bluuebunny/crossref_metadata_2025_split huggingface dataset:
A dual in vitro approach for systematic analysis of mitochondrial toxicity in traditional Chinese medicine injections
Authors: Shen Yunfu , Guo Kaiqiang , Ma Aijun , Huang Zhe , Du Jingjing , Chen Junhe , Lin Qianyu , Wei Chengming , Wang Zi , Zhang Fuming , Zhang Juan , Lin Wanjun , Feng Na , Ma Wenzhe 
Published: September 2022

Abstract
Background
There are technical obstacles in the safety evaluation of traditional Chinese medicine (TCM) injections due to their complex chemical nature and the lack of rapid and accurate in vitro methods.
Objectives
To establish a dual in vitro mitochondrial toxicity assay approach for systematically evaluating mitochondrial toxicity of TCM injections.
Methods
Using this dual in vitro approach, the effect of TCM injections exposure on ATP depletion in HepG2 cells was examined by the conventional “glucose/galactose” assay. The cytotoxicity of TCM injections on HCT116 WT and HCT116 SCO2 KO cell lines was determined by sulforhodamine B (SRB) colorimetric assay. Subsequently, mitochondrial toxicity of ingredients in identified TCM injections was validated by combining molecular docking analysis with experimental validation.
Results
Four of the 35 TCM injections, including Xiyanping, Dengzhanhuasu, Shuanghuanglian, and Yinzhihuang, significantly reduced cellular ATP production in galactose medium in the first assay, and presented less cytotoxic in the respiration deficient cells in the second assay, indicating that they have mitochondrial toxicity. Furthermore, we identified scutellarin, rutin, phillyrin, and baicalin could be the potential mitochondrial toxic ingredients in 4 TCM injections by combining molecular docking analysis with experimental validation.
Conclusions
Collectively, the dual in vitro approach is worth applying to the safety evaluation of more TCM products, and mitochondrial toxic TCM injections and ingredients found in this study deserve more attention.
Classifying literature mentions of biological pathogens as experimentally studied using natural language processing
Authors: Yepes Antonio Jose Jimeno , Verspoor Karin 
Published: September 2022

Abstract
Background
Information pertaining to mechanisms, management and treatment of disease-causing pathogens including viruses and bacteria is readily available from research publications indexed in MEDLINE. However, identifying the literature that specifically characterises these pathogens and their properties based on experimental research, important for understanding of the molecular basis of diseases caused by these agents, requires sifting through a large quantity of articles to exclude incidental mentions of the pathogens, or references to pathogens in other non-experimental contexts such as public health.
Objective
In this work, we lay the foundations for the development of automatic methods for characterising mentions of pathogens in scientific literature, focusing on the task of identifying research that involves active study of a pathogen in an experimental context. There are no manually annotated pathogen corpora available for this purpose, while such resources are necessary to support development of machine learning-based models. We therefore aim to fill this gap, producing a large data set automatically from MEDLINE, and using it to explore automatic methods that specifically support detection of experimentally studied pathogen mentions in research publications.
Methods
We developed a pathogen mention characterisation literature data set —READBiomed-Pathogens— automatically using NCBI resources, which we make available. Resources such as the NCBI Taxonomy, MeSH and GenBank can be used effectively to identify relevant literature about experimentally researched pathogens, more specifically using MeSH to link to MEDLINE citations including titles and abstracts with relevant pathogens. We experiment with several machine learning-based natural language processing (NLP) algorithms leveraging this dataset as training data to model the task of detecting papers that specifically describe active experimental study of a pathogen.
Results
We show that our data set READBiomed-Pathogens can be used to explore natural language processing configurations for experimental pathogen mention characterisation. READBiomed-Pathogens includes citations related to organisms including bacteria, viruses, and a small number of toxins and other disease-causing agents.
Conclusions
We studied the characterisation of experimentally studied pathogens in scientific literature, developing several natural language processing methods supported by an automatically developed data set. As a core contribution of the work, we presented a methodology to automatically construct a data set for pathogen identification using existing biomedical resources. The data set and the annotation code are made publicly available. Performance of the pathogen mention identification and characterisation algorithms were additionally evaluated on a small manually annotated data set shows that the data set that we have generated allows characterising pathogens of interest.
Trial Registration:
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Remote sensing pansharpening with TV-H-1 decomposition and PSO based adaptive weighting method
Authors: Sangani Dhara , Thakker Rajesh , Panchal Shailesh , Gogineni Rajesh Gogineni Dr. Rajesh 
Published: September 2022

