bluuebunny/crossref_metadata_2025_split huggingface dataset:
Early Prediction of Lupus Disease: A Study on the Variations of Decision Tree Models
Authors: Singh Jagjiven Kaur Jasber , Ponnusamy Raja Rajeswari , Ling Elaine Chan Wan , Chin Lim Sern 
Published: September 2022

Abstract

Systematic Lupus Erythematosus (SLE) is an irreversible autoimmune disease that has seen to bring a lot of negative effect on the human body. It has become a very challenging task in predicting the prevalence of Lupus in patients. It has slowly gained popularity among many researchers to study the prevalence of this disease and developing prediction models that not only study the prevalence of the disease but is also able to predict suitable dosage requirements, treatment effectiveness and the severity of the disease in patients. All of these is usually done with medical records or clinical data that has different attributes related and significant to the analysis done. With the advancement in machine learning models and ensemble techniques, accurate prediction models have been developed. However, these models are not able to explain the significant contributing factors as well as correctly classify the severity of the disease. Decision Tree Classifier, Random Forest Classifier and Extreme Gradient Boosting (XGBoost) are the models that will be used in this paper to predict the early prevalence to Lupus Disease in patients using clinical records. The most significant factors affecting Systematic Lupus Erythematosus (SLE) will then be identified to aid medical practitioners to take suitable preventive measures that can manage the complications that arise from the disease. Hence, this paper aims to assess the performance of tree models by performing several experiments on the hyper parameters to develop a more accurate model that is able to classify Lupus Disease in patients in the early stages. Findings revealed that the best model was the Random Forest Classifier with parameter tuning. The most significant factor that affected the presence of Lupus Disease in patients was identified as the Ethnicity and the Renal Outcome or the kidney function of the patients.
Association Between Knee Osteoarthritis and the Risk of Cardiovascular Disease and the Synergistic Adverse Effects of Lack of Exercise
Authors: Park Dojoon , Park Yong-Moon , Ko Seung-Hyun , Choi Youn-Ho , Min Dong-Uk , Ahn Jae-Hyun , Kim Bongseong , Koh Hae-Seok , Han Kyungdo 
Published: September 2022

Abstract
Objectives: We aimed to determine whether knee OA is associated with CVD risk and to evaluate whether the association differs by exercise behavior.
Methods: We used Korea National Health Insurance Service (KNHIS) database and included 201,466 participants (7,572 subjects diagnosed with knee OA) who underwent health screening between 2009 and 2015. Those who had been diagnosed with knee OA or CVD before the index year were excluded. Cox proportional hazard models were used after adjusting for sociodemographic and CVD risk factors to evaluate the association between knee OA and CVD risk. Stratification analysis was further performed to determine the effect of exercise behavior on this relationship.
Results: During a median follow-up of 7.06 ± 2.24 years, 8,743 CVD (2,510 MI and 6,553 stroke) cases developed. Individuals with knee OA had increased risks of CVD (hazard ratio (HR) 1.26, 95% confidence interval (CI) 1.15–1.38), myocardial infarction (MI) (HR 1.20, 95% CI 1.00–1.44), and stroke (HR 1.29, 95% CI 1.16–1.43) compared with those without knee OA. Those with knee OA who did not exercise had an increased risk of CVD (HR 1.25, 95% CI 1.11-1.40), whereas no significant increased CVD risk was observed in those with knee OA who exercised at least once a week (HR 1.11, 95% CI 0.96-1.28).
Conclusion: Knee OA was independently associated with an increased risk of CVD. Lack of exercise might have a synergistic adverse effect on the association between knee OA and CVD.
Predicting microbial community compositions in wastewater treatment plants using artificial neural networks
Authors: Liu Xiaonan , Nie Yong , Wu Xiao-Lei 
Published: September 2022

