bluuebunny/crossref_metadata_2025_split huggingface dataset:
Morbidity amongst South African Hajj pilgrims in 2023—a retrospective cohort study
Authors: Mahomed Ozayr , Jaffer Mohammed Nasir , Parker Salim 
Published: June 2024

AbstractSouth Africans are not accustomed to the dry arid climate and sweltering heat in Saudi Arabia. We conducted a retrospective cohort study to identify the common health conditions pre-Hajj, during the 5 days of Hajj and on return to South Africa from Hajj amongst the 2023 pilgrims. A QR code and a mobile link to a self-administered questionnaire was sent to all 3500 South African pilgrims. Five hundred and seventy-seven pilgrims returned the completed surveys. Mean age of the participants was 48 years (SD 12) with a higher female representation (3:2). Forty eight percent (279) had pre-existing chronic conditions. Forty five percent (259) reported being ill during their stay in the Kingdom, 20% (115) reported having an illness during the main 5 days, whilst 51% (293) reported having an illness within 7 days of returning to South Africa. Only six pilgrims were admitted to hospital after their return home. Respiratory tract linked symptoms were the most frequently reported (95% pre Hajj and 99% post Hajj). Participants who reported having a chronic condition (AOR 1.52 95% CI 1.09–2.11) and engaging in independent exercising prior to Hajj (AOR 1.52–1.07–2.10) were at an increased likelihood of developing an illness within 7 days of returning home. Post travel surveillance swabs to identify potential pathogens that the returning pilgrims are incubating should be explored to guide further interventions.
Efficient and promising oxidative desulfurization of fuel using Fenton like deep eutectic solvent
Authors: Armandsefat Fatemeh , Hamzehzadeh Sholeh , Azizi Najmedin 
Published: June 2024

AbstractOxidative desulfurization (ODS) has emerged as a prominent technique for the removal of sulfur compounds from fuels, aiming to comply with stringent environmental regulations and minimize sulfur dioxide emissions. Herein, Fenton-like deep eutectic solvents (DESs) were synthesized as a catalyst and reaction medium and their application for the ODS process was investigated. The study encompassed the optimization of DES composition, reaction conditions, and the influence of different parameters on the desulfurization efficiency. The experimental findings demonstrated that the Fenton-like DES exhibited outstanding catalytic activity in the oxidative desulfurization of fuel. The optimized conditions involved conducting the reaction at room temperature for 2.5 h, using 200 mg of the prepared DES (HNFM-FeCl4) as both the extraction solvent and catalyst. An oxidant-to-sulfur (O/S) ratio of approximately 3:1 was maintained, with a 30 wt% H2O2 solution utilized as the oxidant. The analysis of the reaction products using GC–MS revealed a remarkable yield of 98% for dibenzothiophene sulfone. The DES provided a suitable medium for the reaction, enhancing the solubility and availability of sulfur compounds. The iron catalyst, in the presence of hydrogen peroxide, facilitated the oxidation of sulfur-containing compounds to their corresponding sulfones, which can be easily separated from the fuel phase. The DES catalysts exhibited stability and recyclability, making them suitable for practical applications in fuel desulfurization processes.
Interaction analysis of subgroup effects in randomized trials: the essential methodological points
Authors: Fingerhut Abraham , Uranues Selman , Dziri Chadly , Ma Junjun , Vernerey Dewi , Kurihara Hayato , Stiegler Philip 
Published: June 2024

