bluuebunny/crossref_metadata_2025_split huggingface dataset:
A bloody interaction: Plasma proteomics reveals gilthead sea bream (Sparus aurata) impairment caused by Sparicotyle chrysophrii
Authors: Riera-Ferrer Enrique , Piazzon M. Carla , Pozo Raquel Del , Palenzuela Oswaldo , Estensoro Itziar , Sitjà-Bobadilla Ariadna 
Published: August 2022

Abstract
Background:Sparicotylosis is an enzootic parasitic disease across the Mediterranean Sea caused by the polyopisthocotylean monogenean Sparicotyle chrysophrii, which affects the gills of gilthead sea bream (GSB; Sparus aurata). Current disease-management, mitigation and treatment strategies are scarce against sparicotylosis. In order to successfully develop more efficient therapeutic strategies against this disease, understanding which molecular mechanisms and metabolic pathways are altered in the host is critical. This study aims to elucidate how S. chrysophrii infection modulates GSB’s plasma proteome and to identify the main altered biological processes involved.Methods:Experimental infections were conducted in a recirculating aquaculture system (RAS), exposing naïve recipient GSB (R; 70 g; N = 50) to effluent water from S. chrysophrii-infected GSB (98 g; N = 50). An additional tank with unexposed naïve fish (C; 70 g; N = 50) was maintained in parallel, with open water flow disconnected from the RAS. Haematological and infection parameters from sampled C and R fish were registered for 10 weeks. Plasma samples from R fish were categorised into 3 different groups according to their infection intensity: low, medium and high (L: 1-50; M: 51-100; H: >100 worms fish-1, respectively). Five plasma samples of each category were selected, in addition to five C samples and underwent a SWATH-MS proteome analysis. Additional assays on haemoglobin, cholesterol and the lytic activity of the alternative complement pathway were performed to validate the proteome analysis findings. Results:The discriminant analysis of the plasma protein abundance revealed a clear separation into 3 groups (H, M/L and C). A pathway analysis was performed with the differentially quantified proteins, indicating that the parasitic infection mainly affected pathways related to haemostasis, the immune system and lipid metabolism and transport.Twenty-two proteins significantly correlated with the infection intensity, highlighting apolipoproteins, globins and complement c3. Validation assays in blood and plasma (haemoglobin, cholesterol and lytic activity of alternative complement pathway) confirmed these correlations.Conclusions:Sparicotylosis profoundly alters the haemostasis, the innate immune system and the lipid metabolism and transport in GSB. This study gives a crucial global overview of the pathogenesis of sparicotylosis and highlights new targets for further research.
Predicting Online Fraudulent Transactions Using Machine Learning
Authors: .T.P Rani , .K Suganthi , Saravanan Magilan , Sahu Ashish Kumar , Sagayam K. Martin , Elngar Ahmed A. 
Published: August 2022

Abstract
Fraudulent online transactions have caused significant damage and loss to individuals and companies over a period of time. There has been an increase in online fraud with the progression of state-of-the-art technologies and worldwide communication. The design of efficient fraud detection algorithms is critical for reducing these losses. Machine learning and statistical techniques play a vital role in the detection of fraudulent transactions. Fraud detection model implementation is particularly challenging due to the lack of data, sensitive nature of data, and the unbalanced class distributions. It is tough to draw inferences and build better models due to the confidentiality of the records. In this paper we aim to focus on the problems: i) the role of sampling in the presence of class imbalance (i.e., nonfraudulent transactions are more in the percentage of the total transactions), ii) building and analyzing various machine learning models, iii) to assess and validate the performances of different fraud detection techniques. This paper has research directions toward applying machine learning for data analysis. We have designed and assessed a prototype of a fraudulent transactions detection system that will be able to meet real-world demands and increase the security of transactions for the customers.
Augmentation of accuracy with the use of different datasets in Artificial Intelligence based corneal ulcer detection
Authors: Daoud Abdullah Akram Raja , Gusseinova Maryamdostu , Celebi Ali Riza Cenk 
Published: August 2022

