bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparative Accuracy Assessment of Dental Surgical Robotics and Dynamic Navigation in Immediate Implant Prosthesis Restoration
Authors: zhang kaiyue , wang dashan , wu yupeng , chen hao , zhao baodong , teng minhua 
Published: September 2023

Abstract
Objectives
To compare dental surgical robotic systems and dynamic navigation in dental implant accuracy and immediate restoration.
Materials and Methods we studied 31 patients to compare dental surgical robotic systems and dynamic navigation in dental implant accuracy and immediate restoration. They underwent either dental robot-assisted implantation (Group A) or dynamic navigation-assisted implantation (Group B) at Qingdao University Hospital from June 2019 to September 2022.
Results
Group A had 20 implants; Group B had 25. Statistical analysis showed non-normal data distribution. Group A's implant neck deviation was 0.58 mm (0.45-0.73 mm), similar to Group B's 0.71 mm (0.51-1.01 mm, p > 0.05). However, Group B had a statistically significant implant apex deviation (0.80 mm vs. 0.58 mm, p < 0.05) and angle deviation (3.15 mm vs. 0.88 mm, p < 0.05).Within six months, Group A showed no prosthetic issues, while Group B had two prosthesis fractures.
Conclusions
Compared to dynamic navigation, dental surgical robotic systems significantly enhance implant placement precision. They are not constrained by tooth position, ensuring maximum adherence to accuracy standards for immediate implant restoration with prefabricated prostheses. When integrated with temporary restoration, this approach reduces chair-side time and enhances patient comfort.
Clinical Relevance
In summary, our study demonstrates that dental surgical robotic systems offer advantages in implant surgery, providing greater accuracy compared to dynamic navigation. These systems are versatile and adaptable, allowing for precise implantation and immediate restoration with prefabricated prostheses, ultimately improving patient outcomes.
Neuron-Specific Enolase as a predictor for outcomes in ischemic stroke: a prognostic biomarker review
Authors: Mochetti Matheus Menão , Porello Estevão Garcia , Corrêa Adriana Aparecida Feltrin , Cabette Marcela Rocha , Perissinotti Iago Dias , Junqueira Lucas Oliveira , Pessoa Adriano Souza , Oliveira Rodrigo Cardoso , Silva Luiz Fernando Ferraz , Souza Heraldo Possolo , Alencar Júlio César Garcia 
Published: September 2023

Abstract
Recent studies have investigated the role of neuron-specific enolase (NSE) as a potential predictor for outcomes in acute ischemic stroke patients. However, up to date, the current body of research has yielded inconclusive results, as its role in cerebrovascular diseases remains poorly understood. Moreover, the limited sample sizes in these studies have restricted the applicability of any potential findings. This study aims to determine whether NSE serum concentrations at Emergency Department (ED) admission correlate with stroke severity, infarcted brain volume, functional outcome, and/or death rates. Literature was searched using Pubmed, Embase and Scopus databases, and all published reports identified as potentially relevant were independently assessed by each reviewer. All relevant original observational studies (cohort, case-control and cross-sectional studies) were included. Eleven studies (1,398 patients) satisfied the entry criteria. Among these, six studies reported a significant correlation between NSE serum levels and stroke severity, while only one study found no association. Regarding infarcted brain volume assessed by imaging, four studies indicated a positive relationship with NSE levels, contrasting with the findings of only one study. In terms of functional outcome and death rates, four studies identified an association, while three others did not reach statistical significance in their findings. These data highlight that NSE levels at the admission to the ED proved to be a promising tool in predicting ischemic stroke patients' outcome in the majority of the studies, although with some discrepancies. Therefore, further research is necessary to establish and define the role of NSE in clinical practice.
Scholtes relaxation method for pessimistic bilevel optimization
Authors: Benchouk Imane , Jolaoso Lateef , Nachi Khadra , Zemkoho Alain Zemkoho Alain 
Published: September 2023

