bluuebunny/crossref_metadata_2025_split huggingface dataset:
SWEPT-SOURCE OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY IN CHOROIDAL MELANOMA
Authors: Pellegrini Marco , Corvi Federico , Invernizzi Alessandro , Ravera Vittoria , Cereda Matteo G. , Staurenghi Giovanni 
Published: June 2018

Purpose:
To describe the imaging features of choroidal melanoma using swept-source optical coherence tomography angiography (SS-OCT-A) and to evaluate its ability to display tumor intrinsic vasculature.

Methods:
Consecutive patients diagnosed with choroidal melanoma underwent a complete ophthalmic evaluation, including best-corrected visual acuity, color fundus photography, B-scan ultrasound, fluorescein angiography, indocyanine green angiography, and SS-OCT-A (PLEX Elite 9000; Carl Zeiss Meditec, Inc, Dublin, CA).

Results:
Twenty-two eyes of 22 consecutive patients were included in the study; 11 cases (50%) were treatment naive. Three lesions (14%) were located at the macula, 14 (63%) between the macula and equator, and 5 (23%) between the equator and the ora serrata. The mean tumor base and thickness were, respectively, 10.3 mm (range 5–15 mm) and 4.3 mm (range 1.5–8.9 mm). Seventeen lesions (77%) were dome shaped, whereas 5 (23%) had a mushroom configuration. Thirteen lesions (59%) were pigmented, 5 (23%) partially pigmented, and 4 (18%) amelanotic. An exudative retinal detachment was documented in 13 eyes (59%). Fluorescein angiography and indocyanine green angiography were performed in 20 patients and disclosed intrinsic microvasculature of the tumor, respectively, in 4 (20%) and 20 (100%) cases. Swept-source optical coherence tomography angiography was performed in 22 eyes and detected microvasculature of choroidal melanoma in all cases. Specifically, intrinsic vasculature could be recognized in 14 eyes (64%) using the automated choroid segmentation, 16 eyes (73%) using the automated whole eye segmentation, and in 22 eyes (100%) with fine manual adjustments of segmentation lines.

Conclusion:
Swept-source optical coherence tomography angiography represents a valid imaging technique to evaluate patients affected by choroidal melanomas. In our series, SS-OCT-A disclosed the intrinsic microvasculature of the tumor in all cases despite their size, location, and history of previous treatments.
INFLUENCE OF INVERTED INTERNAL LIMITING MEMBRANE FLAP TECHNIQUE ON THE OUTER RETINAL LAYER STRUCTURES AFTER A LARGE MACULAR HOLE SURGERY
Authors: Iwasaki Masanori , Kinoshita Takamasa , Miyamoto Hirotomo , Imaizumi Hiroko 
Published: June 2018

Purpose:
To examine the influence of inverted internal limiting membrane (ILM) flap technique on the outer retinal layer structures after macular hole (MH) surgery.

Methods:
We included 24 eyes of 24 patients who underwent vitrectomy for large MHs (≥400 µm) with successful MH closure and observed for at least 6 months. Fourteen eyes were treated with inverted ILM flap technique (inverted group) and 10 with conventional ILM peeling (ILMP group). We evaluated the postoperative recovery rate of the external limiting membrane and ellipsoid zone and the best-corrected visual acuity.

Results:
The postoperative recovery rates of the external limiting membrane and ellipsoid zone in the inverted group were lower than those in the ILMP group (21.4 vs. 70.0%, P = 0.024; 0 vs. 30.0, P = 0.059, respectively). The external limiting membrane recovery period in the inverted group was significantly longer than that in the ILMP group (11.0 ± 1.7 vs. 3.4 ± 2.8 months, P = 0.015). The best-corrected visual acuity change (letters) in the inverted group was significantly smaller than that in the ILMP group (9.0 vs. 22.5, P = 0.040).

Conclusion:
The poorer anatomical and visual results associated with inverted ILM flap compared with ILM peeling suggest the limitation of the ILM flap technique to repair refractory MHs.
“I Got My Diagnosis on a Yellow Post-it Note”
Authors: Hauken May Aasebø , Hølge-Hazelton Bibbi , Larsen Torill M.B. 
Published: June 2018

Background
Cancer in young adults is rare but young adult cancer patients (YACPs) are at an increased risk of severe physical and psychosocial impairments during cancer treatment and survivorship. However, little is known about the onset of this process.

