bluuebunny/crossref_metadata_2025_split huggingface dataset:
Clinical features and prognostic factors of systemic sclerosis in Guangxi, China: Retrospective, single‐center study of long‐term survival in 470 patients
Authors: Chen Juan , Yang Chenxi , Pan Jie , Zhao Cheng , Chen Zhanrui , Wen Jing , Dong Fei , Liao Xiaoling , Lei Ling 
Published: December 2021

AbstractObjectiveSystemic sclerosis (SSc) is an autoimmune disease the prevalence of which varies among populations. We analyzed SSc patients from Guangxi to improve the clinical understanding of this disease.MethodsData of 470 SSc patients admitted to our institution from October 1,2012 to January 1,2019 were examined. The characteristics of these patients were analyzed using Kaplan‐Meier survival analysis. Cox proportional‐hazard regression was used to identify prognostic factors.ResultsThe average age was 50.44 ± 12.31 years, 285 patients (60.6%) were women, 2.1% had pneumoconiosis, 58.2% had pulmonary interstitial disease (ILD), 18.7% had pulmonary hypertension (PH), and 3.6% had renal crisis. These patients had diffuse cutaneous systemic sclerosis (dcSSc, 70.2%) or limited cutaneous systemic sclerosis (29.7%), and PH and renal crisis were more common in the dcSSc group. Patients 50 years old or more had greater prevalences of ILD, PH, and musculoskeletal damage, greater positivity of laboratory biomarkers, and increased mortality (all P < .05). Seventy‐four patients (15.7%) died. The non‐survivors were older, had longer disease duration, had higher prevalences of ILD, restrictive ventilation dysfunction, PH, and renal crisis, and had higher levels of creatine kinase myocardial band (CK‐MB), C‐reactive protein, and immunoglobin A (all P < .05). Renal crisis, PH, and high CK‐MB were independent risk factors for death.ConclusionsPneumoconiosis was more common in SSc patients than the general population from this region. Our patients had a 10‐year cumulative survival rate of 74.9%, higher than reported for patients from the US. Renal crisis, PH, and high CK‐MB level were independent risk factors for death.
Deep learning and satellite imagery predict genetic diversity and differentiation
Authors: Kittlein Marcelo J. , Mora Matías S. , Mapelli Fernando J. , Austrich Ailín , Gaggiotti Oscar E. 
Published: December 2021

Abstract

During the last decade, convolutional neural networks (CNNs) have revolutionised the application of deep learning (DL) methods to classification tasks and object recognition. These procedures can capture key features of image data that are not easily visible to the human eye and use them to classify and predict outcomes with exceptional precision.

Here, we show for the first time that CNNs provide highly accurate predictions for small‐scale genetic differentiation and diversity in Ctenomys australis, a subterranean rodent from central Argentina. Using microsatellite genotypes and high‐resolution satellite imagery, we trained a simple CNN to predict local FST and mean allele richness. To identify landscape features with high impact on predicted values, we applied species distribution models to obtain the distribution of suitable habitat. Subsequent use of a machine learning algorithm (random forest) allowed us to identify the attributes that contribute the most to predictions of population genetic metrics.

Predictions obtained from the CNN accounted for more than 98% of the variation observed both in FST and mean allele richness values. Random forest regression on landscape metrics indicated that features involving connectivity and consistent prevalence of suitable habitat promoted genetic diversity and reduced genetic differentiation in C. australis.

Validation with synthetic data via simulations of genetic differentiation based on the landscape structure of the study area and of a nearby area showed that DL models are able to capture complex relationships between actual data and synthetic data in the same landscape and between synthetic data generated under different landscapes.

Our approach represents an objective and powerful approach to landscape genetics because it can extract information from patterns that are not easily identified by humans. Spatial predictions from the CNN may assist in the identification of areas of interest for biodiversity conservation and management of populations.
Combining laser rangefinder and viewshed technologies to improve ground surveys of invasive tree distributions
Authors: Wyse Sarah V. , Hulme Philip E. , Etherington Thomas R. 
Published: December 2021

