bluuebunny/crossref_metadata_2025_split huggingface dataset:
Predictive Markers of Metabolically Healthy Obesity in Children and Adolescents: Can AST/ALT Ratio Serve as A Simple and Reliable Diagnostic Indicator?
Authors: ÇELİK Nurullah , Ünsal Gülşah , TAŞTANOĞLU Hüseyin 
Published: August 2023

Abstract
This study aimed to estimate the prevalence of Metabolically Healthy Obesity (MHO) according to two different consensus-based criteria and to investigate simple, measurable predictive markers for the diagnosis of MHO. Five hundred and ninety-three obese children and adolescents aged 6–18 years were included in the study. The frequency of MHO was calculated. ROC analysis was used to estimate the predictive value of AST/ALT ratio, waist/hip ratio, MPV, TSH, and Ft4 cut-off value for the diagnosis of MHO. The prevalence of MHO was 21.9% and 10.2% according to 2018 and 2023 consensus-based MHO criteria, respectively. AST/ALT ratio cut-off value for the diagnosis of MHO was calculated as ≥ 1 with 77% sensitivity and 52% specificity using Damanhoury et al.'s criteria (AUC = 0.61, p = 0.02), and 90% sensitivity and 51% specificity using Abiri et al.'s criteria (AUC = 0.70, p = 0.01). Additionally, using binomial regression analysis, only the AST/ALT ratio is independently and significantly associated with the diagnosis of MHO (p = 0.02 for 2018 criteria and p = 0.01 for 2022 criteria). Conclusion: The ALT/AST ratio may be a useful indicator of MHO in children and adolescents.
Decomposition methods for multi-horizon stochastic programming
Authors: Zhang Hongyu , Domènech Èric Mor , Grossmann Ignacio E. , Tomasgard Asgeir 
Published: August 2023

Abstract

Multi-horizon stochastic programming includes short-term and long-term uncertainty in investment planning problems more efficiently than traditional multi-stage stochastic programming. In this paper, we exploit the special structure of multi-horizon stochastic linear programming, and establish that it can be decomposed by Benders decomposition and Lagrangean decomposition. In addition, we propose parallel Lagrangean decomposition with primal reduction that, (1) solves the scenario subproblems in parallel, (2) reduces the primal problem by keeping one copy for each scenario group at each stage, and (3) solves the reduced primal problem in parallel. We compare the parallel Lagrangean decomposition with primal reduction with the standard Lagrangean decomposition and standard Benders decomposition on a stochastic energy system investment planning problem. The computational results show that: (a) the Lagrangean type decomposition algorithms have better convergence at the first iterations to Benders decomposition, and (b) parallel Lagrangean decomposition with primal reduction is up to 9.2 times faster than standard Benders decomposition for a 1% convergence. Based on the computational results, the choice of algorithms for multi-horizon stochastic programming is discussed.
Benchmarking the Bank Branch Efficiency through A New Dynamic Network DEA Model
Authors: Chen Fu-Chiang , Mohammad Mohammad , Mia Md Aslam 
Published: August 2023

Abstract
This study proposes a new model by incorporating a carry-over input and segregating the branch production process into operational and investment divisions. Using a dataset of 121 Taiwanese branches, the findings were as follows: First, the overall efficiencies of bank branches are not on par regardless of investment or operational efficiencies. Second, the operational efficiencies of branches do not differ statistically between regions, although investment efficiencies do. Third, clustering the efficiency of branches based on characteristics provided evidence that branches located in industrial areas have a higher level of operational efficiency (and second highest investment efficiency) compared to other locations.
JEL Classification: L25, C67, G21
Novel and Efficient Hybrid Deep Learning Approach for Solar Photovoltaic Power Forecasting Considering Meteorological Parameters
Authors: Aman Rahma , Rizwan M. , Kumar Astitva 
Published: August 2023