Objective
The aim of this study was to explore how young cancer survivors experience the process of being diagnosed with cancer.

Methods
A qualitative method founded on a phenomenological-hermeneutical approach was used and included in-depth interviews with 20 young adult survivors (aged 24–35 years) with different cancer diagnoses, analyzed by Systematic Text Condensation.

Results
The participants' experiences of the diagnosis process were elaborated according to 3 main themes: (1) “I felt something was wrong, but…,” (2) “The traumatic uncertainty,” and (3) “The day my world collapsed.”

Conclusion
The findings indicate that the YACPs experienced a diagnosis of cancer as a highly traumatic and long-lasting process, characterized by lack of information and uncertainty. The findings indicate that healthcare professionals do not acknowledge the vulnerable phase of life and transitional challenges of YACPs.

Implications for Clinical Practice
The findings highlight the need to raise awareness of cancer in young adulthood in the public and in the primary healthcare system, to shorten the diagnosis process, and to clarify responsibility for age-related information and psychosocial follow-up during the diagnosis process. Further research is highly warranted.
Medication Errors in the Context of Hematopoietic Stem Cell Transplantation
Authors: Pereira Lermontov Simone , Carreiro Brasil Simone , Rezende de Carvalho Magali 
Published: June 2018

Background
There have been numerous efforts by health institutions and professionals to prevent and reduce medication errors.

Objectives
The aim of this study was to identify in the literature the incidence, related factors, consequences, and prevention mechanisms of medication errors in the context of hematopoietic stem cell transplantation.

Methods
This is a systematic review carried out in the databases LILACS, PubMed, PMC, EMBASE, and CINAHL databases, from January 11 to 13, 2017.

Results
Eleven studies were included in this review and presented in 4 categories of analysis. (1) occurrence—most of the medication errors were related to administration and prescription; (2) related factors—multicausal, highlighting issues including polypharmacy, lack of double checking, and similarity between the medications' names; (3) consequences—the main ones were associated with adverse reactions, with prolonged hospitalization time as outcome; (4) preventive measures—related to safe practices in pharmacotherapy, such as double check and application of the 10 rights of medication administration.

Conclusion
Medication administration is an activity of great responsibility for nursing; however, in order to achieve a decrease in medication errors, prevention strategies are necessary for the whole health team.

Implications for Practice
Practice improvements are needed including establishing institutional drug administration protocols and keeping them updated, using a computerized prescription system, and promoting patient safety with staff.
Interplay between coding and exonic splicing regulatory sequences
Authors: Fontrodona Nicolas , Aubé Fabien , Claude Jean-Baptiste , Polvèche Hélène , Lemaire Sébastien , Tranchevent Léon Charles , Modolo Laurent , Mortreux Franck , Bourgeois Cyril F. , Auboeuf Didier 
Published: June 2018

AbstractThe inclusion of exons during the splicing process depends on the binding of splicing factors to short low-complexity regulatory sequences. The relationship between exonic splicing regulatory sequences and coding sequences is still poorly understood. We demonstrate that exons that are coregulated by any splicing factor share a similar nucleotide composition bias. We next demonstrate that coregulated exons preferentially code for amino acids with similar physicochemical properties because of the non-randomness properties of the genetic code. Indeed, amino acids sharing physicochemical properties correspond to codons that have the same nucleotide composition bias. These observations reveal an unanticipated bidirectional interplay between the physicochemical features encoded by exons and exon splicing regulation by splicing factors. We propose that the splicing regulation of an exon by a splicing factor is tightly interconnected with the physicochemical properties of the exon-encoded protein domain depending on the splicing-factor affinity for specific nucleotides.
The Expression of Inflammatory Genes in 3T3-L1 Adipocytes Exhibits a Memory to Stimulation by Macrophage Secretions
Authors: Hancock Dale , Meng Luxi , Holliday Mira , Yuwono Nicole , Zhang Ning , Denyer Gareth 
Published: June 2018