Abstract
In remote sensing due to several constraints of optical imaging sensors high spectral and high spatial features are not present in single image, so high frequency component of panchromatic(PAN) image needs to be restored at the spatial resolution of corresponding multispectral (MS) image. Misbalancing between spatial and spectral details extraction and injection leads to several issues like spectral distortion, intensity variation. Direct fusion methods often generate resultant image with poor contrast and several undesirable effects, while multi resolution analysis (MRA) techniques have achieved success in many fusion algorithms, these methods also suffer from ringing artifact due to strong edge decomposition. In this paper, pansharpening (PS) method is based on total variation-Hilbert − 1(TV-H− 1) model for decomposing MS and PAN image in appropriate cartoon and texture components. To refine the detail map and better quality of fused image adaptive weights are calculated using particle swarm optimization (PSO). For experimental analysis, four different sensors datasets with different regions like city area, hilly area and vegetation area are used. Resultant image and image quality analysis parameters confirm that the proposed method succeed in spectral detail preservation compared with other widely used pansharpening methods.
Ginsenoside Rb1 ameliorates renal tubular epithelial cells ferroptosis in AKI
Authors: Wu Zhifen , Deng Junhui , Tao Chenhui , Tan Wei , Lin Lirong , Zheng Luquan , Yang Xujia , li ke , yang jurong 
Published: September 2022

Abstract
Acute kidney injury (AKI) is mainly caused by ischemia-reperfusion (I/R), with high clinical mortality since there is a lack of definite and effective drug intervention except for renal replacement therapy. Previous research emphasized that ferroptosis is the pivotal process in I/R injury of renal. Ginsenoside Rb1 is a chemical compound belonging to the ginsenoside family and has been proved to have benefits for kidney diseases, but its specific mechanism in AKI is uncovered. Here we discovered Rb1 could mitigate ferroptosis in renal tubular epithelial cells of AKI animals. Mechanically, Rb1 significantly inhibited oxidative stress including promotion of GSH, reduction of malondialdehyde (MDA) and reactive oxygen species (ROS), also reduced inflammatory response, then relieved renal injury in I/R mice. RSL3, the inhibitor of GPX4, or Erastin, the inhibitor of SLC7A11, could induce ferroptosis and eliminate the protective effect of Rb1. Also, a ferroptosis inhibitor or ROS scavenger could simulate the protective effect of Rb1 on renal tubular epithelial cells. In conclusion, we confirmed that Rb1 promotes GSH synthesis by preventing oxidative stress, upregulating the expression of GPX4 and SLC7A11, and ultimately scavengers of ROS or MDA, and reduces the occurrence of ferroptosis in renal tubular epithelial cells. Rb1 has potential benefits for AKI patients.
Serum Rcn3 level is a potential diagnostic biomarker for connective tissue disease-associated interstitial lung disease and reflects the severity of pulmonary function
Authors: Ding Fangping , Yang Liu , Wang Yingfei , Ma Yingmin , Jin Jiawei 
Published: September 2022

