Decade of Medicare : The contribution of private practice dietitians to chronic disease management and diabetes group services

Aim: To review changes in utilisation of dietetics services through the Medicare Chronic Disease Management program over the last decade and describe patient uptake in 2013. Methods: Dietetics service data were extracted from published Medicare statistics for the periods (i) January 2004 to December 2013 and (ii) January to December 2013. Data comprised individual dietetics services by state and patient demography, and group services data for provider professions regarding type 2 diabetes: dietitians, diabetes educators and exercise physiologists. t-test was used to investigate the association of dietetics' individual service utilisation and workforce statistics. Results: Individual dietetics Chronic Disease Management consultations in private practice have increased annually since 2004. Dietetics has remained the third largest provider. In 2013, a total of 302910 individual consultations were conducted; 7% of allied health consultations. Likewise, individual services for Indigenous Australians increased since 2008. Utilisation of group services for type 2 diabetes comprised <2% of dietetics services. Dietitians provided more group services than diabetes educators but considerably fewer than exercise physiologists. Middle-aged and older patients were common, with highest uptake by those aged 55-74 years. Overall, total and per capita utilisation rates were considerably higher in NSW, Victoria and Queensland compared to less populous states, although this disparity has reduced since 2010. Conclusions: As 10 years has elapsed since the program's inception, further evaluation of the policy is needed to examine large variations in dietetics' Chronic Disease Management uptake by state and territory in both individual and group services. © 2015 Dietitians Association of Australia

A University and Middle School Mentor-Scholar Partnership

The State University of New York at Oswego (SUNY Oswego) and the Oswego City School district have created a campus-community partnership through a college program that matches SUNY Oswego students as mentors with at-risk youth in grades 7 and 8 in a structured environment in the school district. The structure is academically based for college students to earn credit based on the tenets of mentoring, youth development, and relationship building. The middle school students, or “mentees” come from an at-risk background that is academic, socially, or behaviorally based. The school district recommends students for inclusion in the program. This innovative program includes a course that is rich with the pedagogy of service-learning, builds leadership characteristics and teamwork through course discussions, workshops, and the mentormentee relationship. The community benefits with increased support to at-risk students and building a pipeline between the college and K–12 community. The Mentor-Scholar Program tracks K–12 impact through state assessments, grades, social-school success outcomes, college mentors course evaluations and grades. The program tracks the impact on college students through grade assessment and reflection. The program was formed five years ago and has grown from thirty mentors with sixty mentees to 120 mentors with 300 mentees this past semester. Initial research shows an increase in attendance and GPA for K–12 students enrolled in the program and leadership skill development for college students

Statistical Methods for High Dimensional Biomedical Data

This dissertation consists of four different topics in the areas of proteomics, genomics, and cardiology. First, a data-based method was developed to assign the subcellular localization of proteins. We applied the method to data on the bacteria Rhodobacter sphaeroides 2.4.1 and compared the results to PSORTb v.3.0. We found that the method compares well to PSORTb and a simulation study revealed that the method is sound and produces accurate results. Next, we investigated genomic features involved in the lethality of the knockout mouse using the random forest technique. We achieved an accuracy rate of 0.725 and found that among other features, the evolutionary age of the gene was a good predictor of lethality. Third, we analyzed DNA breakpoints across eight different cancer types to determine if common hotspots or cancer-type specific hotspots can be well-predicted by various genomic features and investigated which of the genomic features best predict the number of breakpoints. Using the random forest technique, we found that cancer- type specific hotspots are poorly predicted by genomic features but common hotspots can be predicted using the relevant genomic features. Additionally, we found that among the genomic features analyzed, indel rate and substitution rate were consistently chosen as the top predictors of breakpoint frequency. Lastly, we developed a method to predict the hypothetical heart age of a subject based on the subject’s electrocardiogram (ECG). The heart age predictions are consistent with current ECG science and knowledge of cardiac health

Mortality Prediction Analysis among COVID-19 Inpatients Using Clinical Variables and Deep Learning Chest Radiography Imaging Features.

