bluuebunny/crossref_metadata_2025_split huggingface dataset:
Bioinformatics Analysis based on Multiple Databases Identifies Hub Genes Associated with Hepatocellular Carcinoma
Authors: Zeng Lu , Fan Xiude , Wang Xiaoyun , Deng Huan , Zhang Kun , Zhang Xiaoge , He Shan , Li Na , Han Qunying , Liu Zhengwen 
Published: November 2019

Background:
Hepatocellular carcinoma (HCC) is the most common liver cancer and the
mechanisms of hepatocarcinogenesis remain elusive.

Objective:
This study aims to mine hub genes associated with HCC using multiple databases.

Methods:
Data sets GSE45267, GSE60502, GSE74656 were downloaded from GEO database. Differentially
expressed genes (DEGs) between HCC and control in each set were identified by limma software.
The GO term and KEGG pathway enrichment of the DEGs aggregated in the datasets (aggregated
DEGs) were analyzed using DAVID and KOBAS 3.0 databases. Protein-protein interaction (PPI)
network of the aggregated DEGs was constructed using STRING database. GSEA software was used
to verify the biological process. Association between hub genes and HCC prognosis was analyzed using
patients’ information from TCGA database by survminer R package.

Results:
From GSE45267, GSE60502 and GSE74656, 7583, 2349, and 553 DEGs were identified respectively.
A total of 221 aggregated DEGs, which were mainly enriched in 109 GO terms and 29
KEGG pathways, were identified. Cell cycle phase, mitotic cell cycle, cell division, nuclear division
and mitosis were the most significant GO terms. Metabolic pathways, cell cycle, chemical carcinogenesis,
retinol metabolism and fatty acid degradation were the main KEGG pathways. Nine hub genes
(TOP2A, NDC80, CDK1, CCNB1, KIF11, BUB1, CCNB2, CCNA2 and TTK) were selected by PPI
network and all of them were associated with prognosis of HCC patients.

Conclusion:
TOP2A, NDC80, CDK1, CCNB1, KIF11, BUB1, CCNB2, CCNA2 and TTK were hub
genes in HCC, which may be potential biomarkers of HCC and targets of HCC therapy.
Identification and Quantification of Antioxidant Compounds in Clarified Cashew Apple Juice ‘Cajuína’
Authors: Porto-Luz Rayssa G.L. , de Moura Amanda J.B. , da Silva Bibiana , Fett Roseane , da Mota Araújo Marcos A. , dos Reis Moreira-Araújo Regilda S. 
Published: November 2019

Background:Cajuína is obtained through clarification of cashew apple juice (Anacardium occidentale L.), a non-diluted and non-fermented beverage, which is prepared from the edible part of the pseudo-fruit.Objective:This study sought to identify and quantify phenolic compounds, determine vitamin C content, total flavonoid content, and the antioxidant activity of two commercial brands of clarified cashew apple juice (cajuína) produced in Piauí, Brazil.Methods:Spectrophotometric and high performance liquid chromatography were used.Results:A high vitamin C content was found mainly in brand A. A high total phenolic content was observed in brand A, and there was no significant difference in flavonoid content between the two brands. Cajuína had high antioxidant activity, which was confirmed by two methods. The highest value was found for brand A by the DPPH method.Conclusion:We concluded that this clarified cashew apple juice has a high antioxidant compound content, and it is a functional food.
Studies onIn vitroInteraction of Ampicillin andPolyalthia longifoliaLeaf Ethyl Acetate Fraction (PLEAF) by Checkerboard Method Against Methicillin ResistantStaphylococcus aureus(MRSA)
Authors: Kirubakari Balasupramaniam , Chen Yeng , Kanwar Jagat R. , Shin Lai N. , Sasidharan Sreenivasan 
Published: November 2019