Abstract
The power generation from photovoltaic plants depends on varying meteorological conditions. These meteorological conditions such as solar irradiance, temperature, and wind speed, are non-linear and stochastic thus affect estimation of photovoltaic power. Accurate estimation of photovoltaic power is essential for enhancing the functioning of solar power installations. The paper aims to develop a novel deep learning based photovoltaic power forecasting model on different weather conditions. The proposed model utilizes a two-stage deep learning framework for accurate solar power forecasting, which combines the long short-term memory (LSTM) and convolutional neural network (CNN) deep learning architectures. The key role of CNN layer is to identify the weather conditions, i.e., sunny, cloudy and rainy while the LSTM layer learns the patterns of solar power generation that depend on weather variations to estimate photovoltaic power. The proposed hybrid models consider meteorological factors, such as wind speed, sun irradiations, temperature, and humidity, including cloud cover and UV index to provide precise solar power forecasting. The presented hybrid model, a Root Mean Square Error of 0.0254, 0.03465 and 0.0824, Mean Square Error of 0.000645, 0.00120 and 0.00679, R2 of 0.9898, 0.9872 and 0.9358, Mean Average Error of 0.0163 and 0.0236 and 0.2521 for sunny, cloudy and rainy weather conditions respectively. The results demonstrate that presented deep learning based novel solar photovoltaic (SPV) power forecasting model can accurately forecast solar power based on instantaneous changes in generated power patterns, and aid in the optimization of PV power plant operations. The paper presents an effective methodology for forecasting solar power that can contribute to the improvement of solar power generation and management.
A numerical study of Ag − M gO/water hybrid micropolar nanofluid flow with effects of quadratic thermal radiant energy along an exponentially contracting permeable Riga surface: Stability and Entropy optimization
Authors: Mandal Gopinath , Pal Dulal 
Published: August 2023

Abstract
The present analysis deals with the study of non-Newtonian micropolar Silver-Magnesium Oxide (Ag − M gO)/H 2 O hybrid nanofluid flow and heat transfer over an exponentially contracting porous Riga surface. We have highlighted the impacts of micropolar characteristic, quadratic thermal radiant energy in the presence of convective conditions and velocity slip at the boundary on the coefficient of shear stress, coefficient of couple-stress , heat transfer factor (Nusselt number), entropy formation, velocity, micro-rotation or angular velocity and temperature profiles under the assumptions of nanomaterial. We have solved the highly non-linear coupled partial differential equations numerically, using the MATLAB programming platform with a fourth-order method to decode the boundary value problem (bvp4c) with all relevant flow parameters. The second law of thermodynamics is used for the calculation of the irreversibility factor. The results point to the presence of dual-nature solutions with a stable upper solution branch and an unstable bottom solution branch in the contracting sheet area for a given value of the mass suction parameter. In the presence of dual solutions, critical values have been found, and a stability analysis is run to find more stable solutions. Two solutions are found for the limited range of contracting parameter λ when λ S < λ and the solutions terminate at λ = λ c in the contracting region. Additionally, the first solution generates a positive minimum eigenvalue (β 1 > 0) indicating the stability of the solution, whereas the other solution generates a non positive eigenvalue (β 1 < 0). It was found that the shear stress and couple stress coefficients rise with increasing suction value, however the heat transfer factor (Nusselt number) may fall for a stable solution. With an Preprint submitted to 12 July 2023 increase in the solid volume percentage of the hybrid nanofluid and material component , the formation of entropy rises noticeably. Additionally, it is noted that in both solutions, the quadratic thermal radiation parameter, Eckert number, and Biot number all lead to an increase in the temperature with enhancement in thickness of the thermal boundary layer. These findings are important because micropolar hybrid nanofluids are actively used to cool tiny electrical devices like microchips and related equipments.
Silencing KPNA2 Promotes Ferroptosis in Laryngeal Cancer by Activating the FoxO Signaling Pathway Silencing KPNA2 Promotes Ferroptosis in LAC
Authors: Xu Mimi , Hu Xiaoqi , Xiao Zhixue , Zhang Siyi , Lu Zhongming 
Published: August 2023

