bluuebunny/crossref_metadata_2025_split huggingface dataset:
Small Molecule-Mediated Allosteric Activation of the Base Excision Repair Enzyme 8-Oxoguanine DNA Glycosylase: Impact on Mitochondrial Function
Authors: Tian Gaochao , Katchur Steven , Jiang Yong , Briand Jacques , Schaber Michael , Kreatsoulas Constantine , Schwartz Benjamin , Thrall Sara , Davis Alicia , Duvall Sam , Kaufman Brett , Rumsey William 
Published: July 2022

Abstract
8-Oxoguanine DNA glycosylase (OGG1) initiates base excision repair of the oxidative DNA damage product 8-oxoguanine. OGG1 is bifunctional; catalyzing glycosyl bond cleavage, followed by phosphodiester backbone incision via a β-elimination apurinic lyase reaction. The product from the glycosylase reaction, 8-oxoguanine, and its analogues, 8-bromoguanine and 8-aminoguanine, trigger the rate-limiting AP lyase reaction. The precise activation mechanism remains unclear. The product-assisted catalysis hypothesis suggests that 8-oxoguanine and analogues bind at the product recognition (PR) pocket to enhance strand cleavage as catalytic bases. Alternatively, they may allosterically activate OGG1 by binding outside of the PR pocket to induce an active-site conformational change to accelerate apurinic lyase. Herein, steady-state kinetic analyses demonstrated random binding of substrate and activator. 9-Deazaguanine, which can’t function as a substrate-competent base, activated OGG1, albeit with a lower Emax value than 8-bromoguanine and 8-aminoguanine. Random compound screening identified small molecules with Emax values similar to 8-bromoGua. Paraquat-induced mitochondrial dysfunction was attenuated by several small molecule OGG1 activators; benefits included enhanced mitochondrial membrane and DNA integrity, less cytochrome c translocation, ATP preservation, and mitochondrial membrane dynamics. Our results support an allosteric mechanism of OGG1 and not product-assisted catalysis. OGG1 small molecule activators may improve mitochondrial function in oxidative stress-related diseases.
Demonstration of Neural Networks to Reconstruct Temperatures from Simulated Fluorescent Data towards use in Bio-Microfluidics
Authors: Kullberg Jacob , Colton Jacob , Gregory C. Tolex , Bay Austin , Munro Troy 
Published: July 2022

Abstract
Biological systems often have a narrow temperature range of operation, which require highly accurate spatially resolved temperature measurements, often near ±0.1K. However, many temperature sensors cannot meet both accuracy and spatial distribution requirements, often because their accuracy is limited by data fitting and temperature reconstruction models. Machine learning algorithms have the potential to meet this need, but their usage in generating spatial distributions of temperature is severely lacking in the literature. This work presents the first instance of using neural networks to process fluorescent images to map the spatial distribution of temperature. Three standard network architectures were investigated using non-spatially resolved fluorescent thermometry (simply-connected feed-forward network) or during image or pixel identification (U-net and convolutional neural network, CNN). Simulated fluorescent images based on experimental data were generated based on known temperature distributions where Gaussian white noise with a standard deviation of ±0.1 K was added. The poor results from these standard networks motivated the creation of what is termed a moving CNN, with an RMSE error of ±0.23 K, where the elements of the matrix represent the neighboring pixels. Finally, the performance of this MCNN is investigated when trained and applied to three distinctive temperature distributions characteristic within microfluidic devices, where the fluorescent image is simulated at either three or five different wavelengths. The results demonstrate that having a minimum of 103.5 data points per temperature and the broadest range of temperatures during training provides temperature predictions nearest to the true temperatures of the images, with a minimum RMSE of ±0.15 K. When compared to traditional curve fitting techniques, this work demonstrates that greater accuracy when spatially mapping temperature from fluorescent images can be achieved when using convolutional neural networks.
Defining Medication Discontinuation from Prescription Claims Records to Monitor Clinical Progress in Schizophrenia &amp; Bipolar disorder: A Systematic Review
Authors: Musker Michael , Lewis Martin , Schrader Geoff , Isaac Vivian , Strobel Jorg , Bidargaddi Niranjan 
Published: July 2022