Background:Polyalthia longifolia which originates from India is rich with various useful phytochemicals which are valuable for human health. Accordingly, the current study was conducted to evaluate the combinational antimicrobial activity of P. longifolia Ethyl Acetate Fraction (PLEAF) with ampicillin, antioxidant and cytotoxicity activities.Methods:The evaluation of the synergistic activity of PLEAF fraction and ampicillin against MRSA local isolate was conducted with various antimicrobial assays.Results:The Minimum Inhibitory Concentration (MIC) values of PLEAF fraction (62.5 μg/mL) and ampicillin (5000 μg/mL) were found to decrease to 15.63 μg/mL for PLEAF and 2500 μg/mL for ampicillin respectively in the Fractional Inhibitory Concentration (FIC) assay against the MRSA bacteria. The 2,2-diphenyl1-picrylhydrazyl (DPPH) and nitric oxide free radical scavenging activities showed that PLEAF fraction possessed high antioxidant activity and the combinational of PLEAF fraction and ampicillin exhibited moderate antioxidant activity. The total phenolic content (TPC) of PLEAF was 168.22 ± 0.00407 μg GAE/g of PLEAF fraction.Discussion:henolic compounds might be responsible for the observed antioxidant and antimicrobial activity of PLEAF fraction. In addition, in 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) cytotoxicity test against Vero cells the PLEAF fraction was proven to be non-toxic (98.14% of cell viability) and the combination of PLEAF fraction and ampicillin treatment against the Vero cells showed an improved cell viability (52.44%) as compared with ampicillin alone in the treated group.Conclusions:The PLEAF fraction works well in combination with ampicillin to kill the MRSA local resistance strain. PLEAF fraction also showed favourable antioxidant activity and improved Vero cell viability in the presence of ampicillin which is an important attribute of PLEAF fraction to be used in the future combinational therapy.
Green Synthesis of Triazole-Based Chemosensors and their Efficacy Towards Mercury Sensing
Authors: Rani Poonam , Lal Kashmiri , Ghule Vikas D. , Shrivastava Rahul 
Published: November 2019

Background:
The synthesis of small organic molecules based Hg2+ ions receptors have
gained considerable attention because it is one of the most prevalent toxic metals which is continuously
discharged into the environment by different natural and industrial activities. 1,4-Disubstituted
1,2,3-triazoles have been reported as good chemosensors for the detection of various metal ions including
Hg2+ ions.

Methods:
The synthesis of 1,2,3-triazoles (4a-4c) was achieved by Cu(I)-catalyzed azide-alkyne cycloaddition,
and their binding affinity towards various metal ions and anions were studied by UVVisible
titration experiments. The perchlorate salts of metal ions and tetrabutylammonium salts of
anions were utilized for the UV-Visible experiments. DFT studies were performed to understand the
binding and mechanism on the sensing of 4a toward Hg2+ using the B3LYP/6-311G(d,p) method for
4a and B3LYP/LANL2DZ for 4a-Hg2+ species on the Gaussian 09W program.

Results:
The UV-visible experiments indicated that the compounds 4a-4c show a selective response
towards Hg2+ ion in UV-Visible spectra, while other ions did not display such changes in the absorption
spectra. The binding stoichiometry was evaluated by Job’s plot which indicated the 1:1 binding
stoichiometry between receptors (4a-4c) and Hg2+ ion. The detection limit of 4a, 4b and 4c for the
Hg2+ ions was found to be 29.1 nM, 3.5 μM and 1.34 μM, respectively.

Conclusion:
Some 1,2,3-triazole derivatives were synthesized (4a-4c) exhibiting high selectively and
sensitivity towards Hg2+ ions in preference to other ions. Compound 4a has a low detection limit of
29.1 nM and the binding constant of 2.3×106 M-1. Similarly, 4b and 4c also showed selective sensing
towards Hg2+ ions in the μM range. The observed experimental results were corroborated by density
functional theory (DFT) calculations.
Quantitative GC-FID and UHPLC-DAD Evaluation of Bioactive Compounds Extracted from Ginkgo biloba
Authors: Ahn Alessandra von , dos Santos João Henrique Z. 
Published: November 2019

Background:
The official compendium of the quantification of ginkgo flavonoids from
Ginkgo biloba extract has been proposed using HPLC. The drawbacks of this technique appear to be
due to the restricted efficiency in terms of the recovery results and suitability of the system for the
quantification of these compounds. This study investigated the potential advantages and limitations
of the development of efficient extraction methods for the recovery of flavonol glycosides (quercetin,
kaempferol and isorhamnetin) and terpene trilactones (bilobalide, ginkgolide A, ginkgolide B and
ginkgolide C) using extraction, quantification and detection techniques, namely, GC-FID and
UHPLC-DAD, which are alternatives to those techniques available in the literature.

Methods:
Two different extraction methodologies have been developed for the determination of flavonoids
(quercetin, kaempferol and isorhamnetin) and terpene trilactones (bilobalide, ginkgolide A,
ginkgolide B and ginkgolide C) using ultra-high-pressure liquid chromatography coupled to a diode
array detector and gas chromatography coupled to a flame ionization detector.