Abstract
Objective
Ferroptosis induction is a novel approach to oncotherapy, with few studies in laryngeal cancer. This article is forward to providing a new ferroptosis-related biomarker for laryngeal cancer.
Methods
We downloaded the microarray datasets GSE127165 and GSE51985 from the Gene Expression Omnibus database and obtained the differentially expressed genes (DEGs) associated with ferroptosis. The Hub genes were identified after the construction of the protein-protein interaction network and verified by principal component analysis. KPNA2 was selected and verified by Receiver operating characteristic curve and pan-cancer analysis. Then we conducted experimental verification by silencing KPNA2 in ferroptosis-induced laryngeal cancer cells by Erastin.
Results
45 DEGs associated with ferroptosis in laryngeal cancer were obtained, and KPNA2 of 5 hub genes with high degrees in the protein-protein interaction network was further selected, which showed a high expression in pan-cancer including laryngeal cancer, considerable diagnostic efficiency, and a correlation with tumor prognosis and immune infiltration. In ferroptosis-induced laryngeal cancer cells, we found an increased expression of cyclooxygenase 2, iron ions, and malondialdehyde, and a decreased expression of glutathione peroxidase 4 and glutathione when the expression of KPNA2 was suppressed. The FoxO signaling pathway in laryngeal cancer cells was activated by silencing KPNA2.
Conclusion
KPNA2 is possibly a promising therapeutic target for laryngeal cancer, which can suppress ferroptosis in laryngeal cancer by inhibiting the FoxO signaling pathway.
Pre-attentive and attentive auditory event-related potentials in children with attention-deficit hyperactivity disorder and autism
Authors: Schall Ulrich , Fulham Ross , Günther Max , Bergmann Jessica , Thienel Renate , Ortmann Julie , Wall Natalie G , Álvarez Paula Gómez , Youlden Anne-Marie 
Published: August 2023

Abstract
Background
Abnormalities in auditory processing are believed to play a major role in autism and attention-deficit hyperactivity disorder (ADHD). Both conditions often co-occur in children causing difficulties in deciding on the most promising intervention. Event-related potentials (ERPs) have been investigated and are showing promise to act as potential biomarkers for both conditions.
Methods
This study investigated mismatch negativity (MMN) through a passive listening task and P3b in an active auditory go/no-go discrimination task. Recordings were available from 103 children (24 female): 35 with ADHD, 27 autistic, 15 autistic children with co-occurring ADHD, and 26 neurotypical (NT) children. The age range considered was between 4 and 17 years, but varied between groups.
Results
Results revealed increases in MMN and P3b amplitudes with age. Older children with ADHD exhibited smaller P3b amplitudes, while younger autistic children showed reduced MMN amplitudes in response to phoneme changes when compared to their NT counterparts. Notably, autistic children with ADHD did not follow this pattern, instead, they exhibited more similarities to neurotypical children.
Conclusions
The findings of reduced amplitudes of phonetically elicited MMN in autistic children and reduced P3b in children with ADHD suggest that the two respective ERPs can act as potential biomarkers for each condition. However, optimisation and standardisation of the testing protocol as well as longitudinal studies are required in order to translate these findings into clinical practice.
L4-L5 Anatomy Classification System for Lateral Lumbar Interbody Fusion
Authors: Amaral Rodrigo , Pokorny Gabriel , Alvernia Jorge E. , Pimenta Luiz 
Published: August 2023

Abstract
Introduction: Lateral lumbar interbody fusion (LLIF), developed by Dr. Luiz Pimenta in 2006, allows access to the spinal column through the psoas major muscle. The technique has many advantages, such as reduced bone and muscular tissue damage, indirect decompression, larger implants, and lordosis correction capabilities. However, this technique also has drawbacks, with the most notorious being the risk of spinal pathologies due to indirect injury of the lumbar plexus, but with low rates of persistent injuries. Therefore, several groups have proposed classifications to help identify patients at a greater risk of presenting with neurological deficits. The present work aims to propose a new classification system that relies on the simple observation of easily identifiable key structures to guide lateral L4-L5 LLIF decision-making.
Methods: Patients aged > 18 years who underwent preoperative magnetic resonance imaging (MRI) were included. This came to the office between 2022 and 2023, until 50 quality images were acquired. And excluded as follows: Anatomical changes in the vertebral body or major psoas muscles prevent the identification of key structures or poor-quality MRIs. Each anatomy was classified as type I, type II, or type III according to the consensus among the three observers.
Results: Fifty anatomical sites were included in this study. 64% of the L4-L5 anatomies were type I, 22% were type II, and 14% were type III. None of the type 3 L4-L5 anatomies was approached using a lateral technique.
Conclusion: The proposed classification is an easy and simple method for evaluating the feasibility of a lateral approach to L4–LL5.
Groundwater vulnerability assessment of nitrate pollution in the Ankang Basin: using an optimized DRASTIC-LY method
Authors: Hui Tian , Changlai Xiao , Honggen Xu , Xiujuan Liang , Jianli Qian , Longtan Qiao , Weilong Zhang , Hong Zhang 
Published: August 2023

