Does the Congestive Heart Failure Program Improve Patient\u27s Functional Health Status?

Congestive heart failure (CHF) is a significant health problem in the U.S. There are over 2 million Americans diagnosed with CHF, with 200,000 CHF related deaths every year. A CHF Comprehensive Care Program in a Northern California medical center began in August, 1997 to case manage and educate CHF patients and their family members. The program educated CHF patients to self-manage the chronic condition by daily weight, low sodium diet, medication adherence, activity and rest balance, and daily exercise. This study used a one group. Pre and post test study design to evaluate the effectiveness of the CHF program. The results showed statistically significant improvement in three functional health status measures, using the New York Heart Association Classification, Duke Activity Status Index, and 6 Minute Walk Assessment. Recommendations are made for a case management program using family nurse practitioners for CHF patients as well as other chronic diseases

How Well Can We Measure Well-Being?

I will define the meaning of subjective well-being that I believe is the most intrinsic normative good, explain why improving the subjective well-being of sentient individuals ought to be the highest ethical priority, and provide reasons for why finding a way to measure subjective well-being would essentially benefit decision-makers and grassroots altruists. Subjective well-being is a dauntingly nebulous property to attempt to measure with precision, but I will comment on the progress that philosophers and social scientists have made in this field. Although (1) there is no set of well-being criteria that is applicable to every sentient individual (including non-human animals) and (2) most sentient individuals are unable to communicate with us about their level of subjective well-being use or relevant experiential factors, we may yet be able to develop an intrapersonally and interpersonally cardinal method to measure subjective well-being

FM-test: a fuzzy-set-theory-based approach to differential gene expression data analysis

Abstract Background Microarray techniques have revolutionized genomic research by making it possible to monitor the expression of thousands of genes in parallel. As the amount of microarray data being produced is increasing at an exponential rate, there is a great demand for efficient and effective expression data analysis tools. Comparison of gene expression profiles of patients against those of normal counterpart people will enhance our understanding of a disease and identify leads for therapeutic intervention. Results In this paper, we propose an innovative approach, fuzzy membership test (FM-test), based on fuzzy set theory to identify disease associated genes from microarray gene expression profiles. A new concept of FM d-value is defined to quantify the divergence of two sets of values. We further analyze the asymptotic property of FM-test, and then establish the relationship between FM d-value and p-value. We applied FM-test to a diabetes expression dataset and a lung cancer expression dataset, respectively. Within the 10 significant genes identified in diabetes dataset, six of them have been confirmed to be associated with diabetes in the literature and one has been suggested by other researchers. Within the 10 significantly overexpressed genes identified in lung cancer data, most (eight) of them have been confirmed by the literatures which are related to the lung cancer. Conclusion Our experiments on synthetic datasets show that FM-test is effective and robust. The results in diabetes and lung cancer datasets validated the effectiveness of FM-test. FM-test is implemented as a Web-based application and is available for free at http://database.cs.wayne.edu/bioinformatics

Machine Learning Prototype App For Recognition of Fruits

As the incidence of obesity and associated negative health consequences is rising, it becomes crucial to monitor the dietary choices of individuals. Unfortunately, traditional methods to collect this information involve collecting food frequency questionnaires from individuals using paper. Electronic food trackers have been developed to collect food data, but they require participants to manually label and describe the content of their meals, and which may be difficult for researchers to interpret in a standardized fashion. Machine learning, however, provides an easy and efficient method for both participants and researchers to label food items with standardized descriptions. This project aims to create a prototype phone application that can identify and label photos of apples. This is done by making a machine learning model through Turicreate, a python module, which is then implemented into an iOS app through Xcode and Swift. The modules used in Swift include CoreML and AVFoundation. This machine learning application will be incorporated with a MealLogger phone app that is also under development. The MealLogger app will be used to keep track of participants' calorie intake and other personal details throughout the sleep study. The machine learning model will present several potential identities of the foods found in the photo, and the user will only need to select the correct option. This will be a user-friendly method for participants to easily log their food consumption without the hard work of manually inputting each and every description. Some limitations to this project include the wide variety of food, including those within different cultures. To deal with this, the model will include the most generic food categories, which the participant may select, and produce a drop-down menu of more specific dishes under that specified category, with the option of self-input. Additional questionnaires may be implemented according to the food type selected This will allow the process to be quick and easy, but also specific for the purpose of analysis. The release of the application will require a much longer process, but the machine learning prototype presents a first step toward an application that may change data analysis for researchers interested in collecting food intake from individuals living in the real world

Comparison of clinical outcomes and toxicity in endometrial cancer patients treated with adjuvant intensity-modulated radiation therapy or conventional radiotherapy