AbstractQuantifying the spatial extent, location and habitat associations of invasive tree species is critical to predict their future spread and prioritise areas for management. Species–environment relationship analyses are useful tools for understanding and predicting the potential geographical distribution of these species; however, such tools require rigorous and extensive data about species presence and, crucially, the area surveyed.Here, we describe a method for performing ground‐based visual surveys of invasive trees from a set of viewpoints that utilises laser rangefinder and global navigation satellite system (GNSS) technology to detect tree locations. We then highlight the novel use of geographical information system (GIS) viewsheds as a tool to define the area surveyed.Using the invasive conifer,Pinus radiata, as our target, we undertook a ground‐truthing exercise for 50 trees established in the wild to assess the accuracy of the method and determine the suitable spatial resolution for GIS data that would be used in subsequent species–environment relationship analyses. For these trees, location error was positively related to distance from the tree to the viewpoint. The calculated locations for all trees within 600 m of the observer were within 25 m of the location as determined by the GNSS unit, with a median location error of 4 m. These results indicate that data of a resolution suitable for mapping invasive trees can be efficiently collected over large areas. We also outline suggestions and instructions for computing the viewsheds to determine the surveyed area.This approach allows for efficient collection of accurate data on the occurrence of non‐native trees and the land area surveyed. These data can underpin species–environment relationship analyses that then form the basis of risk maps for areas prone to future invasion. Given the speed and accuracy with which data can be obtained using this method, and the use of standard and easily accessible field equipment and GIS software, we recommend this approach to other spatial and invasion ecologists.
hsa_circ_0003410 promotes hepatocellular carcinoma progression by increasing the ratio of M2/M1 macrophages through the miR‐139‐3p/CCL5 axis
Authors: Cao Pei , Ma Bo , Sun Ding , Zhang Weigang , Qiu Junyi , Qin Lei , Xue Xiaofeng 
Published: December 2021

AbstractNoncoding RNAs have been verified to regulate the infiltration of macrophages to accelerate tumor biological progression, however the regulation of macrophages by circular RNAs in hepatocellular carcinoma (HCC) remains unresolved. Using high‐throughput RNA sequencing, we demonstrated that hsa_circ_0003410 was clearly upregulated in HCC. 5‐Ethynyl‐2′‐deoxyuridine and transwell assays showed that hsa_circ_0003410 facilitated the proliferation and migration of HCC cells in vitro. We knocked down the expression of hsa_circ_0003410 in HepG2 cells and performed next‐generation sequencing to determine possible target genes of hsa_circ_0003410. Kyoto Encyclopedia of Genes and Genomes analysis revealed that different genes were mainly enriched in immune‐related pathways. Mechanistically, we identified CCL5 as the target gene of hsa_circ_0003410. RNA‐FISH showed the co‐expression of hsa_circ_0003410 and CCL5. Western blot and ELISA also verified that hsa_circ_0003410 could upregulate the expression of CCL5 protein. Flow cytometry and immunofluorescence assays indicated that CCL5 activated and recruited M2 macrophages and increased the ratio of M2/M1 macrophages to promote the progression of HCC. Animal experiments in vitro also confirmed our results. Taken together, our experiments revealed that noncoding RNAs play a critical role in the HCC microenvironment and can be considered as markers for the diagnosis and prognosis of HCC.
Myelodysplasia cutis as the presenting sign of chronic myelomonocytic leukaemia
Authors: Maglie Roberto , Senatore Stefano , Di Stefano Gioia , Barzacchi Marta , Maio Vincenza , Montefusco Francesca , Baffa Maria Efenesia , Bianchi Beatrice , Santucci Marco , Antiga Emiliano 
Published: December 2021

Abstract
We describe an unusual case of myelodysplasia cutis in a patient with chronic myelomonocytic leukaemia.
Ketogenic diet ameliorates cognitive impairment and neuroinflammation in a mouse model of Alzheimer’s disease
Authors: Xu Yunlong , Jiang Chenyu , Wu Junyan , Liu Peidong , Deng Xiaofei , Zhang Yadong , Peng Bo , Zhu Yingjie 
Published: December 2021

AbstractIntroductionAlzheimer's disease (AD) is the most common neurodegenerative disorder that causes dementia and affects millions of people worldwide. Although it has devastating outcomes for patients and tremendous economic costs to society, there is currently no effective treatment available.AimsThe high‐fat, low‐carbohydrate ketogenic diet (KD) is an established treatment for refractory epilepsy with a proven efficacy. Although the considerable interest has emerged in recent years for applying KD in AD patients, only few interventional studies in animals and humans have addressed the effects of KD on cognitive impairments, and the results were inconclusive. The aim of this study was to explore the impact of KD on cognitive functions and AD pathology in 5XFAD mice—a validated animal model of AD.ResultsFour months of a ketogenic diet improved spatial learning, spatial memory and working memory in 5XFAD mice. The improvement in cognitive functions was associated with a restored number of neurons and synapses in both the hippocampus and the cortex. Ketogenic diet treatment also reduced amyloid plaque deposition and microglial activation, resulting in reduced neuroinflammation. The positive effect of ketogenic diet on cognitive functions depended on the starting time and the duration of the diet. A shorter period (2 months) of ketogenic diet treatment had a weaker effect. Ketogenic diet initiated at late stage of AD (9 months of age) displayed no effect on cognitive improvement.ConclusionsThese findings indicate positive effects of ketogenic diet on both cognitive function and histopathology in Alzheimer's disease, which could be due to reduced microglial activation and neuroinflammation. Our findings provide new insights and therapeutic interventions for the treatment of Alzheimer's disease.
Children's attention to numerical quantities relates to verbal number knowledge: An introduction to the Build‐A‐Train task
Authors: Sokolowski H. Moriah , Merkley Rebecca , Kingissepp Sarah Samantha Bray , Vaikuntharajan Praja , Ansari Daniel 
Published: December 2021

