Hospital Ownership Mix Efficiency in the US: An Exploratory Study

This paper offers an empirical test of ownership mix efficiency in the U.S. hospital services industry. The test compares the benefits of quality assurance with the costs from the attenuation of property rights that result from an increased presence of nonprofit organizations. The empirical results suggest that too many not-for-profit and public hospitals may exist in the typical market area of the U.S. The policy implication is that more quality of care per dollar might be obtained by attracting a greater percentage of for-profit hospitals into some market areas. This conclusion, however, is tempered with several caveats. We discuss these and also make recommendations for further research.

Testing for Ownership Mix Efficiency: The Case of the Nursing Home Industry

This paper offers an empirical test of ownership mix efficiency in the U.S. nursing home industry. We test to compare the benefits of quality assurance with the costs from the attenuation of property rights that result from an increased presence of nonprofit organizations. The empirical results suggest that too few nonprofit nursing homes may exist in the typical market area of the U.S. The policy implication is that more quality of care per dollar might be obtained by attracting a greater percentage of nonprofit nursing homes into most market areas.

A Machine Learning Method of Determining Causal Inference applied to Shifts in Voting Preferences between 2012-2016

This research investigates the application of machine learning techniques to assist in the execution of a synthetic control model. This model was performed to analyze counties within the United States that showed a voter shift from a majority of Democratic voter share to Republican between the 2012 and 2016 election cycles. The following study applies two steps of machine learning analysis. The first, which is the treatment discovery process, leverages a Random Forest to evaluate feature importance. The second step was the execution of the synthetic control model with two predictor variable lists. The first was the parametric method: a hand curated predictor variable list based on domain knowledge. The second was the non-parametric method: all available predictor (descriptive) variables were used. The Random Forest treatment discovery process resulted in two uncommon variables applied as treatment effects: WIC women enrollment and a decrease of vegetable farm acreage. The opportunity to research these atypical treatment variables allows for the potential of surfacing counterfactual arguments for further research. The use of the parametric and non-parametric methods offers a system of comparison for the research in this paper. The result from the decrease in vegetable farm acreage treatment variable was negative for the non-parametric model. However, the parametric model did show strong statistical evidence towards a treatment effect from the decrease in farm acreage. It is likely that the decrease in vegetable farm acreage is a proxy for poverty or a population density metric. These data results suggest that this model was likely suffering from omitted variable bias for representation of one or both of these metrics in the predictor variable list. The WIC women enrollment treatment variable investigation resulted in the synthetic control model having difficulty in forming a synthetic control comparison. These results suggest there is a fundamental difference between those counties used to create the synthetic control and the other counties that saw a treatment effect. Additional research needs to be performed, and it could result in a different application of the data for use in a synthetic control model. The results of this study, while not surfacing causal inference, did open questions for further research. Given the opportunity these joined causal inference and machines learning practices could continue and potential offer assistance to traditional causal modeling methods. Allowing researchers to understand data and relationships between the data more intimately, theoretically allowing for new causal inferences to be discovered

Deep Learning Image Analysis to Isolate and Characterize Different Stages of S-phase in Human Cells

Abstract. This research used deep learning for image analysis by isolating and characterizing distinct DNA replication patterns in human cells. By leveraging high-resolution microscopy images of multiple cells stained with 5-Ethynyl-2′-deoxyuridine (EdU), a replication marker, this analysis utilized Convolutional Neural Networks (CNNs) to perform image segmentation and to provide robust and reliable classification results. First multiple cells in a field of focus were identified using a pretrained CNN called Cellpose. After identifying the location of each cell in the image a python script was created to crop out each cell into individual .tif files. After careful annotation, a CNN was created from scratch using the TensorFlow Keras package and trained on those images to categorize them into five distinct replication patterns. Using a holdout test set our model was able to achieve an accuracy of 86.5%. This analysis method for segmentation and classification enhances the efficiency and reproducibility of DNA replication analysis, allowing for high-throughput processing and analysis of replication foci. This research can enhance image analysis in cell biology by providing a time-efficient and accurate tool to investigate replication dynamics, advance cancer research, and contribute to scientific discovery in various domains

