bluuebunny/crossref_metadata_2025 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

South 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

Oxidative 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

Subgroup 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

Wildfires 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

Effective 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

The 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

This 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

Frailty 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

In 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

To 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

Data 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

In 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

This 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.
Safety assessments and clinical features of PARP inhibitors from real-world data of Japanese patients with ovarian cancer
Authors: Uekusa Ryosuke , Yokoi Akira , Watanabe Eri , Yoshida Kosuke , Yoshihara Masato , Tamauchi Satoshi , Shimizu Yusuke , Ikeda Yoshiki , Yoshikawa Nobuhisa , Niimi Kaoru , Suzuki Shiro , Kajiyama Hiroaki
Published: June 2024

Poly (ADP-ribose) polymerase inhibitors have been increasingly used in ovarian cancer treatment. However, the real-world safety data of these drugs in Japanese patients are limited. This retrospective study included 181 patients with ovarian cancer who received olaparib or niraparib at two independent hospitals in Japan between May 2018 and December 2022. Clinical information and blood sampling data were collected. Regarding patient backgrounds, the olaparib group had higher proportions of patients with serous carcinoma, BRCA positivity, homologous recombination deficiency, and those receiving maintenance therapy after recurrence treatment than the niraparib group. Regarding toxicity properties, the most common reasons for discontinuation in the olaparib group were anemia, fatigue, and nausea, while the reason in the niraparib was thrombocytopenia. Thrombocytopenia caused by niraparib treatment occurred earlier than anemia caused by olaparib treatment. Patients with a low body mass index or who had undergone several previous treatment regimens were more likely to discontinue treatment within the first 3 months. Although we analyzed blood collection data, predicting treatment interruptions due to blood toxicity was challenging. In this study, we revealed the characteristics of patients and the timing of interruptions for each drug, highlighting the importance of carefully managing adverse effects.
Skeletal muscle desmin alterations following revascularization in peripheral artery disease claudicants
Authors: Wilburn Dylan , Miserlis Dimitrios , Fletcher Emma , Papoutsi Evlampia , Ismaeel Ahmed , Bradley Cassandra , Ring Andrew , Wilkinson Trevor , Smith Robert S. , Ferrer Lucas , Haynatzki Gleb , Monteleone Peter , Banerjee Subhash , Brisbois Elizabeth , Bohannon William T. , Koutakis Panagiotis
Published: June 2024

Peripheral artery disease (PAD) is characterized by varying severity of arterial stenosis, exercise induced claudication, malperfused tissue precluding normal healing and skeletal muscle dysfunction. Revascularization interventions improve circulation, but post-reperfusion changes within the skeletal muscle are not well characterized. This study investigates if revascularization enhanced hemodynamics increases walking performance with concurrent improvement of mitochondrial function and reverses abnormal skeletal muscle morphological features that develop with PAD. Fifty-eight patients completed walking performance testing and muscle biopsy before and 6 months after revascularization procedures. Muscle fiber morphology, desmin structure, and mitochondria respiration assessments before and after the revascularization were evaluated. Revascularization improved limb hemodynamics, walking function, and muscle morphology. Qualitatively not all participants recovered normal structural architecture of desmin in the myopathic myofibers after revascularization. Heterogenous responses in the recovery of desmin structure following revascularization may be caused by other underlying factors not reversed with hemodynamic improvements. Revascularization interventions clinically improve patient walking ability and can reverse the multiple subcellular functional and structural abnormalities in muscle cells. Further study is needed to characterize desmin structural remodeling with improvements in skeletal muscle morphology and function.
Towards automatic home-based sleep apnea estimation using deep learning
Authors: Retamales Gabriela , Gavidia Marino E. , Bausch Ben , Montanari Arthur N. , Husch Andreas , Goncalves Jorge
Published: June 2024