Results:
In this study, the Ginkgo biloba extract mass, hydrolysis preparation method (with or without
reflux), and volume of the extraction solution seemed to affect the ginkgo flavonoid recovery.
The UHPLC-based method exhibited higher extraction efficiency for ginkgo flavonoid quantification
compared to the pharmacopoeial method. The developed method exhibited higher extraction efficiency
for terpene quantification compared to the previous method that used extractive solution without
pH adjustment, with less time of extraction and less amount of the sample and organic solvent
aliquots.

Conclusion:
The UHPLC and GC analysis methods established in this study are both effective and
efficient. These methods may improve the quality control procedures for ginkgo extract and commercial
products available in today´s natural health product market. The results indicate that redeveloped
extraction methods can be a viable alternative to traditional extraction methods.
Photoluminescence and Magnetic Properties of Undoped and (Mn, Co) co-doped ZnO Nanoparticles
Authors: Rekaby Mona 
Published: November 2019

Objective:
The influence of Manganese (Mn2+) and Cobalt (Co2+) ions doping on the optical
and magnetic properties of ZnO nanoparticles was studied.

Methods:
Nanoparticle samples of type ZnO, Zn0.97Mn0.03O, Zn0.96Mn0.03Co0.01O, Zn0.95Mn0.03
Co0.02O, Zn0.93Mn0.03Co0.04O, and Zn0.91Mn0.03Co0.06O were synthesized using the wet chemical coprecipitation
method.

Results:
X-ray powder diffraction (XRD) patterns revealed that the prepared samples exhibited a
single phase of hexagonal wurtzite structure without any existence of secondary phases. Transmission
electron microscope (TEM) images clarified that Co doping at high concentrations has the ability
to alter the morphologies of the samples from spherical shaped nanoparticles (NPS) to nanorods
(NRs) shaped particles. The different vibrational modes of the prepared samples were analyzed
through Fourier transform infrared (FTIR) measurements. The optical characteristics and structural
defects of the samples were studied through Photoluminescence (PL) spectroscopy. PL results clarified
that Mn2+ and Co2+ doping quenched the recombination of electron-hole pairs and enhanced the
number of point defects relative to the undoped ZnO sample. Magnetic measurements were carried
out at room temperature using a vibrating sample magnetometer (VSM). (Mn, Co) co-doped ZnO
samples exhibited a ferromagnetic behavior coupled with paramagnetic and weak diamagnetic contributions.

Conclusion:
Mn2+ and Co2+ doping enhanced the room temperature Ferromagnetic (RTFM) behavior
of ZnO. In addition, the signature for antiferromagnetic ordering between the Co ions was revealed.
Moreover, a strong correlation between the magnetic and optical behavior of the (Mn, Co) co-doped
ZnO was analyzed.
A Study on Host Tropism Determinants of Influenza Virus Using Machine Learning
Authors: Kwon Eunmi , Cho Myeongji , Kim Hayeon , Son Hyeon S. 
Published: November 2019

Background:
The host tropism determinants of influenza virus, which cause changes in
the host range and increase the likelihood of interaction with specific hosts, are critical for
understanding the infection and propagation of the virus in diverse host species.

Methods:
Six types of protein sequences of influenza viral strains isolated from three classes of
hosts (avian, human, and swine) were obtained. Random forest, naïve Bayes classification, and knearest
neighbor algorithms were used for host classification. The Java language was used for
sequence analysis programming and identifying host-specific position markers.

Results:
A machine learning technique was explored to derive the physicochemical properties of
amino acids used in host classification and prediction. HA protein was found to play the most
important role in determining host tropism of the influenza virus, and the random forest method
yielded the highest accuracy in host prediction. Conserved amino acids that exhibited host-specific
differences were also selected and verified, and they were found to be useful position markers for
host classification. Finally, ANOVA analysis and post-hoc testing revealed that the
physicochemical properties of amino acids, comprising protein sequences combined with position
markers, differed significantly among hosts.

Conclusion:
The host tropism determinants and position markers described in this study can be
used in related research to classify, identify, and predict the hosts of influenza viruses that are
currently susceptible or likely to be infected in the future.
The Relationship Between HIV Antibody Titer, HIV Viral Load, HIV p24 Antigen, and CD4 T-cell Count Among Iranian HIV-positive Patients
Authors: Moradbeigi Majedeh , SeyedAlinaghi SeyedAhmad , Sajadipour Mansour , Dadras Omid , Shojaei Esfandiar , Ahmadi Parisa , Bayanolhagh Saeed , Baesi Kazem , Rasoolinejad Mehrnaz 
Published: November 2019

Objective:
This study aimed to simultaneously measure and assess the correlation between
the available HIV infection parameters including HIV antibody, p24 Antigen, CD4 cell
count, and viral load at the different stages of HIV disease among HIV-positive individuals in
Iran.

