bluuebunny/crossref_metadata_2025_split huggingface dataset:
The CpG island methylator phenotype increases the risk of high-grade squamous intraepithelial lesions and cervical cancer
Authors: Loaeza-Loaeza Jaqueline , Illades-Aguiar Berenice , del Moral-Hernández Oscar , Castro-Coronel Yaneth , Leyva-Vázquez Marco A. , Dircio-Maldonado Roberto , Ortiz-Ortiz Julio , Hernández-Sotelo Daniel 
Published: January 2022

AbstractBackgroundHigh-risk human papillomavirus (HR-HPV) infection is the main cause of cervical cancer, but additional alterations are necessary for its development. Abnormal DNA methylation has an important role in the origin and dissemination of cervical cancer and other human tumors. In this work, we analyzed the methylation of eight genes (AJAP1, CDH1, CDH13, MAGI2, MGMT, MYOD1, RASSF1A and SOX17) that participate in several biological processes for the maintenance of cell normality. We analyzed DNA methylation by methylation-specific PCR (MSP) and HPV infection using the INNO‑LiPA genotyping kit in 59 samples diagnostic of normal cervical tissue (non-SIL), 107 low-grade squamous intraepithelial lesions (LSILs), 29 high-grade squamous intraepithelial lesions (HSILs) and 51 cervical cancers (CCs).ResultsWe found that all samples of LSIL, HSIL, and CC were HPV-positive, and the genotypes with higher frequencies were 16, 18, 51 and 56. In general, the genes analyzed displayed a significant tendency toward an increase in methylation levels according to increasing cervical lesion severity, except for the CDH13 gene. High CpG island methylator phenotype (CIMP) was associated with a 50.6-fold (95% CI 4.72–2267.3)-increased risk of HSIL and a 122-fold risk of CC (95% CI 10.04–5349.7).ConclusionsWe found that CIMP high was significantly associated with HSIL and CC risk. These results could indicate that CIMP together with HR-HPV infection and other factors participates in the development of HSIL and CC.
HobPre: accurate prediction of human oral bioavailability for small molecules
Authors: Wei Min , Zhang Xudong , Pan Xiaolin , Wang Bo , Ji Changge , Qi Yifei , Zhang John Z. H. 
Published: January 2022

AbstractHuman oral bioavailability (HOB) is a key factor in determining the fate of new drugs in clinical trials. HOB is conventionally measured using expensive and time-consuming experimental tests. The use of computational models to evaluate HOB before the synthesis of new drugs will be beneficial to the drug development process. In this study, a total of 1588 drug molecules with HOB data were collected from the literature for the development of a classifying model that uses the consensus predictions of five random forest models. The consensus model shows excellent prediction accuracies on two independent test sets with two cutoffs of 20% and 50% for classification of molecules. The analysis of the importance of the input variables allowed the identification of the main molecular descriptors that affect the HOB class value. The model is available as a web server at www.icdrug.com/ICDrug/ADMET for quick assessment of oral bioavailability for small molecules. The results from this study provide an accurate and easy-to-use tool for screening of drug candidates based on HOB, which may be used to reduce the risk of failure in late stage of drug development.
Graphical Abstract
Treatment outcomes of HIV patients with hepatitis B and C virus co-infections in Southwest China: an observational cohort study
Authors: Jia Jingya , Zhu Qiuying , Deng Luojia , Lan Guanghua , Johnson Andrew , Chen Huanhuan , Shen Zhiyong , Li Jianjun , Xing Hui , Ruan Yuhua , Li Jing , Lu Hui , Vermund Sten H. , Zhu Jinhui , Qian Han-Zhu 
Published: January 2022