AbstractSubgroup analysis aims to identify subgroups (usually defined by baseline/demographic characteristics), who would (or not) benefit from an intervention under specific conditions. Often performed post hoc (not pre-specified in the protocol), subgroup analyses are prone to elevated type I error due to multiple testing, inadequate power, and inappropriate statistical interpretation. Aside from the well-known Bonferroni correction, subgroup treatment interaction tests can provide useful information to support the hypothesis. Using data from a previously published randomized trial where a p value of 0.015 was found for the comparison between standard and Hemopatch® groups in (the subgroup of) 135 patients who had hand-sewn pancreatic stump closure we first sought to determine whether there was interaction between the number and proportion of the dependent event of interest (POPF) among the subgroup population (patients with hand-sewn stump closure and use of Hemopatch®), Next, we calculated the relative excess risk due to interaction (RERI) and the “attributable proportion” (AP). The p value of the interaction was p = 0.034, the RERI was − 0.77 (p = 0.0204) (the probability of POPF was 0.77 because of the interaction), the RERI was 13% (patients are 13% less likely to sustain POPF because of the interaction), and the AP was − 0.616 (61.6% of patients who did not develop POPF did so because of the interaction). Although no causality can be implied, Hemopatch® may potentially decrease the POPF after distal pancreatectomy when the stump is closed hand-sewn. The hypothesis generated by our subgroup analysis requires confirmation by a specific, randomized trial, including only patients undergoing hand-sewn closure of the pancreatic stump after distal pancreatectomy.Trial registration: INS-621000-0760.
Vegetation change detection and recovery assessment based on post-fire satellite imagery using deep learning
Authors: Priya R. Shanmuga , Vani K. 
Published: June 2024

AbstractWildfires are uncontrolled fires fuelled by dry conditions, high winds, and flammable materials that profoundly impact vegetation, leading to significant consequences including noteworthy changes to ecosystems. In this study, we provide a novel methodology to understand and evaluate post-fire effects on vegetation. In regions affected by wildfires, earth-observation data from various satellite sources can be vital in monitoring vegetation and assessing its impact. These effects can be understood by detecting vegetation change over the years using a novel unsupervised method termed Deep Embedded Clustering (DEC), which enables us to classify regions based on whether there has been a change in vegetation after the fire. Our model achieves an impressive accuracy of 96.17%. Appropriate vegetation indices can be used to evaluate the evolution of vegetation patterns over the years; for this study, we utilized Enhanced Vegetation Index (EVI) based trend analysis showing the greening fraction, which ranges from 0.1 to 22.4 km2 while the browning fraction ranges from 0.1 to 18.1 km2 over the years. Vegetation recovery maps can be created to assess re-vegetation in regions affected by the fire, which is performed via a deep learning-based unsupervised method, Adaptive Generative Adversarial Neural Network Model (AdaptiGAN) on post-fire data collected from various regions affected by wildfire with a training error of 0.075 proving its capability. Based on the results obtained from the study, our approach tends to have notable merits when compared to pre-existing works.
Explainable hypoglycemia prediction models through dynamic structured grammatical evolution
Authors: De La Cruz Marina , Garnica Oscar , Cervigon Carlos , Velasco Jose Manuel , Hidalgo J. Ignacio 
Published: June 2024

AbstractEffective blood glucose management is crucial for people with diabetes to avoid acute complications. Predicting extreme values accurately and in a timely manner is of vital importance to them. People with diabetes are particularly concerned about suffering a hypoglycemia (low value) event and, moreover, that the event will be prolonged in time. It is crucial to predict hyperglycemia (high value) and hypoglycemia events that may cause health damages in the short term and potential permanent damages in the long term. This paper describes our research on predicting hypoglycemia events at 30, 60, 90, and 120 minutes using machine learning methods. We propose using structured Grammatical Evolution and dynamic structured Grammatical Evolution to produce interpretable mathematical expressions that predict a hypoglycemia event. Our proposal generates white-box models induced by a grammar based on if-then-else conditions using blood glucose, heart rate, number of steps, and burned calories as the inputs for the machine learning technique. We apply these techniques to create three types of models: individualized, cluster, and population-based. They all are then compared with the predictions of eleven machine learning techniques. We apply these techniques to a dataset of 24 real patients of the Hospital Universitario Principe de Asturias, Madrid, Spain. The resulting models, presented as if-then-else statements that incorporate numeric, relational, and logical operations between variables and constants, are inherently interpretable. The True Positive Rate and True Negative Rate metrics are above 0.90 for 30-minute predictions, 0.80 for 60 min, and 0.70 for 90 min and 120 min for the three types of models. Individualized models exhibit the best metrics, while cluster and population-based models perform similarly. Structured and dynamic structured grammatical evolution techniques perform similarly for all forecasting horizons. Regarding the comparison of different machine learning techniques, on the shorter forecasting horizons, our proposals have a high probability of winning, a probability that diminishes on the longer time horizons. Structured grammatical evolution provides advanced forecasting models that facilitate model explanation, modification, and retesting, offering flexibility for refining solutions post-creation and a deeper understanding of blood glucose behavior. These models have been integrated into the glUCModel application, designed to serve people with diabetes.
A multi-strategy improved rime optimization algorithm for three-dimensional USV path planning and global optimization
Authors: Gu Gaoquan , Lou Jingjun , Wan Haibo 
Published: June 2024