Abstract
Background: Digitally accessible prescription claims have potential utility in timely identification and active follow up interventions for unsupervised medication discontinuation in consumers with schizophrenia and bipolar disorder, that may prevent relapse and other adverse outcomes. We conducted a systematic review in order to identify and understand definitions of non-adherence from prescription claims records to serve as decision points for interventions. Methods: Electronic searches were conducted using Ovid MEDLINE, PubMed, the Cochrane Central Register of Controlled Trials and PsycINFO on 12 January 2020 to identify studies published in English. Data extraction was done by two reviewers independently. A narrative synthesis and tabulation of outcomes was performed. PROSPERO (CRD42020201708). Findings: Thirty-one studies were eligible for analysis. Three common computing parameters were used to calculate non-adherence over 12 months, the Medication Possession Ratio (MPR) was used most frequently (21 studies - 68%) with a non-adherence threshold <0.8 (or <80% use of the full prescription of medication) in 55% of studies (n=17). All studies defined non-adherence retrospectively, and the main outcome reported by most studies was increased hospitalisation (24 of 31 studies – 77%). Interpretation: Computing non-adherence from prescription claim records is a potential decision point for interventions. While there was variation in methods used to compute non-adherence parameters from prescription claims records, all approaches demonstrated that non-adherence was associated with adverse outcomes, the foremost of which was rehospitalisation and increased costs of care.
Bioinformatics Analysis Identifies Potential Ferroptosis Key Genes in the Pathogenesis of Ankylosing Spondylitis
Authors: He Guoxian , Chen Zexin , Li Jiaxiao , Zhang Lanhui , Liu Suling , Cui Yang 
Published: July 2022

Abstract
Background: Ankylosing spondylitis (AS) is a disabling chronic inflammatory disease. The mechanisms of ferroptosis in AS remain unclear. Method: Using bioinformatics analysis, we aimed to identify key molecules involved in ferroptosis and provide potential therapeutic targets for AS, further exploring the mechanisms of ferroptosis in AS. GSE25101 was downloaded from the Gene Expression Omnibus (GEO) dataset and intersected with ferroptosis genes. Result: A total of 20 differentially expressed genes (DEGs) were screened, most of which were involved in PI3K-Akt, MAPK signalling pathway and endoplasmic reticulum stress response. The following target genes were identified through protein-protein interaction (PPI) network analysis and screening of key modules constructed from genes associated with the PI3K-Akt and MAPK signalling pathway: TP53, PTEN, TLR4, HSPB1, DDIT3, XBP1. In addition, PI3K-Akt and MAPK signaling were both associated with oxidative stress, both of them may play a role in AS pathological ossification related to ferroptosis. We identified only has-miR-205-5p targeting at least two genes. Conclusion: The results of this study indicated that the TP53- and PTEN-related mRNA–miRNA interaction chain could be potentially employed as a biomarker of the inception and progression of ferroptosis in AS.
Priority planting area planning for L. chuanxiong under soil Cd pollution and climate change
Authors: He Ping , Huo Tongtong , Meng Fanyun , Peng Cheng 
Published: July 2022

Abstract
Aims Soil cadmium (Cd) pollution poses substantial threats to cash crops and humans. For decades, L. chuanxiong has been under Cd stress due to natural and human activities, including the soil-forming process. As such, how to plan priority planting areas impacted by soil Cd pollution and explore the future spatial dynamics of planned areas is critical. Methods We developed a comprehensive evaluation system that is universally applicable to all plants stressed by heavy metal pollution by simulating the current and future niche of L. chuanxiong and establishing a scenario simulation model for soil Cd pollution. Results The results revealed that the current first- and secondary-priority planting areas for L. chuanxiong were 2.06 ×103 km2 and 1.64 ×104 km2, respectively. Of these areas, the seven first and twelve secondary counties for current L. chuanxiong cultivation should have higher priorities; these areas include Meishan, Qionglai, Pujiang, and other regions. Additionally, all the priority zones under the current and future scenarios are mainly concentrated in the Chengdu Plain, southeast Sichuan, and northern Chongqing. Future planning results indicate that Renshou, Pingwu, Meishan, Qionglai, Pengshan, and other regions are very important for L. chuanxiong planting, and a pessimistic scenario will negatively impact it. The spatial dynamics of priority areas in 2050 and 2070 clearly fluctuate under different prediction scenarios and are mainly distributed in northern Sichuan and western Chongqing. Conclusions Given these results, taking reasonable measures to manage the planned areas is necessary. This study not only provides a useful reference for the protection and cultivation of L. chuanxiong but also provides a paradigm for other cash crops.
Prediction of Heart Disease Using Data Mining Classifiers
Authors: K UMA , M Hanumanthappa 
Published: July 2022