AbstractBackgroundAntiretroviral therapy (ART) has reduced mortality among people living with HIV (PLWH) in China, but co-infections of hepatitis B virus (HBV) and hepatitis C virus (HCV) may individually or jointly reduce the effect of ART. This study aimed to evaluate the impacts of HBV/HCV coinfections on treatment drop-out and mortality among PLWH on ART.MethodsA retrospective cohort study analysis of 58 239 people living with HIV (PLWH) who initiated antiretroviral therapy (ART) during 2010–2018 was conducted in Guangxi Province, China. Data were from the observational database of the National Free Antiretroviral Treatment Program. Cox proportional hazard models were fitted to evaluate the effects of baseline infection of HBV or HCV or both on death and treatment attrition among PLWH.ResultsOur study showed high prevalence of HBV (11.5%), HCV (6.6%) and HBV-HCV (1.5%) co-infections. The overall mortality rate and treatment attrition rate was 2.95 [95% confidence interval (CI) 2.88–3.02] and 5.92 (95%CI5.82–6.01) per 100 person-years, respectively. Compared with HIV-only patients, HBV-co-infected patients had 42% higher mortality [adjusted hazard ratio (aHR) = 1.42; 95%CI1.32–1.54], HCV-co-infected patients had 65% higher mortality (aHR = 1.65; 95%CI1.47–1.86), and patients with both HCV and HBV co-infections had 123% higher mortality (aHR = 2.23; 95%CI1.87–2.66).ConclusionsHBV and HCV coinfection may have an additive effect on increasing the risk of all-cause death among PLWH who are on ART. It is suggested that there is need for primary prevention and access to effective hepatitis treatment for PLWH.Graphical Abstract
Public and health professional epidemic risk perceptions in countries that are highly vulnerable to epidemics: a systematic review
Authors: Abdelmagid Nada , Checchi Francesco , Roberts Bayard 
Published: January 2022

Abstract
Background
Risk communication interventions during epidemics aim to modify risk perceptions to achieve rapid shifts in population health behaviours. Exposure to frequent and often concurrent epidemics may influence how the public and health professionals perceive and respond to epidemic risks. This review aimed to systematically examine the evidence on risk perceptions of epidemic-prone diseases in countries highly vulnerable to epidemics.

Methods
We conducted a systematic review using PRISMA standards. We included peer-reviewed studies describing or measuring risk perceptions of epidemic-prone diseases among the general adult population or health professionals in 62 countries considered highly vulnerable to epidemics. We searched seven bibliographic databases and applied a four-stage screening and selection process, followed by quality appraisal. We conducted a narrative meta-synthesis and descriptive summary of the evidence, guided by the Social Amplification of Risk Framework.

Results
Fifty-six studies were eligible for the final review. They were conducted in eighteen countries and addressed thirteen epidemic-prone diseases. Forty-five studies were quantitative, six qualitative and five used mixed methods. Forty-one studies described epidemic risk perceptions in the general public and nineteen among health professionals. Perceived severity of epidemic-prone diseases appeared high across public and health professional populations. However, perceived likelihood of acquiring disease varied from low to moderate to high among the general public, and appeared consistently high amongst health professionals. Other occupational groups with high exposure to specific diseases, such as bushmeat handlers, reported even lower perceived likelihood than the general population. Among health professionals, the safety and effectiveness of the work environment and of the broader health system response influenced perceptions. Among the general population, disease severity, familiarity and controllability of diseases were influential factors. However, the evidence on how epidemic risk perceptions are formed or modified in these populations is limited.

Conclusions
The evidence affords some insights into patterns of epidemic risk perception and influencing factors, but inadequately explores what underlies perceptions and their variability, particularly among diseases, populations and over time. Approaches to defining and measuring epidemic risk perceptions are relatively underdeveloped.

Graphical Abstract
DMBT1 expression and neutrophil-to-lymphocyte ratio during necrotizing enterocolitis are influenced by impaired perfusion due to cardiac anomalies
Authors: Diez Sonja , Besendörfer Manuel , Weyerer Veronika , Hartmann Arndt , Moosmann Julia , Weiss Christel , Renner Marcus , Müller Hanna 
Published: January 2022

Abstract
Background
Deleted in malignant brain tumors 1 (DMBT1) is involved in innate immunity and epithelial differentiation. It has been proven to play a role in various states of inflammation or hypoxia of fetal gastrointestinal and pulmonary diseases. Discrimination of pathogenesis in necrotizing enterocolitis (NEC) based on cardiac status improves the understanding of NEC in different patient subgroups. We aimed at examining DMBT1 expressions regarding their association with cardiac status leading to impaired intestinal perfusion, intraoperative bacteria proof, and a fulminant course of NEC.