AbstractThe RIME optimization algorithm (RIME) represents an advanced optimization technique. However, it suffers from issues such as slow convergence speed and susceptibility to falling into local optima. In response to these shortcomings, we propose a multi-strategy enhanced version known as the multi-strategy improved RIME optimization algorithm (MIRIME). Firstly, the Tent chaotic map is utilized to initialize the population, laying the groundwork for global optimization. Secondly, we introduce an adaptive update strategy based on leadership and the dynamic centroid, facilitating the swarm's exploitation in a more favorable direction. To address the problem of population scarcity in later iterations, the lens imaging opposition-based learning control strategy is introduced to enhance population diversity and ensure convergence accuracy. The proposed centroid boundary control strategy not only limits the search boundaries of individuals but also effectively enhances the algorithm's search focus and efficiency. Finally, to demonstrate the performance of MIRIME, we employ CEC 2017 and CEC 2022 test suites to compare it with 11 popular algorithms across different dimensions, verifying its effectiveness. Additionally, to assess the method's practical feasibility, we apply MIRIME to solve the three-dimensional path planning problem for unmanned surface vehicles. Experimental results indicate that MIRIME outperforms other competing algorithms in terms of solution quality and stability, highlighting its superior application potential.
Activated allograft combined with induced membrane technique for the reconstruction of infected segmental bone defects
Authors: Wang Xiaohua , Jia Chao , Wu Hongri , Luo Fei , Hou Tianyong , Li Gang , Lin Sien , Xie Zhao 
Published: June 2024

AbstractThis study was desinged to evaluate the efficacy and safety of activated allograft combined with the induced membrane technique for reconstruction of infected segment bone defects of lower limbs. A retrospective analysis was conducted on 19 patients from May 2015 to February 2017. After debridements, the bone defects were filled with antibiotic bone cement to form the induced membrane. Autologous mesenchymal stem cells were seeded onto allografts to construct activated allograft, which was implanted into the induced membrane after infection was controlled. The clinical efficacy and complications were observed. 19 patients with 20 infected segment bone defect were evaluated. The average deficit size was 11.08 (4–17) cm in length. After a mean follow-up of 71.84 (61–82) months, bone union was achieved in 16 patients (17 sites), resulting in a final union rate of 84.21% (16/19 patients). The average bone union time was 10.18 (5–28) months. There were 2 patients with recurrence of infection, 3 patients with graft absorption, and 1 patient with malunion due to implant breakage. There were no graft-related complications. This study provides clinical significance for the treatment of patients with insufficient autologous bone.
Validation of a polygenic risk score for frailty in the Lothian Birth Cohort 1936 and English longitudinal study of ageing
Authors: Flint J. P. , Welstead M. , Cox S. R. , Russ T. C. , Marshall A. , Luciano M. 
Published: June 2024