Ethanol Activation of Protein Kinase A Regulates GABAA Receptor Subunit Expression in the Cerebral Cortex and Contributes to Ethanol-Induced Hypnosis

Protein kinases are implicated in neuronal cell functions such as modulation of ion channel function, trafficking, and synaptic excitability. Both protein kinase C (PKC) and A (PKA) are involved in regulation of γ-aminobutyric acid type A (GABAA) receptors through phosphorylation. However, the role of PKA in regulating GABAA receptors (GABAA-R) following acute ethanol exposure is not known. The present study investigated the role of PKA in the effects of ethanol on GABAA-R α1 subunit expression in rat cerebral cortical P2 synaptosomal fractions. Additionally, GABA-related behaviors were examined. Rats were administered ethanol (2.0–3.5 g/kg) or saline and PKC, PKA, and GABAA-R α1 subunit levels were measured by western blot analysis. Ethanol (3.5 g/kg) transiently increased GABAA-R α1 subunit expression and PKA RIIβ subunit expression at similar time points whereas PKA RIIα was increased at later time points. In contrast, PKC isoform expression remained unchanged. Notably, lower ethanol doses (2.0 g/kg) had no effect on GABAA-R α1 subunit levels, although PKA type II regulatory subunits RIIα and RIIβ were increased at 10 and 60 min when PKC isozymes are also known to be elevated. To determine if PKA activation was responsible for the ethanol-induced elevation of GABAA-R α1 subunits, the PKA antagonist H89 was administered to rats prior to ethanol exposure. H89 administration prevented ethanol-induced increases in GABAA-R α1 subunit expression. Moreover, increasing PKA activity intracerebroventricularly with Sp-cAMP prior to a hypnotic dose of ethanol increased ethanol-induced loss of righting reflex (LORR) duration. This effect appears to be mediated in part by GABAA-R as increasing PKA activity also increased the duration of muscimol-induced LORR. Overall, these data suggest that PKA mediates ethanol-induced GABAA-R expression and contributes to behavioral effects of ethanol involving GABAA-R

Bupropion Increases Selection of High Effort Activity in Rats Tested on a Progressive Ratio/Chow Feeding Choice Procedure: Implications for Treatment of Effort-Related Motivational Symptoms

Background: Depression and related disorders are characterized by deficits in behavioral activation, exertion of effort, and other psychomotor/motivational dysfunctions. Depressed patients show alterations in effort-related decision making and a bias towards selection of low effort activities. It has been suggested that animal tests of effort-related decision making could be useful as models of motivational dysfunctions seen in psychopathology. Methods: Because clinical studies have suggested that inhibition of catecholamine uptake may be a useful strategy for treatment of effort-related motivational symptoms, the present research assessed the ability of bupropion to increase work output in rats responding on a test of effort-related decision-making (ie, a progressive ratio/chow feeding choice task). With this task, rats can choose between working for a preferred food (high-carbohydrate pellets) by lever pressing on a progressive ratio schedule vs obtaining a less preferred laboratory chow that is freely available in the chamber. Results: Bupropion (10.0–40.0 mg/kg intraperitoneal) significantly increased all measures of progressive ratio lever pressing, but decreased chow intake. These effects were greatest in animals with low baseline levels of work output on the progressive ratio schedule. Because accumbens dopamine is implicated in effort-related processes, the effects of bupropion on markers of accumbens dopamine transmission were examined. Bupropion elevated extracellular dopamine levels in accumbens core as measured by microdialysis and increased phosphorylated dopamine and cyclic-AMP related phosphoprotein 32 kDaltons (pDARPP-32) immunoreactivity in a manner consistent with D1 and D2 receptor stimulation. Conclusion: The ability of bupropion to increase exertion of effort in instrumental behavior may have implications for the pathophysiology and treatment of effort-related motivational symptoms in humans

