bluuebunny/crossref_metadata_2025_split huggingface dataset:
Hyaluronic acid-curcumin conjugate suppresses the fibrotic functions of myofibroblasts from contractive joint by the PTGER2 demethylation
Authors: Yu Dongjie , Zhuang Ze , Ren Jianhua , Hu Xuefeng , Wang Zhe , Zhang Jieyu , Luo Yuansen , Wang Kun , He Ronghan , Wang Yunbing 
Published: April 2019

AbstractJoint contracture is a fibrotic complication induced by joint immobilization and trauma, which is characterized as excessive myofibroblast proliferation in joint capsule. The treatments of joint contracture are unsatisfied and patients are suffered from joint dysfunction. Our previous study has shown that curcumin can inhibit myofibroblast proliferation in vitro, but the major challenge is the low aqueous solubility and biological activity of curcumin. In this study, hyaluronic acid-curcumin (HA-Cur) conjugate was synthesized to suppress myofibroblasts in joint contracture. Cells were isolated from the joint capsules of joint contracture patients and induced to active myofibroblasts by transforming growth factor-β (TGF-β). The anti-fibrotic function and mechanisms of HA-Cur were investigated by immunohistochemistry, reverse transcription-quantitative polymerase chain reaction (PCR), methylation-specific PCR, western blot, transwell migration assay and proliferation assay. Results showed that 30 μM HA-Cur significantly attenuated the fibrotic functions of myofibroblast in joint contracture in vitro by regulating the methylation of prostaglandin E receptor 2 (PTGER2) and inhibiting TGF-β signaling. This may provide a mechanism for the treatment of joint contracture, and provide a molecular target PTGER2 for therapy during the pathogenesis of joint contracture.
Analysis of the Corneal Anterior and Posterior Surface in Patients With Peripheral Hypertrophic Subepithelial Corneal Opacification
Authors: Riedl Jana C. , Musayeva Aytan , Wasielica-Poslednik Joanna , Weyer-Elberich Veronika , Pfeiffer Norbert , Gericke Adrian 
Published: April 2019

Purpose:
To characterize the corneal changes in peripheral hypertrophic subepithelial corneal opacification (PHSCO) considering elevation of the anterior and posterior corneal surface, corneal astigmatism, tear secretion, and endothelial cell density.

Methods:
Thirty-eight eyes of 22 patients with PHSCO on at least 1 eye and 38 eyes of 22 age- and sex-matched healthy subjects were included in this retrospective cross-sectional study. Using the Pentacam system (Oculus, Wetzlar, Germany), measurement of the anterior and posterior corneal surface was performed. In addition, the Schirmer test was conducted, and endothelial cells were counted in the central cornea with a specular microscope (SP-3000P; Topcon, Tokyo, Japan).

Results:
The mean age was 55.2±11.7 years in patients with PHSCO and 54.1±12.4 years in healthy subjects. The corneas of patients with PHSCO showed higher corneal astigmatism of both the corneal anterior and posterior surface (2.9/0.5 vs. 0.8/0.3 D, PHSCO vs. controls, P=<0.001/<0.01). The cornea was thickest in the peripheral 12-o'clock position and the peripheral superior nasal area. Remarkably, central endothelial cell density was markedly reduced in patients with PHSCO (2,372.6 cell/mm2±328.1 vs. 2,673 cells/mm2±287.6, P<0.01, PHSCO vs. controls). Also, the Schirmer test revealed lower tear secretion in patients with PHSCO (9.8±4.4 mm vs. 14.3±5.7 mm, P<0.001, PHSCO vs. controls).

Conclusion:
The astigmatism of both the anterior and posterior corneal surface is increased in patients with PHSCO. Intriguingly, tear secretion and central endothelial cell density are reduced in patients with PHSCO. These measurements may become useful to assess the impact of morphological changes on vision and to track disease progression in PHSCO.
Building an Ambulatory Safety Program at an Academic Health System
Authors: Desai Sonali , Fiumara Karen , Kachalia Allen 
Published: April 2019

Background
Patient safety has traditionally focused on the inpatient setting; however, there is an increased awareness of ambulatory safety risk. However, successful strategies and programs to mitigate risk in the ambulatory setting are lacking.

Program
In 2012, we started building a multidisciplinary ambulatory safety program at an academic health system. Our team was composed of clinical, administrative, and patient safety membership. Based on organizational needs, our program initially focused on the following: (1) safety reporting, (2) safety culture measurement, (3) medication safety, and (4) test result management.

What We Did
We were able to develop initiatives around safety reporting, safety culture survey administration, and medication safety and begin to work on test result management. Internal metrics were developed to measure performance and to drive improvement.