Apnea and hypopnea are common sleep disorders characterized by the obstruction of the airways. Polysomnography (PSG) is a sleep study typically used to compute the Apnea-Hypopnea Index (AHI), the number of times a person has apnea or certain types of hypopnea per hour of sleep, and diagnose the severity of the sleep disorder. Early detection and treatment of apnea can significantly reduce morbidity and mortality. However, long-term PSG monitoring is unfeasible as it is costly and uncomfortable for patients. To address these issues, we propose a method, named DRIVEN, to estimate AHI at home from wearable devices and detect when apnea, hypopnea, and periods of wakefulness occur throughout the night. The method can therefore assist physicians in diagnosing the severity of apneas. Patients can wear a single sensor or a combination of sensors that can be easily measured at home: abdominal movement, thoracic movement, or pulse oximetry. For example, using only two sensors, DRIVEN correctly classifies 72.4% of all test patients into one of the four AHI classes, with 99.3% either correctly classified or placed one class away from the true one. This is a reasonable trade-off between the model’s performance and the patient’s comfort. We use publicly available data from three large sleep studies with a total of 14,370 recordings. DRIVEN consists of a combination of deep convolutional neural networks and a light-gradient-boost machine for classification. It can be implemented for automatic estimation of AHI in unsupervised long-term home monitoring systems, reducing costs to healthcare systems and improving patient care.
Multiplex, high-throughput method to study cancer and immune cell mechanotransduction
Authors: Fabiano Abigail R. , Robbins Spencer C. , Knoblauch Samantha V. , Rowland Schyler J. , Dombroski Jenna A. , King Michael R.
Published: June 2024

Studying cellular mechanoresponses during cancer metastasis is limited by sample variation or complex protocols that current techniques require. Metastasis is governed by mechanotransduction, whereby cells translate external stimuli, such as circulatory fluid shear stress (FSS), into biochemical cues. We present high-throughput, semi-automated methods to expose cells to FSS using the VIAFLO96 multichannel pipetting device custom-fitted with 22 G needles, increasing the maximum FSS 94-fold from the unmodified tips. Specifically, we develop protocols to semi-automatically stain live samples and to fix, permeabilize, and intracellularly process cells for flow cytometry analysis. Our first model system confirmed that the pro-apoptotic effects of TRAIL therapeutics in prostate cancer cells can be enhanced via FSS-induced Piezo1 activation. Our second system implements this multiplex methodology to show that FSS exposure (290 dyn cm−2) increases activation of murine bone marrow-derived dendritic cells. These methodologies greatly improve the mechanobiology workflow, offering a high-throughput, multiplex approach.
Higher-order non-Markovian social contagions in simplicial complexes
Authors: Lin Zhaohua , Han Lilei , Feng Mi , Liu Ying , Tang Ming
Published: June 2024

Higher-order structures such as simplicial complexes are ubiquitous in numerous real-world networks. Empirical evidence reveals that interactions among nodes occur not only through edges but also through higher-dimensional simplicial structures such as triangles. Nevertheless, classic models such as the threshold model fail to capture group interactions within these higher-order structures. In this paper, we propose a higher-order non-Markovian social contagion model, considering both higher-order interactions and the non-Markovian characteristics of real-world spreading processes. We develop a mean-field theory to describe its evolutionary dynamics. Simulation results reveal that the theory is capable of predicting the steady state of the model. Our theoretical analyses indicate that there is an equivalence between the higher-order non-Markovian and the higher-order Markovian social contagions. Besides, we find that non-Markovian recovery can boost the system resilience to withstand a large-scale infection or a small-scale infection under different conditions. This work deepens our understanding of the behaviors of higher-order non-Markovian social contagions in the real world.
Deep-learning-based decomposition of overlapping-sparse images: application at the vertex of simulated neutrino interactions
Authors: Alonso-Monsalve Saúl , Sgalaberna Davide , Zhao Xingyu , Molines Adrien , McGrew Clark , Rubbia André
Published: June 2024