Abstract
The Scholtes relaxation has appeared to be one of the simplest and most efficient ways to solve the optimistic bilevel optimization problem in its Karush-Kuhn-Tucker (KKT) reformulation or the corresponding more general mathematical program with complementarity constraints (MPCC). Inspired by such a success, this paper studies the potential of the Scholtes relaxation in the context of the pessimistic bilevel optimization problem. To proceed, we consider a pessimistic bilevel optimization problem, where all the functions involved are at least continuously differentiable. Then assuming that the lower-level problem is convex, the KKT reformulation of the problem is considered under the Slater constraint qualification. Based on this KKT reformulation, we introduce the corresponding version of the Scholtes relaxation algorithm. We then construct theoretical results ensuring that a sequence of global/local optimal solutions (resp. stationarity points) of the aforementioned Scholtes relaxation converges to a global/local optimal solution (resp. stationarity point) of the KKT reformulation of the pessimistic bilevel optimization. The results are accompanied by technical results ensuring that the Scholtes relaxation algorithm is well-defined or that the corresponding parametric optimization problem can easily be solved. Furthermore, we perform some numerical experiments to assess the performance of the Scholtes relaxation algorithm using various pessimistic bilevel programs. In particular, we study the effectiveness of the algorithm in obtaining solutions that can satisfy the corresponding C-stationarity concept.
Mathematics Subject Classification (2000) 90C26 · 90C31 · 90C33 · 90C46
Public Investment, Political Institution, and Economic Growth under Two-Sided Altruism
Authors: Tamai Toshiki 
Published: September 2023

Abstract
This paper examines how intergenerational conflicts between young and senior people affect fiscal policies and macroeconomic performance in the democratic economy. A two-sided overlapping generations model of endogenous growth with private and public capital is developed to analyze the public investment financed by a distortionary tax under parliament decision-making. Two-sided altruism generates future bias for young people rather than for senior people. The parliament in society is future-biased through the political power of the young relative to the old. Stronger young’s political power leads to higher tax rates for larger public investment because the future-biased young people are willing to receive the benefits from public investment in the future rather than decreased tax in the present. Increased public investment boosts private investment, leading to high economic growth, by the complementarity between two capitals in production. Therefore, it implies that the weaker young’s political power impedes economic growth through smaller public investment as population aging proceeds.
Image Synthesis-based Backdoor Attack Approach in the Face Classification Task
Authors: Na Hyunsik , Choi Daeseon 
Published: September 2023

Abstract
Although deep neural networks (DNNs) are applied in various fields owing to their remarkable performance, recent studies have reported that DNN models are vulnerable to backdoor attacks. They generated backdoored images by adding a backdoor trigger in original training images that activates the backdoor attack. However, most of previous attack methods are noticeable, not natural to the human eye, and easily detected by certain defense methods. In this study, we propose an image synthesis-based backdoor attack approach to resolve these limitations. To overcome the aforementioned limitations, we set a conditional facial region such as hair, eyes, or mouth as a trigger and modified that region using an image synthesis technique that replace the region of original image with the region of target image. Consequently, we achieved an attack success rate of up to 88.37% using 20% of the synthesized backdoored images injected in the training dataset, while maintaining the model accuracy for clean images. Moreover, we analyzed the advantages of the proposed approach by applying image transformation, visualization of activation regions on DNN models, and human tests. In addition to its implementation in both label flipping and clean label attack scenarios, the proposed method can be utilized as an attack approach to threaten security in the face classification task.
Efficient Privacy Protection Scheme with Batch Verification in Smart Grid
Authors: Wang Mingxiang , Zhang Lei 
Published: September 2023

Abstract
Smart grids can establish two-way communication with users, collect their electricity consumption data and provide reasonable pricing, but fine-grained electricity consumption data leads to the leakage of user privacy. In order to protect the privacy of user data and the security of data transmission process, this article proposes an efficient and batch validated privacy protection scheme. In this scheme, legitimate entities transmit encrypted electricity data after mutual authentication. To ensure the correct data is received, we propose a new batch signature verification algorithm. Security analysis shows that our solution achieves user privacy and data security. The simulation experiments provided demonstrate that the efficiency of the proposed scheme can satisfy the requirements of real-time communication.
Multi-parametric radiomics of conventional T1 weighted and susceptibility-weighted imaging for differential diagnosis of idiopathic Parkinson’s disease and multiple system atrophy
Authors: Bu Shuting , Pang Huize , Li Xiaolu , Zhao Mengwan , Wang Juzhou , Liu Yu , Yu Hongmei 
Published: September 2023