Abstract
Purpose
The present research was aimed to investigate the Neural Architecture Search (NASNet) based Vertex AI in the Google Cloud platform for detecting corneal ulcers.
Methods
In the first model, a total of 712 images of corneal ulcers were obtained from the Kaggle platform and grouped according to the category, type, and grade. After the augmentation of a new set of 352 healthy eye images from another Kaggle dataset, the latest model was tested using Vertex AI.
Results
The first model distinguished different categories of corneal ulcers with an accuracy rate of 76.3%, whereas the precision and recall were 80.5% and 57.1%, respectively. The latest model detected corneal ulcers with an accuracy of 98.2%, along with a precision and recall value of 95.1% for both.
Conclusion
The performance of Vertex AI can be altered with the use of additional datasets during training for differentiating corneal ulcers among normal eyes.
Physical as well as psychological distress and coping with situational dilemmas experienced by people infected with COVID-19: a mixed method study
Authors: Hengyotmark Arunee , Kusoom Wichitra 
Published: August 2022

Abstract

Background
The COVID-19 pandemic caused serious health problems that affected people physically, and psychologically around the globe. This study aimed to (1) understand physical distress (PhD), psychological distress (PsD), and coping experiences among people infected with COVID-19 (2) develop a grounded theory (3) examine PhD, PsD, and coping among people infected with COVID-19.
Methods
A sequential exploratory mixed methods strategy was employed. A qualitative procedure was based on a grounded theory, data collection included observation and in-depth interviews with 25 participants, aged 18 years and above. The quantitative one included 180 participants, from Bangkok Metropolitan, and Northeastern region of Thailand from February to May 2022. A snowball sampling technique was used. Quantitative instrument: was built based on grounded data from a qualitative approach. Physical, psychological distress, and coping were assessed. Content analysis was applied using the Strauss and Corbin method, and software ATLAS.ti. Descriptive statistic, Pearson’s correlation, and independent t-test were used.
Results
Six major themes included 10 sub-themes. The six major themes appeared (1) severity of COVID-19 symptoms (2) death anxiety (3) uncertainty (4) barrier to healthcare access (5) compliance, and self-regulation coping (6) post-COVID-19 effects. PhD, PsD, and coping were at a moderate level. All PhD, PsD, and coping were in positive relations. Comparison of PsD, and coping between the non-severe group and severe group was significantly different (p < 0.001, p < 0.05). Post COVID-19 effects were fatigue, dyspnea, high blood sugar level, impairment of lungs, liver, and kidney.
Conclusions
The findings of the study supported 6 major themes including 10 sub-themes. All PhD, PsD, and coping were in positive relations. The PsD, and coping of the severe group was higher than non-severe group. Suggestions, healthcare providers should be concerned with sufficient healthcare services, physical and psychological supports. The interventions are needed for their recovery from physical and psychological effects.
Interspecific competition between invasive Spodoptera frugiperda and indigenous Helicoverpa armigera in maize fields of China
Authors: Song Yifei , Li Hui , He Limei , Zhang Haowen , Yang Xianming , Wu Kongming 
Published: August 2022

Abstract
Background
Interspecific interactions (e.g., competition, mutualism) with native species are key determinants of the ability of invasive species to colonize new habitats. Since the fall armyworm (FAW), Spodoptera frugiperda, invaded China in December 2018, it has coexisted in maize fields with the native cotton bollworm (CBW), Helicoverpa armigera, but the dynamics and mechanisms of their competition are not well understood.
Results
We evaluated interspecific competition between FAW and CBW by analyzing their predation function response in the laboratory, survival rates when their larvae co-infested the same maize plant, and the population dynamics of both in the same maize field. In the predation tests, FAW and CBW larvae preyed on each other. The predation functional response of the sixth instar FAW or CBW larvae preying on 1st–3rd instar larvae were consistent with the Holling Ⅲ model. The theoretical maximum predation of 6th instar FAW larvae preying on 1st–3rd instar CBW larvae was 71.4, 32.3 and 30.3 individuals, respectively, while that of 6th instar CBW larvae on 1st–3rd instar FAW larvae was 38.5, 28.6 and 17.0 individuals, respectively. Field co-infestation trials showed that the older larvae had a higher survival rate when the two pests co-infested the same maize plants, but young larval survival was higher for FAW than CBW. In the maize field from 2019 to 2021, FAW populations were significantly higher than those of CBW.
Conclusions
Our results showed that S. frugiperda larvae had a predation advantage over H. armigera in the laboratory and field and might be an important reason for its dominance in Chinese maize fields, especially in southern parts.
Metabolic Syndrome and The Likelihood of Knee Pain and Functional Disability: Evidence from a Large Middle Eastern Population-Based Study
Authors: Ibrahim Talal , Ahmed Abdulaziz F , Nofal Mariam , Hegazy Abdelsalam , Ghomrawi Hassan M. K. 
Published: August 2022

