bluuebunny/crossref_metadata_2025_split huggingface dataset:
Top-up Design and Health Care Expenditure: Evidence from Cardiac Stents
Authors: Jin Ginger Zhe , Lien Hsienming , Tao Xuezhen 
Published: October 2023

Abstract
Since 2006, Taiwan’s National Health Insurance (NHI) has covered the full cost of baseline treatment in cardiac stents (bare-metal stents, BMS). Still, it requires patients to pay the incremental cost of more expensive treatments (drug-eluting stents, DES). Within this “top-up” design, we study how hospitals responded to a 26% cut of the NHI reimbursement rate in 2009. We find that minor teaching hospitals that were more revenue reliant on cardiac patients, relative to other minor teaching hospitals, increased BMS usage per stent patient and raised DES prices, which, in turn, reduced the use of DES stents. These changes helped such minor teaching hospitals recoup up to 32.7% of the revenue loss from the NHI rate cut in 2009-2010. In comparison, we find little effects in the major teaching hospitals. Overall, the rate cut was effective in reducing NHI expenditure without any substantial changes in patient outcomes, although some minor teaching hospitals made moral hazard adjustments in response.
JEL: G22, I11, I18
Binary Classification of Drug Sensitivity Prediction Using Multimodal Deep Learning
Authors: Aqeeli Noura , Alzaid Eman 
Published: October 2023

Abstract
Precision medicine has revolutionized cancer treatment by tailoring cancer therapy to individual patients. The field of drug sensitivity prediction has witnessed significant growth, encompassing diverse contributions that range from multi-omics integration to novel computational network architectures. Many existing studies focused on predicting the continuous value of the half maximal inhibitory concentration (IC50), but few have focused on predicting the binary class of the response (sensitive or resistant). This study presents a Binary Multimodal Deep Learning classifier (BinaryMDL) trained on diverse data sources, including drug screening, gene expression, mutation, and clinical data, to predict binary drug responses. The training dataset comprises 166 drugs and 603 cell lines. Unlike prior studies, we addressed the issue of data imbalance by employing the class weight technique, which shows an improvement in the model’s consideration of the minority class. Furthermore, our model’s predictive ability improved when incorporating clinical data. The resulting model BinaryMDL showed a performance improvement compared to other studies, achieving an AUC of 0.890 and an auPR of 0.614. Our work advances binary drug sensitivity prediction, highlighting the potential of multimodal deep learning for precision cancer therapy.
Efficient directions for oil-producing developing countries to technically achieve low-carbon transition
Authors: Aliyeva Simuzar , Yoon Naeun , Sohn So Young 
Published: October 2023

Abstract
The transition of oil-producing developing countries to low-carbon energy industry is an important issue in the era of climate change. However, their transition is difficult due to economic and technological issues. This study applied data envelopment analysis and topic modeling to help establish effective low-carbon energy transition strategies to ensure sustainable technological development. First, the technological innovation efficiency for transition of 22 oil-producing developing countries was compared using data envelopment analysis, where research and development investment ratio, amount of natural gas reserves, and oil production were used as input variables, and the number of low-carbon energy-related Patent Cooperation Treaty applications from 2015 to 2019, categorized into five technological fields, were considered as output variables. The countries were clustered based on the DEA peer weights. Then, the technical areas to be benchmarked by the followers per group were identified using topic modeling and technological strategies were suggested to improve the efficiency of transition. Our research suggests that patent trends in frontier countries show a strong relationship with relevant policies and standards, leading to the introduction of technologies that support these standards. The findings of this study can contribute to establishing national low-carbon energy policies in oil-producing developing countries.
The Properties of Absorbable Scaffold Harvested with Human Amniotic Fluid Stem Cells on Rat Model: Innovation for Pelvic Reconstruction Surgery
Authors: Lo Tsia-Shu , Chen Yi-Pin , Harun Fazlin , Shaw Steven W. , Lin Yi-Hao 
Published: October 2023