Abstract
Objectives This study aims to investigate the potential of radiomics with multiple parameters from conventional T1 weighted imaging (T1WI) and susceptibility weighted imaging (SWI) in distinguishing between idiopathic Parkinson's disease (IPD) and multiple system atrophy (MSA).Methods A total of 201 participants, including 57 patients with IPD, 74 with MSA, and 70 healthy control (HCs) individuals, underwent T1WI and SWI scans. From the 12 subcortical nuclei (e.g. red nucleus, substantia nigra, subthalamic nucleus, putamen, globus pallidus, and caudate nucleus), 2640 radiomic features were extracted from both T1WI and SWI scans. Three classification models - logistic regression (LR), support vector machine (SVM), and light gradient boosting machine (LGBM) - were used to distinguish between MSA and IPD, and MSA, IPD, and HC, respectively, based on T1WI features, SWI features, and a combination of T1WI and SWI features. Five-fold cross-validation was used to evaluate the performance of the models with metrics such as sensitivity, specificity, accuracy, and area under the receiver operating curve (AUC). During each fold, the ANOVA and least absolute shrinkage and selection operator (LASSO) methods were used to identify the most relevant subset of features for the model training process.Results The LGBM model trained by the features combination of T1WI and SWI exhibited the most outstanding differential performance in both the three-class classification task of MSA vs. PD vs. HC and the binary classification task of MSA vs. PD, with an accuracy of 0.814 and 0.854, and an AUC of 0.904 and 0.881, respectively. The texture-based differences (GLCM) of the SN and the shape-based differences of the GP were highly effective in discriminating between the three classes and two classes, respectively.Conclusions Radiomic features combining T1WI and SWI can achieve a satisfactory differential diagnosis for PD, MSA, and HC groups, as well as for PD and MSA groups, thus providing a useful tool for clinical decision-making based on routine MRI sequences.
Pseudomonas aeruginosa PR23 isolated from oil contaminated soil tolerate and degrades mixture of polyaromatic hydrocarbons and express novel proteins
Authors: Jindal Shanky , Aggarwal Kamal Krishan 
Published: September 2023

Abstract

Pseudomonas aeruginosa PR23 isolated from the hydrocarbon contaminated soil tolerate and degrades mixture of polyaromatic hydrocarbons (PAHs) having initial concentration of 1300 ppm. The degradation and intermediates formed were assessed through GC-MS analysis. The isolated strain was able to degrade 59.2% mixture of PAHs in 3 days and 71.6% by day 15. Effect of PAHs on protein expression in Pseudomonas aeruginosa PR23 was studied through nano LC-MS/MS. Thirty-six proteins were observed that showed more than 2-fold increased expression in the presence of mixture of PAHs. Out of these proteins, 7 proteins are reported for their role in degradation of naphthalene, phenanthrene, and pyrene. Data also showed the presence of 16 proteins that are uniquely expressed in the presence of mixture of PAHs. A Twin-arginine translocation signal peptide (tat system) known for transportation of folded proteins across the cell membrane showed more than 8-fold increased expression in the presence of mixture of PAHs. Results indicate that isolated strain adopts the conditions in the presence of mixture of PAHs by modulating its metabolic and physiological processes. These findings suggest that Pseudomonas aeruginosa PR23 may be a suitable candidate to be used for the development of strategies for bioremediation of mixture of PAHs.
A Novel Solar Power Generation Forecasting Using Inductive Analysis and Bi-lstm Model
Authors: Vanitha R , Swaroopan N M Jothi 
Published: September 2023