Materials and methods:
Fifty HIV-positive individuals were classified into three stages (1, 2, and
3) according to the HIV disease stages classification, available in Control of Disease and Prevention
(CDC) guideline. 10 ml of the venous blood sample was collected to run the tests for HIV antibody
and p24 Ag levels, CD4 cell counts, and viral load. Pearson’s correlation test was employed
to calculate the coefficients for the in-between correlation of different HIV parameters in
each stage.

Results:
Of 50 participants, 17 (34%), 25 (50%), and 8 (16%) patients belonged to stages 1, 2, and
3, respectively. Sexual relationship was the main route of HIV transmission among the patients
(36%); however, injecting drug use (20%) was also reported frequently. There was no significant
correlation between the parameters of HIV disease in different stages in the present study.

Conclusion:
The findings showed no correlation between HIV parameters in the present study.
Considering the fact that the association of HIV antibodies with HIV disease progression in infected
individuals is independent of HIV-1 RNA levels, combined measurement of HIV-1 RNA
and CD4 cell counts should be routinely carried out in HIV infected patients follow up.
Development of a Small Aviation Two-stroke Kerosene Engine Fuel Injection Controller
Authors: Chen Linlin , Sun Qun , Gao Weidong , Wang Chong 
Published: November 2019

Background:
The study of kerosene fuel for gasoline engines is of great significance to the
supply, management, storage, transportation of military fuel, as well as its safety. A small aviation twostroke
kerosene engine fuel injection controller is the key technology of kerosene engines. It is very
important to improve the performance of kerosene engine by controlling the air-fuel ratio accurately.

Objective:
In this study, the initial injection pulse spectrum was firstly obtained by numerical calculation
in the absence of kerosene injection pulse spectrum, and then the injection controller was designed
based on the initial injection pulse spectrum.

Methodology:
Firstly, a numerical model of the whole engine was established by using BOOST software.
The air mass flow data of the inlet was obtained through numerical calculation. The amount of
initial engine fuel injection was calculated according to the requirements of air-fuel ratios in each
working condition, from which an initial injection pulse spectrum was obtained. Then, based on Freescale
16-bit embedded micro-controller MC9S12DP512, a kerosene engine fuel injection controller
was developed, together with the circuit. According to the initial fuel injection pulse spectrum, a twodimensional
interpolation algorithm was developed by using assembly language and C language mixed
programming, and the anti-electromagnetic interference ability of the controller was further enhanced.
Finally, the accuracy of the initial injection pulse spectrum and the performance and reliability of the
injection controller of the kerosene engine were verified by the kerosene engine bench test.

Results:
The experimental results show that the numerical model was accurate, and the development
time of the injection controller reduced by using the numerical model to calculate the initial injection
pulse spectra.

Conclusion:
The developed controller was observed to be stable and reliable, which can meet the
control requirement.
Effective Substrate Loading for Saccharification of Corn Cob and Concurrent Production of Lignocellulolytic Enzymes by Fusarium oxysporum and Sporothrix carnis
Authors: Olajuyigbe Folasade M. , Fatokun Cornelius O. , Oni Oluwatosin I. 
Published: November 2019

Background:
One of the critical challenges of cost-effective bioethanol production from
lignocellulosic biomass is the decreasing yield of reducing sugars caused by increasing substrate loading.
Hence, it is crucial to determine the best substrate concentration for efficient saccharification of
lignocellulosic wastes.

Objective:
This paper reports the saccharification of corn cob by two lignocellulolytic fungi (Fusarium
oxysporum and Sporothrix carnis) and concurrent production of lignocellulolytic enzymes at varying
substrate concentrations.

Methods:
F. oxysporum and S. carnis were cultivated on corn cob based media at 30°C and 160 rpm
for 144 h. The lignocellulosic composition of corn cob was determined. Saccharification of varying
concentrations of substrate was determined by evaluating the release of reducing sugar while the production
of cellulase and xylanase was monitored.

