bluuebunny/crossref_metadata_2025_split huggingface dataset:
Grading of Oral Epithelial dysplasia: Comparison of WHO three tier grading and the binary grading of Classical and Differentiated dysplasia
Authors: Selvan Preetha , Ranganathan Kannan 
Published: June 2024

Abstract

Background: The WHO Oral Epithelial Dysplasia (OED) three-tier grading is widely used to assess the risk of malignant transformation. There are other grading systems that address the limitations of the WHO grading, but these have not been widely used, and need to be validated. These include the binary systems of low/high grade dysplasia and differentiated/classical dysplasia. This study was done to evaluate the correlation and reproducibility of the grading, between these three systems
Methods: Twenty-four OED cases were histologically graded, independently by two oral pathologists. The grading systems used were: the WHO three tier, the binary grading system proposed by Kujan’s (high and low risk) and the binary system of differentiated and classic dysplasia by Arsenic and Kurrer’s . The results were tabulated and kappa statistics was analysed using SPSSTM (version 25) to assess the inter and intra-observer variability
Results: Among the twenty-four cases evaluated, 25% (6 cases) were classified as differentiated dysplasia, while the remaining 75% (18 cases) were categorized as classical dysplasia. All the six cases of differentiated dysplasia were high risk by the Kujan’s binary categorization, and by the WHO grading five of the six were severe and one was moderate dysplasia. In the binary system -Kujan’s; of the 24 cases 12 cases were graded as high-risk dysplasia and 12 as low-risk dysplasia by both the observers. The six cases of differentiated dysplasia were all high risk according to the binary categorization by both the observers.
Conclusion: The grading of differentiated dysplasia correlates well with the WHO three tier and Kujan’s binary system. These findings highlight the usefulness of identifying differentiated dysplasia and necessity to further validate these grading systems for better prediction models of malignant transformation
FOXF1 inhibits invasion and metastasis of lung adenocarcinoma cells and enhances anti-tumor immunity via MFAP4/FAK signal axis
Authors: Wang Zhenyu , Xie MengXia , Jia Zhongyue , Tao Ziwei , Zhao Ping , Ying Muying 
Published: June 2024

Abstract

Based on the joint analysis of multi-omic data and the biological experiments, we demonstrate that FOXF1 inhibits invasion and metastasis of lung adenocarcinoma cells and enhances anti-tumor immunity via regulating MFAP4/FAK signal axis in this study. The levels of FOXF1 and MFAP4 are significantly down-regulated in LUAD, and the increased levels of two genes can improve the clinical prognosis of LUAD patients. Fluorescein reporter gene determination, chromatin immunoprecipitation and gene co-expression analysis indicate that MFAP4 level is positively regulated by transcription factor FOXF1. The function enrichment analysis shows that the levels of FOXF1 and MFAP4 are closely associated with an enrichment of tumor metastasis signatures. FOXF1 can inhibit the migration and invasion of LAUD cells by transcriptionally activating MFAP4 expression. And the overexpression of FOXF1/MFAP4 can reduce Focal Adhesion Kinase (FAK) phosphorylation, while their knockdown result in the opposite effects. The increased levels of FOXF1/MFAP4 enhance the antitumor immunity by increasing the infiltration of dendritic cells (DCs) and CD4 + T cells, and the interactions between LUAD cells and immune cells, and activating multiple anti-tumor immunity-related pathways. In conclusion, our study reveals the potential function of FOXF1/MFAP4/FAK signal axis in inhibiting metastasis of LUAD cells and modulating anti-tumor immunity of LUAD patients.
Phytochemical Screening of Coconut Water and Bitter Leaf Extract and Antibiogram of the Mixture (Coconut Water + Bitter Leaf) on Salmonella Typhi and Streptococcus Spp
Authors: Matthew Emmanuel Oluwatimileyin , Ozigi Ibrahim Abdulkarim , Uthman Abdulgafar Ishola , ADELEYE ADEYERI DAVID 
Published: June 2024