Abstract
Fist size muscle acquires the name of an important part of the human body by pumping blood to all the parts of the body. Cardiovascular disease is the major cause of death in the nation. Though the data available in the health field is vast, still there is a need to develop decision supporting system to maintain, analyze, and knowledge evaluation. One such technique that can be used to address such a problem is data mining. Data mining techniques can help to classify the whether a patient having heart disease or not. This paper explores the different classification techniques for heart disease prediction. Logistic Regression, Support Vector Machine, Naïve Bayes, Nearest Neighbor, and Decision Tree methods are applied. To assess performance of the classifiers various measures are taken includes accuracy, recall, precision and F1-score. Methods: An abundant data generated by the healthcare industry leads to exploring the required information to make a decision-making system using data mining techniques. Classification methods such as SVM, Naive Bayes, KNN, Logistic Regression, and Decision Tree are applied to predict heart disease with a different dataset. Results: Five classification techniques are applied to predict the two different heart disease datasets. By inference, the different classifiers work differently on selected attributes of datasets in terms of accuracy, precision, recall, and F1-score measurements. Conclusion: Our research focuses on comparing the results of classification techniques applied to two different heart disease datasets. Various performance metrics were used to compare and contrast the two datasets of heart disease.
microRNA152-3p protects against ischemia/reperfusion-induced BBB destruction possibly targeting MAP3K2/JNK/c-Jun pathway
Authors: Li Fei , Zhou Fangfang , Yang Binbin 
Published: July 2022

Abstract
In the current study, we reported that overexpression of miR-152-3p effectively ameliorated neurological deficit and protected blood-brain barrier(BBB) integrity in middle cerebral artery occlusion (MCAO) rats. In in vitro model, the level of miR-152-3p was significantly decreased in bEnd.3 after oxygen–glucose deprivation/reperfusion (OGD/R) insult. miR-152-3p overexpressed bEnd.3 cell monolayers were protected from OGD/R-induced microvascular hyperpermeability. miR-152-3p-mediated protective effect was associated with lower apoptosis of endothelia by negatively modulating MAP3K2/JNK/c-Jun pathway.
Sinus of Valsalva aneurysms complicated by infective endocarditis: one rupturing into the left ventricle and the other dissecting into the interventricular septum
Authors: Gao Huanhuan , Cheng Yan , Xie Linyuan , Mou Yun , Hu Zhiqiang , Zheng Zhelan 
Published: July 2022

Abstract
Background: Sinus of Valsalva aneurysm (SVA) is an uncommon cardiac abnormality and can be a serious complication of infection or trauma or after cardiac surgery or a procedure. The clinical features and symptoms are inextricably linked to the progression and site of rupture of the aneurysm.Case presentations: We present two rare cases of ruptured SVA, both of which were associated with infective endocarditis. In one case the SVA ruptured into the left ventricle and in the other case dissected into the interventricular septum.Conclusions: These two cases highlight the expanding clinical view of SVA complicated by infective endocarditis using echocardiography and hemodynamic assessment to guide surgical planning.
Analysis of Water-Energy-Crop Nexus Indicators in Irrigated Sugarcane of Awash Basin, Ethiopia
Authors: Kedir Yusuf , Berhanu Belete , Alamirew Tena 
Published: July 2022