Abstract
Objectives
Metabolic Syndrome (MetS) has been associated with knee osteoarthritis (KOA) in animal studies, but epidemiologic evidence of the association remains controversial. We investigated the association between MetS and knee pain and functional disability, the hallmarks of KOA, in a Middle Eastern population with high MetS rates.
Methods
Between 01/2016 and 03/2019, a population-based sample of adult individuals was recruited prospectively. Data collected included age, sex, blood pressure, body mass index (BMI), waist circumference (WC), and comprehensive metabolic panel blood tests. Knee symptoms were assessed using The Western Ontario and McMaster Arthritis index (WOMAC) The Adult Treatment Panel III criteria was applied to determine if participants had MetS. Multivariable regression was used to determine the association of MetS, and its components, with the WOMAC total and subscale scores.
Results
A total of 6,000 participants were enrolled, and 15.5% had MetS. Multivariate regression demonstrated that participants with MetS had significantly higher WOMAC total and subscale scores after adjusting for demographic variables; however, these associations were not significant after adjusting for BMI. Multivariate regression examining the association between MetS components and the WOMAC scores showed that increased WC and decreased high-density lipoprotein (HDL) were significantly associated with worse WOMAC total and subscale scores even after adjusting for BMI.
Conclusions
This study demonstrated that the proposed effect fo MetS on knee symptoms may be mainly mechanically induced by obesity. However, the association of lower HDL with worse knee scores may suggest a metabolic pathway as well.
Exploring healthcare providers perceptions of gamified self-regulated learning experiences on smartphone devices
Authors: Tuti Timothy , Muinga Naomi , Wanyama Conrad , English Mike , Nagraj Shobhana , Paton Chris , Winters Niall 
Published: August 2022

Abstract
There is an urgent need for more and better-trained health workers in Sub-Saharan Africa (SSA). A lack of training opportunities significantly contributes to the poor quality of neonatal care outcomes in this region. Smartphone-based learning shows potential to address this training need. However, before widespread adoption can happen, it is important to obtain input from healthcare providers who will be future users of this technology. This study explored the perceptions of healthcare providers on how to enhance the design and implementation of more engaging learning experiences on smartphone-based gamified learning, which is a largely under-researched topic in low resource settings. It also explored how healthcare providers perceived such contextualised learning experiences could influence their self-regulatory learning(SRL) efforts as they embark on bridging their knowledge gaps. We used Nominal Group Technique and semi-structured interviews to explore how healthcare providers perceived their learning experiences using gamified smartphone-based learning with a particular focus on how the platform could support SRL. We found that the provision of highly contextual feedback that elaborates on the consequences of action, inaction or erroneous action optimises healthcare providers SRL experiences and improves both their engagement and effort regulation. For this to be effective at scale in SSA contexts, a theory of change that validates healthcare providers learning by building confidence in the knowledge gained and that transfers to a routine clinical setting would be necessary. We posit that the context-reflective, co-designed conceptual model emerging from this study could support uptake of smartphone-based self-regulated learning platforms and aid in making gains in patient outcomes in SSA.
Scene text detection using HRNet and spatial attention mechanism
Authors: Tang Qingsong , Jiang Zhangyan , Pan Bolin , Guo Jinting , Jiang Wuming 
Published: August 2022