Abstract

Background Nigeria, a significant player in the global economy, has a rich agricultural heritage that includes diverse crops with potential medicinal properties. Among these, coconut (Cocos nucifera) and bitter leaf (Vernonia amygdalina) are notable for their traditional medicinal uses. This study aimed to evaluate the phytochemical composition and antibacterial efficacy of extracts from these plants, individually and in combination, against Salmonella typhi and Streptococcus pyogenes, addressing the need for alternative antibacterial therapies in the face of rising antibiotic resistance.Methods Bitter leaf and coconut were collected from Bakindogo market, Kaduna, and authenticated by a botanist. The plants were prepared and macerated, with the extracts subjected to phytochemical screening using standard methods. The antibacterial activity was assessed through agar disk diffusion, determining the zones of inhibition at various extract concentrations. The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) were established for both pathogens.Results Phytochemical analysis revealed the presence of alkaloids, flavonoids, tannins, saponins, terpenoids, and carbohydrates in both extracts, with anthraquinones present only in Vernonia amygdalina. The extracts showed significant antibacterial activity, particularly the mixture of Vernonia amygdalina and Cocos nucifera water. The mixture exhibited higher efficacy against Salmonella typhi, with zones of inhibition ranging from 9.5mm to 17mm, compared to Streptococcus pyogenes, which showed inhibition zones from 9.0mm to 13mm. The MIC for Salmonella typhi was 25mg/ml, with an MBC of 100mg/ml, indicating bactericidal properties at higher concentrations. Streptococcus pyogenes showed an MIC of 100mg/ml, but the MBC was not applicable, suggesting bacteriostatic effects.Conclusions The study confirms the significant antibacterial potential of Vernonia amygdalina and Cocos nucifera water extracts, particularly against Salmonella typhi. These findings support their traditional use and highlight their potential as alternative antibacterial agents. Further research and pharmaceutical exploration are recommended to fully harness these plants' medicinal properties, particularly for developing new treatments for bacterial infections.
SGLT2 inhibition reduces cardiac surgery-associated acute kidney injury: An open-label randomized study.
Authors: Snel Lars Ivar Pieter , Oosterom-Eijmael Maartina J.P. , Rampanelli Elena , Lankadeva Yugeesh R. , Plummer Mark P. , Preckel Benedikt , Hermanides Jeroen , Raalte Daniël van , Hulst Abraham H. 
Published: June 2024

Abstract

Background Currently, no effective preventative strategies exist to mitigate Cardiac surgery-associated acute kidney injury CSA-AKI. Sodium-glucose transporter-2 (SGLT2) inhibitors reduced acute kidney injury (AKI) incidence in large, randomized placebo-controlled outcome trials. We hypothesized that perioperative SGLT2 inhibition could also reduce CSA-AKI.
Methods In this open-label phase IV, randomized, parallel-group, pilot study, adult patients undergoing elective cardiac surgery with cardiopulmonary bypass were randomized to receive either daily an SGLT2 inhibitor, (empagliflozin (10 mg; oral)) from three days prior to surgery until postoperative day two or standard-of-care. Serum and urine biomarkers for acute kidney injury were measured, including NGAL, KIM-1, and HIF-1 α . Additional outcomes included AKI incidence according to Kidney Disease: Improving Global Outcomes (KDIGO) criteria as well as metabolic parameters, including ketone body concentrations and glycemic control.
Results Between March 2022 and April 2023, 55 patients were included (sex: 73% male, age: 66 ± 10 years, BMI: 28 ± 4 kg/m2, empagliflozin n = 25, control n = 30) in the intention-to-treat analysis. Empagliflozin significantly reduced the incidence of CSA-AKI (20% vs 66.7%; absolute difference 46.7%, 95% CI, -69.7 – -23.6; P=.001). Following surgery, NGAL, and KIM-1 were found to increase in both arms, whereas a significant increment in serum HIF-1α after surgery was solely observed in the control group. We observed no between-group differences in the incidence of (euglycemic) ketoacidosis or hypoglycemic events.
Conclusions Perioperative SGLT2 inhibition, compared with standard of care, significantly reduced the incidence of CSA-AKI. These findings warrant validation in large-scale, double-blind, placebo-controlled, randomized trials
Overdamping of vibration resonances by liquid crystal elastomers
Authors: Elmadih Waiel , Terentjev Andrew , Liang Hsin-Ling , Terentjev Eugene 
Published: June 2024