Methods
Twenty-eight patients with NEC were treated surgically between 2010 and 2019 at our institution. DMBT1 expression was examined in intestinal sections using immunohistochemistry to detect DMBT1 protein. Associations of clinical parameters and DMBT1 expression were analyzed.

Results
We examined DMBT1 levels in 10 patients without cardiac defects and 18 patients with persisting ductus arteriosus (PDA) and congenital heart defects (CHD). Compared to patients without cardiac malformations, DMBT1 levels tended to score higher in patients with PDA/CHD (p = 0.2113) and were negatively correlated with C-reactive protein in these infants (p = 0.0172; r = − 0.5533). The number of DMBT1-expressing macrophages was elevated in the PDA/CHD-subgroup (p = 0.0399). Ratios of neutrophils and monocytes to lymphocytes were significantly higher in infants with PDA/CHD (p = 0.0319 and 0.0493). DMBT1 expression was significantly associated with positive bacterial culture of intraoperative swabs (p = 0.0252) and DMBT1 expression of the serosa was associated with a fulminant course of NEC (p = 0.0239).

Conclusions
This study demonstrates that DMBT1 expression may be influenced by cardiac anomalies with an impaired intestinal perfusion in the neonatal intestine. NEC in PDA/CHD infants is associated with more DMBT1-positive macrophages and a significantly elevated neutrophil-to-lymphocyte ratio.
Modeling the public attitude towards organic foods: a big data and text mining approach
Authors: Singh Anupam , Glińska-Neweś Aldona 
Published: January 2022

AbstractThis study aims to identify the topics that users post on Twitter about organic foods and to analyze the emotion-based sentiment of those tweets. The study addresses a call for an application of big data and text mining in different fields of research, as well as proposes more objective research methods in studies on food consumption. There is a growing interest in understanding consumer choices for foods which are caused by the predominant contribution of the food industry to climate change. So far, customer attitudes towards organic food have been studied mostly with self-reported methods, such as questionnaires and interviews, which have many limitations. Therefore, in the present study, we used big data and text mining techniques as more objective methods to analyze the public attitude about organic foods. A total of 43,724 Twitter posts were extracted with streaming Application Programming Interface (API). Latent Dirichlet Allocation (LDA) algorithm was applied for topic modeling. A test of topic significance was performed to evaluate the quality of the topics. Public sentiment was analyzed based on the NRC emotion lexicon by utilizingSyuzhetpackage. Topic modeling results showed that people discuss on variety of themes related to organic foods such as plant-based diet, saving the planet, organic farming and standardization, authenticity, and food delivery, etc. Sentiment analysis results suggest that people view organic foods positively, though there are also people who are skeptical about the claims that organic foods are natural and free from chemicals and pesticides. The study contributes to the field of consumer behavior by implementing research methods grounded in text mining and big data. The study contributes also to the advancement of research in the field of sustainable food consumption by providing a fresh perspective on public attitude toward organic foods, filling the gaps in existing literature and research.
Programming big data analysis: principles and solutions
Authors: Belcastro Loris , Cantini Riccardo , Marozzo Fabrizio , Orsino Alessio , Talia Domenico , Trunfio Paolo 
Published: January 2022

AbstractIn the age of the Internet of Things and social media platforms, huge amounts of digital data are generated by and collected from many sources, including sensors, mobile devices, wearable trackers and security cameras. This data, commonly referred to as Big Data, is challenging current storage, processing, and analysis capabilities. New models, languages, systems and algorithms continue to be developed to effectively collect, store, analyze and learn from Big Data. Most of the recent surveys provide a global analysis of the tools that are used in the main phases of Big Data management (generation, acquisition, storage, querying and visualization of data). Differently, this work analyzes and reviews parallel and distributed paradigms, languages and systems used today to analyze and learn from Big Data on scalable computers. In particular, we provide an in-depth analysis of the properties of the main parallel programming paradigms (MapReduce, workflow, BSP, message passing, and SQL-like) and, through programming examples, we describe the most used systems for Big Data analysis (e.g., Hadoop, Spark, and Storm). Furthermore, we discuss and compare the different systems by highlighting the main features of each of them, their diffusion (community of developers and users) and the main advantages and disadvantages of using them to implement Big Data analysis applications. The final goal of this work is to help designers and developers in identifying and selecting the best/appropriate programming solution based on their skills, hardware availability, application domains and purposes, and also considering the support provided by the developer community.
Machine learning approaches in Covid-19 severity risk prediction in Morocco
Authors: Laatifi Mariam , Douzi Samira , Bouklouz Abdelaziz , Ezzine Hind , Jaafari Jaafar , Zaid Younes , El Ouahidi Bouabid , Naciri Mariam 
Published: January 2022