Abstract

This study retains the basic structure of DRASTIC model and obtains more specific evaluation results by adding land-use type and groundwater resource yield parameters, modifying the rating scale and weight of nine parameters. Comparison of the modified DRASTIC-LY vulnerability map with the map of the original DRASTIC-LY method revealed differences in 40.49% of the study area. The risk map shows that the very high vulnerability area decreased from 2.79 to 1.67%, while the high vulnerability area increased from 18.70 to 28.86%. Areas with low vulnerability increased by 10.15%, while areas with medium vulnerability decreased by 15.01%. The areas with very high groundwater vulnerability are mainly distributed in the Hanbin area on the north bank of the Han River, the areas with high are mainly concentrated on both sides of the Fujia River, while the areas with low are distributed in most areas in the west of the basin. The Pearson's correlation factor was 0.0583 in the original DRASTIC model, 0.1113 in the DRASTIC-LY method and 0.8291 in the modified DRASTIC-LY model, which indicated that the revised DRASTIC-LY model was more appropriate than the original model. The results can help the government with the protection of water resources.
Analysis of the effects of COVID-19 restriction policies on drinking water consumption by smart water network data filtering
Authors: Evangelista Stefania , Nardi Mariantonia , Padulano Roberta , Di Cristo Cristiana , Del Giudice Giuseppe 
Published: August 2023

Abstract

People's habits changed during the COVID-19 pandemic and the consequent containment policies, with numerous implications in all fields. In particular, restrictions had important consequences for drinking water consumption. The present work analyses this influence in the Soccavo district of Naples (Campania), in Italy, during the two periods of strongest restrictions in 2020: the national Lockdown (March 11–May 3) and the autumn Red Zone (November 16–December 6). A large amount of data, referred to single-household flowmeters connected to a Smart Water Grid acquisition system, was collected for the years 2019 (considered the average reference year) and 2020. The first step was the preliminary filtering of the data, by identification and elimination of anomalies and outliers, as well as anomalous annual patterns, through clustering and classification. The second step consisted of the comparison of the same meters in two consecutive years considering the daily and weekly average hourly patterns, the average daily patterns of midweek days, Saturdays, and Sundays, respectively, and the total daily volumes. The results are consistent with those in the literature. Some general trends in literature data were sought and pointed out in the present paper.
Regulatory Non-coding RNAs Involved in Oxidative Stress and
Neuroinflammation: An Intriguing Crosstalk in Parkinson’s Disease
Authors: Khish Naser Salari , Ghiasizadeh Pooran , Rasti Abolhasan , Moghimi Omid , Zadeh Arash Zeynali , Bahiraee Alireza , Ebrahimi Reyhane 
Published: August 2023

:
Parkinson’s disease (PD) is a common neurodegenerative disorder characterized
by the accumulation of α-synuclein and the degeneration of dopaminergic neurons
in the substantia nigra. Although the molecular bases for PD development are not fully
recognized, extensive evidence has suggested that the development of PD is strongly associated
with neuroinflammation. It is noteworthy that while neuroinflammation might
not be a primary factor in all patients with PD, it seems to be a driving force for disease
progression, and therefore, exploring the role of pathways involved in neuroinflammation
is of great importance. Besides, the importance of non-coding RNAs (ncRNAs),
such as microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and competing endogenous
RNAs (ceRNAs), has been widely studied with a focus on the pathogenesis of
PD. However, there is no comprehensive review regarding the role of neuroinflammation-
related ncRNAs as prospective biomarkers and therapeutic targets involved in the
pathogenesis of PD, even though the number of studies connecting ncRNAs to neuroinflammatory
pathways and oxidative stress has markedly increased in the last few years.
Hence, the present narrative review intended to describe the crosstalk between regulatory
ncRNAs and neuroinflammatory targets with respect to PD to find and propose novel
combining biomarkers or therapeutic targets in clinical settings.
Myricitrin versus EGCG in the Treatment of Obesity: Target Mining and Molecular Mechanism Exploring based on Network Pharmacology
Authors: Yin Peipei , Huang Jiangping , Yang Kang , Deng Chuang , Yang Lingguang 
Published: August 2023

