Conversations with Veterans in VA LTC Settings” is proposed in partnership with the US Veterans Health Administration (VA) National Center for Ethics in Health Care and the Geriatrics and Extended Care Program Offices, together with the VA Office of Nursing Services. The three projects in this program are designed to support a new system-wide mandate requiring providers to conduct and systematically record conversations with veterans about their preferences for care, particularly life-sustaining treatments. These treatments include cardiac resuscitation, mechanical ventilation, and other forms of life support. However, veteran preferences for care go beyond whether or not they receive life-sustaining treatments to include issues such as whether or not they want to be hospitalized if they are acutely ill, and what kinds of comfort care they would like to receive. Methods Three projects, all focused on improving the provision of veteran-centered care, are proposed. The projects will be conducted in Community Living Centers (VA-owned nursing homes) and VA Home-Based Primary Care programs in five regional networks in the Veterans Health Administration. In all the projects, we will use data from context and barrier and facilitator assessments to design feedback reports for staff to help them understand how well they are meeting the requirement to have conversations with veterans about their preferences and to document them appropriately. We will also use learning collaboratives—meetings in which staff teams come together and problem-solve issues they encounter in how to get veterans’ preferences expressed and documented, and acted on—to support action planning to improve performance. Discussion We will use data over time to track implementation success, measured as the proportions of veterans in Community Living Centers (CLCs) and Home-Based Primary Care (HBPC) who have a documented goals of care conversation soon after admission. We will work with our operational partners to spread approaches that work throughout the Veterans Health Administration.http://deepblue.lib.umich.edu/bitstream/2027.42/134645/1/13012_2016_Article_497.pd

Development of tools to measure dignity for older people in acute hospitals

Background: Dignity is a concept that applies to all patients. Older patients can be particularly vulnerable to experiencing a loss of dignity in hospital. Previous tools developed to measure dignity have been aimed at palliative and end of life care. No tools for measuring dignity in acute hospital care have been reported. Objectives: To develop tools for measuring patient dignity in acute hospitals. Setting: A large UK acute hospital. We purposively selected 17 wards where at least 50% of patients are 65 or over. Methods: Three methods of capturing data related to dignity were developed: an electronic patient dignity survey (possible score range 6-24); a format for non-participant observations; and individual face-to-face semi-structured patient and staff interviews (reported elsewhere). Results: 5693 surveys were completed. Mean score increased from 22.00 pre-intervention to 23.03 after intervention (p Conclusions: We have developed a simple format for a dignity survey and observations. Overall, most patients reported electronically that they received dignified care in hospital. However, observations identified a high percentage of interactions categorised as neu-tral/basic care, which, while not actively diminishing dignity, will not enhance dignity. There is an opportunity make these interactions more positive

Reappraising ‘the good death’ for populations in the age of ageing

This is the second in an occasional series of paired commentaries in Age and Ageing, the Journal of the British Geriatrics Society and the Journal of the American Geriatrics Society (JAGS). The aim is to address issues of current significance and to foster dialogue and increased understanding between academics and clinicians working in comparative international settings. Both commentaries address the urgent need to improve palliative care for older people, with a critique of some stereotypes surrounding palliative care and the ‘good death’. The companion commentary, published in JAGS, was written by Alexander Smith and Vyjeyanthi Periyakoil, and is grounded in their experience as academic clinicians (Smith AK, Periyakoil V. Should we bury ‘The Good Death’? Journal of the American Geriatrics Society 2018; in press). In the present paper, we offer a perspective on the outcome and wider consequences of misalignment between current UK policy and aspirations for end of life care in relation to epidemiological trends and patient experience of death and dying

The Beck Depression Inventory (BDI-II) and a single screening question as screening tools for depressive disorder in Dutch advanced cancer patients

Item does not contain fulltextPURPOSE: Depression is highly prevalent in advanced cancer patients, but the diagnosis of depressive disorder in patients with advanced cancer is difficult. Screening instruments could facilitate diagnosing depressive disorder in patients with advanced cancer. The aim of this study was to determine the validity of the Beck Depression Inventory (BDI-II) and a single screening question as screening tools for depressive disorder in advanced cancer patients. METHODS: Patients with advanced metastatic disease, visiting the outpatient palliative care department, were asked to fill out a self-questionnaire containing the Beck Depression Inventory (BDI-II) and a single screening question "Are you feeling depressed?" The mood section of the PRIME-MD was used as a gold standard. RESULTS: Sixty-one patients with advanced metastatic disease were eligible to be included in the study. Complete data were obtained from 46 patients. The area under the curve of the receiver operating characteristics analysis of the BDI-II was 0.82. The optimal cut-off point of the BDI-II was 16 with a sensitivity of 90% and a specificity of 69%. The single screening question showed a sensitivity of 50% and a specificity of 94%. CONCLUSIONS: The BDI-II seems an adequate screening tool for a depressive disorder in advanced cancer patients. The sensitivity of a single screening question is poor.1 februari 201