ABSTRACTObesity is characterized by increased output of inflammatory compounds from adipose tissue. Whilst the relative contribution of adipocytes and resident macrophages to this phenomenon is debated, there is no doubt that the secretions of each cell type can stimulate the expression of inflammatory genes in the other. We hypothesized that mechanisms must exist to prevent an escalating positive feedback loop between the two cell types, so that after an initial exposure to macrophage secretions, adipocytes would become desensitized to subsequent inflammatory stimulation.We used microarrays to investigate the response of 3T3-L1 adipocytes to macrophage secretions (macrophage conditioned medium, MCM). MCM caused a rapid (<4 hours) and high amplitude (over 100-fold) rise in the expression of several inflammatory genes. For some genes, generally cytokines, expression returned to basal levels within 24 h following removal of the MCM, but other transcripts, notably those for acute phase proteins and extracellular matrix remodeling proteins, remained highly expressed even during the washout period.Unexpectedly, some cytokine genes (e.g., iNOS, IL-6) showed an enhanced expression to a second exposure of MCM, illustrating that the transcriptome response of 3T3-L1 adipocytes retains a memory to the first stimulus. We characterized the parameters that give rise to the memory phenomenon, finding that additional stimuli do not augment or abrogate the effect. The memory is preserved for several days after the initial exposure and it is not due to a change in sensitivity to the MCM but, rather, a change in the capacity of the signal-target system. The possible mechanisms of the memory are discussed, along with the physiological ramifications should the phenomenon be replicatedin vivo.
Searching for a UV-filter in the eyes of high flying birds
Authors: Zawadzka Malgorzata , Ràcz Beatrix , Ambrosini Dario , Gørbitz Carl Henrik , Morth Jens Preben , Wilkins Alistair L. , Østeby Anja , Elgstøen Katja Benedikte Prestø , Lundanes Elsa , Rise Frode , Ringvold Amund , Wilson Steven Ray 
Published: June 2018

AbstractAn ultraviolet (UV)-absorbing compound of unknown identity is present in the aqueous humor of geese and other birds flying at high altitudes. A goose aqueous humor extract, that was believed to contain the UV protective compound which was designated as “compound X”, was fractionated and examined using a variety of spectroscopic techniques including LC-MS and high field one- and two dimensional-NMR methods. A series of compounds were identified but none of them appeared to be the UV protective “compound X”. It may be that the level of the UV protective compound in goose aqueous humor is much less than the compounds identified in our investigation, or it may have been degraded by the isolation and chromatographic purification protocols used in our investigations.
Dynamic Modeling and Stochastic Simulation of Metabolic Networks
Authors: Clement Emalie J. , Soliman Ghada A. , Wysocki Beata J. , Davis Paul H. , Wysocki Tadeusz A. 
Published: June 2018

AbstractIncreased technological methods have enabled the investigation of biology at nanoscale levels. Nevertheless, such systems necessitate the use of computational methods to comprehend the complex interactions occurring. Traditionally, dynamics of metabolic systems are described by ordinary differential equations producing a deterministic result which neglects the intrinsic heterogeneity of biological systems. More recently, stochastic modeling approaches have gained popularity with the capacity to provide more realistic outcomes. Yet, solving stochastic algorithms tend to be computationally intensive processes. Employing the queueing theory, an approach commonly used to evaluate telecommunication networks, reduces the computational power required to generate simulated results, while simultaneously reducing expansion of errors inherent to classical deterministic approaches. Herein, we present the application of queueing theory to efficiently simulate stochastic metabolic networks. For the current model, we utilize glycolysis to demonstrate the power of the proposed modeling methods, and we describe simulation and pharmacological inhibition in glycolysis to further exemplify modeling capabilities.Author SummaryComputational biology is increasingly used to understand biological occurances and complex dynamics. Biological modeling, in general, aims to represent a biological system with computational approaches, as realistically and accurate as current methods allow. Metabolomics and metabolic systems have emerged as an important aspect of cellular biology, allowing a more sentive view for understanding the complex interactions occurring intracellularly as a result of normal or perturbed (or diseased) states. To understand metabolic changes, many researchers have commonly used Ordianary Differential Equations to produce in silico models of the in vitro system of interest. While these have been beneficial to date, continuing to advance computational methods of analyzing such systems is of interest. Stochastic models that include randomness have been known to produce more reaslistic results, yet the difficulty and intesive time component urges additional methods and techniques to be developed. In the present research, we propose using queueing networks as a technique to model complex metabolic systems, doing such with a model of glycolysis, a core metabolic pathway.
Single haplotype admixture models using large scale HLA genotype frequencies to reproduce human admixture
Authors: Simanovsky Alexandra Litinsky , Madbouly Abeer , Halagan Michael , Maiers Martin , Louzoun Yoram 
Published: June 2018

