bluuebunny/crossref_metadata_2025_split huggingface dataset:
LC/MS/MS and GC/MS/MS metabolic profiling of Leontodon hispidulus, in vitro and in silico anticancer activity evaluation targeting hexokinase 2 enzyme
Authors: Abd-El-Aziz Noha Mokhtar , Hifnawy Mohamed Saeed , Lotfy Rehab Ahmed , Younis Inas Youssef 
Published: March 2024

AbstractLeontodon hispidulus Boiss is a wild annual plant growing in Egypt. The present study aims for the first time, to evaluate the phytochemical profile of the main secondary metabolites of the optimized ethanolic extract of the plant using Quadrupole Time-of-Flight Liquid chromatography-mass spectrometry and Gas chromatography-mass spectrometry. It also aims to assess the anticancer activity of its different fractions against the prostate carcinoma cell line. Moreover, an in-silico docking study was performed using the Hexokinase-two enzyme. LC-qToF-MS analysis revealed the tentative identification of 36 phenolic compounds including the glycosides of (luteolin, quercetin, kaempferol, apigenin, isorhamnetin, and daidzein), coumarines (esculin, esculetin, and daphnetin), and phenolic acids (chlorogenic, caffeic, quinic, P-coumaric, and rosmarinic). GC–MS/MS analysis revealed the presence of 18 compounds where palmitic acid, myristic acid, alpha-amyrin, and beta-amyrin were the major ones. The cytotoxic activity results revealed that methylene chloride and ethyl acetate fractions showed the highest cytotoxic activity against the PC3 cell line, with IC50 values of 19, and 19.6 μg/ml, respectively. Interestingly, the docking study demonstrated that apigenin-7-O-glucoside, luteolin-7-O-glucoside, kaempferol-3-O-glucuronide, quercetin-4′-O-glucoside, esculin, rosmarinic acid, chlorogenic acid, and α-amyrin exhibited high affinity to the selected target, HEK-2 enzyme.
Correlation between magnetic resonance imaging proton density fat fraction (MRI-PDFF) and liver biopsy to assess hepatic steatosis in obesity
Authors: Wibulpolprasert Pornphan , Subpinyo Benya , Chirnaksorn Supphamat , Shantavasinkul Prapimporn Chattranukulchai , Putadechakum Supanee , Phongkitkarun Sith , Sritara Chanika , Angkathunyakul Napat , Sumritpradit Preeda 
Published: March 2024

AbstractObesity is highly associated with Non-alcoholic fatty liver disease (NAFLD) and increased risk of liver cirrhosis and liver cancer-related death. We determined the diagnostic performance of the complex-based chemical shift technique MRI-PDFF for quantifying liver fat and its correlation with histopathologic findings in an obese population within 24 h before bariatric surgery. This was a prospective, cross-sectional, Institutional Review Board-approved study of PDFF-MRI of the liver and MRI-DIXON image volume before bariatric surgery. Liver tissues were obtained during bariatric surgery. The prevalence of NAFLD in the investigated cohort was as high as 94%. Histologic hepatic steatosis grades 0, 1, 2, and 3 were observed in 3 (6%), 25 (50%), 14 (28%), and 8 (16%) of 50 obese patients, respectively. The mean percentages of MRI-PDFF from the anterior and posterior right hepatic lobe and left lobe vs. isolate left hepatic lobe were 15.6% (standard deviation [SD], 9.28%) vs. 16.29% (SD, 9.25%). There was a strong correlation between the percentage of steatotic hepatocytes and MRI-PDFF in the left hepatic lobe (r = 0.82, p < 0.001) and the mean value (r = 0.78, p < 0.001). There was a strong correlation between MRI-derived subcutaneous adipose tissue volume and total body fat mass by dual-energy X-ray absorptiometry, especially at the L2–3 and L4 level (r = 0.85, p < 0.001). MRI-PDFF showed good performance in assessing hepatic steatosis and was an excellent noninvasive technique for monitoring hepatic steatosis in an obese population.
Prediction of pacemaker-induced cardiomyopathy using a convolutional neural network based on clinical findings prior to pacemaker implantation
Authors: Oida Mitsunori , Mizutani Takuya , Hasumi Eriko , Fujiu Katsuhito , Goto Kosaku , Kani Kunihiro , Oshima Tsukasa , Matsubara Takumi J. , Shimizu Yu , Oguri Gaku , Kojima Toshiya , Komuro Issei 
Published: March 2024