AbstractFrailty is a complex trait. Twin studies and high-powered Genome Wide Association Studies conducted in the UK Biobank have demonstrated a strong genetic basis of frailty. The present study utilized summary statistics from a Genome Wide Association Study on the Frailty Index to create and test the predictive power of frailty polygenic risk scores (PRS) in two independent samples – the Lothian Birth Cohort 1936 (LBC1936) and the English Longitudinal Study of Ageing (ELSA) aged 67–84 years. Multiple regression models were built to test the predictive power of frailty PRS at five time points. Frailty PRS significantly predicted frailty, measured via the FI, at all-time points in LBC1936 and ELSA, explaining 2.1% (β = 0.15, 95%CI, 0.085–0.21) and 1.8% (β = 0.14, 95%CI, 0.10–0.17) of the variance, respectively, at age ~ 68/ ~ 70 years (p < 0.001). This work demonstrates that frailty PRS can predict frailty in two independent cohorts, particularly at early ages (~ 68/ ~ 70). PRS have the potential to be valuable instruments for identifying those at risk for frailty and could be important for controlling for genetic confounders in epidemiological studies.
Effect of two-photon absorption on trapping of plasmonic nanoparticles
Authors: Mirzaei-Ghormish S. , Qaderi K. , Smalley D. 
Published: June 2024

AbstractIn this paper, we introduce a theoretical framework for optical trapping that integrates nonlinear polarization within the dipole approximation. This theory represents the most comprehensive analytic model to date capable of resolving the discrepancies between the observed and simulated trapping of plasmonic nanoparticles. Our theory elucidates how two-photon absorption can account for the stable trapping of gold nanoparticles, including their longitudinal stability, especially near their plasmon resonance. Furthermore, the experimentally observed split potential wells in the transverse plane, which are attributed to two-photon absorption, are in close agreement with our model’s predictions. Finally, this study provides new insights into the mechanism of optical trapping under conditions of intense light–matter interactions.
Research of 2D-COS with metabolomics modifications through deep learning for traceability of wine
Authors: Wang Zhuo-Kang , Ta Na , Wei Hai-Cheng , Wang Jin-Hang , Zhao Jing , Li Min 
Published: June 2024

AbstractTo tackle the difficulty of extracting features from one-dimensional spectral signals using traditional spectral analysis, a metabolomics analysis method is proposed to locate two-dimensional correlated spectral feature bands and combine it with deep learning classification for wine origin traceability. Metabolomics analysis was performed on 180 wine samples from 6 different wine regions using UPLC-Q-TOF-MS. Indole, Sulfacetamide, and caffeine were selected as the main differential components. By analyzing the molecular structure of these components and referring to the main functional groups on the infrared spectrum, characteristic band regions with wavelengths in the range of 1000–1400 nm and 1500–1800 nm were selected. Draw two-dimensional correlation spectra (2D-COS) separately, generate synchronous correlation spectra and asynchronous correlation spectra, establish convolutional neural network (CNN) classification models, and achieve the purpose of wine origin traceability. The experimental results demonstrate that combining two segments of two-dimensional characteristic spectra determined by metabolomics screening with convolutional neural networks yields optimal classification results. This validates the effectiveness of using metabolomics screening to determine spectral feature regions in tracing wine origin. This approach effectively removes irrelevant variables while retaining crucial chemical information, enhancing spectral resolution. This integrated approach strengthens the classification model's understanding of samples, significantly increasing accuracy.
Advanced CKD detection through optimized metaheuristic modeling in healthcare informatics
Authors: Bilal Anas , Alzahrani Abdulkareem , Almuhaimeed Abdullah , Khan Ali Haider , Ahmad Zohaib , Long Haixia 
Published: June 2024

AbstractData categorization is a top concern in medical data to predict and detect illnesses; thus, it is applied in modern healthcare informatics. In modern informatics, machine learning and deep learning models have enjoyed great attention for categorizing medical data and improving illness detection. However, the existing techniques, such as features with high dimensionality, computational complexity, and long-term execution duration, raise fundamental problems. This study presents a novel classification model employing metaheuristic methods to maximize efficient positives on Chronic Kidney Disease diagnosis. The medical data is initially massively pre-processed, where the data is purified with various mechanisms, including missing values resolution, data transformation, and the employment of normalization procedures. The focus of such processes is to leverage the handling of the missing values and prepare the data for deep analysis. We adopt the Binary Grey Wolf Optimization method, a reliable subset selection feature using metaheuristics. This operation is aimed at improving illness prediction accuracy. In the classification step, the model adopts the Extreme Learning Machine with hidden nodes through data optimization to predict the presence of CKD. The complete classifier evaluation employs established measures, including recall, specificity, kappa, F-score, and accuracy, in addition to the feature selection. Data related to the study show that the proposed approach records high levels of accuracy, which is better than the existing models.
Causes and risk factors for same-day discharge failure after total hip and knee arthroplasty: a meta-analysis
Authors: Lamo-Espinosa José María , Mariscal Gonzalo , Gómez-Álvarez Jorge , Benlloch María , San-Julián Mikel 
Published: June 2024