Image decomposition plays a crucial role in various computer vision tasks, enabling the analysis and manipulation of visual content at a fundamental level. Overlapping and sparse images pose unique challenges for decomposition algorithms due to the scarcity of meaningful information to extract components. Here, we present a solution based on deep learning to accurately extract individual objects within multi-dimensional overlapping-sparse images, with a direct application to the decomposition of overlaid elementary particles obtained from imaging detectors. Our approach allows us to identify and measure independent particles at the vertex of neutrino interactions, where one expects to observe images with indiscernible overlapping charged particles. By decomposing the image of the detector activity at the vertex through deep learning, we infer the kinematic parameters of the low-momentum particles and enhance the reconstructed energy resolution of the neutrino event. Finally, we combine our approach with a fully-differentiable generative model to improve the image decomposition further and the resolution of the measured parameters. This improvement is crucial to search for asymmetries between matter and antimatter.
Control of cell membrane receptor condensation by adhesion to supported bilayers with nanoscale topography
Authors: Li Long , Hou Ruihan , Shi Xinghua , Ji Jing , Różycki Bartosz , Hu Jinglei , Song Fan
Published: June 2024

Developing physical methods to modulate biomolecular clusters and condensates on cell membranes is of great importance for understanding physiological and pathological processes as well as for stimulating novel therapeutic strategies. Here, we propose an effective means to control receptor condensation on the cell membrane via specific adhesion to a supported lipid bilayer (SLB) with nanoscale topography. The specific adhesion is mediated by receptors in the cell membrane that bind their ligands anchored in the SLB. Using Monte Carlo simulations and mean-field theory, we demonstrate that the nanoscale topography of the SLB can enhance condensation of the receptors associated with lipid nanodomains. Our results indicate that SLBs with nanoscale topography proves an effective physical stimulus for tuning condensation of membrane adhesion proteins and lipids in cell membranes, and can serve as a feasible option to control and direct cellular activities, e.g., stem cell differentiation for biomedical and therapeutic applications.
Photoinduced decarboxylative germylation of α-fluoroacrylic acids: access to germylated monofluoroalkenes
Authors: Lu Xiao-Yu , Qian Yu-Jun , Sun Hai-Lun , Su Meng-Xue , Wang Zi-Zhen , Jiang Fan , Zhou Xin-Yue , Sun Yan-Xi , Shi Wan-Li , Wan Ji-Ru
Published: June 2024

Herein, a novel strategy is presented for the photoinduced decarboxylative and dehydrogenative cross-coupling of a wide range of α-fluoroacrylic acids with hydrogermanes.
Isolation of a copper photocatalyst on a metal–organic cage for the sulfonylation of aryl halides resulting from visible-light-mediated C(sp2)–S cross-coupling
Authors: Zhao Lehua , Zhang Yu , Wang Huali , Wang Jing , He Cheng , Zhao Liang , Duan Chunying
Published: June 2024

A Ce-based metal–organic tetrahedron was assembled in a controlled manner for preserving 2,2′-bipyridyl groups to form an isolated Cu-based photocatalyst for achieving efficient visible-light-mediated photocatalytic C(sp2)–S cross-coupling.
Synthesis of dihydrotropone derivatives using an anionic 8π-electrocyclic reaction
Authors: Kato Ranmaru , Suzuki Takahiro , Tanino Keiji
Published: June 2024

A new method to synthesise functionalised cycloheptatrienes was established using the anionic 8π-electrocyclic reaction of β-ketoester-derived dienyne substrates.
Green synthesis of a magnesium single-atom catalyst from Spinacia oleracea chlorophyll extracts for sustainable electrocatalytic nitrate reduction to ammonia
Authors: Kumar Kanhai , Tripathi Pragyan , Raj Gokul , Kalyan Dova , Gorle Demudu Babu , Mohan Nikhil George , Makineni Surendra Kumar , Ramanujam Kothandaraman , Singh Abhishek Kumar , Nanda Karuna Kar
Published: June 2024

Magnesium single atoms derived from natural leaves for sustainable ammonia production.
Co/Li2S of chemical confinement decorated electrochemically stable Co(OH)F nanoarrays enabling dendrite-free flexible 3D Li anodes
Authors: Song Liubin , Wang Yixuan , Qian Huaming , Bai Mengxin , Chen Qinchuan , Xiao Minzhi , Zhao Tingting , Xiao Zhongliang , Wang Jingjing , Li Xifei
Published: June 2024

The 3D CoS2–Co(OH)F/CC skeleton suppresses Li dendrite growth.
On the relation between performance and permanent demagnetisation in spoke type machines with ferrite magnets
Authors: Silva Marcelo D. , Eriksson Sandra
Published: June 2024