AbstractThe purpose of this study is to develop and test machine learning-based models for COVID-19 severity prediction. COVID-19 test samples from 337 COVID-19 positive patients at Cheikh Zaid Hospital were grouped according to the severity of their illness. Ours is the first study to estimate illness severity by combining biological and non-biological data from patients with COVID-19. Moreover the use of ML for therapeutic purposes in Morocco is currently restricted, and ours is the first study to investigate the severity of COVID-19. When data analysis approaches were used to uncover patterns and essential characteristics in the data, C-reactive protein, platelets, and D-dimers were determined to be the most associated to COVID-19 severity prediction. In this research, many data reduction algorithms were used, and Machine Learning models were trained to predict the severity of sickness using patient data. A new feature engineering method based on topological data analysis called Uniform Manifold Approximation and Projection (UMAP) shown that it achieves better results. It has 100% accuracy, specificity, sensitivity, and ROC curve in conducting a prognostic prediction using different machine learning classifiers such as X_GBoost, AdaBoost, Random Forest, and ExtraTrees. The proposed approach aims to assist hospitals and medical facilities in determining who should be seen first and who has a higher priority for admission to the hospital.
IoT information theft prediction using ensemble feature selection
Authors: Leevy Joffrey L. , Hancock John , Khoshgoftaar Taghi M. , Peterson Jared M. 
Published: January 2022

AbstractThe recent years have seen a proliferation of Internet of Things (IoT) devices and an associated security risk from an increasing volume of malicious traffic worldwide. For this reason, datasets such as Bot-IoT were created to train machine learning classifiers to identify attack traffic in IoT networks. In this study, we build predictive models with Bot-IoT to detect attacks represented by dataset instances from the Information Theft category, as well as dataset instances from the data exfiltration and keylogging subcategories. Our contribution is centered on the evaluation of ensemble feature selection techniques (FSTs) on classification performance for these specific attack instances. A group or ensemble of FSTs will often perform better than the best individual technique. The classifiers that we use are a diverse set of four ensemble learners (Light GBM, CatBoost, XGBoost, and random forest (RF)) and four non-ensemble learners (logistic regression (LR), decision tree (DT), Naive Bayes (NB), and a multi-layer perceptron (MLP)). The metrics used for evaluating classification performance are area under the receiver operating characteristic curve (AUC) and Area Under the precision-recall curve (AUPRC). For the most part, we determined that our ensemble FSTs do not affect classification performance but are beneficial because feature reduction eases computational burden and provides insight through improved data visualization.
A model for examining middle school students’ STEM integration behavior in a national technology competition
Authors: Ku Chih-Jung , Hsu Ying-Shao , Chang Mei-Chen , Lin Kuen-Yi 
Published: January 2022

Abstract
Background
Research on teaching and learning for science, technology, engineering, and mathematics (STEM) subjects has increased, and has demonstrated the importance of integrating interdisciplinary knowledge and skills. Our research model was based on the theory of planned behavior (TPB) and the data were analyzed by partial least squares-structural equation modeling. The present study aims to identify factors that play an important role in students’ ability to integrate STEM knowledge and skills.

Results
Data were collected from participants who had won awards in local contests and represented their regions in a national technology competition. The reliability and validity of our instrument, the Students’ STEM Integration Scale, were verified. The findings demonstrated that students’ intentions to integrate STEM knowledge and skills to solve complex problems can be predicted by their attitude and perceived behavioral control.

