bluuebunny/crossref_metadata_2025_split huggingface dataset:
Differences Between Encoding and Retrieval Failure in Mild Cognitive Impairment: Results from Quantitative Electroencephalography and Magnetic Resonance Volumetry
Authors: Han Su-Hyun , Pyun Jungmin , Yeo Soeun , Kang Dong Won , Jeong Ho Tae , Kang Seung Wan , Kim SangYun , Youn Young Chul 
Published: July 2020

Abstract
Background: Memory impairments in mild cognitive impairment can be classified into encoding (EF) and retrieval (RF) failure. We explored differences between the two using quantitative electroencephalography (qEEG) and magnetic resonance imaging volumetry.Methods: We compared qEEG power spectra and connectivity between 87 participants with EF and 78 with RF, and gray matter (GM) volume differences between participants with mild cognitive impairment and 71 controls. Demographic and cognitive assessment results between groups were compared with Student’s t-tests, and obtained qEEG features were analyzed according to statistically significant differences between the groups using iSyncBrain ™. GM volume changes underlying EF and RF in amnestic mild cognitive impairment were compared on processed magnetic resonance images of cognitively normal subjects using Student’s t-tests, with age and total intracranial volume classified as nuisance covariates. We used a voxel-based morphometry analysis to demonstrate significant atrophic GM areas in the two types of patients with amnestic mild cognitive impairment. Results were considered statistically significant at p < 0.05 and were corrected for family-wise errors to avoid multiple-comparison problems.Results: qEEG showed higher frontal theta and lower beta2 band power and higher theta and lower beta connectivity in patients with EF. GM volume reductions due to EF in the left thalamus and bilateral hippocampi and reductions due to RF in the left thalamus, right superior frontal lobe, right superior temporal lobe, and right middle cingulum were observed. Conclusions: Patients with mild cognitive impairment differ functionally and structurally according to their specific memory impairments. EF findings are more consistent with the prodromal Alzheimer's disease stage than RF findings. Since this study is a cross-sectional study, prospective follow-up studies are needed to investigate whether different types of memory impairments can predict the underlying pathology of amnestic MCI. Additionally, insufficient sample size may lead to ambiguous statistical finidings in direct comparisons, a larger patient cohort could more robustly identify differences in GM volume reductions between the EF and the RF group.
Evolving trajectories of COVID-19 curves in India: Prediction using autoregressive integrated moving average modeling.
Authors: Bhandari Sudhir , Tak Amit , Gupta Jitendra , Patel Bhoopendra , Shukla Jyotsna , Shaktawat Ajit Singh , Singhal Sanjay , Saini Abhishek , Kakkar Shivankan , Dube Amitabh , Dia Sunita , Dia Mahendra , Wehner Todd C 
Published: July 2020

Abstract
The forecasting of Coronavirus Disease-19 (COVID-19) dynamics is a centerpiece in evidence based disease management. Numerous approaches that use mathematical modeling have been used to predict the outcome of the pandemic, including data driven models, empirical and hybrid models. This study was aimed at prediction COVID-19 evolution in India using a model based on autoregressive integrated moving average (ARIMA). Retrieving real time data from the Johns Hopkins dashboard from 11 Mar 2020 to 25 Jun 2020 (N = 107 time points) to fit the model. The ARIMA (1,3,2) and ARIMA (3,3,1) model fit best for cumulative cases and deaths respectively with minimum Akaike Informaton Criteria. The prediction of cumulative cases and deaths for next 10 days from 26 Jun 2020 to 05 Jul 2020 showed a trend toward continuous increment. The predicted root mean square error (PredRMSE) and base root mean square error (BaseRMSE) of ARIMA(1,3,2) model was 21137 and 166330 respectively. Similarly, PredRMSE and BaseRMSE of ARIMA(3,3,1) model was 668.7 and 5431 respectively. We propose that data on COVID-19 be collected continuously, and that forecasting continue in real time. The COVID-19 forecast assist government in resource optimization and evidence based decision making for a subsequent state of affairs.
Are Australian medical interns adequately prepared for the optimal use of personal protective equipment?
Authors: Barratt Ruth 
Published: July 2020