The use of rare earth elements (REE) in permanent magnets (PMs) raises problems in several domains. The supply chain of these is fragile, the prices have shown volatility and its manufacturing has a bigger impact on climate change when compared to the manufacturing of other PMs. Instead, ferrite PMs have been researched as an alternative. This alternative shows a relatively higher demagnetisation risk when compared to REE PMs. Thus, a detailed study on permanent demagnetisation during winding faults is crucial. The authors use the finite element method to evaluate different machine designs, developed under mechanical constraints, and explore several strategies to mitigate permanent demagnetisation. Also, the importance of avoiding permanent demagnetisation is changed gradually in the optimisation process. The results show that the protection of the PM and performance optimisation are irreconcilable goals. It also highlights the impact of the stator design in decreasing demagnetisation. Additionally, it is shown that the classic notion of avoiding demagnetisation is an ineffective strategy for designing high‐performance machines with ferrite magnets, and instead, it should be integrated into the optimisation process and weighted according to the application demands.
Firearms forensics is becoming more quantitative
Authors: Feder Toni
Published: June 2024

Science over subjectivity could increase juries’ confidence in gun identification.
Gravitational patterns reveal a tumultuous lunar past
Authors: Sieben Jennifer
Published: June 2024

Geophysical models and data analysis show that subsurface titanium causes anomalies in the Moon’s gravitational field.
Educational interventions to reduce depression and anxiety in older adults with cancer in the community: a systematic review, meta-analysis and meta-regression of randomised controlled trials
Authors: Low Chen Ee , Pillay Renish M , Teo Finn Jing Jie , Loh Caitlin Yuen Ling , Yau Chun En , Yan Bin Lee Ainsley Ryan , Ho Cyrus Su Hui , Chen Matthew Zhixuan
Published: June 2024

Background

Older adults make up half of those with cancer and are prone to mood disorders, such as depression and severe anxiety, resulting in negative repercussions on their health-related quality-of-life (HRQOL). Educational interventions have been shown to reduce adverse psychological outcomes. We examined the effect of educational interventions on the severity of psychological outcomes in older adults with cancer (OAC) in the community.

Method

This PRISMA-adherent systematic review involved a search of PubMed, MedLine, Embase and PsycINFO for randomised controlled trials (RCTs) that evaluated educational interventions impacting the severity of depression, anxiety and HRQOL in OAC. Random effects meta-analyses and meta-regressions were used for the primary analysis.

Results

Fifteen RCTs were included. Meta-analyses showed a statistically insignificant decrease in the severity of depression (SMD = −0.30, 95%CI: −0.69; 0.09), anxiety (SMD = −0.30, 95%CI: −0.73; 0.13) and improvement in overall HRQOL scores (SMD = 0.44, 95%CI: −0.16; 1.04). However, subgroup analyses revealed that these interventions were particularly effective in reducing the severity of depression and anxiety in specific groups, such as OAC aged 60–65, those with early-stage cancer, those with lung cancer and those treated with chemotherapy. A systematic review found that having attained a higher education and income level increased the efficacy of interventions in decreasing the severity of adverse psychological outcomes.

Conclusion

Although overall meta-analyses were statistically insignificant, subgroup meta-analyses highlighted a few specific subgroups that the educational interventions were effective for. Future interventions can be implemented to target these vulnerable groups.
Disaster and Social Work in Nepal: A Discussion
Authors: Karki Dilip , Yadav Raj , Davis Cindy
Published: June 2024

Despite social work’s about three decades of history and most populations being at risk of multiple natural disasters, Nepali social workers are yet to establish their foothold in the sector of disaster in Nepal. Part of this has happened due to ineffective curriculum on disaster and social work. And hence, this article proposes how the curriculum for disaster and social work can be advanced in the future.
Management of acute severe ulcerative colitis—an update for generalist and specialist clinicians
Authors: Kuriakose Kuzhiyanjal Anish J , Limdi Jimmy K
Published: June 2024

Background

Acute severe ulcerative colitis (ASUC) is a potentially life-threatening medical emergency that occurs in up to 25% of patients with ulcerative colitis. Although intravenous corticosteroids remain the cornerstone of therapy, 30–40% of patients will not respond and need timely consideration of rescue therapy with (currently) either infliximab or ciclosporin or indeed colectomy, underscoring the importance of multidisciplinary care to ensure favourable outcomes for patients. We discuss the current evidence and present an approach to the management of ASUC for general and specialist clinicians caring for patients with ASUC.