Abstract
Pressures on limited resources of water and energy in agriculture forced researchers to look for alternative approaches towards the solutions integrating the resources. Since the emancipation of WEF nexus concept, several methods including indicator approaches have been developed and in use to analyze their linkages. The aims of the study were to determine and categorically compare performances of sugarcane producing irrigation schemes and technologies in Awash basin of Ethiopia. Water applied, input energy used and productivity data of eleven irrigation schemes found in Wonji, Metehara and Kessem sugar factories were used for the for the study and performances of each scheme were evaluated. Then, based on statistical analyses, irrigation schemes were grouped into gravity surface, pump surface and pump sprinklers. After normalizing the original data through min-max, performance scores were determined based on equal weight and entropy methods. Categorical assessments revealed that gravity surface schemes had highest total input energy use and energy productivity scores while pump sprinkler schemes were better on water applied, Relative Irrigation Supply (RIS) and water productivity indicators. However, regarding composite performance indicators or WEF nexus indices, pump surface schemes scored the highest followed by gravity surface schemes while sprinklers were the least. The study proved the emphasis of the sugar industries of the basin for crop productivity with the expenses of energy and water utilizations. Hence, improvements on water managements of gravity surface schemes and energy utilization of sprinkler schemes were recommended as key solutions for balanced resource use as well as overall sustainability of the sector.
Fault diagnosis of a photovoltaic system using recurrent neural networks
Authors: DJEGHADER Reda , MSABAH Ilyes LOUAHEM , BENZAHIOUL Samia , METATLA Abderrezak 
Published: July 2022

Abstract
The developed work in this paper is a part of the detection and identification of faults in systems by modern techniques of artificial intelligence, in a first step we have developed an MLP (Multi- Layer Perceptron ), type neural network to detect shunt faults and shading phenomenon in photovoltaic systems, and in the second part of the work we developed another RNN (recurrent neural networks ) type network in order to identify single and combined faults in PV systems. The results obtained clearly show the performance of the networks developed for the rapid detection of the appearance of faults with the estimation of their times as well as the robust decision to identify the type of faults in the PV system.
Gut microbiota and COVID-19: A systematic review
Authors: SeyedAlinaghi SeyedAhmad , Afzalian Arian , Pashaei Zahra , Varshochi Sanaz , Karimi Amirali , Mojdeganlou Hengameh , Mojdeganlou Paniz , Razi Armin , Ghanadinezhad Farzaneh , Shojaei Alireza , Amiri Ava , Dashti Mohsen , Ghasemzadeh Afsaneh , Dadras Omid , Mehraeen Esmaeil , Afsahi Amir Masoud 
Published: July 2022

Abstract
Introduction: Alteration in humans’ gut microbiota was reported in patients infected with SARS-CoV-2. The gut and upper respiratory tract microbiota harbor a dynamic and complex population of microorganisms and have strong interaction with host immune system homeostasis. However, our knowledge about microbiota and its association with SARS-CoV-2 is still limited. We aimed to systematically review the effects of gut microbiota on the COVID-19 infection and its severity and the impact that SARS-CoV-2 could have on the gut microbiota.Methods: We searched the keywords in the online databases of Web of Science, Scopus, PubMed, and Cochrane on December 31st, 2021. After duplicate removal, we performed the screening process in two stages; title/abstract and then full-text screening. The data of the eligible studies were extracted into a pre-designed word table. This study adhered to the PRISMA checklist.Results: Sixty-three publications were included in this review. It appeared that COVID-19 infection could affect the microbiome in patients, particularly in moderate and severe cases. The severity of COVID-19 disease may also be influenced by the microbiota's composition through immunological responses or some other unknown mechanisms.Conclusion: Our study showed that there was a significant change in the composition of the upper respiratory tract and gut microbiome in patients with COVID-19 compared to healthy individuals. These apparent changes suggested gut and upper respiratory tract microbiota as a possible indicator for diagnosis and strategy for the treatment of COVID-19.
Quantitative detection of SARS-CoV-2 antigen: an effective approach for evaluating the infectivity of COVID- 19 convalescent patients
Authors: Wang Di , Lu Hailong , Li Yaju , Shen Jiazhen , Jiang Guangjie , Xiang Jin , Qin Huanhuan , Guan Ming 
Published: July 2022