Abstract
To better extract the features from text instances with various shapes, a scene text detector using High Resolution Net (HRNet) and spatial attention mechanism is proposed in this paper. Specifically, we use HRNetV2-W18 as the backbone network to extract the text feature in text instances with complex shapes. Considering that the scene text instance is usually small, to avoid too small feature size, we optimize HRNet through deformable convolution and Smooth Maximum Unit (SMU) activation function, so that the network can retain more detail information and location information of the text instance. In addition, a Text Region Attention Model (TRAM) is added after the backbone to make it pay more attention to the text location information and a loss function is added to TRAM, so that the network can learn the features better. The experimental results illustrate that the proposed method can compete with the state-of-the-art methods. Code is available at: https://github.com/zhangyan1005/HR-DBNet.
The clinical condition rather than cerebrospinal fluid parameters guides the optimal duration of antimicrobial therapy among neonates with bacterial meningitis: a single-center retrospective study
Authors: Hu Ya , Li Xingyuan , Wei Hong , Li Hongdong 
Published: August 2022

Abstract
Background
Neonatal bacterial meningitis is serious and accounts for high morbidity and mortality in developing countries, but the duration of antimicrobial treatment is not well established. We aimed to compare the clinical characteristics and outcomes of neonatal bacterial meningitis with normal cerebrospinal fluid (CSF) and abnormal CSF at discharge.
Methods
We enrolled neonates with bacterial meningitis who were admitted to the neonatology department of Children’s Hospital of Chongqing Medical University between August 1, 2019 and January 1, 2021. The children’s clinical data, laboratory data and outcomes were recorded and analyzed.
Results
Fifty-five neonates met the inclusion criteria in our study. Besides CSF protein levels, there was no significant difference in clinical symptoms, the blood parameters (white blood cell count, neutrophils, platelet, C-reaction protein, and procalcitonin), CSF WBC counts at admission and discharge period, the duration of antibiotics, and hospital stays between the two groups (P > 0.05). For the sequelae of bacterial meningitis, short-term and long-term complications also showed no statistically significant differences between the two groups.
Conclusions
The duration of antimicrobial therapy in neonatal bacterial meningitis depends on the clinical condition of the patient, rather than the CSF parameters. It may improve the reasonable utilization of the antibiotics in neonatal bacterial meningitis.
Microvascular alterations in the diagnosis of systemic sclerosis: a new approach
Authors: Liu Yi-Nuo , Xu Yu-Ling , Luo Wei , Kang Min , Ying Ping , Pan Yi-Cong , Liang Rong-Bin , Shu Hui-Ye , Zhang Li-Juan , Shao Yi 
Published: August 2022

Abstract
The study investigated the conjunctival and fundus retinal vascular density changes in individuals with systemic sclerosis (SSc) by optical coherence tomography angiography (OCTA) to clarify the relationship between these changes and clinical features of SSc. Twelve SSc patients and twelve healthy controls (HCs) participated (24 eyes each). OCTA photographs each eye's conjunctiva and macular retina. Images were segmented using annular partitioning, Early Treatment Diabetic Retinopathy Study partitioning (ETDRS), and hemispheric quadrants. Superficial and deep retina layers of fundus microvascular (MIR), macrovessel (MAR), total MIR (TMI), and temporal conjunctival vessel densities were assessed and compared across groups. OCTA results showed no significant difference in conjunctival blood vessel density between groups (P > 0.05). MIR and TMIR densities differed significantly (P < 0.05). Using a new segmentation method, we found that the SR, IL, S, L, C1 and C2 regions in both the superficial and deep layers, the R and C3 regions in the superficial layer, and the S, L and C4 regions in the deep layer of the retina were lower than that in the patient group (P < 0.05). Deep MAR and conjunctival vascular densities correlated positively (r = 0.5206; P < 0.05). OCTA results show lower microvascular density in active SSc patients' superficial and deep retinal layers. A potential indicator for the diagnosis of SSc may be the vascular density measurement obtained by OCTA.
Association of Metallothionein 2A rs10636 with low mean cor-puscular volume (MCV), low mean corpuscular haemoglobin (MCH) in healthy Taiwanese
Authors: Chen Rong-Fu , Chen Po-Ming , Pan Chau-Shiung , Huang Chieh-Cheng , Chiang En-Pei Isabel 
Published: August 2022

