bluuebunny/crossref_metadata_2025_split huggingface dataset:
Novel Class of Antibiotics Disassembles the Lipopolysaccharide Transport Bridge
Authors: Zerbe Oliver , Schuster Matthias , Brabet Emile , Oi Kathryn , Desjonquères Nicolas , Moehle Kerstin , Poupon Karen , Hell Sopie , Gable Stéphane , Rithié Virginie , Dillinger Séverine , Zbinden Peter , Luther Anatol , Li Claudia , Stiegeler Sarah , D'Arco Carolin , Locher Hans , Remus Tobias , DiMaio Selena , Motta Paola , Wach Achim , Jung Françoise , Upert Gregory , Obrecht Daniel , Benghezal Mohammed 
Published: April 2022

Abstract
The rapid rise of multi-resistant bacteria poses a significant threat to our health system. The development of drugs against novel targets is urgently needed to replenish the clinical arsenal with effective antibiotics. The naturally occurring peptide thanatin kills Gram-negative bacteria by targeting the periplasmic protein bridge, in particular the lipopolysaccharide transport protein A (LptA).
Using the thanatin scaffold together with phenotypic medicinal chemistry and a target-focused approach, we developed potent antimicrobial macrocyclic peptides with drug-like properties that exhibited low frequencies for development of resistance both in vitro and in vivo. Binding affinities to Escherichia coli LptA of the thanatin analogs correlated well with minimal inhibitory concentrations for both parent and thanatin-resistant E. coli strains. Mode of action studies revealed that the antimicrobial activity of the antibiotics involves the specific disruption of the periplasmic Lpt protein bridge.
Our studies validate the Lpt protein bridge as a novel target for macrocyclic peptide antibiotics that can avoid current mechanisms of drug resistance. The peptides warrant further preclinical and clinical studies addressing WHO priority 1 carbapenem-resistant Enterobacteriaceae, in particular for Klebsiella pneumoniae lung infections.
The effect of personality traits on patient-reported outcomes (PRO) in patients with atrial fibrillation: A cross-sectional study
Authors: Yan Qing , Li Yuan , Fan Jiali , Wu Wenhuan , Xu Pan , Zhang Juanli , Zhou Tian , Xue Jiahong 
Published: April 2022

Abstract
Background
Atrial Fibrillation (AF) is known to aggregate with a negative emotional state. Patient-reported outcomes (PROs) are identified as important tools to evaluate the efficacy and endpoints of AF management. This study explored the association between personality traits and PROs in AF.
Methods
In this cross-sectional study, we assessed personality traits (choleric personality, depressive personality, mucinous personality, and sanguine personality) using the Eysenck Personality Questionnaire (EPQ) in AF patients. The Atrial Fibrillation Effect on Quality of Life (AFEQT) was used to measure the Quality of Life (QoL) in AF patients (range: 0 [worst] to 100 [best]). The General Anxiety Scale (GAD-7) and a 9-item Patient Health Questionnaire (PHQ-9) were explored to assess subjects’ depression and anxiety levels. The association of personality traits with quality of life and emotional state was measured by multivariate analysis.
Results
Among 124 subjects (age: 67.48 ± 11.59 years, 50.00% male) with newly diagnosed AF, 69 (55.65%) had paroxysmal AF, 51 (41.13%) had persistent AF, and 4 (3.22%) did not know their AF type. Of these, 10.48% of patients (n = 13) were of a sanguine personality. Compared to patients with other personality characteristics, average AFEQT scores were the highest in AF patients with sanguine personality (P < 0.05), and the levels of anxiety and depression were the lowest (P < 0.05). Furthermore, multiple linear regression analyses suggested that a sanguine personality was one of the main risk factors affecting the QoL after adjustment for clinical background (β = 12.40, 95%CI: 1.87 to 22.93, P < 0.05). Overall, sanguine personalities in AF patients were associated with improved QoL and a better emotional state.
Conclusion
Personality traits may be an essential factor affecting PROs in individuals with AF.
Analysis of Genomes and Transcriptomes of Clear Cell Renal Cell Carcinomas Identifies Mutations and Gene Expression Changes in TGF-beta Pathway
Authors: Che Xiangyu , Li Jianyi , Xu Yingkun , Wang Qifei , Wu Guangzhen 
Published: April 2022