Abstract
Background: Activated sludge (AS) of wastewater treatment plants (WWTP) is one of the world’s largest artificial microbial ecosystems and the microbial community of the AS system is closely related to WWTP performance. However, how to predict its community structure is still unclear.
Results: Here, we used artificial neural networks (ANN) to predict the microbial compositions of AS systems collected from WWTPs located worldwide. The predictive accuracy R21:1 of Shannon-Wiener index reached 60.42%, and the average R21:1 of amplicon sequence variants (ASVs) appearing in at least 10% of samples and core taxa were 35.09% and 42.99%, respectively. We also found that the predictability of ASVs was significantly positively correlated with their relative abundance and occurrence frequency, but significantly negatively correlated with potential migration rate. The typical functional groups such as nitrifiers, denitrifiers, polyphosphate-accumulating organisms (PAOs), glycogen-accumulating organisms (GAOs), and filamentous organisms in AS systems could also be well recovered using ANN models, with R21:1 ranging from 32.62% to 56.81%. Furthermore, we found that industry wastewater source (IndConInf) had good predictive abilities, although its correlation with ASVs in the Mantel test analysis was weak, which suggested important factors that cannot be identified using traditional methods may be highlight by the ANN model.
Conclusions: We demonstrated that the microbial compositions and major functional groups of AS systems are predictable using our approach, and IndConInf has a significant impact on the prediction. Our results provide a better understanding of the factors affecting AS communities through the prediction of the microbial community of AS systems, which could lead to insights for improved operating parameters and control of community structure.
Approach to the prevalence and comorbidity of neurodevelopmental disorders in primary care consultations on the island of Menorca in 6-year-old children
Authors: Francés Lorena , Caules Jessica , Ruiz Antoni , Soler C. Virgínia , Hervás Amaia , Fernández Alberto , Rodríguez-Quiroga Alberto , Quintero Javier 
Published: September 2022

Abstract
Background
Few studies estimate the real prevalence of neurodevelopmental disorders according to DSM-5 in Spain and worldwide. However, there are disparate prevalence figures. We consider research in this field essential to improve early detection, secondary prevention, and health planning.
Methods
The Minikid ADHD and TICS- Mini International Neuropsychiatric Interview for Children and Adolescents, The Autism Spectrum Quotient (Children’s version, AQ- Child) and a protocol of general medical questions were administered for screening purposes. The PROLEXIA battery in ages between 4–6 years was used to directly assess children. Parents provided information on emotional, medical, and school aspects. The final population evaluated using these tools was made up of 291 6-year-old subjects.
Results
The overall risk of presenting a neurodevelopmental disorder was 55.4% of the sample. A 23.4% risk of presenting attention deficit hyperactivity disorder (ADHD) in any modality (inattentive, hyperactive-impulsive and combined), a 2.8% risk of developing ASD, a 30.6% risk of presenting a learning disorder with reading difficulties, a 5.5% risk of tics and a 22.5% of language problems (incomprehensible language or minor language problems) were detected. The most common combinations were learning and language difficulties in 6.9% of the sample. The second most frequent combination was the presence of learning and language difficulties and the risk of ADHD in 4.5%.
Conclusions
The prevalence of risks detected in our sample seems to be consistent with national and international studies. A significant proportion had never been previously diagnosed, so it would be convenient to insist on early detection programs.
lncRNA LINC00652 is a significant prognostic and diagnostic biomarker in the Isfahan Breast cancer patients, correlated to ID1: integrated bioinformatics and experimental approach
Authors: Saeri Mahdies , Ramezani Zahra , Abbasi Samaneh , Foroutan Shamim , Azadeh Mansoureh 
Published: September 2022

Abstract
Background
The primary goal of this study is to compare the levels of expression of ID1, LINC00101, and LINC00652 in Isfahan Breast cancer samples to control samples. Understanding the relationship between variations in RNA expression levels and the clinicopathological statuses of Isfahan patients is another important goal of this research.
Method
R Studio evaluated 104 breast cancer tissue and 17 control samples in the GSE42568 microarray datasets to identify a relevant dysregulated gene. The limma package was used to conduct differential expression analysis. The DEGs were used to test mRNA-lncRNA interactions using the co-lncRNA online database. The qRT-PCR experiment aimed to determine the degree of expression of ID1, LINC001013, and LINC00652.
Result
Based on bioinformatics and experimental analyses, ID1 has a significant low-expression in the BC samples. Furthermore, lncRNAs LINC00101 and LINC00652 have significant high expression in the BC samples. ID1 and LINC00652 might be the two potential diagnostic (ROC analysis) and prognostic (Clinicopathological analysis) biomarkers of BC.
Discussion
According to ROC and clinicopathological analyses, ID1 and LINC00652 could be the two significant prognosis and diagnosis biomarkers in Isfahan Breast cancer patients.
Hybrid Modeling of Hetero-Agglomeration Processes: A Framework for Model Selection and Arrangement
Authors: Rhein Frank , Hibbe Leonard , Nirschl Hermann 
Published: September 2022