Abstract

The current practice of restoring the anatomical structure in the treatment of pelvic floor dysfunction includes implantation of synthetic sling, which carries complications. This study aimed to develop biological substitutes to improve tissue function using scaffolds as a support to the host cells, through formation of new tissue. Human amniotic fluid stem cells (hAFSCs) were seeded on synthetic mesh-scaffold of AlloDerm Regenerative Tissue Matrix (RTM), Poly-DL-lactico-glycolic acid (PLGA) mesh (VICRYL) and Polydioxanone (PDS) meshes. In vitro study evaluates the metabolic activity of hAFSCs seeded mesh-scaffolds. In vivo study involving Sprague-Dawley rats was performed by assigning into 7 groups of sham control with fascia operation, AlloDerm implant, PDS implant, PLGA implant, AlloDerm harvest with hAFSC (AlloDerm-SC), PDS harvest with hAFSC(PDS-SC) and PLGS harvest with hAFSC (PGLA-SC). In vitro study reveals cell viability and proliferation of hAFSC on mesh scaffolds varies between meshes, with AlloDerm growing the fastest. The biomechanical properties of tissue-mesh-complex tension strength declined over time, showing highest tension strength on week-1, deteriorated similar to control group on week-12. All hAFSC-seeded mesh provides higher tension strength, compared to without. This study shed the potential of synthetic mesh as a scaffold for hAFSC for the surgical treatment of pelvic floor dysfunction.
The Paradox of Noise: An Empirical Study of Noise-Infusion Mechanisms to Improve Generalization, Stability, and Privacy in Federated Learning
Authors: Jafarigol Elaheh , Trafalis Theodore B. 
Published: October 2023

AbstractIn a data-centric era, concerns regarding privacy and ethical data handling grow as machine learning relies more on personal information. This empirical study investigates the privacy, generalization, and stability of deep learning models in the presence of additive noise in federated learning frameworks. Our main objective is to provide strategies to measure the generalization, stability, and privacy-preserving capabilities of these models and further improve them.To this end, five noise infusion mechanisms at varying noise levels within centralized and federated learning settings are explored. As model complexity is a key component of the generalization and stability of deep learning models during training and evaluation, a comparative analysis of three Convolutional Neural Network (CNN) architectures is provided.The paper introduces Signal-to-Noise Ratio (SNR) as a quantitative measure of the trade-off between privacy and training accuracy of noise-infused models, aiming to find the noise level that yields optimal privacy and accuracy. Moreover, the Price of Stability and Price of Anarchy are defined in the context of privacy-preserving deep learning, contributing to the systematic investigation of the noise infusion strategies to enhance privacy without compromising performance. Our research sheds light on the delicate balance between these critical factors, fostering a deeper understanding of the implications of noise-based regularization in machine learning. By leveraging noise as a tool for regularization and privacy enhancement, we aim to contribute to the development of robust, privacy-aware algorithms, ensuring that AI-driven solutions prioritize both utility and privacy.
Intellectual Structure of Explainable Artificial Intelligence: a Bibliometric Reference to Research Constituents
Authors: Chen Hsin-Yuan , Sharma Chetan , Sharma Shamneesh , Sharma Komal , Sethi Ganesh Kumar 
Published: October 2023

Abstract
The need for easier-to-understand and more interpretable AI systems has led to the rise of explainable artificial intelligence (XAI) in recent years. In the realm of explainable artificial intelligence, this paper briefly reviews the work carried out, along with a conceptual framework. The researchers conducted a systematic review of 4781 research publications obtained from the Scopus database spanning the years 2004 to 2023 using the VOSViewer tool. The research shows that there has been exponential growth in terms of publications from the year 2018. The study establishes its prominence by studying the publication activities based on the year of publication and region, citation analysis, research designs, data analysis techniques, and findings from the selected articles.
Reserpine-induced rat model of depression: behavioral, physiological and dopamine receptor PET based validation
Authors: Telega Lidia Miguel , Berti Raissa , Blazhenets Ganna , Domogalla Lisa-Charlotte , Steinacker Nils , Omrane M. Aymen , Meyer Philipp T. , Coenen Volker A. , Eder Ann-Christin , Döbrössy Máté D. 
Published: October 2023