Abstract
The occurrence of clear cell renal cell carcinoma (ccRCC) is related to the change of transforming growth factor-β (TGF-β) signal pathway. We adopted an integrated approach to identify and verify the effects of changes in this pathway in ccRCC, and provide direction for finding new therapeutic targets. We performed transcriptome analysis on 539 cases of ccRCC from The Cancer Genome Atlas (TCGA) and divided the samples into different TGF-β clusters according to unsupervised hierarchical clustering. We found that 76 of the 85 TGF-β pathway genes were dysregulated, and 55 genes could be used as protective factors or risk factors to affect the prognosis of ccRCC. The survival time of tumor patients with low TGF-β score was shorter than that of tumor patients with high TGF-β score. The TGF-β score was related to the expression of ccRCC key genes and deacetylation genes. The sensitivity of tumor patients to targeted drugs was different between TGF-β high score group and TGF-β low score group. The prognostic model based on TGF-β pathway gene can predict the prognosis of ccRCC patients. Grouping patients with ccRCC according to TGF-β score is of great significance to evaluate the prognosis of patients, select targeted drugs and find new targets.
CircMBOAT2 promotes FASN-mediated lipid metabolism reprogramming and progression in intrahepatic cholangiocarcinoma
Authors: Yu Xiaopeng , Tong Huanjun , Chen Jialu , Tang Chenwei , Wang Shuqing , Si Yu , Wang Shouhua , Tang Zhaohui 
Published: April 2022

Abstract
Background: The carcinogenic role of FASN by regulating lipid metabolism reprogramming has been well established in multiple tumors. However, whether mechanisms during intrahepatic cholangiocarcinoma (ICC) progression such as circRNAs regulate FASN expression remains unknown.Methods: Five paired ICC and adjacent normal tissues were used to screen the lipid metabolism-associated differentially expressed circRNAs by high-throughput RNA sequencing; the biological role of circMBOAT2 was determined by gain or loss of function experiments. Fluorescence in situ hybridization (FISH), RNA immunoprecipitation (RIP) and RNA pull-down assays were used to analyze the interaction of circMBOAT2 with PTBP1 and PTBP1 with FASN. Coimmunoprecipitation (co-IP) was used to investigate the ubiquitin binding of PTBP1. Non-targeted lipidomics was applied to detect changes in metabolite composition.Results: CircMBOAT2 (has_circ_0007334 in circBase) was frequently upregulated in ICC tissues and correlated with tumor size. Knockdown circMBOAT2 inhibits proliferation of ICC cells. Mechanistically, circMBOAT2 combines with PTBP1 and protect PTBP1 from ubiquitin/proteasome-dependent degradation, impairing the function of PTBP1 to transfer FASN mRNA from the nucleus to the cytoplasm. Moreover, circMBOAT2 and FASN have the same effect on fatty acid profile, unsaturated fatty acids instead of saturated fatty acids are primarily regulated and associated with malignant behaviors of ICC cells.Conclusions: Our results identified that circMBOAT2 stabilized PTBP1 and facilitated ICC lipid metabolic reprogramming via the modulation of FASN mRNA cytoplasmic export, suggesting that circMBOAT2 may serve as an available therapeutic target for ICC with active lipid metabolism.
Development of a Novel Intra-Operative Evaluation Method for the Directions and Degrees of Malrotation in Supracondylar Humerus Fractures
Authors: Tang Yifu , Li Xiaoning , Guo Qiang , Huang Yuzhao , Kuang Wei , Luo Ling 
Published: April 2022

Abstract
Introduction
: The malrotation of supracondylar humerus fractures (SCHF) often occurred, prolonging the reduction time during operation. An effective method should be established to achieve accurate reduction rapidly.
Methods
Three dimensional (3D) printed humerus models were used to obtain the linear correlation equation between the width and projection degrees. Then, transverse fracture was performed in the peak of the humeral olecranon fossa of 3D printed models, which was examined by C-arm in the lateral 45° oblique position. Then, ten images in models and five cases in clinics with internal or external rotation within 90° were selected, as their rotation direction and degrees were evaluated by five orthopedic surgeons. Finally, the intraclass correlation coefficient (ICC) and Kappa statistic were used to analyze the accuracy of this novel method.
Results
After linear regression analysis, the equation was obtained to show the correlation between the distal fracture line width and the rotation degrees. With the application of this method, the observers correctly predicted the rotation direction in 96% and correctly predicted the rotation degrees in 86% of models with a superior ICC value. Especially in the rotation range from − 45° to 45°, the accuracy of rotation direction and degrees could reach to 100% and 93.3%. Also, it has an equally accuracy (24/30) in five clinical cases.
Conclusions
This method provided a simple and efficient method for correcting intraoperative rotational displacement in patients with SCHF. By using this method, it can rapidly correct the malrotation displacement during operation, in order to distinctly shorten operation time, reduce bleeding volume and potential skeletal muscle injuries.
A novel and accurate deep learning based Covid-19 diagnostic model for heart patients only
Authors: Hassan Ahmed , Elhoseny Mohamed , Kayed Mohammed 
Published: April 2022