AbstractRisk factors for pacemaker-induced cardiomyopathy (PICM) have been previously reported, including a high burden of right ventricular pacing, lower left ventricular ejection fraction, a wide QRS duration, and left bundle branch block before pacemaker implantation (PMI). However, predicting the development of PICM remains challenging. This study aimed to use a convolutional neural network (CNN) model, based on clinical findings before PMI, to predict the development of PICM. Out of a total of 561 patients with dual-chamber PMI, 165 (mean age 71.6 years, 89 men [53.9%]) who underwent echocardiography both before and after dual-chamber PMI were enrolled. During a mean follow-up period of 1.7 years, 47 patients developed PICM. A CNN algorithm for prediction of the development of PICM was constructed based on a dataset prior to PMI that included 31 variables such as age, sex, body mass index, left ventricular ejection fraction, left ventricular end-diastolic diameter, left ventricular end-systolic diameter, left atrial diameter, severity of mitral regurgitation, severity of tricuspid regurgitation, ischemic heart disease, diabetes mellitus, hypertension, heart failure, New York Heart Association class, atrial fibrillation, the etiology of bradycardia (sick sinus syndrome or atrioventricular block) , right ventricular (RV) lead tip position (apex, septum, left bundle, His bundle, RV outflow tract), left bundle branch block, QRS duration, white blood cell count, haemoglobin, platelet count, serum total protein, albumin, aspartate transaminase, alanine transaminase, estimated glomerular filtration rate, sodium, potassium, C-reactive protein, and brain natriuretic peptide. The accuracy, sensitivity, specificity, and area under the curve of the CNN model were 75.8%, 55.6%, 83.3% and 0.78 respectively. The CNN model could accurately predict the development of PICM using clinical findings before PMI. This model could be useful for screening patients at risk of developing PICM, ensuring timely upgrades to physiological pacing to avoid missing the optimal intervention window.
Source-credibility information and social norms improve truth discernment and reduce engagement with misinformation online
Authors: Prike Toby , Butler Lucy H. , Ecker Ullrich K. H. 
Published: March 2024

AbstractMisinformation on social media is a pervasive challenge. In this study (N = 415) a social-media simulation was used to test two potential interventions for countering misinformation: a credibility badge and a social norm. The credibility badge was implemented by associating accounts, including participants’, with a credibility score. Participants’ credibility score was dynamically updated depending on their engagement with true and false posts. To implement the social-norm intervention, participants were provided with both a descriptive norm (i.e., most people do not share misinformation) and an injunctive norm (i.e., sharing misinformation is the wrong thing to do). Both interventions were effective. The social-norm intervention led to reduced belief in false claims and improved discrimination between true and false claims. It also had some positive impact on social-media engagement, although some effects were not robust to alternative analysis specifications. The presence of credibility badges led to greater belief in true claims, lower belief in false claims, and improved discrimination. The credibility-badge intervention also had robust positive impacts on social-media engagement, leading to increased flagging and decreased liking and sharing of false posts. Cumulatively, the results suggest that both interventions have potential to combat misinformation and improve the social-media information landscape.
Accuracy of generative deep learning model for macular anatomy prediction from optical coherence tomography images in macular hole surgery
Authors: Kwon Han Jo , Heo Jun , Park Su Hwan , Park Sung Who , Byon Iksoo 
Published: March 2024

AbstractThis study aims to propose a generative deep learning model (GDLM) based on a variational autoencoder that predicts macular optical coherence tomography (OCT) images following full-thickness macular hole (FTMH) surgery and evaluate its clinical accuracy. Preoperative and 6-month postoperative swept-source OCT data were collected from 150 patients with successfully closed FTMH using 6 × 6 mm2 macular volume scan datasets. Randomly selected and augmented 120,000 training and 5000 validation pairs of OCT images were used to train the GDLM. We assessed the accuracy and F1 score of concordance for neurosensory retinal areas, performed Bland–Altman analysis of foveolar height (FH) and mean foveal thickness (MFT), and predicted postoperative external limiting membrane (ELM) and ellipsoid zone (EZ) restoration accuracy between artificial intelligence (AI)-OCT and ground truth (GT)-OCT images. Accuracy and F1 scores were 94.7% and 0.891, respectively. Average FH (228.2 vs. 233.4 μm, P = 0.587) and MFT (271.4 vs. 273.3 μm, P = 0.819) were similar between AI- and GT-OCT images, within 30.0% differences of 95% limits of agreement. ELM and EZ recovery prediction accuracy was 88.0% and 92.0%, respectively. The proposed GDLM accurately predicted macular OCT images following FTMH surgery, aiding patient and surgeon understanding of postoperative macular features.
Diet, lifestyle and gut microbiota composition among Malaysian women with gestational diabetes mellitus: a prospective cohort study
Authors: Kunasegaran Thubasni , Balasubramaniam Vinod R. M. T. , Thirunavuk Arasoo Valliammai Jayanthi , Palanisamy Uma Devi , Tan Yen Ker , Ramadas Amutha 
Published: March 2024