Abstract
High-efficiency integration of solar power into large-scale regional power grids depends on accurate photovoltaic power prediction. However, the inefficient and intermittent power output generation significantly affect the economic integration of solar generation within existing power system, therefore, an effective forecasting method is needed to enhance solar power prediction in both good and bad weather condition. This paper proposed a novel intelligent model for accurate power forecasting in PV solar systems based on Bidirectional LSTMs (Bi-LSTMs). A combination of a data-driven Bi-LSTM model and inductive data analysis is employed to predict photovoltaic power generation. This approach takes into account various factors that can affect or limit power generation, including sun irradiance, ambient and relative temperature. The solar power output is evaluated for the good and bad datasets. The input data is pre-processed and combined with generation data and weather data where missing values are removed to avoid performance reduction. Through Inductive analysis, the dataset is separated into good and bad using a k-means clustering algorithm, which divides the data based on its similarity. An efficient output in good weather means the solar panel is functioning correctly, and bad data means the solar panel malfunctions or dust blocks the solar panel. Three deep learning models (GRU, Stacked GRU, and Bi-LSTM) are evaluated on historical weather data series. Bi-LSTM (0.0096) achieves the lowest MSE and RMSE value for good and bad data compared to stacked GRU (0.0129) and GRU (0.0044). After the evaluation, our proposed forecasting Inductive analysis with Bi-LSTM algorithm provides an optimal performance than other deep learning models.
The causal relationship between genetic prediction of iron homeostasis and Parkinson's disease: a two-sample Mendelian randomization study
Authors: Chen Hong , Wang Xie , Chang Ze , Zhang Juan , Xie Daojun 
Published: September 2023

Abstract
Background
The specific etiology of Parkinson's disease (PD), a degenerative disease of the central nervous system, is still unclear, and it is currently believed that its main pathological basis is a decrease in dopamine concentration in the striatum of the brain.Although many previous studies have suggested that iron, as an important nutrient, is involved in the development of PD, there has been a lack of validated genetic evidence on whether there is a causal association between iron homeostasis indices (ferritin, serum iron, TIBC, and TSAT) and PD.
Methods
We used Mendelian randomization (MR) as an analytical method to efficiently assess the genetic association of exposure and outcome, based on the largest genome-wide association study (GWAS) data so far, for the causal association between iron homeostasis indicators and Parkinson's disease, controlling for confounders, by using genetic instrumental variables, that is, single-nucleotide polymorphisms (SNPs), which are randomly assigned and are not subject to any causative effect.
Results
By coordinated analysis of 86 SNPs associated with iron homeostasis markers and 12,858,066 SNPs associated with PD, a total of 56 SNPs were finally screened for genome-wide significance of iron homeostasis associated with Parkinson's disease.The results of the IVW analysis suggested that total iron binding capacity ( β= -0.142; 95%Cl = -0.197 to 0.481; P = 0.4138421 ), transferrin saturation ( β= -0.316 ; 95%Cl = -0.861 to 0.229; P = 0.2553290 ), ferritin ( β= -0.387 ; 95%Cl = -1.179 to 0.405; P = 0.33783807 ) were not genetically causally associated with PD, serum iron ( β= -0.524; 95%Cl = -0.046 to -0.002; P = 0.03191512 ) was considered to have genetic causality with PD. Cochran's Q test for MR-IVW suggested that TIBC ( P = 0.1618872), TSAT ( P = 0.7099448), ferritin ( P = 0.09768154), serum iron ( P = 0.8557510) were not heterogeneous with the results of Mr analysis of PD. MR-PRESSO global test showed that TIBC ( P = 0.404), TSAT ( P = 0.759), ferritin ( P = 0.113), serum iron ( P = 0.87 ) were not detected the presence of horizontal pleiotropy.
Conclusion
Our study found that of the four iron homeostasis markers, TIBC, TSAT, and ferritin were not genetically causally associated with PD, whereas there was a genetically causal association between serum iron and PD, and the increase of serum iron level may reduce the risk of PD.
Gestational fallopian tubal choriocarcinoma cured surgically: a case report
Authors: Xie Xiaoyan , Peng Meilian , Wang Yan , Cai Liangzhi , Xu Mu , Shi Yuequan , Xu Shuxia , Lin Jiansong , Sun Pengming , Wu Qibin 
Published: September 2023