Abstract
BackgroundThe current COVID-19 pandemic has demonstrated that personal protective equipment (PPE) is essential, to prevent the acquisition and transmission of infectious diseases, yet its use is often sub-optimal in the clinical setting. Training and education are important to ensure and sustain the safe and effective use of PPE by medical interns, but current methods are often inadequate in providing the relevant knowledge and skills. The purpose of this study was to explore new graduates’ experiences of the use of PPE and identify opportunities for improvement in medical student education and training programmes, to improve occupational and patient safety. MethodsThis study was undertaken in 2018 in a large tertiary-care teaching hospital in XXXX, Australia, to explore medical interns’ self-reported experiences of PPE use, at the beginning of their first postgraduate year. Focus groups were conducted immediately after theoretical and practical PPE training, during hospital orientation. Transcripts of recorded discussions were analysed, using a thematic approach that drew on the COM-B (capability, opportunity, motivation - behaviour) framework for behaviour. Results 80% of 90 eligible interns participated. Many interns had not previously received formal training in the specific skills required for optimal PPE use and had developed potentially unsafe habits. Their experiences as medical students in clinical areas contrasted sharply with recommended practice taught at hospital orientation and impacted on their ability to cultivate correct PPE use. ConclusionsUndergraduate teaching should be consistent with best practice PPE use, and include practical training that embeds correct and safe practices.
Patterns of Social Reactions to COVID-19 Pandemic; Reasons & Proposed Ways to Overcome
Authors: Ahmed Kaenat 
Published: July 2020

This study on the patterns of social reactions to a pandemic has been done amid the spread of COVID-19 globally. Worldwide, everyone reacts differently to every single situation that takes place, especially those as serious as a pandemic. Every individual builds a social reaction unique to his/her concerns. The purpose of this study is to explore the types of reactions people have been showing amid the pandemic from COVID-19, which could help policy makers all over the world to figure out and adapt to the situation according to their own needs and concerns rather than applying a single solution universally. Due to novelty of the topic, and hence limited availability of formal research, this study mostly relies upon reports in print and electronic media about the emerging situation. The study suggests that worldwide, there have been four broad categories of social reactions to a pandemic which include fear, panic, indifference and fatalism. After studying these social reactions, the research comes up with strategies to help resolve these concerns which will eventually help everyone accept the situation and tackle it wisely.
A RAZÃO E A EMOÇÃO NA VIAGEM DO TURISTA
Authors: Silva Maria Aldenora da , Coriolano Luzia Neide Menezes Teixeira 
Published: July 2020

A interferência dos sistemas sensoriais – visão, audição, paladar, olfato e tato – em escolhas de atividades, produtos e serviços serve de argumento para experiências e motivações em viagens turísticas. A literatura sobre o tema, embora incipiente e vasqueira, é encontrada em análises de temas turísticos. Razão e emoção entram como componentes da oferta de produtos e serviços turísticos, modernamente. Não obstante, a relevância do tema é abalizada com narrações de experiências compartilhadas pelos distintos componentes da cadeia produtiva do turismo. O artigo analisa as categorias razão e emoção, assim como os influxos em decisões de compras de consumidores de pacotes turísticos. Concentra-se nos aspectos: mudanças no setor de turismo e contribuição para a economia; perfil do novo consumidor e identificação de determinante nas escolhas; e na influência dos sistemas sensoriais nas escolhas de turistas, para, consequentemente, ensejar vantagem competitiva para as empresas. Explora teorias com o propósito de depreender as mudanças de consumo nas escolhas de equipamentos turísticos pelas novas gerações de consumidores.
Genome-wide Core Proteome Analysis of Brucella melitensis Strains for Potential Drug Target Prediction
Authors: Rahman Noor , Shah Mohibullah , Muhammad Ijaz , Khan Haroon , Imran Muhammad 
Published: July 2020

Introduction:
Brucella melitensis is a facultative intracellular bacterial pathogen that causes
abortion in goats and sheep and Malta fever in humans. In humans, chronic infection occurs through
contact with infected animals or their waste products.

Methods:
The subtractive genomic approach is considered as a powerful and useful method for the
identification of potential drug and vaccine targets. In this study, an attempt has been made through a
subtractive proteomic strategy to identify novel drug targets in Brucella melitensis strains. Total 2604
core proteins of 56 strains of B. melitensis were taken, of which 545 non-human homologs were found to
be essential for pathogen growth. Metabolic pathway analysis of these essential proteins revealed that 129
proteins are exclusively involved in 21 unique metabolic pathways in B. melitensis reference strain.