AbstractThe study addressed a significant gap in the profiling and understanding of the gut microbiota’s influence on Malaysian Malay women with gestational diabetes mellitus (GDM). This prospective cohort study aimed to explore the intricate relationship between gut microbiota, dietary choices, and lifestyle factors among Malay women, both with and without GDM. The research specifically focused on participants during the second (T0) and third (T1) trimesters of pregnancy in Johor Bahru, Malaysia. In Part 1 of the study, a diverse pool of pregnant women at T0 was categorized into two groups: those diagnosed with GDM and those without GDM, with a total sample size of 105 individuals. The assessments encompassed demographic, clinical, lifestyle, and dietary factors at the T0 and T1 trimesters. Part 2 of the study delved into microbiome analysis, targeting a better understanding of the gut microbiota among the participants. Stool samples were randomly collected from 50% of the individuals in each group (GDM and non-GDM) at T0 and T1. The collected samples underwent processing, and 16s rRNA metagenomic analysis was employed to study the microbial composition. The results suggested an association between elevated body weight and glucose levels, poor sleep quality, lack of physical activity, greater intake of iron and meat, and reduced fruit consumption among women with GDM compared to non-GDM groups. The microbiome analysis revealed changes in microbial composition over time, with reduced diversity observed in the GDM group during the third trimester. The genera Lactiplantibacillus, Parvibacter, Prevotellaceae UCG001, and Vagococcus positively correlated with physical activity levels in GDM women in the second trimester. Similarly, the genus Victivallis exhibited a strong positive correlation with gravida and parity. On the contrary, the genus Bacteroides and Roseburia showed a negative correlation with omega-3 polyunsaturated fatty acids (PUFAs) in women without GDM in the third trimester. The study highlighted the multifaceted nature of GDM, involving a combination of lifestyle factors, dietary choices, and changes in gut microbiota composition. The findings emphasized the importance of considering these interconnected elements in understanding and managing gestational diabetes among Malaysian Malay women. Further exploration is essential to comprehend the mechanisms underlying this relationship and develop targeted interventions for effective GDM management.
Comparison of different machine learning classification models for predicting deep vein thrombosis in lower extremity fractures
Authors: Wei Conghui , Wang Jialiang , Yu Pengfei , Li Ang , Xiong Ziying , Yuan Zhen , Yu Lingling , Luo Jun 
Published: March 2024

AbstractDeep vein thrombosis (DVT) is a common complication in patients with lower extremity fractures. Once it occurs, it will seriously affect the quality of life and postoperative recovery of patients. Therefore, early prediction and prevention of DVT can effectively improve the prognosis of patients. This study constructed different machine learning models to explore their effectiveness in predicting DVT. Five prediction models were applied to the study, including Extreme Gradient Boosting (XGBoost) model, Logistic Regression (LR) model, RandomForest (RF) model, Multilayer Perceptron (MLP) model, and Support Vector Machine (SVM) model. Afterwards, the performance of the obtained prediction models was evaluated by area under the curve (AUC), accuracy, sensitivity, specificity, F1 score, and Kappa. The prediction performances of the models based on machine learning are as follows: XGBoost model (AUC = 0.979, accuracy = 0.931), LR model (AUC = 0.821, accuracy = 0.758), RF model (AUC = 0.970, accuracy = 0.921), MLP model (AUC = 0.830, accuracy = 0.756), SVM model (AUC = 0.713, accuracy = 0.661). On our data set, the XGBoost model has the best performance. However, the model still needs external verification research before clinical application.
Transcriptomic investigation of the interaction between a biocontrol yeast, Papiliotrema terrestris strain PT22AV, and the postharvest fungal pathogen Penicillium expansum on apple
Authors: Ianiri Giuseppe , Barone Giuseppe , Palmieri Davide , Quiquero Michela , Gaeta Ilenia , De Curtis Filippo , Castoria Raffaello 
Published: March 2024