The emergence of the COVID-19 pandemic over a relatively brief interval illustrates the need for rapid data-driven approaches to facilitate clinical decision making. We examined a machine learning process to predict inpatient mortality among COVID-19 patients using clinical and chest radiographic data. Modeling was performed with a de-identified dataset of encounters prior to widespread vaccine availability. Non-imaging predictors included demographics, pre-admission clinical history, and past medical history variables. Imaging features were extracted from chest radiographs by applying a deep convolutional neural network with transfer learning. A multi-layer perceptron combining 64 deep learning features from chest radiographs with 98 patient clinical features was trained to predict mortality. The Local Interpretable Model-Agnostic Explanations (LIME) method was used to explain model predictions. Non-imaging data alone predicted mortality with an ROC-AUC of 0.87 ± 0.03 (mean ± SD), while the addition of imaging data improved prediction slightly (ROC-AUC: 0.91 ± 0.02). The application of LIME to the combined imaging and clinical model found HbA1c values to contribute the most to model prediction (17.1 ± 1.7%), while imaging contributed 8.8 ± 2.8%. Age, gender, and BMI contributed 8.7%, 8.2%, and 7.1%, respectively. Our findings demonstrate a viable explainable AI approach to quantify the contributions of imaging and clinical data to COVID mortality predictions

Cardiovascular disease self-management: pilot testing of an mHealth healthy eating program

Cardiac rehabilitation (CR) is crucial in the management of cardiovascular disease (CVD), yet attendance is poor. Mobile technology (mHealth) offers a potential solution to increase reach of CR. This paper presents two development studies to determine mobile phone usage in adults with CVD and to evaluate the acceptability of an mHealth healthy eating CR program. Methods: CR attendees were surveyed to determine mobile phone usage rates. A second single-subject pilot study investigated perceptions of a 4-week theory-based healthy eating mHealth program and explored pre-post changes in self-efficacy. Results: 74 adults with CVD completed the survey (50/74 male; mean age 63 &plusmn; 10). Nearly all had mobile phones (70/74; 95%) and used the Internet (69/74; 93%), and most were interested in receiving CR by text message (57/74; 77%). 20 participants took part in the healthy eating pilot study. Participants read all/most of the text messages, and most (19/20) thought using mobile technology was a good way to deliver the program. The website was not widely used as visiting the website was reported to be time consuming. Exploratory t-tests revealed an increase in heart healthy eating self-efficacy post program, in particular the environmental self-efficacy subset (Mean = 0.62, SD = 0.74, p = 0.001). Conclusions: Text messaging was seen as a simple and acceptable way to deliver nutrition information and behavior change strategies; however, future research is needed to determine the effectiveness of such programs

Steroidogenic Enzyme and Steroid Receptor Expression in the Equine Accessory Sex Glands

The expression pattern and distribution of sex steroid receptors and steroidogenic enzymes during development of the equine accessory sex glands has not previously been described. We hypothesized that equine steroidogenic enzyme and sex steroid receptor expression is dependent on reproductive status. Accessory sex glands were harvested from mature stallions, pre-pubertal colts, geldings, and fetuses. Expression of mRNA for estrogen receptor 1 (ESR1), estrogen receptor 2 (ESR2), androgen receptor (AR), 3β-Hydroxysteroid dehydrogenase/Δ5-4 isomerase (3βHSD), P450,17α hydroxylase, 17–20 lyase (CYP17), and aromatase (CYP19) were quantified by RT-PCR, and protein localization of AR, ER-α, ER-β, and 3βHSD were investigated by immunohistochemistry. Expression of AR, ESR2, CYP17, or CYP19 in the ampulla was not different across reproductive statuses (p \u3e 0.1), while expression of ESR1 was higher in the ampulla of geldings and fetuses than those of stallions or colts (p \u3c 0.05). AR, ESR1 and ESR2 expression were decreased in stallion vesicular glands compared to the fetus or gelding, while AR, ESR1, and CYP17 expression were decreased in the bulbourethral glands compared to other glands. ESR1 expression was increased in the prostate compared to the bulbourethral glands, and no differences were seen with CYP19 or 3β-HSD. In conclusion, sex steroid receptors are expressed in all equine male accessory sex glands in all stages of life, while the steroidogenic enzymes were weakly and variably expressed

Regulatory complexity revealed by integrated cytological and RNA-seq analyses of meiotic substages in mouse spermatocytes