Abstract
Background.
Reserpine, a monoamine depleting agent, has been used in preclinical research for many years to create animal models of depression and to test experimental antidepressant strategies. Nevertheless, evidence of the potential use and validity of reserpine as a chronic pharmacological model for depression is lacking, and there are no comprehensive studies of the behavioral effects in conjunction with molecular outcomes.
Results. Experiment 1.
Following baseline behavior testing sensitive to depressive-like phenotype and locomotion (Phase 1), 27 Sprague-Dawley (SD) rats received i.p. either vehicle solution (0.0 mg/kg), low (0.2 mg/kg) or high (0.8 mg/kg) resrpine dose for 20 days using a pre-determined schedule and reassessed for emerging behavioral phenotypes (Phase 2). After a 10 days washout period, a final behavioral assessment was done (Phase 3), and the brains were collected for gene-expression assessment. Experiment 2. In a similar timetable as in Experiment 1 but without the behavioral testing, 12 SD rats underwent repetitive dopamine D2 receptor (D2R) PET scanning with [18F]DMFP post Phases 1–3. The binding potential (BPND) of [18F]DMFP was quantified by kinetic analysis as a marker of striatal D2R availability. Weight and welfare were monitored throughout the experiment. Significant, dose-dependent weight loss and behavioral deficits including both motor (hypo-locomotion) and non-motor behavior (anhedonia, mild anxiety and reduced socialization) were found for both the low and high dose groups with significant decrease in D2R mRNA expression in the accumbal region for the low reserpine group. In addition, both drug treated groups showed a substantial increase in [18F]DMFP BPND (in line with dopamine depletion) during injection phase and washout phases compared to baseline and Controls.
Conclusions.
The longitudinal design of the study demonstrated that chronic RES administration induced anxiety and depressive symptoms that were moderately maintained even after the wash out period. The data suggests that the low dose RES administration can induce a rodent model of depression with good face validity.
Comparative analysis of biochemical composition, amino acids, and fatty acids among diploid, triploid, and tetraploid Crassostrea gigas
Authors: FU jingjing , Zhang Enshuo , Yu Wensong , Wang Weijun , Sun Youmei , Dong Luyao , Zhang Yousen , Sun Guohua , Li Zan , Luo Qihao , Yang Jianmin 
Published: October 2023

Abstract
Tetraploid oysters are artificially produced oysters that do not exist in nature. The successful breeding of 100% triploid oysters resolved the difficulties with traditional drug–induced triploids such as the presence of drug residues and the low triploid induction rate. However, little is known concerning the biochemical composition and nutrient contents of such tetraploids. Therefore, we investigated compositional differences among diploid, triploid, and tetraploid Crassostrea gigas as well as between males and females of diploids and tetraploids. The findings indicated that glycogen, EPA, ∑PUFA and Omega-3 contents were significantly higher in triploid oysters than in diploid or tetraploid; tetraploid oysters had significantly higher C14:0, essential amino acid, and flavor-presenting amino acid contents than diploids or triploids, while diploid oysters had the highest protein content. For both diploid and tetraploids, females had significantly higher levels of glutamate, methionine, and phenylalanine than males but lower levels of glycine and alanine. In addition, female oysters had significantly more EPA, DHA, omega-3 and total fatty acids, a result that may be due to the fact that gonadal development in male oysters requires more energy to sustain growth, consumes greater amounts of nutrients, and accumulates more proteins. With these results, important information is provided for the production of C. gigas, as well as the basis and backing for genetic breeding of oysters.
A Nomogram for Predicting Coronary Artery Lesions in Patients with Kawasaki Disease
Authors: Xuan Wenjie , Liu Xiaoqun , Yao Yinping , Wang Yayun , Lin Jinjing , Chen Xiaohong , Yao Huanying 
Published: October 2023