Abstract
BackgroundThis study evaluated the quantitative detection of SARS-CoV-2 antigen by the MAGLUMI chemiluminescent immunoassay (MAG-CLIA) in COVID-19 patients during the peak of COVID-19 Shanghai epidemics in a tertiary hospital in Shanghai.MethodsAnalytical performances of the MAG-CLIA were evaluated, including precision, limit of quantitation (LoQ), linearity and specificity. Nasopharyngeal specimens from 232 patients who were SARS-CoV-2 RT-qPCR positive and from 477 healthy donors were included to evaluate the diagnostic performance. The performance of the Wondfo antigen-detecting lateral flow test (LFT) was evaluated in parallel. The longitudinal studies were performed by monitoring antigen concentrations alongside with RT-qPCR results in 14 COVID-19 participants for up to 22 days. The critical antigen concentration in determining virus infectivity was evaluated at the reference cycle threshold (Ct) of 35.ResultsCOVID-19 patients were well-identified using an optimal threshold of 0.64 pg/mL antigen concentration, with sensitivity and specificity of 95.7% (95% CI: 92.2%-97.9%) and 98.3% (95% CI: 96.7%-99.3%), respectively, while the Wondfo LFT exhibited those of 34.9% (95% CI: 28.8%-41.4%) and 100% (95% CI: 99.23%-100%), respectively. Close dynamic consistence was observed between SARS-CoV-2 Ag and viral load time series in the longitudinal studies. The critical value of 8.82 pg/mL antigen showed adequate sensitivity and specificity in evaluating the infectivity of convalescent patients.ConclusionsThe MAG-CLIA SARS-CoV-2 Ag detection is an effective and alternative approach for rapid diagnosis and enables us to evaluate the infectivity of convalescent patients.
ALDH3B1 protects interfollicular epidermal cells against lipid peroxidation via the NRF2 pathway
Authors: wu zhenjie , Chen Aoyu , Zhang Guang , Liu Chunyan , Yin Siyuan , Song Ru , Ma Jiaxu , Cao Guoqi , Sun Rui , Liu Jian , Wang Yibing 
Published: July 2022

Abstract

Reactive oxygen species (ROS) production is critical for the initiation of wound repair; However, persistent high levels of reactive oxygen species can lead to lipid peroxidation of cells and thus affect wound healing. Iron is a transition metal that is an essential component of almost all living cells and organisms. when present in excess in cells and tissues, iron disrupts redox homeostasis and catalyses the generation of ROS, leading to increased lipid peroxidation. In this study, we found that after treating interepithelial follicular (IFE) cells with different concentrations of transferrin (0 µg/ml, 1 µg/ml, 10 µg/ml, 100 µg/ml, and 1 mg/ml), we increased the intracellular iron content, and our findings regarding viability and function did not differ significantly between groups of cells. It was also found that the level of lipid peroxidation in IFE cells did not increase. We speculate that there is a protective mechanism within IFE cells that reduces the occurrence of intracellular lipid peroxidation. We found that the elevated intracellular iron content of IFE cells was accompanied by elevated ALDH3B1 expression. We investigated the effect of ALDH3B1 on the level of lipid peroxidation in IFE cells and found that the elevated expression of ALDH3B1 could decrease the damage to IFE cells by lipid peroxidation. In addition, the NRF2 pathway was found to affect the expression of ALDH3B1, which in turn affected lipid peroxidation in IFE cells. In conclusion, these findings suggest that in IFE cells, activation of the NRF2 pathway can increase the expression of ALDH3B1 and thus reduce the production of intracellular ROS and the occurrence of intracellular lipid peroxidation. Therefore, ALDH3B1 may be a potential target for the treatment of chronic wounds.
Deep Emotion Detection Sentiment Analysis of Persian Literary Text
Authors: Khodaei Azadeh , Bastanfard Azam , Saboohi Hadi , Aligholizadeh Hossein 
Published: July 2022

Abstract
A large amount of training data is needed to achieve accurate results in processing deep learning models. Although deep learning has played a successful role in sentiment analysis of texts, a new era in natural language processing has begun with the rise of pre-trained language models. Pre-trained models provide access to a large amount of training data. Emotion analysis in Persian text is challenging because, besides being one of the low resource languages, it needs more accurate methods for processing texts. In this study, a dataset that includes the emotional sentences of Persian literary texts was created. We compared two different methods for analyzing the emotion of Persian sentences. The first method uses the FastText and the Bi-LSTM model for emotion classification. In the second approach, we employ the XLM-R based on a deep bidirectional transformer to extract features from text and apply the Catboost algorithm to guide the model to focus on the most relevant class. The proposed model can be used in all languages, especially low-resource languages and imbalanced datasets. According to the experimental results, the proposed model achieved better binary classification results and employing the XLM-R, and Catboost algorithm yielded remarkable improvement in the multi-classification accuracy.