Safety Reporting
When evaluating our ambulatory safety reports, we discovered that less than one-third of staff filing safety reports requested feedback. From 2013 to 2018, we tested various strategies to increase the rates of feedback to staff and ultimately found that a decentralized process that was supported by the ambulatory safety program could achieve rates of feedback of 90%.

Safety Culture Measurement
We administered the Agency for Healthcare Research and Quality Medical Office Survey in 2012, 2014, and 2016, achieving a more than 70% response rate across 70 unique ambulatory areas. Data from these surveys were shared with senior hospital leadership, local departmental directors, and managers and ultimately with frontline staff focusing on two key survey areas: communication openness and communication about error.

Medication Safety
From 2012 to 2014, our rates of ambulatory medication reconciliation increased to more than 90% in both primary care and specialty practices in our homegrown electronic medical record system. From 2015 to 2016, rates of ambulatory medication reconciliation in our new vendor-based electronic medical record were 73% as of August 2017.

Conclusions
We were able to build an infrastructure to focus and support ambulatory safety efforts on safety reporting, safety culture change, and medication reconciliation with a team dedicated to ambulatory-focused safety risks and encountered many challenges along the way. Currently, we are expanding our program to concentrate on test result follow-up to prevent missed and delayed diagnosis and medication error reduction.
Effects of an Intensive Discharge Intervention on Medication Adherence, Glycemic Control, and Readmission Rates in Patients With Type 2 Diabetes
Authors: Magny-Normilus Cherlie , Nolido Nyryan V. , Borges Jorge C. , Brady Maureen , Labonville Stephanie , Williams Deborah , Soukup Jane , Lipsitz Stuart , Hudson Margo , Schnipper Jeffrey L. 
Published: April 2019

Objectives
Patients with diabetes are at particularly high risk for adverse outcomes after hospitalization. The goals of this study were to design, implement, and evaluate a multipronged transitional care intervention among hospitalized patients with diabetes.

Methods
We randomly assigned inpatients likely to be discharged home on insulin to an intensive transitional care intervention or usual care. The primary outcome was 90-day postdischarge insulin adherence, using prescription refill information to calculate a medication possession ratio. Unadjusted analyses were conducted using Wilcoxon rank sum; adjusted analyses used multivariable linear regression and weighted propensity scoring methods, with general estimating equations to account for clustering by admitting physician.

Results
One hundred eighty patients participated. The mean (SD) medication possession ratio to all insulin types was 84.5% (22.6) among intervention and 76.4% (25.1) among usual care patients (difference = 8.1, 95% confidence interval = −1.0 to 17.2, P = 0.06), with a smaller difference for adherence to all medications (86.3% versus 82.0%). A1c levels decreased in both groups but was larger in the intervention arm (1.09 and 0.11, respectively) (difference = −0.98, 95% confidence interval = −2.03 to −0.07, P = 0.04). Differences between study arms were not significant for rates of hypoglycemic episodes, 30-day readmissions, or emergency department visits. In adjusted/clustered analyses, the difference in A1c reduction remained statistically significant, whereas differences in all other outcomes remained nonsignificant.

Conclusions
The intervention was associated with improvements in glycemic control, with nonsignificant trends toward greater medication adherence. Further research is needed to optimize and successfully implement interventions to improve patient safety and health outcomes during care transitions.
The Mental Health Trigger Tool: Development and Testing of a Specialized Trigger Tool for Mental Health Settings
Authors: Sajith Sreedharan Geetha , Fung Daniel Shuen Sheng , Chua Hong Choon 
Published: April 2019

Objective
Tools generally used in measuring patient safety incidents in general healthcare settings are not considered suitable for mental health settings. The aim of this study was to develop and evaluate a specialized trigger tool for mental health settings that could detect both traditionally defined adverse events (AEs) and other mental health–related patient safety incidents (MHPSIs).

Methods
We first defined and categorized AEs and MHPSIs based on existing literature and then developed a trigger list, initially consisting of 50 items, which was subsequently reduced to 25 items after a pilot study. We then explored the properties of this final 25-item trigger tool, the Mental Health Trigger Tool (MHTT), through a retrospective review of 515 patient records with a two-stage review process similar to Global Trigger Tool methodology. We used findings of an alternative method of review which consisted of page-to-page reviews of patient records in the analysis of properties of MHTT.

Results
Using the MHTT, at least one AE was identified in 98 patient records (19%) and at least one MHPSI was identified in 58 patient records (11%). The MHTT had a sensitivity of 98.6% and its specificity was 100%. The probability of finding an AE/MHPSI when any trigger was detected in a patient record with MHTT was 33.8% and that of individual triggers ranged from 0% to 100%.