Abstract
Using radiographic changes of COVID-19 in the medical images, artificial intelligence techniques such as deep learning are used to extract some graphical features of COVID-19 and present a Covid-19 diagnostic tool. Differently from previous works that focus on using deep learning to analyze CT scans or X-ray images, this paper uses deep learning to scan electro diagram (ECG) images to diagnose Covid-19. Covid-19 patients with heart disease are the most people exposed to violent symptoms of Covid-19 and death. This shows that there is a special, unclear relation (until now) and parameters between covid-19 and heart disease. So, as previous works, using a general diagnostic model to detect covid-19 from all patients, based on the same rules, is not accurate as we prove later in the practical section of our paper because the model faces dispersion in the data during the training process. So, this paper aims to propose a novel model that focuses on diagnosing accurately Covid-19 for heart patients only to increase the accuracy and to reduce the waiting time of a heart patient to perform a covid-19 diagnosis. Also, we handle the only one existed dataset that contains ECGs of Covid-19 patients and produce a new version, with the help of a heart diseases expert, which consists of two classes: ECGs of heart patients with positive Covid-19 and ECGs of heart patients with negative Covid-19 cases. This dataset will help medical experts and data scientists to study the relation between Covid-19 and heart patients. We achieve overall accuracy, sensitivity and specificity 99%, 98.67% and 100%, respectively.
A novel method for drug-target interaction prediction based on graph transformers model
Authors: Wang Guishen , Wang Hongmei , Guo Fang , Du Mengyan , Cao Chen 
Published: April 2022

Abstract
Background:drug-target interactions prediction(DTIs) becomes more and moreimportant for accelerating drug research and drug repositioning. drug-targetinteraction network is a typical model for DTIs prediction. As many differenttypes of relationships exist between drug and target, drug-target interactionnetwork can be used for modeling drug-target interaction relationship. Recentworks on drug-target interaction network are mostly concentrate on drug node ortarget node and neglecting the relationships between drug-target. Results:We propose a novel prediction method for modeling the relationshipbetween drug and target independently. Firstly, we use different level relationshipsof drugs and targets to construct feature of drug-target interaction. Then, we useline graph to model drug-target interaction. After that, we introduce graphtransformer network to predict drug-target interaction. Conclusions:We introduce line graph to model the relationship between drug andtarget. After transformed drug-target interaction from links into nodes, we usegraph transformer network to fulfill drug-target interaction prediction task. Keywords: drug-target interaction; graph attention network; line graph
Can the thyme oil be an alternative treatment for human demodicosis?
Authors: Akkucuk Serife , OM Kaya 
Published: April 2022

Abstract
Background: Demodex mites can cause ocular demodicosis in humans. Natural products such as tea tree oil are used for ocular demodicosis, which can take a long time to treat. The aim of our study is to investigate the effects of some essential oils on in vitro mite lifespan. Methods: Eyelashes were examined under a light microscope. Mites were determined and the killing times of mineral oil, tea tree oil, black seed oil, St. John's Wort oil, sage oil and thyme oil were recorded. The active ingredients of thyme, tea tree and sage oils were determined by GC / MS method.Results: It was determined that 1% concentration of thyme oil has more effective killing time than 5% concentrations of tea tree oil and sage oil (p <0.0001). The most important active ingredients were carvacrol in thyme oil, terpinen-4-ol in tea tree oil, and 1.8-cineol in sage oil. Conclusion: Thyme oil was the most effective oil in killing mites. In addition, the first steps were taken that thyme oil and its products can be used in therapy.
Temperature mediated influence of mycotoxigenic fungi on the life cycle attributes of Callosobruchus maculatus F. in stored chickpea
Authors: Zahra Tatheer , Batool Muazzama , Tanveer Maryam , Naaz Farah , Saeed Qamar 
Published: April 2022