Abstract
Background
As an acute systemic vasculitis, Kawasaki disease (KD) could develop coronary artery lesions (CAL) sometimes. However, its etiology was still unidentified. This study was to construct a predictive model based on clinical features and laboratory parameters, and then perform a rapid risk assessment of CAL.
Methods
We collected clinical and laboratory data retrospectively for all patients with KD who were hospitalized at our hospital from January 2016 to June 2023. All the patients were divided into CAL and non-CAL groups and then randomly assigned to a training set and a verification set. The independent risk variables of CAL were identified by univariate analysis and multivariate logistic regression analysis of the training set. These components were then utilized to build a predictive nomogram. Calibration curve and receiver operating characteristic curve were used to evaluate the performance of the model. The predictive nomogram was further validated in verification set.
Results
In the training set, 49 KD patients (19.9%) showed CAL. The proportion of fever days ≥ 10, C-reactive protein, total bilirubin were significantly higher, whereas age was younger, hemoglobin and albumin were lower in the CAL group than the non-CAL group. Younger age, fever days ≥ 10, higher C-reactive protein, lower hemoglobin and albumin were identified as independent risk factors. The nomogram constructed using these factors showed satisfactory calibration degree and discriminatory power (the area under the curve, 0.764). In the verification set, the area under the curve was 0.798.
Conclusions
Younger age, fever days longer than 10, lower hemoglobin and albumin levels, higher C-reactive protein levels were independent risk factors for CAL in KD patients. The predictive nomogram constructed utilizing 5 relevant risk factors could be conveniently used to facilitate the individualized prediction of CAL in KD patients.
Antioxidant potential of resveratrol on plasmodium berghei-induced malaria in diabetic male Wistar rats
Authors: Jimoh A. , Abdullahi S. , Dawud F. A. , Jimoh A.O , Adamu Z.I , Muhammed Z , Abubakar A , Ahmad A , Danboyi T , Tanko Y , Ayo J.O 
Published: October 2023

Abstract
Objective The aim of this study was to elucidate the antioxidant and antimalarial effects of resveratrol in diabetes and malaria comorbidity.
Methods 40 males Wistar rats were divided into 8 groups. Induction of diabetes was done in 7 of the groups with high-fat diet (HFD) and 20% fructose solution as drinking water for 8 weeks. Thereafter, the seven groups were injected with single streptozotocin (30 mg/kg) intraperitoneally. Inoculation of malaria was achieved by injecting 0.2 ml of Plasmodium bergheiparasite mixture into the rats intraperitoneally. Meaningful values were set at P < 0.05.
Results There were augments in mean body weight across the groups from day 0-6 after inoculation (day of sacrifice), but it was significantly reduced when compared to the normal control. The blood glucose levels (BGL)of the resveratrol groups significantly decreased on day 6 after inoculation, when compared with vitamin C and diabetic controls. For the average infected erythrocytes, the diabetic control had the greater (P < 0.05) count, when compared across the groups. The resveratrol groups had significantly (P < 0.05) lower values compared to the diabetic control. superoxide dismutase and malondialdehyde levels were significantly (P < 0.05) reduced in resveratrol 50 mg/kg rats, related to diabetic controls. Although malondialdehyde concentrations was markedly higher in resveratrol 100 mg/kg. Serum plasmodium lactate dehydrogenase (pLDH) enzymes concentrations of resveratrol groups were reduced (P < 0.05) when related with the diabetic controls.
Conclusion It was concluded that resveratrol reduced BGL and suppressed the proliferation of P. berghei parasites in diabetic Wistar rats. Resveratrol exerted some modulatory effects on the malondialdehyde and superoxide dismutase and ameliorated adverse effect of P. berghei-induced malaria in diabetic Wistar rats.
Circular RNA circADARB1 enhances radiotherapy resistance in nasopharyngeal carcinoma via suppressing ferroptosis
Authors: Wang Dan , Tang Le , Chen Mingjian , Gong Zhaojian , Fan Chunmei , Qu Hongke , Liu Yixuan , Shi Lei , Mo Yongzhen , Wang Yumin , Yan Qijia , Chen Pan , Xiang Bo , Liao Qianjin , Zeng Zhaoyang , Li Guiyuan , Wu Steven Xu , Jiang Weihong , Xiong Wei 
Published: October 2023