Abstract
Modeling of hetero-agglomeration processes is invaluable for a variety of applications in particle technology. Traditionally, population balance equations (PBE) are employed, however, calculation of kinetic rates is challenging due to heterogeneous surface properties and insufficient material data. This study investigates how the integration of machine learning (ML) techniques - resulting in so-called hybrid models (HM) - can help to integrate experimental data and close this gap. A variety of ML algorithms can either be used to estimate kinetic rates for the PBE (serial HM) or to correct the PBE's output (parallel HM). As the optimal choice of the HM architecture is highly problem-dependent, we propose a general and objective framework for model selection and arrangement. A repeated nested cross-validation with integrated hyper-parameter optimization ensures a fair and meaningful comparison between different HMs. This framework was subsequently applied to experimental data of magnetic seeded filtration, where prediction errors of the pure PBE were reduced by applying the hybrid modeling approach. The framework helped to identify that for the given data set, serial outperforms parallel arrangement and that more advanced ML algorithms provide better interpolation ability. Additionally, it enables to draw inferences to general properties of the underlying PBE model and a statistical investigation of hyper-parameter optimization that paves the way for further improvements.
Investigation on ratios of some common diseases and health risks relating to dioxin exposure in Da Nang, Vietnam
Authors: Sau Trinh Khac , Hung Dinh Viet , Le Quan Van , Quang Le Van 
Published: September 2022

Abstract
Analyze the current status of disease patterns and health risks of people in Thanh Khe district (TK group) residing near areas where Agent Orange (AO)/dioxin was stored in Da Nang airport compared with people in Son Tra district (ST group - control group), Da Nang city. A cross-sectional descriptive study. This is a survey of 402 families residing in the TK group and 400 ones residing in the ST group. Subjects in the age group 45–69 accounted for a high proportion, of which women accounted for the majority, the residence time was stable and long, mainly over 15 years in both groups. People in the TK group had direct and indirect exposure to AO/dioxin accounted for a higher proportion than in the ST group. The incidence of some diseases that are recommended to be related to AO/dioxin such as hypertension (33.6%), diabetes mellitus (8.96%), and other diseases: stomach (23.0%), joints (40.3%), ear-nose-throat (ENT, 11.2%), urinary system (7.21%) compared with the ST group. Exposure to AO/dioxin has changed the disease pattern and increased some diseases in people residing near areas where AO/dioxin was previously stored.
Identification of Biomarker Genes Based on Multi-Omics Analysis in Non- Small Cell Lung Cancer
Authors: Xia Ji , He Hai-bin , Liu Ying , Wang Yi , Shu Kun-Xian , Ma Ming-Yue 
Published: September 2022

Abstract
Background: Non-small cell lung cancer (NSCLC) is a complex disease with a high mortality rate and a poor prognosis, but its molecular mechanisms and effective biomarkers are still unclear. Comprehensive analysis of multiple histological data can effectively exclude random events and is helpful in improving the reliability of the findings. In this study, we used three types of omics data, RNA-seq, microRNA-seq, and DNA methylation data, from public databases to explore the potential biomarker genes oftwo major subtypes of NSCLC.
Results: Through the combined differential analysis of multi-omics, we found 873 and 1378 potential high-risk genes in LUAD and LUSC, respectively. Then, we used WGCNA and PPI analyses to identify hub-genes and LASSO regressionto construct prognostic models, and we obtained 15 prognostic genes. We also used survival analysis, univariate COX analysis, and GEO datasets to validate prognostic genes. Finally, we found ten genes associated with NSCLC, and eight of them have been reported in previous research.
Conclusions: In this study, we have provided a reliable analysis method for predicting biomarker genes of complex diseases. Two novel biomarker genes were identified: NES and ESAM. The two genes were both gene expression down-regulation and DNA methylation up-regulation, and regulated by miR-122and miR-154. Moreover, the NES gene can contribute to the clinical diagnosis and prognosis of NSCLC.
Handling and preservation of buffy coat isolated T cells for homologous dendritic cell co-cultures
Authors: Alobaid Meshal A. 
Published: September 2022