Conclusions
The MHTT may offer an effective, practical, and easy-to-use method in identifying and measuring safety incidents in mental health settings.
A brief history of liquid computers
Authors: Adamatzky Andrew 
Published: April 2019

A substrate does not have to be solid to compute. It is possible to make a computer purely from a liquid. I demonstrate this using a variety of experimental prototypes where a liquid carries signals, actuates mechanical computing devices and hosts chemical reactions. We show hydraulic mathematical machines that compute functions based on mass transfer analogies. I discuss several prototypes of computing devices that employ fluid flows and jets. They are fluid mappers, where the fluid flow explores a geometrically constrained space to find an optimal way around, e.g. the shortest path in a maze, and fluid logic devices where fluid jet streams interact at the junctions of inlets and results of the computation are represented by fluid jets at selected outlets. Fluid mappers and fluidic logic devices compute continuously valued functions albeit discretized. There is also an opportunity to do discrete operation directly by representing information by droplets and liquid marbles (droplets coated by hydrophobic powder). There, computation is implemented at the sites, in time and space, where droplets collide one with another. The liquid computers mentioned above use liquid as signal carrier or actuator: the exact nature of the liquid is not that important. What is inside the liquid becomes crucial when reaction–diffusion liquid-phase computing devices come into play: there, the liquid hosts families of chemical species that interact with each other in a massive-parallel fashion. I shall illustrate a range of computational tasks, including computational geometry, implementable by excitation wave fronts in nonlinear active chemical medium. The overview will enable scientists and engineers to understand how vast is the variety of liquid computers and will inspire them to design their own experimental laboratory prototypes.This article is part of the theme issue ‘Liquid brains, solid brains: How distributed cognitive architectures process information’.
Statistical physics of liquid brains
Authors: Piñero Jordi , Solé Ricard 
Published: April 2019

Liquid neural networks (or ‘liquid brains’) are a widespread class of cognitive living networks characterized by a common feature: the agents (ants or immune cells, for example) move in space. Thus, no fixed, long-term agent-agent connections are maintained, in contrast with standard neural systems. How is this class of systems capable of displaying cognitive abilities, from learning to decision-making? In this paper, the collective dynamics, memory and learning properties of liquid brains is explored under the perspective of statistical physics. Using a comparative approach, we review the generic properties of three large classes of systems, namely: standard neural networks (solid brains), ant colonies and the immune system. It is shown that, despite their intrinsic physical differences, these systems share key properties with standard neural systems in terms of formal descriptions, but strongly depart in other ways. On one hand, the attractors found in liquid brains are not always based on connection weights but instead on population abundances. However, some liquid systems use fluctuations in ways similar to those found in cortical networks, suggesting a relevant role for criticality as a way of rapidly reacting to external signals.This article is part of the theme issue ‘Liquid brains, solid brains: How distributed cognitive architectures process information’.
Modular structure within groups causes information loss but can improve decision accuracy
Authors: Kao Albert B. , Couzin Iain D. 
Published: April 2019

Many animal groups exhibit signatures of persistent internal modular structure, whereby individuals consistently interact with certain groupmates more than others. In such groups, information relevant to a collective decision may spread unevenly through the group, but how this impacts the quality of the resulting decision is not well understood. Here, we explicitly model modularity within animal groups and examine how it affects the amount of information represented in collective decisions, as well as the accuracy of those decisions. We find that modular structure necessarily causes a loss of information, effectively silencing the input from a fraction of the group. However, the effect of this information loss on collective accuracy depends on the informational environment in which the decision is made. In simple environments, the information loss is detrimental to collective accuracy. By contrast, in complex environments, modularity tends to improve accuracy. This is because small group sizes typically maximize collective accuracy in such environments, and modular structure allows a large group to behave like a smaller group (in terms of its decision-making). These results suggest that in naturalistic environments containing correlated information, large animal groups may be able to exploit modular structure to improve decision accuracy while retaining other benefits of large group size.This article is part of the theme issue ‘Liquid brains, solid brains: How distributed cognitive architectures process information’.
The computational stance in biology
Authors: Wood C. C. 
Published: April 2019

The goal of this article is to call attention to, and to express caution about, the extensive use of computation as an explanatory concept in contemporary biology. Inspired by Dennett's ‘intentional stance’ in the philosophy of mind, I suggest that a ‘computational stance’ can be a productive approach to evaluating the value of computational concepts in biology. Such an approach allows the value of computational ideas to be assessed without being diverted by arguments about whether a particular biological system is ‘actually computing’ or not. Because there is sufficient difference of agreement among computer scientists about the essential elements that constitute computation, any doctrinaire position about the application of computational ideas seems misguided. Closely related to the concept of computation is the concept of information processing. Indeed, some influential computer scientists contend that there is no fundamental difference between the two concepts. I will argue that despite the lack of widely accepted, general definitions of information processing and computation: (1) information processing and computation are not fully equivalent and there is value in maintaining a distinction between them and (2) that such value is particularly evident in applications of information processing and computation to biology.This article is part of the theme issue ‘Liquid brains, solid brains: How distributed cognitive architectures process information’.
The brain: a concept in flux
Authors: Pagán Oné R. 
Published: April 2019