Results:
Of these, 31 proteins were found to be involved in 10 metabolic pathways that are unique to
the pathogen. We selected Nitrate reductase subunit-β, Urease subunit α-2, Pantoate-β-alanine ligase,
Isochorismatase, 2-dehydro-3-deoxyphosphooctonate aldolase and Serine O-acetyltransferase as drug
targets in Brucella melitensis strains. Among these druggable targets, we selected only Pantoate-β-
alanine ligase as high confidence target based on intensive literature curation, which is nonhomologous
to the human gut metagenome involved in biosynthesis of secondary metabolites pathway. Pantothenate
synthetase is the best chemotherapeutic target to combat Brucellulosis.

Conclusion:
Furthermore, in vitro and in vivo validation is needed for the evaluation of lead compounds
against Brucella melitensis strains.
An Ensembled SVM Based Approach for Predicting Adverse Drug Reactions
Authors: Joshi Pratik , Vedhanayagam Masilamani , Ramesh Raj 
Published: July 2020

Background:
Preventing adverse drug reactions (ADRs) is imperative for the safety of
the people. The problem of under-reporting the ADRs has been prevalent across the world, making it
difficult to develop the prediction models, which are unbiased. As a result, most of the models are
skewed to the negative samples leading to high accuracy but poor performance in other metrics such
as precision, recall, F1 score, and AUROC score.

Objective:
In this work, we have proposed a novel way of predicting the ADRs by balancing the dataset.

Method:
The whole data set has been partitioned into balanced smaller data sets. SVMs with
optimal kernel have been learned using each of the balanced data sets and the prediction of given
ADR for the given drug has been obtained by voting from the ensembled optimal SVMs learned.

Results:
We have found that results are encouraging and comparable with the competing methods in
the literature and obtained the average sensitivity of 0.97 for all the ADRs. The model has been
interpreted and explained with SHAP values by various plots.

Conclusion:
A novel way of predicting ADRs by balancing the dataset has been proposed thereby
reducing the effect of unbalanced datasets.
Deep Learning Model for Pathogen Classification Using Feature Fusion and Data Augmentation
Authors: Ahmad Fareed , Farooq Amjad , Khan Muhammad Usman Ghani 
Published: July 2020

Background:
Bacterial pathogens are deadly for animals and humans. The ease of their dissemination, coupled
with their high capacity for ailment and death in infected individuals, makes them a threat to society.

Objective:
Due to high similarity among genera and species of pathogens, it is sometimes difficult for microbiologists to
differentiate between them. Their automatic classification using deep-learning models can help in reliable, and accurate
outcomes.

Method:
Deep-learning models, namely; AlexNet, GoogleNet, ResNet101, and InceptionV3 are used with numerous
variations including training model from scratch, fine-tuning without pre-trained weights, fine-tuning along with freezing
weights of initial layers, fine-tuning along with adjusting weights of all layers and augmenting the dataset by random
translation and reflection. Moreover, as the dataset is small, fine-tuning and data augmentation strategies are applied to
avoid overfitting and produce a generalized model. A merged feature vector is produced using two best-performing models
and accuracy is calculated by xgboost algorithm on the feature vector by applying cross-validation.

Results:
Fine-tuned models where augmentation is applied produces the best results. Out of these, two-best-performing
deep models i.e. (ResNet101, and InceptionV3) selected for feature fusion, produced a similar validation accuracy of 95.83
with a loss of 0.0213 and 0.1066, and a testing accuracy of 97.92 and 93.75, respectively. The proposed model used xgboost
to attained a classification accuracy of 98.17% by using 35-folds cross-validation.

Conclusion:
The automatic classification using these models can help experts in the correct identification of pathogens.
Consequently, they can help in controlling epidemics and thereby minimizing the socio-economic impact on the community.
Bioinformatics Analysis Identifies CPZ as a Tumor Immunology Biomarker for Gastric Cancer
Authors: Gu Yuan , Gao Ying , Tang Xiaodan , Xia Huizhong , Shi Kunhe 
Published: July 2020

Background:
Gastric cancer (GC) is one of the most common malignancies worldwide. However, the biomarkers for the
prognosis and diagnosis of Gastric cancer were still need.

Objective:
The present study aimed to evaluate whether CPZ could be a potential biomarker for GC.