BACKGROUND: The continuous and non-synchronous nature of postnatal male germ-cell development has impeded stage-specific resolution of molecular events of mammalian meiotic prophase in the testis. Here the juvenile onset of spermatogenesis in mice is analyzed by combining cytological and transcriptomic data in a novel computational analysis that allows decomposition of the transcriptional programs of spermatogonia and meiotic prophase substages. RESULTS: Germ cells from testes of individual mice were obtained at two-day intervals from 8 to 18 days post-partum (dpp), prepared as surface-spread chromatin and immunolabeled for meiotic stage-specific protein markers (STRA8, SYCP3, phosphorylated H2AFX, and HISTH1T). Eight stages were discriminated cytologically by combinatorial antibody labeling, and RNA-seq was performed on the same samples. Independent principal component analyses of cytological and transcriptomic data yielded similar patterns for both data types, providing strong evidence for substage-specific gene expression signatures. A novel permutation-based maximum covariance analysis (PMCA) was developed to map co-expressed transcripts to one or more of the eight meiotic prophase substages, thereby linking distinct molecular programs to cytologically defined cell states. Expression of meiosis-specific genes is not substage-limited, suggesting regulation of substage transitions at other levels. CONCLUSIONS: This integrated analysis provides a general method for resolving complex cell populations. Here it revealed not only features of meiotic substage-specific gene expression, but also a network of substage-specific transcription factors and relationships to potential target genes. BMC Genomics 2016 Aug 12; 17(1):628

CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison

Large, labeled datasets have driven deep learning methods to achieve expert-level performance on a variety of medical imaging tasks. We present CheXpert, a large dataset that contains 224,316 chest radiographs of 65,240 patients. We design a labeler to automatically detect the presence of 14 observations in radiology reports, capturing uncertainties inherent in radiograph interpretation. We investigate different approaches to using the uncertainty labels for training convolutional neural networks that output the probability of these observations given the available frontal and lateral radiographs. On a validation set of 200 chest radiographic studies which were manually annotated by 3 board-certified radiologists, we find that different uncertainty approaches are useful for different pathologies. We then evaluate our best model on a test set composed of 500 chest radiographic studies annotated by a consensus of 5 board-certified radiologists, and compare the performance of our model to that of 3 additional radiologists in the detection of 5 selected pathologies. On Cardiomegaly, Edema, and Pleural Effusion, the model ROC and PR curves lie above all 3 radiologist operating points. We release the dataset to the public as a standard benchmark to evaluate performance of chest radiograph interpretation models. The dataset is freely available at https://stanfordmlgroup.github.io/competitions/chexpert .Comment: Published in AAAI 201

Development of an evidence-based mHealth weight management program using a formative research process

BACKGROUND: There is a critical need for weight management programs that are effective, cost efficient, accessible, and acceptable to adults from diverse ethnic and socioeconomic backgrounds. mHealth (delivered via mobile phone and Internet) weight management programs have potential to address this need. To maximize the success and cost-effectiveness of such an mHealth approach it is vital to develop program content based on effective behavior change techniques, proven weight management programs, and closely aligned with participants’ needs. OBJECTIVE: This study aims to develop an evidence-based mHealth weight management program (Horizon) using formative research and a structured content development process. METHODS: The Horizon mHealth weight management program involved the modification of the group-based UK Weight Action Program (WAP) for delivery via short message service (SMS) and the Internet. We used an iterative development process with mixed methods entailing two phases: (1) expert input on evidence of effective programs and behavior change theory; and (2) target population input via focus group (n=20 participants), one-on-one phone interviews (n=5), and a quantitative online survey (n=120). RESULTS: Expert review determined that core components of a successful program should include: (1) self-monitoring of behavior; (2) prompting intention formation; (3) promoting specific goal setting; (4) providing feedback on performance; and (5) promoting review of behavioral goals. Subsequent target group input confirmed that participants liked the concept of an mHealth weight management program and expressed preferences for the program to be personalized, with immediate (prompt) and informative text messages, practical and localized physical activity and dietary information, culturally appropriate language and messages, offer social support (group activities or blogs) and weight tracking functions. Most target users expressed a preference for at least one text message per day. We present the prototype mHealth weight management program (Horizon) that aligns with those inputs. CONCLUSIONS: The Horizon prototype described in this paper could be used as a basis for other mHealth weight management programs. The next priority will be to further develop the program and conduct a full randomized controlled trial of effectiveness