Abstract
Background
Although reticulocalbin 3 (Rcn3) has a critical role in alveolar epithelial function as well as in pathogenesis of pulmonary fibrosis, no study has yet examined its diagnostic and prognostic values for interstitial lung disease (ILD). This study aimed to evaluate Rcn3 as a potential marker for differential diagnosis in the patients with idiopathic pulmonary fibrosis (IPF) and connective tissue disease-associated interstitial lung disease (CTD-ILD) and reflecting the severity of disease.
Methods
This was a retrospective observational study included 71 ILD patients and 19 healthy controls. These patients were stratified into IPF group (39) and CTD-ILD group (32). The severity of ILD was evaluated through pulmonary function test.
Results
Serum Rcn3 levels were statistically higher in CTD-ILD patients than those in IPF patients (p = 0.012) and healthy controls (p = 0.028). There were statistic negative correlations between serum Rcn3 and TLC, FVC and DLCO in patients with CTD-ILD (r = -0.367, p = 0.039; r = -0.391, p = 0.027; r = -0.370, p = 0.037, respectively), but not in IPF patients. ROC analysis demonstrated that serum Rcn3 had superior diagnostic value for CTD-ILD and an Rcn3 cutoff value of 2.73 ng/mL had a sensitivity of 69%, a specificity of 69% and an accuracy of 45% for diagnose of CTD-ILD.
Conclusions
Serum Rcn3 levels might be a clinically useful biomarker in screening and evaluating CTD-ILD.
Quantum metrology with N-qubit W superposition state under local and nonlocal operation
Authors: Li Yan , Ren Zhihong 
Published: September 2022

Abstract
We study the performance of N-qubit W superposition state in quantum metrology. Taking advantage of the general Ising-type Hamiltonian (including local and nonlocal operation), we analytically present the Quantum Fisher information (QFI) of N-qubit W superposition state under different situations and then investigate its phase sensitivity. The results show that the phase sensitivity under local operation displays a crossover from W state to GHZ state, where it is same as W state in few-qubit case (N ≤ 6) but asymptotically equal to GHZ state for large qubit case (N >> 1). Interestingly, the 4-qubit W superposition state is found to have the same sensitivity with 4-qubit GHZ state, but be more robust than latter. Besides, the optimal theoretical proposals are provided for ideal metrology. Under the phase/amplitude damping channel, the phase sensitivity of W superposition state (except for N=3) is ultimately decreased to standard quantum limit, while it turns worse in depolarizing channel. Finally, the tunable phase sensitivity under nonlocal operation is studied and the general Heisenberg limit is surpassed with the increasing interaction strength γ . Meanwhile, a plateau of QFI and phase sensitivity is found for all large qubit W superposition state, which is similar to the study of GHZ state and again verifies the common feature of GHZ-type states in quantum metrology.
Study the electrocatalytic performance in alkaline medium and enhanced glucose electro-oxidation by bismuth nanostructures for potential application in direct glucose fuel cell
Authors: Dey Santanu , Pramanik Subhamay , Chakraborty Pradipta , Rana Dhiraj Kumar , Mandal Manas Kumar , Basu Soumen 
Published: September 2022

Abstract
Although direct glucose fuel cell (DGFC) is considered as a promising energy supply system, the poor catalytic activity and substandard stability of most catalysts during glucose electro-oxidation is a serious drawback for its possible applications. In this study, different bismuth nanostructures are proposed as efficient anode electrocatalysts for the oxidation of glucose in alkaline medium. Bismuth nanospheres with smaller diameter were prepared by hydrothermal method, giving rise to active surface area and electrocatalytic performance as well toward glucose electro-oxidation. Among the prepared Bi electrocatalysts of different morphologies, uniformly spherical three dimensional structure displayed improved current density as high as 79.78 mAcm− 2 during glucose oxidation in 1 M NaOH + 6 mM glucose solution tested at 100 mVs− 1 CV scan rate. The cycling stability of the prepared catalyst was examined for 100 cycles and it showed the 79.5% retention of activity after 100 cycles. In our investigation, bismuth nanospheres having smaller diameter showed best catalytic performance. The notable electrocatalytic performance of Bi nanostructures (mainly Bi nanosphere) may make possible to design and progress of renewable power device.
Patient and Health Worker Perspectives on Quality of HIV Care and Treatment Services in Haiti
Authors: Puttkammer Nancy , Demes Joseph Adrien Emmanuel , Dervis Witson , Chéry Jean Marcxime , Elusdort Josette , Haight Elizabeth , Honoré Jean Guy , Simoni Jane M. 
Published: September 2022