One of the most important aspects of the scientific endeavour is the definition of specific concepts as precisely as possible. However, it is also important not to lose sight of two facts: (i) we divide the study of nature into manageable parts in order to better understand it owing to our limited cognitive capacities and (ii) definitions are inherently arbitrary and heavily influenced by cultural norms, language, the current political climate, and even personal preferences, among many other factors. As a consequence of these facts, clear-cut definitions, despite their evident importance, are oftentimes quite difficult to formulate. One of the most illustrative examples about the difficulty of articulating precise scientific definitions is trying to define the concept of a brain. Even though the current thinking about the brain is beginning to take into account a variety of organisms, a vertebrocentric bias still tends to dominate the scientific discourse about this concept. Here I will briefly explore the evolution of our ‘thoughts about the brain’, highlighting the difficulty of constructing a universally (or even a generally) accepted formal definition of it and using planarians as one of the earliest examples of organisms proposed to possess a ‘traditional’, vertebrate-style brain. I also suggest that the time is right to attempt to expand our view of what a brain is, going beyond exclusively structural and taxa-specific criteria. Thus, I propose a classification that could represent a starting point in an effort to expand our current definitions of the brain, hopefully to help initiate conversations leading to changes of perspective on how we think about this concept.
This article is part of the theme issue ‘Liquid brains, solid brains: How distributed cognitive architectures process information’.
Regulatory network-based imputation of dropouts in single-cell RNA sequencing data
Authors: Leote Ana Carolina , Wu Xiaohui , Beyer Andreas 
Published: April 2019

AbstractSingle-cell RNA sequencing (scRNA-seq) methods are typically unable to quantify the expression levels of all genes in a cell, creating a need for the computational prediction of missing values (‘dropout imputation’). Most existing dropout imputation methods are limited in the sense that they exclusively use the scRNA-seq dataset at hand and do not exploit external gene-gene relationship information. Further, it is unknown if all genes equally benefit from imputation or which imputation method works best for a given gene.Here, we show that a transcriptional regulatory network learned from external, independent gene expression data improves dropout imputation. Using a variety of human scRNA-seq datasets we demonstrate that our network-based approach outperforms published state-of-the-art methods. The network-based approach performs particularly well for lowly expressed genes, including cell-type-specific transcriptional regulators. Further, the cell-to-cell variation of 12.6% to 48.2% of the genes could not be adequately imputed by any of the methods that we tested. In those cases gene expression levels were best predicted by the mean expression across all cells, i.e. assuming no measurable expression variation between cells. These findings suggest that different imputation methods are optimal for different genes. We thus implemented an R-package called ADImpute (available via Bioconductor https://bioconductor.org/packages/release/bioc/html/ADImpute.html) that automatically determines the best imputation method for each gene in a dataset.Our work represents a paradigm shift by demonstrating that there is no single best imputation method. Instead, we propose that imputation should maximally exploit external information and be adapted to gene-specific features, such as expression level and expression variation across cells.Author summarySingle-cell RNA-sequencing (scRNA-seq) allows for gene expression to be quantified in individual cells and thus plays a critical role in revealing differences between cells within tissues and characterizing them in healthy and pathological conditions. Because scRNA-seq captures the RNA content of individual cells, lowly expressed genes, for which few RNA molecules are present in the cell, are easily missed. These events are called ‘dropouts’ and considerably hinder analysis of the resulting data. In this work, we propose to make use of gene-gene relationships, learnt from external and more complete datasets, to estimate the true expression of genes that could not be quantified in a given cell. We show that this approach generally outperforms previously published methods, but also that different genes are better estimated with different methods. To allow the community to use our proposed method and combine it with existing ones, we created the R package ADImpute, available through Bioconductor.
Synaptic weight decay with selective consolidation enables fast learning without catastrophic forgetting
Authors: Leimer Pascal , Herzog Michael , Senn Walter 
Published: April 2019

AbstractLearning can interfere with pre-existing memories that in classical neural networks may lead to catastrophic forgetting. Different from these networks, biological synapses show an early decay of long-term potentiation, combined with a tag & capture mechanism for selective consolidation. We present a 2-component synaptic plasticity model that, by an early decay and a tag & capture mechanism, enables context-dependent fast learning without catastrophic forgetting. For reinforcement learning in multiple contexts, the fast learning combined with an early weight decay maximizes the expected reward while minimizing interferences between subsequent contexts. Fast learning, enabled by a highly plastic weight component, improves performance for a given context. Between contexts this plastic component decays to prevent interference, but selective consolidation into a stable component protects old memories. As a downside of these mechanisms, learning is hampered when consolidation is triggered prematurely by interleaving easy and difficult tasks, consistent with human psychophysical experiments.