Results:
Cellulose, hemicellulose and lignin contents of corn cob were 37.8±1.56%, 42.2±1.68% and
12.7±1.23%, respectively. Yields of reducing sugar by F. oxysporum and S. carnis were 5.03 µmol/mL
and 6.16 µmol/mL; and 6.26 µmol/mL and 6.58 μmol/mL at 10.0 and 25.0% substrate concentration,
respectively. The production of cellulase and xylanase was exponential as corn cob concentration increased
from 0.5% to 10.0% yielding 586.93 U/mL and 1559.18 U/mL from F. oxysporum, with 590.7
U/mL and 1573.95 U/mL from S. carnis, respectively.

Conclusion:
The study shows that the most efficient saccharification of corn cob by F. oxysporum and
S. carnis was achieved at 10.0% substrate concentration. This suggests that two separate saccharification
processes at this concentration will result in higher yields of enzyme and reducing sugars than a
single process involving higher concentration.
Application of Machine Learning Techniques to Predict Binding Affinity for Drug Targets: A Study of Cyclin-Dependent Kinase 2
Authors: Bitencourt-Ferreira Gabriela , Duarte da Silva Amauri , Filgueira de Azevedo Walter 
Published: November 2019

Background:
The elucidation of the structure of cyclin-dependent kinase 2 (CDK2) made
it possible to develop targeted scoring functions for virtual screening aimed to identify new inhibitors
for this enzyme. CDK2 is a protein target for the development of drugs intended to modulate cellcycle
progression and control. Such drugs have potential anticancer activities.

Objective:
Our goal here is to review recent applications of machine learning methods to predict ligand-
binding affinity for protein targets. To assess the predictive performance of classical scoring
functions and targeted scoring functions, we focused our analysis on CDK2 structures.

Methods:
We have experimental structural data for hundreds of binary complexes of CDK2 with
different ligands, many of them with inhibition constant information. We investigate here computational
methods to calculate the binding affinity of CDK2 through classical scoring functions and machine-
learning models.

Results:
Analysis of the predictive performance of classical scoring functions available in docking
programs such as Molegro Virtual Docker, AutoDock4, and Autodock Vina indicated that these
methods failed to predict binding affinity with significant correlation with experimental data. Targeted
scoring functions developed through supervised machine learning techniques showed a significant
correlation with experimental data.

Conclusion:
Here, we described the application of supervised machine learning techniques to generate
a scoring function to predict binding affinity. Machine learning models showed superior predictive
performance when compared with classical scoring functions. Analysis of the computational models
obtained through machine learning could capture essential structural features responsible for binding
affinity against CDK2.
Recent advances in graphene based nano-composites for automotive and off-highway vehicle applications
Authors: Kandasamy Ramkumar 
Published: November 2019

:
Nano-composites comprised of a polymer matrix and various types of nanosized fillers have remained as one of the most important engineering materials and continue to draw great interest in the research community and industry. In particular, graphene based nano-composites with high thermal conductivity, excellent mechanical, electrical and optical properties, becomes important and promising filler for making the next generation, high performance composite materials.

:
Automotive and off-highway machinery industry are extensively viewed as being the industry in which the highest volume of advanced composite materials such as graphene based nano-composites will be used in the future to produce lighter, stronger, more energy-efficient, and safe vehicles. Design, modeling, analyzing and methods for large-scale production of the graphene based nano-composites in automotive and off-highway machinery applications considering it’s mechanical, functional and interface properties between the graphene and polymer matrix under severe loading conditions is challenging, potentially due to nonlinear properties, joining of dissimilar materials and high demand of computations. While graphene based material strategies have been investigated and demonstrated to be effective for structural application in various industries includes electronics, electromechanical and energy systems, currently there is limited research highlighting the specific knowledge available for design engineers and researchers concerned with lightweight and stronger solutions by use of graphene based materials for automotive off-highway vehicle applications.

:
The present review presents, an overview of the latest studies that utilize graphene-based nanomaterials and their composites in automotive and off-highway machinery applications. First, the paper describes the concept of traditional composites used in present engineering industries considering its advantages and limitations, then highlights the key benefits of using nanostructured carbon material, such as graphene through some recent studies available in the literature. Then depicts the various mechanisms of integrating graphene as polymer reinforcements within composite materials, which have been found based on the survey, and their related modeling, designing, and manufacturing capabilities suitable for automotive and off-highway machinery industry. Finally outlines the available experimental evidence for graphene based composites. In order to lay the groundwork for future work in this exciting area, the paper discusses close by several future prospects as well as the current challenges in this field.