Abstract
The use of T cells is increasing both in healthcare and in research yet the preservation methodologies for longer periods of times are yet to be optimized. In order to overcome these issues, we have optimized a protocol in sample handling and preservation of T cells in order to perform a successful donor homologous co-culture with DCs and preserve these cells for subsequent testing. This method will help in saving time and effort as well as the ease of use for experiments requiring use of T cells in mono or co-cultures. Handling and preservation of T cells using our methodology showed stability and viability of these cells in co-cultures. Data showed viability of > 93% before and after liquid nitrogen preservation. Moreover, preserved cells had no unspecific activation which can be seen in unchanged expression of the T cell activation marker CD25. T cell proliferation profile showed that preserved T cells used in DC-T cell co-cultures (LPS stimulated DCs) had the ability to interact and proliferate indicating potency of these cells. This provides evidence of the efficiency of our handling and preservation methodology in maintaining cell viability and stability. Preserving donor T cells would facilitate reuse of these cells in donor homologous co-cultures reducing inconvenience of multiple donations of fresh blood and provides accessibility of the same population of T cells for experiments that requires repetition, commercial availability of the cells or for preservation of cells for clinical therapies such as chimeric antigen receptor T cells.
Application of the Xi robotic platform for familial adenomatous polyposis with ultra-low rectal cancer: exploration of minimally invasive and refined therapies
Authors: Xiong Huan , Wang Jiaqi , Hu Hanqing , Yuan Ziming , Wang Yuliuming , Qiao Tianyu , Ma Tianyi , Wang Chunlin , Wang Zitong , Tang Qingchao 
Published: September 2022

Abstract
When a Familial adenomatous polyposis (FAP) patient's rectal polyp undergoes malignant transformation, the surgeon needs to consider how to balance the quality of surgery with the patient's quality of life. Here we present a case of robotic surgery in a patient with familial adenomatous polyposis and ultra-low rectal cancer. Fiberoptic colonoscopy found that hundreds of polyp-like bulges were diffusely distributed throughout the colon, and a malignant mass was found at the end of the rectum. The patient underwent total colectomy with abdominoperineal extended radical resection for rectal cancer using the Xi robotic platform. The patient recovered well in the postoperative period. The ileostomy was well used. And the patient was in good health and metastasis-free at nine months postoperatively. We identified total colectomy combined with extended radical rectal resection under the assistance of the da Vinci robot platform is of great benefit to the patient.
Neiella litorisoli sp. nov., an alginate lyase – excreting bacterium from South China Sea
Authors: Sun Jingran , Mo Kunlian , Li Xue , Hu Yonghua , Liu Zhiyuan , Huang Huiqin 
Published: September 2022

Abstract
A Gram-stain-negative, rod-shaped bacterium, designated HB171785T, was isolated from soil sample collected from Qishui Bay, Hainan, China. The strain grew optimally at pH 7–8, 40–45°C and with NaCl 3–4%. The predominant isoprenoid quinone was found to be Q-8 and the major fatty acids were C16:0, C16:1 ω7c / C16:1 ω6c, C18:1 ω7c / C18:1 ω6c and C12:0 3OH. The polar lipids contained diphosphatidylglycerol, phosphatidylglycerol and phosphatidylethanolamine. The size of the draft genome was 4.32 Mbp with G + C content 49.7%. Phylogenetic analysis of 16S rRNA gene sequence indicated that strain HB171785T belonged to the order Alteromonadales, and the closest phylogenetically related species were Neiella marina j221T, “Neiella holothuriorum” 126T and Echinimonas agarilytica LMG2520T with the similarities of 98.2, 96.0 and 95.0%, respectively. The highest values of average nucleotide identity (ANI), digital DNA-DNA hybridization (dDDH) and average amino acid identity (AAI) were found to be 82.9, 25.4 and 89.2%, respectively, between the isolate and its closest neighbor Neiella marina j221T. The combined phylogenetic relatedness, phenotypic and genotypic features supported the conclusion that strain HB171785T represents a novel species of the genus Neiella, for which the name Neiella litorisoli sp. nov. is proposed. The type strain is HB171785T (= MCCC 1K04625T = KCTC 82319T).
Identification and Verification of Key MiRNAs Associated with Intervertebral
Disc Degeneration
Authors: Liu Jianwei , Li Rong , Lyv Peizhen 
Published: September 2022

Background:
Intervertebral Disc Degeneration (IDD) is a heterogeneous spinal disease
whose underlying molecular mechanism is unclear.