PurposeTo evaluate the treatment outcomes and toxicity in endometrial cancer patients treated with hysterectomy and adjuvant intensity-modulated radiation therapy (IMRT) or conventional radiotherapy (CRT).MethodsThere were 101 patients with stage IA-IIIC2 endometrial carcinoma treated with hysterectomy and adjuvant radiotherapy. In total, 36 patients received adjuvant CRT and 65 were treated with adjuvant IMRT. The endpoints were overall survival, local failure-free survival, and disease-free survival. Patients were assessed for acute toxicity weekly according to the Common Terminology Criteria for Adverse Events version 3.0. Late toxicity was evaluated according to the Radiation Therapy Oncology Group and the European Organization for Research and Treatment of Cancer Late Radiation Morbidity Scoring Schema.ResultsThe 5-year overall survival, local failure-free survival, and disease-free survival for the CRT group and the IMRT group were 82.9% versus 93.5% (p = 0.26), 93.7% versus 89.3% (p = 0.68), and 88.0% versus 82.8% (p = 0.83), respectively. Four (11.1%) patients had Grade 3 or greater acute gastrointestinal (GI) toxicity and three (8.3%) patients had Grade 3 or greater acute genitourinary (GU) toxicity in the CRT group, whereas four (6.2%) patients had Grade 3 or greater acute GI toxicity in the IMRT group and no patient had severe GU toxicity. There was one (2.8%) patient who had Grade 3 or greater late GI toxicity and one (2.8%) patient had Grade 3 or greater late GU toxicity in the CRT group, whereas no patient had severe GI or GU toxicity in the IMRT group.ConclusionAdjuvant IMRT for endometrial cancer patients had comparable clinical outcomes with CRT and had less acute and late toxicity

Speech in interaction: Mandarin particle Le as a marker of intersubjectivity

Adopting a discourse-analysis approach, we argue in line with van den Berg and Wu (2006) in showing that the particle le in Mandarin serves as a common ground coordination device, exhibiting a high degree of intersubjectivity, which is absent in the use of verbal -le. However, in view of the limit imposed by the data type of previous research, we observe the turn-taking behavior in natural, spontaneous, spoken data to further consolidate van den Berg and Wu‟s proposal. We argue that use of natural, spontaneous spoken data is essential in furthering our understanding of linguistic forms and their associated functions. Through this study, we hope we will be able to show and confirm the importance of data type in both the theoretical and pedagogical aspects of linguistic research

MCM-test: a fuzzy-set-theory-based approach to differential analysis of gene pathways

Abstract Background Gene pathway can be defined as a group of genes that interact with each other to perform some biological processes. Along with the efforts to identify the individual genes that play vital roles in a particular disease, there is a growing interest in identifying the roles of gene pathways in such diseases. Results This paper proposes an innovative fuzzy-set-theory-based approach, Multi-dimensional Cluster Misclassification test (MCM-test), to measure the significance of gene pathways in a particular disease. Experiments have been conducted on both synthetic data and real world data. Results on published diabetes gene expression dataset and a list of predefined pathways from KEGG identified OXPHOS pathway involved in oxidative phosphorylation in mitochondria and other mitochondrial related pathways to be deregulated in diabetes patients. Our results support the previously supported notion that mitochondrial dysfunction is an important event in insulin resistance and type-2 diabetes. Conclusion Our experiments results suggest that MCM-test can be successfully used in pathway level differential analysis of gene expression datasets. This approach also provides a new solution to the general problem of measuring the difference between two groups of data, which is one of the most essential problems in most areas of research

The natural stilbenoid (-)-hopeaphenol inhibits cellular entry of SARS-CoV-2 USA-WA1/2020, B.1.1.7, and B.1.351 variants

Antivirals are urgently needed to combat the global SARS-CoV-2/COVID- 19 pandemic, supplement existing vaccine efforts, and target emerging SARS-CoV-2 variants of concern. Small molecules that interfere with binding of the viral spike receptor binding domain (RBD) to the host angiotensin-converting enzyme II (ACE2) receptor may be effective inhibitors of SARS-CoV-2 cell entry. Here, we screened 512 pure compounds derived from natural products using a high-throughput RBD/ACE2 binding assay and identified (-)-hopeaphenol, a resveratrol tetramer, in addition to vatalbinoside A and vaticanol B, as potent and selective inhibitors of RBD/ACE2 binding and viral entry. For example, (-)-hopeaphenol disrupted RBD/ACE2 binding with a 50% inhibitory concentration (IC50) of 0.11 mM, in contrast to an IC50 of 28.3 mM against the unrelated host ligand/receptor binding pair PD-1/PD-L1 (selectivity index, 257.3). When assessed against the USA-WA1/2020 variant, (-)-hopeaphenol also inhibited entry of a VSVDG-GFP reporter pseudovirus expressing SARS-CoV-2 spike into ACE2-expressing Vero-E6 cells and in vitro replication of infectious virus in cytopathic effect and yield reduction assays (50% effective concentrations [EC50s], 10.2 to 23.4 mM) without cytotoxicity and approaching the activities of the control antiviral remdesivir (EC50s, 1.0 to 7.3 mM). Notably, (-)-hopeaphenol also inhibited two emerging variants of concern, B.1.1.7/Alpha and B.1.351/Beta in both viral and spike-containing pseudovirus assays with similar or improved activities over the USA-WA1/2020 variant. These results identify (-)-hopeaphenol and related stilbenoid analogues as potent and selective inhibitors of viral entry across multiple SARS-CoV-2 variants of concern