Abstract
Background: Poor quality of care is a barrier to engagement in HIV care and treatment in low- and middle-income country settings. This study involved focus group discussions (FGD) with patients and health workers in two large urban hospitals to describe quality of patient education and psychosocial support services within Haiti’s national HIV antiretroviral therapy (ART) program. The purpose of this qualitative study was to illuminate key gaps and salient “ingredients” for improving quality of care. Methods: The study included 8 FGDs with a total of 26 male patients and 32 female patients and 15 smaller FGDs with 57 health workers. The analysis used a directed content analysis method, with the goal of extending existing conceptual frameworks on quality of care through rich description.Results: Dimension of safety, patient-centeredness, accessibility, and equity were most salient. Patients noted risks to privacy with both clinic and community-based services as well as concerns with ART side effects, while health workers described risks to their own safety in providing community-based services. While patients cited examples of positive interactions with health workers that centered their needs and perspectives, they also noted concerns that inhibited trust and satisfaction with services. Health workers described difficult working conditions that challenged their ability to provide patient-centered services. Patients sought favored relationships with health workers to help them navigate the health care system, but this undermined the sense of fairness. Both patients and health workers described frustration with lack of resources to assist patients in dire poverty, and health workers described great pressure to help patients from their “own pockets.”Conclusions: These concerns reflected the embeddedness of patient – provider interactions within a health system marked by scarcity, power dynamics between patients and HWs, and social stigma related to HIV. Reinforcing respectful and welcoming atmosphere, timely service, privacy protection, and building patient perception of fairness in access to support could help to build patient satisfaction and care engagement in Haiti. Improving working conditions for health workers is also critical to achieving quality.
500 V Breakdown Voltage in β-Ga 2 O 3 LDMOSFET With 108 MW/cm 2 Power Figure of Merit
Authors: Rik Nesa Abedi , Orouji Ali A. , Madadi Dariush 
Published: September 2022

Abstract
Our work presents a silicon-on-insulator (SOI) laterally diffused metal-oxide-semiconductor field-effect transistor (LDMOSFET) with β-Ga2O3, which is a large bandgap semiconductor (β-LDMOSFET) for increasing breakdown voltage (VBR) and power figure of merit. The characteristics of β-LDMOSFET were analyzed to those of a standard LDMOSFET, such as VBR, ON-resistance (RON), power figure of merit (PFOM), and radio frequency (RF) performances. The fundamental purpose of this research is to use the high bandgap semiconductor instead of silicon material due to its large breakdown field of about 9 MV/cm. The β-LDMOSFET structure outperforms performance in the VBR, increasing to 500 V versus 84.4 V in a standard LDMOSFET design. The suggested β-LDMOSFET has RON ~ 2.3 mΩ.cm− 2 and increased the PFOM (VBR2/RON) to 108.6 MW/cm2.
Cost-effective and Smart AAA-Class RGB-LED-based Sun Simulator for Real-Time Characterization of Solar Cells
Authors: Nayeri Saeed , Rostami Ali , Moghadam Javad Javadi 
Published: September 2022