AbstractWhich dimension of a set of objects is more salient to young children: number or size? The ‘Build‐A‐Train’ task was developed and used to examine whether children spontaneously use a number or physical size approach on an un‐cued matching task. In the Build‐A‐Train task, an experimenter assembles a train using one to five blocks of a particular length and asks the child to build the same train. The child's blocks differ in length from the experimenter's blocks, causing the child to build a train that matches based on either the number of blocks or length of the train, as it is not possible to match on both. One hundred and nineteen children between 2 years 2 months and 6 years 0 months of age (M = 4.05, SD = 0.84) completed the Build‐A‐Train task, and the Give‐a‐Number task, a classic task used to assess children's conceptual knowledge of verbal number words. Across train lengths and verbal number knowledge levels, children used a number approach more than a size approach on the Build‐A‐Train task. However, children were especially likely to use a number approach over a size approach when they knew the verbal number word that corresponded to the quantity of blocks in the train, particularly for quantities smaller than four. Therefore, children's attention to number relates to their knowledge of verbal number words. The Build‐A‐Train task and findings from the current study set a foundation for future longitudinal research to investigate the causal relationship between children's acquisition of symbolic mathematical concepts and attention to number.
The advantages of physical exercise as a preventive strategy against NAFLD in postmenopausal women
Authors: Molina‐Molina Emilio , Furtado Guilherme Eustaquio. , Jones John G. , Portincasa Piero , Vieira‐Pedrosa Ana , Teixeira Ana Maria , Barros Marcelo Paes , Bachi André Luís Lacerda , Sardão Vilma A. 
Published: December 2021

AbstractBackgroundThe prevalence and severity of nonalcoholic fatty liver disease (NAFLD) increase in women after menopause. This narrative review discusses the causes and consequences of NAFLD in postmenopausal women and describes how physical activity can contribute to its prevention.MethodsThe authors followed the narrative review method to perform a critical and objective analysis of the current knowledge on the topic. The Medical Subject Heading keywords ‘physical exercise’, ‘menopause’, ‘hormone replacement therapy’, ‘estradiol’ and ‘NAFLD’ were used to establish a conceptual framework. The databases used to collect relevant references included Medline and specialized high‐impact journals.ResultsHigher visceral adiposity, higher rate of lipolysis in adipose tissue after oestrogen drop and changes in the expression of housekeeping proteins involved in hepatic lipid management are observed in women after menopause, contributing to NAFLD. Excessive liver steatosis leads to hepatic insulin resistance, oxidative stress and inflammation, accelerating NAFLD progression. Physical activity brings beneficial effects against several postmenopausal‐associated complications, including NAFLD progression. Aerobic and resistance exercises partially counteract alterations induced by metabolic syndrome in sedentary postmenopausal women, impacting NAFLD progression and severity.ConclusionsWith the increased global obesity epidemic in developing countries, NAFLD is becoming a severe problem with increased prevalence in women after menopause. Evidence shows that physical activity may delay NAFLD development and severity in postmenopausal women, although the prescription of age‐appropriate physical activity programmes is advisable to assure the health benefits.
Microstructural changes in prodromal dementia with Lewy bodies compared to normal ageing: Multiparametric quantitative MRI evidences
Authors: Chabran Eléna , Mondino Mary , Noblet Vincent , Degiorgis Laetitia , Loureiro de Sousa Paulo , Blanc Frédéric 
Published: December 2021

AbstractDementia with Lewy bodies (DLB) patients show few significant macroscopic structural changes, especially at the early stages of the disease, making quantitative MRI especially interesting to explore more subtle changes that are not detectable by conventional volumetric techniques. Microstructural alterations have been reported in DLB at the dementia stage, but no study to date was conducted in prodromal patients. Here, quantitative MRI data were collected from 46 DLB prodromal patients and 20 healthy elderly subjects, who also underwent a detailed clinical examination including the Mayo Clinic Fluctuation Scale. We conducted voxel‐wise between‐group comparisons in diffusion tensor imaging (DTI) metrics and in R2* mapping, along with a multivariate analysis combining the two modalities. We highlighted multiple grey matter and white matter microstructural changes in DLB patients at the prodromal stage, compared to control subjects. Our multivariate analysis identified three distinct regional patterns of DTI and R2* changes (anterior, anteromedial, posterior) in DLB patients, that could reflect different neuropathological processes across brain regions. We also observed an association between R2* alterations in the thalamus, and the severity of fluctuations, in the DLB group. These preliminary findings are promising and require future investigations to better understand the biological underpinnings of microstructural alterations.
An ensemble artificial intelligence‐enabled MIoT for automated diagnosis of malaria parasite
Authors: Nayak Soumya Ranjan , Nayak Janmenjoy , Vimal S. , Arora Vaibhav , Sinha Utkarsh 
Published: December 2021