Abstract

Environmental factors (biotic and abiotic) are a major source of grain reserve depletion. Fungi and insect pests both damage grains synergistically in the store. Fungal and insect pest infestations pose nutritional damage to the stored food which becomes unpalatable for the consumer. There is a need to develop a timeline for synergistic damage caused by insect pest and mycotoxigenic fungi to manage them in stored products. For this purpose, interaction of mycotoxigenic fungi (Aspergillus flavus, Aspergillus niger, Penicillium digitatum and Alternaria alternata) along with C. maculatus (Fabricius) (Bruchidae: Coleoptera) was studied at wide range of temperature. Development of C. maculatus was observed on fungus inoculated and non-inoculated C. arietinum seeds under different temperatures. C. maculatus was found attracted for completion of its life cycle attributes (Fecundity, larval emergence, pupae and adults) in fungus inoculated grains. Population of C. maculatus was decreased by increase in temperature but high temperatures pose to more fungi developments. Meanwhile, more egg laying was observed at 27 ºC and 33 ºC. At tested temperatures, larval emergence was high as compared other checked life attributes. Infestation of A. flavus and A. niger was also increased with different life stages of C. maculatus at all checked temperatures. Penicillium digitatum and Alternaria alternata infestation were increased in the C. arietinum at 27 ºC and 30 ºC respectively. This study will help in measuring the control practices of fungi and insect pest infestations in stored chick pea in Pakistan.
Operational taxon pairs in microbial co-occurrence networks imply complicated bacterial associations
Authors: Jiang Min-Zhi , Zhu Hai-Zhen , Zhou Nan , Liu Chang , Jiang Cheng-Ying , Wang Yulin , Liu Shuang-Jiang 
Published: April 2022

Abstract
Background
Microbial co-occurrence networks inferred from the abundance data of environmental and human gut samples are widely applied to describe microbial associations and to predict potential microbial interactions. However, the microbial associations and predicted interactions were hardly demonstrated with cultivated microbial isolates. Experimental demonstration of the microbial associations and the predicted interactions needs extensive isolation and cultivation of microbes and matches the isolates to operational taxon units (OTUs) from co-occurrence networks. The high workloads of bacterial isolation and cultivation, the shortage of methods to match cultivated bacterial isolates to co-occurrence network OTUs, and the complexity of the networks themselves were factors that constrained experimental demonstrations.
Results
Here, we integrated droplet microfluidics and bar-coding logistics for high-throughput bacterial isolation and cultivation from samples. The co-occurrence network complexity of samples was reduced by dilution of samples. Phylogenetic tree topology was applied to match bacterial isolates to zero-level OTUs (Zotus). We collected wetland samples from Beijing Olympic Park (BOP) including Potamogeton perfoliatus plants, their roots and root peripheral sediments. Droplets of series diluted homogenates of wetland samples were inoculated into 126 of 96-well plates containing R2A and TSB media. After 10 days of cultivation, 65 plates (each with > 30% wells of the 96 wells showed microbial growth) were elected to reconstruct co-occurrence networks. The prevalent co-occurred zero-level OTUs (Zotus) pairs were selected from the networks and were matched to bacterial isolates. We cultivated 129 bacterial isolates belonged to 15 taxa at species level. Thirty-six bacterial isolate combinations corresponding to 19 Zotus pairs were evaluated for interactions on agar plates. Results suggested that positively associated Zotu pairs in the co-occurrence network implied complicated relations including neutralism, competition, and mutualism. We further tested in broth the bacterial isolate combinations between BOP-1, BOP-5, BOP-11 or BOP-16 and BOP-108 that represented the Zotu1-Zotu10 pair. Results revealed that this Zotu1-Zotu10 pair had also competitive, neutral, or mutual relation, depending on bacterial isolate combination and on cultivation time.
Conclusion
This study established a workflow for experimental demonstration of relations between Zotu pairs from microbial co-occurrence networks. Using a wetland sample, we reconstructed microbial co-occurrence networks and cultivated extensively bacterial isolates, and further demonstrated that positively associated Zotu pairs had neutral, competitive, and mutual relations during bacterial cultivations on solid agar plates and in liquid broth.
Text-based recommender system with explanatory capabilities
Authors: Pérez-Núñez Pablo , Díez Jorge , Bahamonde Antonio , Luaces Oscar 
Published: April 2022