Method:
Kaplan-Meier plotter (http://kmplot.com/analysis/) was used to determine the correlation between CPZ expression and overall
survival (OS) and disease-free survival (DFS) time in GC [9]. We analyzed CPZ expression in different types of cancer and the
correlation of CPZ expression with the abundance of immune infiltrates, including B cells, CD4+ T cells, CD8+ T cells,
neutrophils, macrophages, and dendritic cells, via gene modules using TIMER Database.

Results:
The present study identified that CPZ was overexpressed in multiple types of human cancer, including Gastric cancer. We
found that overexpression of CPZ correlates to the poor prognosis of patients with STAD. Furthermore, our analyses show that
immune infiltration levels and diverse immune marker sets are correlated with levels of CPZ expression in STAD.
Bioinformatics analysis revealed that CPZ was involved in regulating multiple pathways, including PI3K-Akt signaling pathway, cGMP-PKG signaling pathway, Rap1 signaling pathway, TGF-beta signaling pathway, regulation of cell adhesion,
extracellular matrix organization, collagen fibril organization, collagen catabolic process.

Conclusion:
This study for the first time provides useful information to understand the potential roles of CPZ in tumor immunology and
validate it to be a potential biomarker for GC.
Characterization and Prediction of Presynaptic and Postsynaptic Neurotoxins Based on Reduced Amino Acids and Biological Properties
Authors: Cao Yiyin , Yu Chunlu , Huang Shenghui , Wang Shiyuan , Zuo Yongchun , Yang Lei 
Published: July 2020

Background:
Presynaptic and postsynaptic neurotoxins are two important neurotoxins. Due to the important
role of presynaptic and postsynaptic neurotoxins in pharmacology and neuroscience, identification of them becomes very
important in biology.

Method:
In this study, the statistical test and F-score were used to calculate the difference between amino acids and
biological properties. The support vector machine was used to predict the presynaptic and postsynaptic neurotoxins by
using the reduced amino acid alphabet types.

Results:
By using the reduced amino acid alphabet as the input parameters of support vector machine, the overall accuracy
of our classifier had increased to 91.07%, which was the highest overall accuracy in this study. When compared with the
other published methods, better predictive results were obtained by our classifier.

Conclusion:
In summary, we analyzed the differences between two neurotoxins in amino acids and biological properties,
and constructed a classifier that could predict these two neurotoxins by using the reduced amino acid alphabet.
Renal Phosphate Handling in Antiretroviral-naive HIV-Infected Patients
Authors: Adedeji Tewogbade Adeoye , Adebisi Simeon Adelani , Adedeji Nife Olamide , Jeje Olusola Akanni , Owolabi Rotimi Samuel 
Published: July 2020

Background:
Human immunodeficiency virus (HIV) infection impairs renal function,
thereby affecting renal phosphate metabolism.

Objectives:
We prospectively estimated the prevalence of phosphate abnormalities (mild, moderate
to life-threatening hypophosphataemia, and hyperphosphataemia) before initiating antiretroviral
therapy (ART).

Methods:
A cross-sectional analysis was performed on 170 consecutive newly diagnosed ARTnaive,
HIV-infected patients attending our HIV/AIDS clinics over a period of one year. Fifty (50)
screened HIV-negative blood donors were used for comparison (controls). Blood and urine were
collected simultaneously for phosphate and creatinine assay to estimate fractional phosphate excretion
(FEPi %) and glomerular filtration rate (eGFR).

Results:
eGFR showed significant difference between patients’ and controls’ medians (47.89ml/
min/1.73m2 versus 60ml/min/1.73m2, p <0.001); which denotes a moderate chronic kidney disease
in the patients. Of the 170 patients, 78 (45.9%) had normal plasma phosphate (0.6-1.4
mmol/L); 85 (50%) had hyperphosphataemia. Grades 1, 2 and 3 hypophosphataemia was observed
in 3 (1.8%), 3 (1.8%), and 1(0.5%) patient(s) respectively. None had grade 4 hypophosphataemia.
Overall, the patients had significantly higher median of plasma phosphate than the controls, 1.4
mmol/L (IQR: 1.0 – 2.2) versus 1.1 mmol/L (IQR: 0.3 – 1.6), p <0.001, implying hyperphosphataemia
in the patients; significantly lower median urine phosphate than the controls, 1.5 mmol/L
(IQR: 0.7 -2.1) versus 8.4 mmol/L (IQR: 3.4 – 16), p <0.001), justifying the hyperphosphataemia
is from phosphate retention; but a non-significantly lower median FEPi% than the controls, 0.96%
(IQR: 0.3 -2.2) versus 1.4% (IQR: 1.2 -1.6), p > 0.05. Predictors of FEPi% were age (Odds ratio,
OR 0.9, p = 0.009); weight (OR 2.0, p < 0.001); CD4+ cells count predicted urine phosphate
among males (p = 0.029).