Abstract
Background
Fallopian choriocarcinoma is extremely rare and is still treated with a combination of chemotherapy and surgery. We report a case of gestational fallopian tubal choriocarcinoma treated solely with surgery and describe its clinical characteristics, treatment, and follow-up prognosis.
Case presentation
We report a patient who developed fallopian tube choriocarcinoma during the perimenopausal period. After surgical treatment, the patient was diagnosed with histopathology using immunohistochemistry staining combined with short tandem repeatdetection, and the diagnosis was confirmed as Gestational fallopian tubal choriocarcinoma. Postoperative monitoring serum β- HCG levels, this patient’s β- HCG value showed an exponential decrease and dropped to the normal range within 6 weeks for 3 years. Therefore, the patient did not receive chemotherapy
Conclusions
This report provides ideas for the simple surgical treatment of gestational fallopian tubal choriocarcinoma that meets conditions, which can reduce unnecessary chemotherapy side effects for such patients.
Is metabolic dysfunction-associated fatty liver disease a protective factor in hepatocellular carcinoma patients after hepatectomy? A retrospective study based on different analysis methods
Authors: Kong Qingyan , Gao Qianqian , Li Wenjie , Chen Zheyu 
Published: September 2023

Abstract
Background
Metabolic dysfunction-associated fatty liver disease (MAFLD) has been recognized as an accurate reflection of the pathogenesis of fatty liver disease by a consensus document. However, the relationship between MAFLD and the prognosis of patients with hepatocellular carcinoma (HCC) remains unclear. Therefore, the main purpose of this study was to investigate the impact of MAFLD on the prognosis of HCC patients after hepatectomy.
Methods
To identify risk factors associated with poor prognosis, we used a multivariate Cox proportional hazards model. We also conducted propensity score matching (PSM) analysis to balance the baseline characteristics between the MAFLD and non-MAFLD groups. Kaplan‒Meier survival curves were used to compare the prognosis of the two matched groups.
Results
This study involved 909 HCC patients who underwent hepatectomy between January 2011 and December 2017 at the West China Hospital of Sichuan University, including 97 patients with MAFLD (MAFLD group) and 812 patients without MAFLD (non-MAFLD group). Our multivariate analysis results indicated that MAFLD might be a protective factor for overall survival (OS) (p = 0.032), but not for recurrence-free survival (RFS) (p = 0.214). After conducting PSM analysis, we found that the RFS rates in the MAFLD group were higher at the 1-, 3-, and 5-year intervals, with rates of 72.7%, 59.0%, and 49.9%, respectively, than those in the non-MAFLD group, which had rates of 69.8%, 54.3%, and 43.2%, respectively. Therefore, MAFLD might improve the 1-year, 3-year, and 5-year RFS rates of HCC patients who undergo hepatectomy. In the subgroup analysis, we found that patients in the overweight group had better RFS than those in the lean/normal group (p＜0.001).
Conclusions
In summary, our study suggests that MAFLD might be a protective factor for the OS of HCC patients who underwent hepatectomy through multivariate analysis. Furthermore, after PSM analysis, MAFLD was found to improve the 1-year, 3-year, and 5-year RFS rates of HCC patients who underwent hepatectomy, especially those who were overweight. These findings may have implications for the management and treatment of HCC patients with MAFLD.
Unmasking the Complexity: Primary Systemic Lupus Erythematosus Coexisting with Drug-Induced Lupus – A Unique Case Report"
Authors: Siddiqui Aisha , Gul Muhammad Hamza , Siddiqui Farina Fatima , Oza Yugam Rajeshkumar , Saher Najam us 
Published: September 2023

Abstract
Systemic lupus erythematous (SLE) is a chronic autoimmune disease that affects multiple systems in the body, and it follows a relapsing-remitting course. Certain medications can trigger SLE in individuals who are genetically susceptible to the condition, leading to drug-induced lupus (DIL). Currently, over 100 drugs are suspected to induce DIL, and the list of drugs continues to grow as new drugs arise. In this case, we present the case of a 50-year-old patient who had primary SLE and subsequently developed drug-induced SLE after receiving ATT treatment. Previous literature has shown that the risk of drug-induced lupus increases with age, and our patient's age supports this finding. Therefore, the patient was admitted and treated as a case of primary SLE with Superimposed Drug-Induced SLE.