Abstract
Recommender systems have proven their usefulness both for companies and customers. The former increase their sales and the latter get a more satisfying shopping experience. These systems can benefit from the advent of explainable artificial intelligence, since a well-explained recommendation will be more convincing and may broaden the customer’s purchasing options. Many approaches offer justifications for their recommendations based on the similarity (in some sense) between users, past purchases, etc., which require some knowledge of the users. In this paper we present a recommender system with explanatory capabilities which is able to deal with the so-called cold-start problem, since it does not require any previous knowledge of the user. Our method learns the relationship between the products and some relevant words appearing in the textual reviews written by previous customers for those products. Then, starting from the textual query of a user’s request for recommendation, our approach elaborates a list of products and explains each recommendation on the basis of the compatibility between the query’s words and the relevant terms for each product.
LEV4REC: A Low-Code Environment to Support the Development of Recommender Systems
Authors: Sipio Claudio Di , Rocco Juri , Ruscio Davide , Nguyen Phuong T. 
Published: April 2022

Abstract
Recommender systems (RSs) are complex software systems that suggest relevant items of interest given a specific application domain to users. The development of RSs encompasses the execution of different steps, including data preprocessing, choice of appropriate algorithms, item delivery, to name a few. Though RSs can alleviate the curse of information overload, existing approaches resemble black-box systems, where the end-user is not supposed to customize the overall process. To fill the gap, we proposed LEV4REC, an initial MDE-based prototype for supporting the mentioned activities needed to conceive an RS from the design phase to the actual deployment of the system, including the parameters fine-tuning. As a first step, the user defines a coarse-grain model that allows the configuration of the desired RS, which then can be finalized by fine-tuning different parameters. LEV4REC eventually generates the source code of the RS, being ready for actual deployment. LEV4REC is provided as a plugin extension for two different IDEs and an initial web interface. To study the capabilities of the approach, we utilized LEV4REC in curating two existing RSs, which have been designed on top of two different algorithms, i.e., collaborative filtering and feed-forward neural network. Experimental results show that our proposed tool can create systems that can provide suitable recommendations, thereby conforming to their original design. LEV4REC is capable of enabling developers to refine the produced system by experimenting with different algorithms, experimental settings, and evaluation metrics.
Study on the Land Surface Cover Dynamics of Built-up Areas and its Implication for Sustainable Urban Planning in Hawassa City, Ethiopia
Authors: Gerezgher Tikabo Gebreyesus , Habtemariam Kumlachew Yeshitila , Mengistu Aramde Fetene , Jauregui Cristina Herrero 
Published: April 2022

Abstract
Rapid and uncontrolled urbanization is one of the drivers responsible for land cover change dynamics in Ethiopia. In most of the cities and towns of Ethiopia, the proportion of different types of urban land surface cover and trends of change in each are unknown and thus hindering environmentally sustainable urban planning. This study describes different land surface cover types and their dynamics of change, and subsequent influence on the Land Surface Temperature of Hawassa city between 2011 and 2021. The i-Tree canopy and Landsat 5 (TM) and Landsat 8 (OLI) images were used for 2011 and 2021 to analyze the land surface cover and surface temperature change, respectively. The results show that bare soil is the dominant land surface cover type (23.4%), followed by tree canopy cover (21.4%), while impervious roads occupied the smallest land surface area cover (3.4%) along with water bodies (1%). In 10 years most of the land surface cover types increased positively, including tree covers by + 9.8%. The only exceptions were bare soil and herbaceous cover, which decreased drastically by -34.6% and − 2.8%, respectively. As a result of tree cover increment, the average and maximum Land Surface Temperature showed a declining trend between the two periods. This study shows that increasing tree cover in line with the expansion of urbanization is inversely correlated with the land surface temperature which implies that integrating green coverage along with the built-up area can reduce heatwave in the fast growing urban areas. Therefore, the Hawassa city administration should set tree cover targets to achieve the desired balance between green and grey infrastructure and enhance the climate resilience level of the study area.