AbstractIn recent decades, the trend toward early same-day discharge (SDD) after surgery has dramatically increased. Efforts to develop adequate risk stratification tools to guide decision-making regarding SDD versus prolonged hospitalization after total hip arthroplasty (THA) remain largely incomplete. The purpose of this report is to identify the most frequent causes and risk factors associated with SDD failure in patients undergoing THA and total knee arthroplasty (TKA). A systematic search following PRISMA guidelines of four bibliographic databases was conducted for comparative studies between patients who were successfully discharged on the same day and those who failed. Outcomes of interests were causes and risk factors associated with same-day discharge failure. Odds ratios (OR) were calculated for dichotomous variables, whereas mean differences (MD) were calculated for continuous variables. Meta-analysis was performed using RevMan software. Random effects were used if there was evidence of heterogeneity. Eight studies with 3492 patients were included. The most common cause of SDD failure was orthostatic hypotension, followed by inadequate physical condition, nausea/vomiting, pain, and urinary retention. Female sex was a risk factor for failure (OR 0.77, 95% CI 0.63–0.93), especially in the THA subgroup. ASA score IV (OR 0.33, 95% CI 0.14–0.76) and III (OR 0.72, 95% CI 0.52–0.99) were risk factors, as were having > 2 allergies and smoking patients. General anesthesia increased failure risk (OR 0.58, 95% CI 0.42–0.80), while spinal anesthesia was protective (OR 1.62, 95% CI 1.17–2.24). The direct anterior and posterior approaches showed no significant differences. In conclusion, orthostatic hypotension was the primary cause of SDD failure. Risk factors identified for SDD failure in orthopedic surgery include female sex, ASA III and IV classifications, a higher number of allergies, smoking patients and the use of general anesthesia. These factors can be addressed to enhance SDD outcomes.
Construction and validation of a clinical risk model based on machine learning for screening characteristic factors of lymphovascular space invasion in endometrial cancer
Authors: Wang Fang , Pang Rui , Shi Shaohong , Zhang Yang 
Published: June 2024

AbstractThis study aimed to identify factors that affect lymphovascular space invasion (LVSI) in endometrial cancer (EC) using machine learning technology, and to build a clinical risk assessment model based on these factors. Samples were collected from May 2017 to March 2022, including 312 EC patients who received treatment at Xuzhou Medical University Affiliated Hospital of Lianyungang. Of these, 219 cases were collected for the training group and 93 for the validation group. Clinical data and laboratory indicators were analyzed. Logistic regression and least absolute shrinkage and selection operator (LASSO) regression were used to analyze risk factors and construct risk models. The LVSI and non-LVSI groups showed statistical significance in clinical data and laboratory indicators (P < 0.05). Multivariable logistic regression analysis identified independent risk factors for LVSI in EC, which were myometrial infiltration depth, cervical stromal invasion, lymphocyte count (LYM), monocyte count (MONO), albumin (ALB), and fibrinogen (FIB) (P < 0.05). LASSO regression identified 19 key feature factors for model construction. In the training and validation groups, the risk scores for the logistic and LASSO models were significantly higher in the LVSI group compared with that in the non-LVSI group (P < 0.001). The model was built based on machine learning and can effectively predict LVSI in EC and enhance preoperative decision-making. The reliability of the model was demonstrated by the significant difference in risk scores between LVSI and non-LVSI patients in both the training and validation groups.