AbstractThe Human Leukocyte Antigen (HLA) is the most polymorphic region in humans. Anthropologists use HLA to trace populations’ migration and evolution. However, recent admixture between populations masks the ancestral haplotype frequency distribution.We present an HLA-based method based on high-resolution HLA haplotype frequencies to resolve population admixture using a non-negative matrix factorization formalism and validated using haplotype frequencies from 56 populations. The result is a minimal set of original populations decoding roughly 90% of the total variance in the studied admixtures. These original populations agree with the geographical distribution, phylogenies and recent admixture events of the studied groups.With the growing population of multi-ethnic individuals, the matching process for stem-cell and solid organ transplants is becoming more challenging. The presented algorithm provides a framework that facilitates the breakdown of highly admixed populations into original groups, which can be used to better match the rapidly growing population of multi-ethnic individuals worldwide.Author SummaryHuman Leukocyte Antigen (HLA) is known to be the most polymorphic region in the human genome. Anthropologists frequently use HLA to trace migration and evolution of different populations. This is due to the high linkage among HLA genes leading to the transmission of intact haplotypes from parents to offspring, hence preserving key population ancestral features.We developed a new HLA-based method to identify admixture models in mixed populations using high-resolution HLA haplotype frequencies. Our results highlight that a single highly polymorphic locus can contain enough information to map clearly human admixture and the population genetics of the different human populations, and reproduces results based on SNP arrays.The presented algorithm is validated using haplotype frequencies sampled from 56 worldwide populations. Under such factorization we demonstrate that 90% of the variance in these populations can be explained using a much-reduced set of 8 ethnic groups. We demonstrate that the estimated ethnic groups and admixture models agree with the geographical distribution, population phylogenies and recent historic admixture events of the studied populations.
BO-LSTM: Classifying relations via long short-term memory networks along biomedical ontologies
Authors: Lamurias Andre , Clarke Luka A. , Couto Francisco M. 
Published: June 2018

AbstractRecent studies have proposed deep learning techniques, namely recurrent neural networks, to improve biomedical text mining tasks. However, these techniques rarely take advantage of existing domain-specific resources, such as ontologies. In Life and Health Sciences there is a vast and valuable set of such resources publicly available, which are continuously being updated. Biomedical ontologies are nowadays a mainstream approach to formalize existing knowledge about entities, such as genes, chemicals, phenotypes, and disorders. These resources contain supplementary information that may not be yet encoded in training data, particularly in domains with limited labeled data.We propose a new model, BO-LSTM, that takes advantage of domain-specific ontologies, by representing each entity as the sequence of its ancestors in the ontology. We implemented BO-LSTM as a recurrent neural network with long short-term memory units and using an open biomedical ontology, which in our case-study was Chemical Entities of Biological Interest (ChEBI). We assessed the performance of BO-LSTM on detecting and classifying drug-drug interactions in a publicly available corpus from an international challenge, composed of 792 drug descriptions and 233 scientific abstracts. By using the domain-specific ontology in addition to word embeddings and WordNet, BO-LSTM improved both the F1-score of the detection and classification of drug-drug interactions, particularly in a document set with a limited number of annotations. Our findings demonstrate that besides the high performance of current deep learning techniques, domain-specific ontologies can still be useful to mitigate the lack of labeled data.Author summaryA high quantity of biomedical information is only available in documents such as scientific articles and patents. Due to the rate at which new documents are produced, we need automatic methods to extract useful information from them. Text mining is a subfield of information retrieval which aims at extracting relevant information from text. Scientific literature is a challenge to text mining because of the complexity and specificity of the topics approached. In recent years, deep learning has obtained promising results in various text mining tasks by exploring large datasets. On the other hand, ontologies provide a detailed and sound representation of a domain and have been developed to diverse biomedical domains. We propose a model that combines deep learning algorithms with biomedical ontologies to identify relations between concepts in text. We demonstrate the potential of this model to extract drug-drug interactions from abstracts and drug descriptions. This model can be applied to other biomedical domains using an annotated corpus of documents and an ontology related to that domain to train a new classifier.
Coupled Mixed Model for Joint Genetic Analysis of Complex Disorders with Two Independently Collected Data Sets
Authors: Wang Haohan , Pei Fen , Vanyukov Michael M. , Bahar Ivet , Wu Wei , Xing Eric P. 
Published: June 2018