Conclusions
This work highlights factors which are associated with students’ intentions to integrate interdisciplinary knowledge and skills, and serves as a reference for research on the gap between intentions and actual behavior. The findings could help teachers and instructors design STEM-based activities to enhance students’ attitudes, perceived behavioral control, and intentions, to improve their ability to integrate STEM knowledge and skills.
Ozonized biochar filtrate effects on the growth of Pseudomonas putida and cyanobacteria Synechococcus elongatus PCC 7942
Authors: Sacko Oumar , Engle Nancy L. , Tschaplinski Timothy J. , Kumar Sandeep , Lee James Weifu 
Published: January 2022

AbstractBackgroundBiochar ozonization was previously shown to dramatically increase its cation exchange capacity, thus improving its nutrient retention capacity. The potential soil application of ozonized biochar warrants the need for a toxicity study that investigates its effects on microorganisms.ResultsIn the study presented here, we found that the filtrates collected from ozonized pine 400 biochar and ozonized rogue biochar did not have any inhibitory effects on the soil environmental bacteriaPseudomonas putida,even at high dissolved organic carbon (DOC) concentrations of 300 ppm. However, the growth ofSynechococcus elongatusPCC 7942 was inhibited by the ozonized biochar filtrates at DOC concentrations greater than 75 ppm. Further tests showed the presence of some potential inhibitory compounds (terephthalic acid andp-toluic acid) in the filtrate of non-ozonized pine 400 biochar; these compounds were greatly reduced upon wet-ozonization of the biochar material. Nutrient detection tests also showed that dry-ozonization of rogue biochar enhanced the availability of nitrate and phosphate in its filtrate, a property that may be desirable for soil application.ConclusionOzonized biochar substances can support soil environmental bacteriumPseudomonas putidagrowth, since ozonization detoxifies the potential inhibitory aromatic molecules.Graphical Abstract
An increase in circulating levels of branched-chain amino acids during hemodialysis with regard to protein breakdown: three case reports
Authors: Fujiwara Masako , Ando Itiro , Shishido You , Imai Yutaka , Terawaki Hiroyuki 
Published: January 2022

Abstract
Background
Hemodialysis (HD) is a protein catabolic event. However, the amino acid (AA) kinetics during HD sessions involved in protein breakdown have not been well investigated in patients with and without diabetes mellitus (DM).

Case presentation
Three patients (two patients with DM and one patient without DM) underwent fasting HD. Plasma levels of branched-chain AAs (BCAA; leucine, isoleucine, and valine), major non-essential AAs (alanine and glutamine, including glutamate), insulin, and ketone bodies were measured every hour during each HD session. After the start of the HD session, the plasma levels of insulin and all BCAAs dropped simultaneously. There was a significant subsequent increase in the plasma level of leucine and isoleucine levels, while valine levels remained constant. However, the recovery in levels of BCAAs during HD indicated a profound amount of BCAAs entering the blood from body tissues such as muscles. BCAAs may have surpassed their removal by HD. Ketone body levels increased continuously from the start of the sessions and reached high values in patients with DM. Synchronous changes in insulin depletion and an increase in the levels of ketone bodies may indicate disruption of energy metabolism.

Conclusions
This is the first report to demonstrate the time course of the changes in circulating levels of BCAAs and related metabolites in energy homeostasis during HD. An increase in BCAA levels during HD was found to be due to their transfer from the body tissue which suggested protein breakdown.
Efficacy of the entomopathogenic nematode isolate Heterorhabditis taysearae to control the cotton leafworm, Spodoptera littoralis (Boisd.) (Lepidoptera: Noctuidae)
Authors: Abd El Azim Amany M. 
Published: January 2022

Abstract
Background
Entomopathogenic nematodes (EPNs) are promising alternatives since they have many characteristics as a biological control agent against insect pests.

Results
Among indigenous EPN isolated, adapted to local environmental conditions by employing the Galleria baiting strategy, only one sample was positive for the presence of EPNs. The new isolate was identified at the species level using DNA sequencing of the internal transcribed spacer region and the Basic Local Alignment Search Tool search of GenBank showed that the isolate had a high similarity (99%) with that sequence available for Heterorhabditis taysearae. The pathogenicity of the EPN isolate was tested against the cotton leaf worm, Spodoptera littoralis (Boisd.) (Lepidoptera: Noctuidae), using different concentrations (60, 90, 120 and 150 IJs/larvae). Data showed that 150 IJs/larvae caused 100% mortality rate, followed by 120 IJs/larvae (90%), while 60 IJs/larvae showed the lowest rate (60%) after 72 h.

Conclusions
The present study indicated that the native isolate of EPN could be recommended against S. littoralis as an efficient tool in its control programs.