Abstract
Human metallothionein-2A (MT2A) protein participates in metal homeostasis, detoxification, oxidative stress reduction, and immune defense. It decreases heavy metal ions and reactive oxygen species (ROS) during injury of cells and tissues. The single nucleotide poly-morphisms at the MT2A gene have been associated in various human diseases including cancer.; The current study aimed to elucidate associations between MT2A genotypes with the clinical, biochemical, and molecular characteristics that potentially related to lowered MT2A ex-pression. One hundred and twenty-one healthy Taiwanese subjects were enrolled from Changhua Show-Chwan Memorial Hospital. Clinical, biochemical and molecular characteristics including the frequent minor allele SNPs, rs28366003 and rs10636, within the MT2A gene were determined.; The genotype distribution of MT2A rs10636 fits the Hardy-Weinberg equilibrium. The significant associations with gradually decline of mean corpuscular volume (MCV) and mean corpuscular hemoglobin (MCH) were identified with MT2A rs10636 using analysis of variance (ANOVA) with Tukey’s analysis as a post hoc test. We further validated the correlations between the expressions of genes in erythropoiesis, cholesterol synthesis, platelet synthesis, insulin with MT2A using the web-based Gene Expression Profiling Interactive Analysis (GEPIA) databases. The results revealed that hypoxia-inducible factor 1α (HIF-1α), erythropoietin (EPO), lipoprotein lipase (LPL), and lecithin-cholesterol acyltransferase (LCAT) mRNA ex-pression are significantly correlated with MT2A mRNA expression.; In conclusion, these results suggested that genetic variations of MT2A rs10636 might be an important risk factor for erythropoiesis in the Taiwanese general population.
Identification and prediction of Parkinson’s disease subtypes and progression using machine learning in two cohorts.
Authors: Faghri Faraz , Dadu Anant , Satone Vipul K , Kaur Rachneet , Hashemi Sayed , Leonard Hampton , Iwaki Hirotaka , Makarious Mary , Billingsley Kimberley , Bandres-Ciga Sara , Sargent Lana , Noyce Alastair , Daneshmand Ali , Blauwendraat Cornelis , Marek Ken , Scholz Sonja , Singleton Andrew , Nalls Mike , Campbell Roy 
Published: August 2022

Abstract
Background: The clinical manifestations of Parkinson’s disease (PD) are characterized by heterogeneity in age at onset, disease duration, rate of progression, and the constellation of motor versus non-motor features. There is an unmet need for the characterization of distinct disease subtypes as well as improved, individualized predictions of the disease course. The emergence of machine learning to detect hidden patterns in complex, multi-dimensional datasets provides unparalleled opportunities to address this critical need. Methods and Findings: We used unsupervised and supervised machine learning methods on comprehensive, longitudinal clinical data from the Parkinson’s Disease Progression Marker Initiative (PPMI) (n = 294 cases) to identify patient subtypes and to predict disease progression. The resulting models were validated in an independent, clinically well-characterized cohort from the Parkinson’s Disease Biomarker Program (PDBP) (n = 263 cases). Our analysis distinguished three distinct disease subtypes with highly predictable progression rates, corresponding to slow, moderate, and fast disease progression. We achieved highly accurate projections of disease progression five years after initial diagnosis with an average area under the curve (AUC) of 0.92 (95% CI: 0.95 ± 0.01 for the slower progressing group (PDvec1), 0.87 ± 0.03 for moderate progressors, and 0.95 ± 0.02 for the fast progressing group (PDvec3). We identified serum neurofilament light (Nfl) as a significant indicator of fast disease progression among other key biomarkers of interest. We replicated these findings in an independent validation cohort, released the analytical code, and developed models in an open science manner. ﻿Conclusions: Our data-driven study provides insights to deconstruct PD heterogeneity. This approach could have immediate implications for clinical trials by improving the detection of significant clinical outcomes that might have been masked by cohort heterogeneity. We anticipate that machine learning models will improve patient counseling, clinical trial design, allocation of healthcare resources, and ultimately individualized patient care.