Background:Myricitrin is a flavonol glycoside possessing beneficial effects on obesity, a rising global health issue that affects millions of people worldwide. However, the involving target and mechanism remain unclear.Objective:In the present study, the anti-obesity targets and molecular mechanisms of Myricitrin, along with another flavanol Epigallocatechin gallate (EGCG), were explored through network pharmacology, bioinformatics, and molecular docking.Methods:The potential targets for Myricitrin and EGCG were obtained from Pharmmaper, SwissTargetPrediction, TargetNet, SEA, Super-PRED, TCMSP, and STICH databases. Meanwhile, DEG targets were retrieved from GEO datasets, and obesity targets were collected from DrugBank, TTD, DisGeNet, OMIM, GeneCards, PharmGKB, and CTD databases. GO and KEGG pathway enrichment analyses were conducted through Metascape online tool. Protein-protein interaction (PPI) networks were also constructed for compound, DEG, and obesity targets to screen the core targets through MCODE analysis. The further screened-out key targets were finally verified through the compound-target-pathway-disease network, mRNA expression level, target-organ correlation, and molecular docking analyses.Results:In total, 538 and 660 targets were identified for Myricitrin and EGCG, respectively, and 725 DEG targets and 1880 obesity targets were retrieved. GO and KEGG analysis revealed that Myricitrin and EGCG targets were enriched in the pathways correlating with obesity, cancer, diabetes, and cardiovascular disease. Furthermore, the intersection core targets for Myricitrin and EGCG function mainly through the regulation of responses to hormones and involving pathways in cancer. Above all, androgen receptor (AR), cyclin D1 (CCND1), early growth response protein 1 (EGR1), and estrogen receptor (ERS1) were identified as key targets in the compound-target-pathway-disease network for both Myricitrin and EGCG, with significant different mRNA expression between weight loss and control groups. Target-organ correlation analysis exhibited that AR and CCND1 showed high expression in adipocytes. Molecular docking also revealed good binding abilities between Myricitrin and EGCG, and all four receptor proteins.Conclusion:The present research integrated network pharmacology and bioinformatics approach to reveal the key targets of Myricitrin and EGCG against obesity. The results provided novel insights into the molecular mechanism of Myricitrin and EGCG in obesity prevention and treatment and laid the foundations for the exploitation of flavonoid-containing herbal resources.
Influence of Supplementation of Ecklonia cava Polyphenols on Learning,
Memory, and Brain Fatty Acid Composition in Mice
Authors: Lee Jung Woo , Lee Jung Im , Lim Sun Young 
Published: August 2023

Aims:
The objective of this study was to determine the effects of intake of polyphenols from Ecklonia cava on spatial task performance and nervous fatty acid composition in mice fed with a high-fat diet.

Materials and Methods:
Thirty mice were randomly divided into three groups; each group consisted of ten mice. The control group was fed 5% soybean oil as a fat source, whereas the high fat (HF) group was fed a 15% lard diet and the polyphenol (ECP) group was maintained on the HF diet plus 1% E. cava polyphenols.

Results:
The ECP group exhibited a short escape latency and better memory retention in the Morris water maze test compared with the control and HF groups (P<0.05). In addition, the ECP group showed a greater increase in avoidance latency than that of the HF group (P<0.05). Moreover, the consumption of polyphenols from E. cava presented higher levels of DHA in the brain and retina (P<0.05).

Conclusion:
This study suggested the positive effects of polyphenols from E. cava on memory retention, which might be partially attributed to the increased levels of DHA in the brain.