A New Strategy to Generate Functional Insulin-Producing Cell Lines by Somatic Gene Transfer into Pancreatic Progenitors

BACKGROUND: There is increasing interest in developing human cell lines to be used to better understand cell biology, but also for drug screening, toxicology analysis and future cell therapy. In the endocrine pancreatic field, functional human beta cell lines are extremely scarce. On the other hand, rodent insulin producing beta cells have been generated during the past years with great success. Many of such cell lines were produced by using transgenic mice expressing SV40T antigen under the control of the insulin promoter, an approach clearly inadequate in human. Our objective was to develop and validate in rodent an alternative transgenic-like approach, applicable to human tissue, by performing somatic gene transfer into pancreatic progenitors that will develop into beta cells. METHODS AND FINDINGS: In this study, rat embryonic pancreases were transduced with recombinant lentiviral vector expressing the SV40T antigen under the control of the insulin promoter. Transduced tissues were next transplanted under the kidney capsule of immuno-incompetent mice allowing insulinoma development from which beta cell lines were established. Gene expression profile, insulin content and glucose dependent secretion, normalization of glycemia upon transplantation into diabetic mice validated the approach to generate beta cell lines. CONCLUSIONS: Somatic gene transfer into pancreatic progenitors represents an alternative strategy to generate functional beta cell lines in rodent. Moreover, this approach can be generalized to derive cells lines from various tissues and most importantly from tissues of human origin

A broader role for AmyR in Aspergillus niger: regulation of the utilisation of d-glucose or d-galactose containing oligo- and polysaccharides

AmyR is commonly considered a regulator of starch degradation whose activity is induced by the presence of maltose, the disaccharide building block of starch. In this study, we demonstrate that the role of AmyR extends beyond starch degradation. Enzyme activity assays, genes expression analysis and growth profiling on d-glucose- and d-galactose-containing oligo- and polysaccharides showed that AmyR regulates the expression of some of the Aspergillus niger genes encoding α- and β-glucosidases, α- and β- galactosidases, as well as genes encoding α-amlyases and glucoamylases. In addition, we provide evidence that d-glucose or a metabolic product thereof may be the inducer of the AmyR system in A. niger and not maltose, as is commonly assumed

Leveraging Rural Energy Investment for Parasitic Disease Control: Schistosome Ova Inactivation and Energy Co-Benefits of Anaerobic Digesters in Rural China

Cooking and heating remain the most energy intensive activities among the world's poor, and thus improved access to clean energies for these tasks has been highlighted as a key requirement of attaining the major objectives of the UN Millennium Development Goals. A move towards clean energy technologies such as biogas systems (which produce methane from human and animal waste) has the potential to provide immediate benefits for the control of neglected tropical diseases. Here, an assessment of the parasitic disease and energy benefits of biogas systems in Sichuan Province, China, is presented, highlighting how the public health sector can leverage the proliferation of rural energy projects for infectious disease control. ova) counted at the influent of two biogas systems were removed in the systems when adjusted for system residence time, an approximate 1-log removal attributable to sedimentation. Combined, these inactivation/removal processes underscore the promise of biogas infrastructure for reducing parasite contamination resulting from nightsoil use. When interviewed an average of 4 years after construction, villagers attributed large changes in fuel usage to the installation of biogas systems. Household coal usage decreased by 68%, wood by 74%, and crop waste by 6%. With reported energy savings valued at roughly 600 CNY per year, 2–3 years were required to recoup the capital costs of biogas systems. In villages without subsidies, no new biogas systems were implemented.Sustainable strategies that integrate rural energy needs and sanitation offer tremendous promise for long-term control of parasitic diseases, while simultaneously reducing energy costs and improving quality of life. Government policies can enhance the financial viability of such strategies by introducing fiscal incentives for joint sanitation/sustainable energy projects, along with their associated public outreach and education programs