AbstractBiocontrol strategies offer a promising alternative to control plant pathogens achieving food safety and security. In this study we apply a RNAseq analysis during interaction between the biocontrol agent (BCA) Papiliotrema terrestris, the pathogen Penicillium expansum, and the host Malus domestica. Analysis of the BCA finds overall 802 upregulated DEGs (differentially expressed genes) when grown in apple tissue, with the majority being involved in nutrients uptake and oxidative stress response. This suggests that these processes are crucial for the BCA to colonize the fruit wounds and outcompete the pathogen. As to P. expansum analysis, 1017 DEGs are upregulated when grown in apple tissue, with the most represented GO categories being transcription, oxidation reduction process, and transmembrane transport. Analysis of the host M. domestica finds a higher number of DEGs in response to the pathogen compared to the BCA, with overexpression of genes involved in host defense signaling pathways in the presence of both of them, and a prevalence of pattern-triggered immunity (PTI) and effector-triggered immunity (ETI) only during interaction with P. expansum. This analysis contributes to advance the knowledge on the molecular mechanisms that underlie biocontrol activity and the tritrophic interaction of the BCA with the pathogen and the host.
A genetic mouse model of lean-NAFLD unveils sexual dimorphism in the liver-heart axis
Authors: Burelle Charlotte , Clapatiuc Valentin , Deschênes Sonia , Cuillerier Alexanne , De Loof Marine , Higgins Marie-Ève , Boël Hugues , Daneault Caroline , Chouinard Billie , Clavet Marie-Élaine , Tessier Nolwenn , Croteau Isabelle , Chabot Geneviève , Martel Catherine , Sirois Martin G. , Lesage Sylvie , Burelle Yan , Ruiz Matthieu 
Published: March 2024

AbstractLean patients with NAFLD may develop cardiac complications independently of pre-existent metabolic disruptions and comorbidities. To address the underlying mechanisms independent of the development of obesity, we used a murine model of hepatic mitochondrial deficiency. The liver-heart axis was studied as these mice develop microvesicular steatosis without obesity. Our results unveil a sex-dependent phenotypic remodeling beyond liver damage. Males, more than females, show fasting hypoglycemia and increased insulin sensitivity. They exhibit diastolic dysfunction, remodeling of the circulating lipoproteins and cardiac lipidome. Conversely, females do not manifest cardiac dysfunction but exhibit cardiometabolic impairments supported by impaired mitochondrial integrity and β-oxidation, remodeling of circulating lipoproteins and intracardiac accumulation of deleterious triglycerides. This study underscores metabolic defects in the liver resulting in significant sex-dependent cardiac abnormalities independent of obesity. This experimental model may prove useful to better understand the sex-related variability, notably in the heart, involved in the progression of lean-NAFLD.
Multiplexed orbital angular momentum beams demultiplexing using hybrid optical-electronic convolutional neural network
Authors: Ye Jiachi , Kang Haoyan , Cai Qian , Hu Zibo , Solyanik-Gorgone Maria , Wang Hao , Heidari Elham , Patil Chandraman , Miri Mohammad-Ali , Asadizanjani Navid , Sorger Volker , Dalir Hamed 
Published: March 2024

AbstractAdvancements in optical communications have increasingly focused on leveraging spatial-structured beams such as orbital angular momentum (OAM) beams for high-capacity data transmission. Conventional electronic convolutional neural networks exhibit constraints in efficiently demultiplexing OAM signals. Here, we introduce a hybrid optical-electronic convolutional neural network that is capable of completing Fourier optics convolution and realizing intensity-recognition-based demultiplexing of multiplexed OAM beams under variable simulated atmospheric turbulent conditions. The core part of our demultiplexing system includes a 4F optics system employing a Fourier optics convolution layer. This optical spatial-filtering-based convolutional neural network is utilized to realize the training and demultiplexing of the 4-bit OAM-coded signals under simulated atmospheric turbulent conditions. The current system shows a demultiplexing accuracy of 72.84% under strong turbulence scenarios with 3.2 times faster training time than all electronic convolutional neural networks.
The foundations of the Patagonian icefields
Authors: Fürst Johannes J. , Farías-Barahona David , Blindow Norbert , Casassa Gino , Gacitúa Guisella , Koppes Michèle , Lodolo Emanuele , Millan Romain , Minowa Masahiro , Mouginot Jérémie , Pȩtlicki Michał , Rignot Eric , Rivera Andres , Skvarca Pedro , Stuefer Martin , Sugiyama Shin , Uribe José , Zamora Rodrigo , Braun Matthias H. , Gillet-Chaulet Fabien , Malz Philipp , Meier Wolfgang J.-H. , Schaefer Marius 
Published: March 2024