Abstract
Background
Nasopharyngeal carcinoma (NPC) is a prevalent head and neck malignancy in Southeast Asia. Radiotherapy is the primary clinical treatment for NPC, but radiotherapy resistance stands as the fundamental reason for treatment failure and patient mortality. Nevertheless, the mechanism underlying radiotherapy resistance in NPC remains elusive.
Methods
The expression of circADARB1 in NPC tissues was examined by quantitative real-time polymerase chain reaction and in situ hybridization. Colonies assay, MTT, ROS and lipid peroxidation level measurement, and nude mouse tumor model were employed to explore the role of circADARB1 enhances radiotherapy resistance via suppressing ferroptosis in vitro or in vivo. The target proteins regulated by circADARB1 were screened using mass spectrometry. The interactions between HSP90B1 and SLC7A11 or GPX4 were verified through co-immunoprecipitation and immunofluorescence assay.
Results
We identified a novel circular RNA, circADARB1, which is significantly upregulated in NPC tissues, is closely associated with unfavorable prognosis and radiotherapy resistance in NPC patients. Intriguingly, in vitro and in vivo experiments demonstrated that circADARB1 promotes radiotherapy resistance in NPC by inhibiting ferroptosis. Mechanistically, circADARB1 upregulates the expression of heat shock protein HSP90B1 through competitive adsorption of miR-615-5p. Elevated intracellular levels of HSP90B1 serve to repair misfolded proteins such as SLC7A11 and GPX4 which are typically affected by radiotherapy, thereby maintaining the stability and biological function of these proteins. In turn, SLC7A11 facilitates the transport of cystine into cells and the synthesis glutathione, while GPX4 utilizes glutathione to catalyze the reduction of intracellular lipid peroxidation induced by radiotherapy, which suppresses ferroptosis in cells and ultimately leads to radiotherapy resistance in NPC cells. Building on these findings, we have developed innovative polymer-coated quantum dot biomimetic nanoparticles that carry siRNA sequences targeting circADARB1 and ferrous ion (Fe2+). The results demonstrated that targeted inhibition of circADARB1, along with an increase in intracellular Fe2+, synergistically enhances ferroptosis of NPC cells following radiotherapy.
Conclusions
This work demonstrates that circADARB1 as a promising novel target for augmenting sensitivity of NPC to radiotherapy.
JCDS, A Hybrid Reanalysis-Forecast Meteorological Forcing Data for Advancing Climate Adaptation in Agriculture
Authors: Iizumi Toshichika , Takimoto Takahiro , Masaki Yoshimitsu , Maruyama Atsushi , Kayaba Nobuyuki , Takaya Yuhei , Masutomi Yuji 
Published: October 2023

Abstract

Variations in the growing season climate are well suited for testing agricultural adaptation measures. Adaptations for mitigating adverse events such as heatwaves and droughts allow stakeholders to plan for disruptions to production due to both seasonal extremes and long-term changes in climate. Climate impact models (e.g., crop models) can be used to develop adaptation responses. To run these models, historical records and climate forecasts need to be combined as a single daily time series. We introduce the JCDS dataset: a daily, 0.5°, global, reanalysis-forecast dataset comprising meteorological forcing data from 2010 to 2021. The dataset is a hybrid of the Japanese 55-yr Reanalysis (JRA55) and the Japan Meteorological Agency/Meteorological Research Institute Coupled Prediction System version 2 (JMA/MRI-CPS2) 5-member ensemble forecast that is issued twice per month. Both are bias-corrected using the delta method and integrated with a climatology derived from S14FD baseline meteorological forcing data. The JCDS dataset offers a framework for monitoring and forecasting applications, facilitating the transition of many climate-susceptible systems toward greater climate resilience.
A diagnostic model of polycystic ovary syndrome was constructed based on the genes associated with pyroptosis
Authors: Chen Xun , Zhang Wen , Ma Yu , Chen Yue , Xu Boqun 
Published: October 2023

Abstract
Background
Polycystic ovary syndrome (PCOS) is a complex disease, and the underlying mechanisms remain unclear. It has been suggested that genes involved in pyroptosis may play a regulatory role in PCOS. However, the exact contribution of pyroptosis to PCOS is not fully understood.
Methods
To investigate this, we obtained three mRNA expression profiles from the Gene Expression Synthesis (GEO) database and analyzed the differential expression of pyroptosis-related genes (PRGs) between PCOS patients and normal individuals. We employed four machine learning algorithms (GLM, RF, SVM, and XGB) to identify disease signature genes.
Results
A predictive model and a nomogram were developed based on PRGs to accurately predict PCOS. The XGB method demonstrated the highest accuracy in validating the model using two independent datasets, which was further supported by decision curve analysis. Consensus clustering revealed two distinct subgroups within PCOS cases, with Cluster2 exhibiting higher immune infiltration compared to Cluster1. Differential expression analysis identified DEGs between the two subtypes, and pathway enrichment analysis was conducted on the model genes.
Conclusion
This study provides preliminary insights into the association between PCOS and pyroptosis, and presents a precise predictive model for PCOS.