AbstractRapid advancements in Information and Communication Technologies (ICT) and artificial intelligence (AI) applications permeating to all spheres of life, including medical prognosis, have led modern clinical systems to tread the path of advanced Internet of Medical Things (IoMT) by infusing advanced learning technologies, particularly deep learning. Automated diagnosis of malarial infection using AI‐enabled IoMT holds the promise of sustainable prognosis by reducing diagnosis error significantly with improved recognition accuracy. Existing automated diagnostic systems usually employ classical deep learning models wherein setting parameter values such as automatic learning rate selection, weight management etc. are a major concern. To address these issues, this paper proposes a collaborative ensemble AI‐enabled IoMT automated diagnosis model to classify malaria parasitized from microscopic images. The proposed model consists of two main stages. In the first stage, a Snapshot ensemble learning model is conjured upon by a combination of three distinct layers of Convolutional, Batch Normalization, and Relu networks; that alters the learning rate aggressively during training phase thus providing different network weights that gives multiple models by training a single model. In the second stage, an ensemble of three transfer learning models is constructed, and finally the average ensemble result is obtained. The learning rates at both these stages are empirically selected through Cosine Annealing. Experiment on the malaria parasite image dataset demonstrates the superiority of the proposed model with respect to a baseline algorithm.
Exploring the connection between language use and oral performance during study abroad: Results from the Daily Language Questionnaire 2
Authors: García‐Amaya Lorenzo 
Published: December 2021

AbstractMany second language (L2) learners participate in study abroad (SA) experiences believing their choice will be synonymous with increased interaction in the L2, from which enhanced linguistic gains will ensue. Nonetheless, one open question is whether SA participants actually engage in sustained L2 interaction while they are abroad. This paper reports the results of a longitudinal study in which 31 SA participants (L1 = English; L2 = Spanish) completed the Daily Language Questionnaire 2 (DLQ 2), an online questionnaire designed in Qualtrics, every day during their 6‐week SA experience in Spain. We show that (i) learners reported substantially less L2 use at the end than at the start of the SA and (ii) learners reported substantially less L2 use on “no‐class” days compared to class days. We additionally tested correlations between the self‐reported values of L2 use and the learners' L2 oral abilities. We found moderate positive correlations between L2 usage (per the DLQ 2) and initial‐ability scores (per the Elicited Imitation Task); these findings are contextualized within theories of L2 interaction. Finally, we use the data of the present study to offer recommendations for administrators and instructors looking to refine the experience offered to SA participants.
Capturing learning from public involvement with people experiencing homelessness to help shape new physiotherapy research: Utilizing a reflective model with an under‐served, vulnerable population
Authors: Dawes Jo , Barron Duncan S. , Lee Laurence E. 
Published: December 2021

AbstractIntroductionPeople experiencing homelessness (PEH) have poorer health than housed people but face barriers accessing care and being involved in research. As an often‐ignored group, their contribution to help shape research that is for and about them is essential, as it can strengthen the research proposal, in turn facilitating research and outcomes that are relevant to this vulnerable group.MethodsSix people with experience of homelessness attended a PPI consultation aided by Pathway, a UK homeless peer advocacy charity, which coordinates an ‘Experts by Experience’ group. We present reflections on conducting PPI with PEH that informed the development of a physiotherapy research proposal. Kolb's Experiential Learning Cycle guided reflections across four stages: (1) describing the PPI experience; (2) reviewing and reflecting on the PPI experience; (3) learning from the PPI experience; and (4) planning and trying out learning.ResultsReflections highlighted the importance of: working closely with an advocacy organisation and leader to reach under‐served people; the diversity of experiences; using familiar venues, contingency and budget planning; flexibility and ‘allowing time; talking less; listening more’; planning for early and ongoing PPI, and the potential of mobile ‘one‐off’ PPI outreach models to reach vulnerable groups.ConclusionKolb's Experiential Learning Cycle aided team reflection on feedback from PEH, which helped refine and strengthen a physiotherapy research proposal. The project was unfunded. However, a reflective model helped maximize learning and impact including for future PPI and research. The novel application of Kolb's Experiential Learning Cycle provided structure, facilitated reflection and enhanced individual and collective learning and may benefit capturing learning from PPI with other vulnerable populations.Patient or Public ContributionWe highlight how a PPI consultation with people with experience of homelessness helped shape a funding proposal. Additionally, the reflections of the experts by experience team leader are included.