AbstractThe two vast Patagonian icefields are a global hotspot for ice-loss. However, not much is known about the total ice volume they store - let alone its spatial distribution. One reason is that the abundant record of direct thickness measurements has never been systematically exploited. Here, this record is combined with remotely-sensed information on past ice thickness mapped from glacier retreat. Both datasets are incorporated in a state-of-the-art, mass-conservation approach to produce a well-informed map of the basal topography beneath the icefields. Its major asset is the reliability increase of thicknesses values along the many marine- and lake-terminating glaciers. For these, frontal ice-discharge is notably lower than previously reported. This finding implies that direct climatic control was more influential for past ice loss. We redact a total volume for both icefields in 2000 of 5351 km3. Despite the wealth of observations used in this assessment, relative volume uncertainties remain elevated.
Modular synthesis of C2-symmetric nitrogen-containing polyaromatics via visible light-enabled reductive aza-6π electrocyclization
Authors: Zhang Qun-Liang , Fan Qing-Tian , Zhou Yirong , Zhang Jingli , Zhang Fang-Lin 
Published: March 2024

Here, an unprecedented one-step modular construction of C2-symmetric N-PACs through reduction/aza-6π electrocyclization was reported and the resultant C2-symmetric N-PAC could be converted into a powerful organic photocatalyst.
Fault‐tolerant operation for five‐phase permanent magnet synchronous machine drives with five‐phase six‐leg inverter
Authors: Wu Lijian , Yi Jiali , Lyu Zekai , Hu Sideng 
Published: March 2024

AbstractFive‐phase permanent magnet synchronous machine (PMSM) can operate under open‐circuit faults with the appropriate current regulations. However, the traditional current regulations based on the five‐phase half‐bridge inverter have to satisfy the constraint of zero neutral point current, which limits the fault‐tolerant performance of the machine. Thus, this paper investigates the current regulation based on the five‐phase six‐leg (FPSL) inverter, which relieves the zero‐neutral point current limitation, and both current regulations considering the maximum torque (MT) and minimum copper loss (MC) are derived under different open‐circuit conditions. The cost of the system has increased, but with the new regulations, better fault‐tolerant operation capability of the machine can be obtained under all kinds of open‐circuit faults. Moreover, with the introduction of the neutral point current, the machine can work under triple‐phase open‐circuit faults. In conclusion, the proposed control scheme based on the FPSL inverter expands the fault‐tolerant operation capability of the five‐phase PMSM. The experiment results prove the effectiveness of the proposed current regulation on increasing average torque or reducing copper loss under fault‐tolerant operation.
Zonotope approximation based flexible cluster division method for load‐side resource scheduling
Authors: Yao Zongjun , Zhang Tieyan , Huang Jinfeng 
Published: March 2024

AbstractIn view of the insufficient flexibility and adjustment capabilities caused by the high proportion of renewable energy connected to the distribution network and the limitations of the source‐side adjustment method, a load‐side resource flexibility cluster partitioning method based on zonotope approximation is proposed to cope with the flexibility adjustment requirements brought by uncertain resources. First, the constraint space of load‐side resources such as energy storage, multi‐source mobile energy storage, electric vehicles, and temperature‐controlled loads is described as a convex polyhedral form general model based on the virtual battery framework, and the zonotope approximation is used to integrate its constraint space with the Minkowski sum. Secondly, modularity and flexibility supply and demand balance index are proposed to perform flexibility clustering of load‐side resources. Then, the minimum comprehensive operating cost of the cluster aggregator is set as the optimization goal, the cluster constraint space is equivalent to the cost function, and the optimal charging and discharging power and flexibility adjustment capacity of the load‐side resources are obtained. Finally, the method proposed here is verified through numerical example simulation to effectively balance aggregation accuracy and calculation speed.