Conclusion:
HIV infection likely induces renal insufficiency with reduced renal phosphate clearance.
Thus, hyperphosphataemia is highly prevalent, and there is mild to moderate hypophosphataemia
but its life-threatening form (grade 4) is rare among ART-naive HIV patients.
Mobile-Based and Cloud-Based System for Self-management of People With Type 2 Diabetes: Development and Usability Evaluation
Authors: Salari Raheleh , R Niakan Kalhori Sharareh , GhaziSaeedi Marjan , Jeddi Marjan , Nazari Mahin , Fatehi Farhad 
Published: July 2020

Background
As the use of smartphones and mobile apps is increasing, mobile health (mHealth) can be used as a cost-effective option to provide behavioral interventions aimed at educating and promoting self-management for chronic diseases such as diabetes. Although many mobile software apps have been developed for this purpose, they usually lack a theoretical foundation and do not follow the guidelines suggested for evidence-based practice. Therefore, this study aimed to develop a theory-based self-management app for people with type 2 diabetes and provide an app based on a needs assessment analysis.

Objective
This paper describes the development and usability evaluation of a cloud-based and mobile-based diabetes self-management app designed to help people with diabetes change their health behavior and also enable remote monitoring by health care providers.

Methods
The development of this mHealth solution comprises 3 phases. Phase I: feature extraction of the Android apps that had a user rating of 4 stars or more and review of papers related to mHealth for diabetes self-management were performed followed by seeking expert opinions about the extracted features to determine the essential features of the app. Phase II: design and implementation included selecting which behavioral change and structural theories were to be applied the app and design of the website. Phase III: evaluation of the usability and user experience of the mobile app by people with diabetes and the portal by health care providers using the User Experience Questionnaire.

Results
The developed mobile app includes modules that support several features. A person’s data were entered or collected and viewed in the form of graphs and tables. The theoretical foundation of behavioral intervention is the transtheoretical model. Users were able to receive customized messages based on the behavioral change preparation stage using the Kreuter algorithm. The clinician’s portal was used by health care providers to monitor the patients. The results of the usability evaluation revealed overall user satisfaction with the app.

Conclusions
Mobile- and cloud-based systems may be an effective tool for facilitating the modification of self-management of chronic care. The results of this study showed that the usability of mobile- and cloud-based systems can be satisfactory and promising. Given that the study used a behavioral model, assessment of the effectiveness of behavior change over time requires further research with long-term follow-up.
Developing a Heart Transplantation Self-Management Support Mobile Health App in Taiwan: Qualitative Study
Authors: Chen Yi-Wen , Wei Jeng , Chen Hwei-Ling , Cheng Ching-Hui , Hou I-Ching 
Published: July 2020

Background
Heart transplantation (HTx) is the most effective treatment for end-stage heart failure patients. After transplantation, patients face physiological, psychological, social, and other health care problems. Mobile health (mHealth) apps can change the delivery of conventional health care to ubiquitous care and improve health care quality. However, a dearth of mHealth apps exists for patients with HTx worldwide, including in Taiwan.

Objective
The aim of this study was to investigate the information needed and to develop a preliminary framework for an mHealth app for post-HTx patients.

Methods
A qualitative approach with individual in-depth interviews was conducted at a heart center in the regional hospital of northern Taiwan from June to November 2017. Patients that had undergone HTx and their health professionals were recruited for purposeful sampling. A semistructured interview guideline was used for individual interviews and transcribed. Thematic analysis was used for data analysis.

Results
A total of 21 subjects, including 17 patients and 4 health professionals, were recruited for the study. The following five major themes were identified: reminding, querying, experience sharing, diet, and expert consulting. Minor themes included a desire to use the app with artificial intelligence and integration with professional management.

Conclusions
An intelligent mHealth app that addresses the five main themes and integrates the processes of using a mobile app could facilitate HTx self-management for Taiwanese patients.