Abstract

This work aims to compare the capability of vibration attenuation by standard elastomeric polymers, and by the new anomalously damping nematic liquid crystal elastomers. We use the most mainstream materials in both categories, and design two testing platforms: the ASTM-standard constrained layer plate resonance geometry, and the attenuation of resonances in a commercial device (electric drill) where the damping polymers were inserted into the casing. In the standard plate resonance testing, we find that LCE outperforms all standard damping materials, moreover, it brings the vibrating plate into the overdamped condition, which is unique for a non-fluid dissipative system. In the attenuation of high-frequency vibrations of a device, we also found LCE dissipates these vibrations much better, although we did not find the optimal insertion configuration for the damping polymer, and did not reach overdamping.
A Strongly Polynomial-Time Algorithm forWeighted General Factors with Three Feasible Degrees*
Authors: Shao Shuai , Živný Stanislav 
Published: June 2024

Abstract

General factors are a generalization of matchings. Given a graph G with a set π(v) of feasible degrees, called a degree constraint, for each vertex v of G, the general factor problem is to find a (spanning) subgraph F of G such that degF(v) ∈ π(v) for every v of G. When all degree constraints are symmetric Δ-matroids, the problem is solvable in polynomial time. The weighted general factor problem is to find a general factor of the maximum total weight in an edge-weighted graph. In this paper, we present a strongly polynomial-time algorithm for a type of weighted general factor problems with real-valued edge weights that is provably not reducible to the weighted matching problem by gadget constructions.
Predictors of high-flow nasal cannula failure in COVID-19 patients in a northern Peruvian hospital.
Authors: Calle-Peña Sara Teresita , Tavara Edwin David Diaz , Aguirre-Milachay Edwin , León-Figueroa Darwin A. , Valladares-Garrido Mario J. 
Published: June 2024

Abstract

Objectives: To determine the predictors of high-flow nasal cannula (HFNC) failure in COVID-19 patients in a hospital in northern Peru.
Methodology: A retrospective cohort study was conducted during the months of March and May 2021. Data collection was based on a follow-up of 156 hospitalized patients with a diagnosis of COVID-19 who were users of HFNC, and outcomes and factors were analyzed from medical records. Epidemiological, analytical, and HFNC use-related characteristics were described using measures of absolute and relative frequencies, measures of central tendency, and dispersion. A multivariate Poisson regression analysis with robust variance and a 95% confidence interval was performed.
Results: We found that age, SpO2/FiO2 and work of breathing (WOB) at admission, degree of involvement and type of infiltrate on CT scan, lymphocytes, c-reactive protein, and D-dimer were significantly associated with failure of HFNC (p< 0.05). In addition, WOB, PaO2/FiO2, SaO2/FiO2, and ROX index were variables that presented statistical significance (p<0.0001). In the multivariate analysis model, a risk of failure of HFNC was determined with age >= 60 years [RRa 1.39 (1.05–1.85)] and PaO2/FiO2 score less than 100 [RRa 1.65 (0.99–2.76)].
Conclusions: Predictors to failure of HFNC are age older than 60 years and minimally significantly lower PaO2/FiO2 than 100.
Analyzing the COVID-19 intensive care unit bed occupancy with random regression coefficient Poisson models
Authors: Diz-Rosales Naomi , Lombardía María José , Morales Domingo 
Published: June 2024

Abstract

COVID-19 has shown the need for spatio-temporal monitoring of the saturation of healthcare capacity in epidemic contexts. However, the scarcity and lack of homogeneity of data is a challenge. Under a random regression coefficient Poisson model, this work derives area-level predictors of occupied bed counts and occupancy ratios in intensive care units, and introduces bootstrap estimators of mean squared errors. Maximum likelihood estimators of model parameters and mode predictors of random effects are calculated using a Laplace approximation algorithm. Simulation experiments are conducted to investigate the behaviour of the fitting algorithm, the predictors and the mean squared error estimators. A forward prediction study is also initiated, defining the out-of-sample predictors and their mean squared errors. The new statistical methodology is applied to data from the Spanish region Castilla y León. The target is to estimate and predict the proportions of beds occupied by COVID-19 patients in intensive care units by health areas and days.
Mathematics Subject Classiffication: 62F40 Bootstrap, jacknife and other resampling methods; 62J12 Generalized linear models; 62P10 Applications of statistics to biology and medical sciences.
Efficient, Calibrated, and Interpretable Uncertainty Quantification for Deep Learning Classification
Authors: Hollis Andrew , Pazdernik Karl , Wilson Alyson , Smith Ralph C. 
Published: June 2024