AbstractTo characterize the solar cell parameters, the light emitting diode (LED)-based sun simulators have been developed using different types of LEDs and geometrical configurations in the various wavelength range. However, the optimal spectral match, spatial homogeneity, and temporal stability have not been attained in these systems. Moreover, their design is very complex, heavy, expensive, energy and time-consuming, and needs a professional operator. Here, we have introduced a cost-effective and smart RGB LED-based sun simulator that is controlled by an ARM microprocessor, which is adjustable using user-friendly windows software. The LED panel cover 22 peak wavelength in the range of 245–940 nm at 0.2 sun intensity compared with AM1.5G global solar spectrum sunlight containing 4 UV LED (245 nm), 56 RGB LED with a 10 mm center-to-center distance from each other (14 different peaks with 22nm intervals in the range of 380-680nm), five IR LEDs with different wavelengths, including 730, 740, 810, 850, and 940 nm in the area of 10×15 cm2. According to the IEC60904-9 and ASTM standards, we achieved the AAA-class performance in all three categories including spectral match, spatial homogeneity, and temporal instability for test planes 4×4 cm2and 8 ×8 cm2at a distance of 4 cm from the LED panel. We successfully measured theI-Vparameter of the standard solar cell 3×3 cm in the real-time condition. This system is ultra-low cost, simple, user-friendly, and can be built and used in any research lab characterizing and monitoring solar cell parameters in real-time conditions.
Optimization of femtosecond laser drilling process for DD6 single crystal alloy
Authors: Du Tianhan , Liang Xiaoqing , Yu Yanqing , Zhou Liucheng , Cai Zhenbin , Wang Lingfeng , Jia Wentong , Pan Xinlei 
Published: September 2022

Abstract
In this paper, we explore the optimal combination of femtosecond laser drilling parameters for micro-hole processing on DD6 single-crystal high-temperature alloy and analyze the significance of parameter variations on the microstructure characteristics of the holes. The L25(56) orthogonal test was performed by controlling six parameters during femtosecond laser ring processing: Average power, Overlap rate, Defocus rate, Feed amount, Gas pressure, and End position. It is found that the entrance of the micro-holes has good roundness, while a large variation in diameter is observed. The exit of the holes exhibits poor roundness, and the diameter variation is intensified compared with the entrance, there are some with jagged edges. After the extreme difference analysis, it is concluded that the defocus has the most significant effect on the hole drilling by femtosecond laser ring processing for DD6 single crystal high-temperature alloy. The increase of laser average power leads to the decrease of hole taper, and the feed of 0.02 mm is beneficial for processing small taper through-hole. In addition, the outlet diameter of the holes increases with the increasing of gas pressure after the pressure is greater than 0.25 MPa, and the effect of the end is smaller and other regular conclusions. Through the integrated equilibrium method, the optimized combination of the femtosecond laser drilling parameters for 400 µm through-hole with minimum taper is as follows: power 7.84 W, overlap 99%, defocus + 0.4 mm, pressure 0.25 MPa, feed 0.02 mm and end + 0.2 mm.
Unbalanced Data Processing and Machine Learning in Credit Card Fraud Detection
Authors: Peng Haili , Wang Jing 
Published: September 2022

Abstract
Imbalanced data often performs poorly in the model and can prevent the model from capturing a few classes of samples, so it is crucial to process imbalanced data. This paper is a credit card fraud detection based on imbalanced data, comparing different processing methods for imbalanced data and using machine learning to detect credit card fraud, and finally arriving at optimal results. Since credit card fraud data is mostly a dichotomous problem and highly imbalanced, it means that the machine learning model favors the majority of the samples and treats the fraudulent transactions in the credit card fraud data as correct transactions. The treatment of imbalanced data is crucial because of the low percentage of fraudulent data. We used different methods for imbalanced data such as oversampling, undersampling, combined sampling and using class weights to improve the class imbalance and applied these methods to credit card fraud detection and calculated Accuracy, Precision, Recall, F1 score and AUC. Because of the severe imbalance in the data, the model is biased towards majority of the samples, so the accuracy of the model will be high. Because we focus more on the probability that a minority class of the sample is correctly classified, to check the performance of the model, we will use the F1 score, the Area Under the Precision-Recall Curve (AUPRC). and recall as measures instead of accuracy. The results demonstrate that the model achieves the best performance by resampling the credit card fraud data, and finding the optimal weights for different category samples by grid search when setting the category weights leads to a significant improvement in the performance of the logistic regression model, and the random forest outperforms all the machine learning models that are compared.