AbstractIn the last decade, Genome-wide Association studies (GWASs) have contributed to decoding the human genome by uncovering many genetic variations associated with various diseases. Many follow-up investigations involvejoint analysisof multiple independently generated GWAS data sets. While most of the computational approaches developed for joint analysis are based on summary statistics, the joint analysis based on individual-level data with consideration of confounding factors remains to be a challenge. In this study, we propose a method, called Coupled Mixed Model (CMM), that enables a joint GWAS analysis on two independently collected sets of GWAS data with different phenotypes. The CMM method does not require the data sets to have the same phenotypes as it aims to infer the unknown phenotypes using a set of multivariate sparse mixed models. Moreover, CMM addresses the confounding variables due to population stratication, family structures, and cryptic relatedness, as well as those arising during data collection such as batch effects that frequently appear in joint genetic studies. We evaluate the performance of CMM using simulation experiments. In real data analysis, we illustrate the utility of CMM by an application to evaluating common genetic associations for Alzheimers disease and substance use disorder using datasets independently collected for the two complex human disorders. Comparison of the results with those from previous experiments and analyses supports the utility of our method and provides new insights into the diseases.The software is available athttps://github.com/HaohanWang/CMM
FMRFa receptor stimulated Ca2+signals alter the activity of flight modulating central dopaminergic neurons inDrosophila melanogaster
Authors: Ravi Preethi , Trivedi Deepti , Hasan Gaiti 
Published: June 2018

AbstractNeuropeptide signaling influences animal behavior by modulating neuronal activity and thus altering circuit dynamics. Insect flight is a key innate behavior that very likely requires robust neuromodulation. Cellular and molecular components that help modulate flight behavior are therefore of interest and require investigation. In a genetic RNAi screen for G-protein coupled receptors that regulate flight bout durations, we earlier identified several receptors, including the receptor for the neuropeptide FMRFa (FMRFaR). To further investigate modulation of insect flight by FMRFa we generated CRISPR-Cas9 mutants in the gene encoding theDrosophilaFMRFaR. The mutants exhibit significant flight deficits with a focus in dopaminergic cells. Expression of a receptor specific RNAi in adult central dopaminergic neurons resulted in progressive loss of sustained flight. Further, genetic and cellular assays demonstrated that FMRFaR stimulates intracellular calcium signaling through the IP3R and helps maintain neuronal excitability in a subset of dopaminergic neurons for positive modulation of flight bout durations.Author summaryNeuropeptides play an important role in modulating neuronal properties such as excitability and synaptic strength and thereby influence innate behavioral outputs. In flying insects, neuromodulation of flight has been primarily attributed to monoamines. In this study, we have used the genetically amenable fruit fly,Drosophila melanogasterto identify a neuropeptide receptor that is required in adults to modulate flight behavior. We show from both knockdown and knockout studies that the neuropeptide receptor,FMRFaR, present on a few central dopaminergic neurons, modulates the duration of flight bouts. Overexpression of putative downstream molecules, the IP3R, an intracellular Ca2+-release channel, and CaMKII, a protein kinase, significantly rescue the flight deficits induced by knockdown of theFMRFaR. Our data support the idea that FMRFaR and CaMKII help maintain optimal membrane excitability of adult dopaminergic neurons required to sustain longer durations of flight bouts. We speculate that the ability to maintain longer flight bouts in natural conditions enhances the individual’s capacity to search and reach food sources as well as find sites suitable for egg laying.