Abstract

Deep learning models often produce accurate classifiers but do not always produce outputs that are easily interpretable by decision-makers or appropriate estimates of uncertainty or confidence for the classification decision. Furthermore, for some larger architectures, existing uncertainty quantification (UQ) approaches can be computationally expensive. In this paper, we propose two new variational inference loss functions for fitting deep learning classification models and obtaining approximate Bayesian uncertainty quantification. Using text and image datasets, we demonstrate that the proposed framework is more computationally tractable than standard UQ approaches like dropout, and, compared with the competing approaches we consider, yields outputs that better reflect our prior conception of class similarity making the outputs more interpretable and easier to explain to decision-makers. We also demonstrate that the proposed framework balances producing well-calibrated class probabilities with producing posterior credible sets for the class labels that have appropriate coverage.
Refining Pre-Seismic Thermal Anomaly Detection from MODIS Data with Standard Deviation Thresholds and RETIRA Model
Authors: Wongpornchai Pisanu , Suwanprasit Chanida 
Published: June 2024

Abstract

Earthquakes arise from tectonic plate movements and seismic waves, profoundly affecting populations and infrastructure. Remote sensing technologies are invaluable for investigating these phenomena by monitoring the earth surface processes. This study examines pre-seismic thermal anomalies preceding a 4.9 Mw earthquake on February 20, 2019, in Wang Nuea district, Lampang province, Thailand. The Moderate Resolution Imaging Spectroradiometer (MODIS) and applied standard deviation thresholds along with the Robust Estimator of Thermal Infrared Anomalies (RETIRA) index were integrated. Anomalies were categorized based on standard deviation thresholds: normal (≤ 1.0 S.D.), moderate (1.5 S.D.), significant (2.0 S.D.), high (2.5 S.D.), and extreme (≥ 3.0 S.D.). Our findings revealed significant temperature increases near fault lines and lineament structures over the eight days before the earthquake, normalizing post-event. The average temperature elevation was 2.968°C above the pixel-wise mean temperature. A targeted survey of 13 locations identified that 23.08% were along faults or lineaments, 69.23% were devoid of such features, and 7.69% yielded indeterminate results. Non-fault areas, typically dynamic farmlands, were also associated with thermal anomalies. These insights underscore the utility of remote sensing in earthquake studies and its potential to enhance disaster readiness and mitigation efforts.
High strength anhydrite cement based on lime mud from water treatment process: one step synthesis in water environment, characterization and technological parameters
Authors: Kamarou Maksim , Tan Hongbin , Moskovskikh Dmitry , Ma Xiaoling , Yang Feihua , Beletskii Evgenii , Alekseeva Elena , Yudin Sergey , Romanovski Valentin 
Published: June 2024

Abstract

In the process of water treatment from surface water sources, lime mud as waste is formed. This waste contains CaO, Ca(OH)2 and CaCO3. The article proposes a comprehensive method for processing lime mud into high strength anhydrite cement. The method involves the interaction of lime mud with waste sulfuric acid from the production of polymer fibers using a structure-controlled method in the (CaO·Ca(OH)2·CaCO3) – H2SO4 – H2O system at a temperature of 40°C. X-ray diffraction analysis showed the presence of CaSO4 and CaSO4·0.62H2O phases with a purity of 99.8%. The structure-controlled method makes it possible to control the formation and growth of calcium sulfate crystals of the required shape and size, due to which it is possible to obtain anhydrite cement with desired properties. Combined grinding of synthetic anhydrite with activator additives makes it possible to obtain anhydrite cement with a strength of up to 28.5 MPa.
A pause in the weakening of the Atlantic meridional overturning circulation since the early 2010s
Authors: Lee Sang-Ki , Kim Dongmin , Gomez Fabian , Lopez Hosmay , Volkov Denis , Dong Shenfu , Lumpkin Rick , Yeager Stephen 
Published: June 2024