Objectives:
This study aimed to identify, profile, and analyze microRNAs (miRNAs) related to IDD.

Method:
Microarray Gene Expression IDD data (GSE63492) were downloaded from Gene Expression
Omnibus datasets. We employed Weighted Gene Co-Expression Network Analysis (WGCNA)
to construct a miRNA co-expression network, and the miRNAs related to the IDD stage were detected.
The number of differentially expressed miRNAs between normal and degenerated nucleus
pulposus tissues was calculated. Twenty-three clinical specimens were used to validate the expression
of miRNAs using qRT-PCR.

Results:
WGCNA identified 48 miRNAs significantly related to the IDD stage, and 94 miRNAs
that were significantly different between normal and degenerated nucleus pulposus tissues. We selected
32 overlapping miRNAs and identified 347 corresponding target genes. The integrative analysis
revealed the biological function and pathways of these targeted genes. Analysis of clinical specimens
validated that hsa-miR-4534 was upregulated in IDD, whereas hsa-miR-1827 and hsa-miR-
185-5p were downregulated in IDD.

Conclusion:
This study has identified a subset of miRNAs that are related to IDD pathogenesis and
hub miRNAs that are keys to the IDD co-expression network, which may potentially be utilized as
indicators for treatment.
Advances in Nanomaterial-based Biosensors for Determination of
Glycated Hemoglobin
Authors: Noviana Eka , Siswanto Soni , Budi Hastuti Agustina Ari Murti 
Published: September 2022

Abstract:
Diabetes is a major public health burden whose prevalence has been steadily increasing over the past decades. Glycated hemoglobin (HbA1c) is currently the gold standard for diagnostics and monitoring of glycemic control in diabetes patients. HbA1c biosensors are often considered to be cost-effective alternatives for smaller testing laboratories or clinics unable to access other refer-ence methods. Many of these sensors deploy nanomaterials as recognition elements, detection la-bels, and/or transducers for achieving sensitive and selective detection of HbA1c. Nanomaterials have emerged as important sensor components due to their excellent optical and electrical proper-ties, tunable morphologies, and easy integration into multiple sensing platforms. In this review, we discuss the advantages of using nanomaterials to construct HbA1c sensors and various sensing strategies for HbA1c measurements. Key gaps between the current technologies with what is need-ed moving forward are also summarized.
Assessment of Obstructive Sleep Apnea among Adult Hypertensive
Patients on Follow-up at Jimma Medical Center
Authors: Etefa Tesema , Gerema Urge , Ayele Mengistu , Getachew Bekalu , Dereje Diriba , Hamba Niguse , Tesfaye Solomon 
Published: September 2022

Background:
Obstructive Sleep Apnea (OSA) is a disorder caused by the repetitive collapse of the upper airway during sleep. The pathophysiology of health problems related to OSA is most strongly linked to irregular hypoxia, which results in cell function damage. In our investigation, no determinants of the OSA were found. The pathophysiology of OSA-related health problems is most significantly associated with irregular hypoxia, which induces damage to cell functions. Determinants of the OSA were not identified in our study.

Objective:
The aim of this study was to assess obstructive sleep apnea among adult hypertensive patients on follows up at Jimma Medical center (JMC) in 2020.

Methods:
An institution-based descriptive cross-sectional study design was carried out at the JMC clinic during follow-up care. All hypertensive patients who attended the JMC's chronic follow-up clinic were our baseline populations, while those who gave their consent and met our inclusion criteria during the study period were enrolled as study participants. The data were sorted and entered into the computer using Epi-data version 3.1 and exported to the Statistical Package for Social Sciences (SPSS) version 20.0 for analysis. Frequency, percentage, and mean were calculated for descriptive statistics.

Result:
A total of 291 adult hypertension patients on follow-up care at the JMC were included in the study, comprising 155 (53.3%) men and 136 (46.7%) women. The age of the participants ranged from 2874 years, and the mean age was 51 years. Of the 291 hypertensive patients screened for OSA using the STOP-Bang questionnaire, 187 (64.3%) were classified as high risk for OSA.

Conclusion:
The present study showed that the prevalence of OSA is considerably high, with remarkable fluctuations and increases with age. It is also associated with gender. Men are most affected by OSA compared to women.