Abstract

The current state-of-the-art climate models suggest that the anthropogenic weakening of the Atlantic Meridional Overturning Circulation (AMOC) has already begun since the mid-1980s. However, direct observational records during the past two decades have shown remarkable resilience of the AMOC. To shed light on this apparent contradiction, here we attempt to attribute the interdecadal variation of the historical AMOC to the anthropogenic and natural signals, by analyzing multiple climate and surface-forced ocean model simulations together with direct observational data. Our analysis suggests that an extensive weakening of the AMOC occurred in the 2000s, as evident from the surface-forced ocean model simulations, and was primarily driven by anthropogenic forcing and possibly augmented by natural variability. However, since the early 2010s, the natural component of the AMOC has greatly strengthened due to the development of a strong positive North Atlantic Oscillation. The enhanced natural AMOC signal in turn acted to oppose the anthropogenic weakening signal, leading to a near stalling of the AMOC weakening. Further analysis suggests that the tug-of-war between the natural and anthropogenic signals will likely continue in the next several years.
Leveraging Feature Sensitivity and Relevance: A Hybrid Feature Selection Approach for Improved Model Performance in Supervised Classification
Authors: Saranya G , Rajendran Rakesh , Jaganathan Subash Chandra Bose , Pandimurugan V 
Published: June 2024

Abstract

Many feature selection algorithms primarily give importance to identifying relevant features and eliminating redundant features. This hybrid work determines the significant features, based on the estimated individual feature sensitivities and the degree of relevance between the feature and target outcome. The majority of works currently in existence employ mutual information (MI) to calculate the degree of information between two variables. By scaling the range of the MI to [0,1], Symmetrical Uncertainty (SU) can be viewed as the normalized MI. In this proposed work, Symmetrical Uncertainty-Relevance (SU-R) is used to measure the relevance between each feature and the target outcome. Per Feature Sensitivity Analysis (PFS) is used to measure the individual feature sensitivity with the target outcome. Features are ranked based on the sum of the ranks of features calculated individually using Symmetrical Uncertainty-Relevance (SU-R) and Per Feature Sensitivity analysis (PFS). Less significant features are iteratively eliminated starting from discarding the least ranked feature identified using the combination of SU-R and PFS Analysis.To evaluate how well our proposed method identifies important features, we assess the influence of each feature on the model's performance using metrics like F1 score and accuracy. This evaluation is conducted on two diverse public datasets from the UCI Machine Learning repository, allowing us to assess the method's robustness across different data types.This hybrid work identified the best 450 significant features out of 754 in the Parkinson’s disease dataset, and the top 150 features out of 562 in the smart phone dataset. The efficacy of the SVM classifier with the selected number of significant features with the proposed hybrid PF and SU-R technique outperforms the SVM when applied with existing feature selection methods.
A novel dynamics modeling method for the heavy cargo airdrop based on reduced multibody system transfer matrix method
Authors: Wang Youyu , Wang Guoping , Sun Yutian , Gu Lilin , Zhang Jin 
Published: June 2024

Abstract

Airdrop technology transfers cargo to a predetermined location using a parachute deceleration system. Relevant researchers establish multibody system dynamics models using ordinary dynamics methods to simulate different particular airdrop system configurations, which require global dynamics equations. The system matrix order is equal to the number of degrees of freedom of the airdrop system. The reduced multibody system transfer matrix method (RMSTMM) is developed to model and simulate airdrop system in this paper, which avoids the symbolically deriving dynamics equations. It does not require global dynamics equations and has high programming and computing efficiency. The effectiveness of recursive algorithm of RMSTMM is validated. The multibody system dynamics modeling and simulation for the whole process of a heavy cargo airdrop are presented by the dynamics topology figures. The variation regularity of displacement, velocity, attitude angle, and overload of the cargo under windless and windy conditions are analyzed. The paper develops a theoretical foundation for rapid modeling, simulation, design, and optimization for the heavy cargo airdrop.
2.53W of 261nm Continuous wave generation in a Pr:YLF laser pumped by blue laser diode at 444.2nm
Authors: Yao Yi , zheng quan , chen xi , wang jinyan , xiao huidong , wang yan , wang yuning , liu huizhen , tian donghe 
Published: June 2024

Abstract

We demonstrate the generation of a watt-level continuous wave deep ultraviolet laser at 261nm by efficient frequency doubling of a blue-diode-pumped Pr:YLF laser at 522nm for the first time. With a novel coating method and LD collimating method, a TEM00 mode deep UV laser radiation at 261nm with an output power of 2.53W was obtained. To the best of our knowledge, this is the first report on a watt-level Pr:YLF laser operating at 261nm.