Sources of data

The information in this review is derived from data published in peer- reviewed academic journals and registered clinical trials.

Areas of agreement

Management of acute severe colitis requires a multidisciplinary approach with early initiation with steroids and timely escalation of treatment to either medical rescue therapy or surgery.

Areas of controversy

Balancing the risks of delayed surgery vs. optimizing medical therapy, including accelerated dosing schedules for biologics, remains ambiguous.

Growing points

The position on newer molecules like Janus Kinase inhibitors, such as tofacitinib, is a growing area with early real-world data showing promise for steroid refractory ASUC.

Areas timely for developing research

Developing predictive biomarkers and clinical risk scores for personalized rescue therapy selection is an evolving area of research.
A Simple Validated LC-UV Method for the Simultaneous Determination of Brimonidine and Brinzolamide in the Presence of Brinzolamide’s Degradation Product (Major Metabolite) in Rabbit Aqueous Humor
Authors: Wahab Mubarak Marwa A , Mohammad M Abdul-Azim , El-Bagary Ramzia I , Elkady Ehab , Abo-Talib Nisreen F
Published: June 2024

A sensitive, specific, reliable and low-cost LC-UV method has been developed and validated for simultaneous quantification of brimonidine tartrate (BM) and brinzolamide (BZ) in rabbit aqueous humor (AH) in the presence of N-desethyl-brinzolamide (NDBZ); BZ is a major degradation product, and it is also considered to be its major metabolite. Dorzolamide hydrochloride (DZ) was used as an internal standard (IS). The analytes were extracted from rabbit AH samples by a simple pre-treatment utilizing ZnSO4.7H2O as a deproteinizing agent. The analytes were separated on a cyanopropyl Waters column (4.6 × 200 mm, 5 μm) with an isocratic mobile phase consisting of 25 mM ammonium acetate pH 4.5 (adjusted with 85% phosphoric acid):methanol:acetonitrile (94:4.5:1.5, v/v) at a flow rate of 1.0 mL min−1. The detection was done at 254 nm. The lower limit of quantification in rabbit AH was 100.0 ng mL−1. The method was validated according to EMA guidelines. The method was confirmed to be accurate, precise and linear over a range of 100.0–1000.0 ng mL−1 for BM and BZ. The method developed herein is simple, safe, eco-friendly, rapid and accurately applied for the quantification of BM and BZ, along with the successful separation of NDBZ, which is considered as a potential irritant degradation product of BZ.
Dose-Response Relationship Between Exercise Duration and Enhanced Function and Cognition in Acutely Hospitalized Older Adults: A Secondary Analysis of a Randomized Clinical Trial
Authors: Sáez de Asteasu Mikel L , Martínez-Velilla Nicolás , Zambom-Ferraresi Fabricio , Galbete Arkaitz , Ramírez-Vélez Robinson , Cadore Eduardo L , Abizanda Pedro , Gómez-Pavón Javier , Izquierdo Mikel
Published: June 2024

Background and Objectives

Exercise may reverse functional decline in hospitalized older adults, but the optimal duration is unclear. This study examined the potential relationship between in-hospital multicomponent exercise program duration and changes in physical function, cognition, and muscle function to maximize exercise-related health benefits in acutely hospitalized older patients.

Research Design and Methods

This secondary analysis of a multicenter randomized controlled trial examined the relationship between the duration of an in-hospital multicomponent exercise program and changes in physical function, cognition, and muscle strength in 570 acutely hospitalized older adults. Participants completed 3, 4, or 5–7 consecutive days of exercise based on the progression of their acute medical illness. The acute clinical condition of the older patients was similar across the study groups (i.e., 3/4/5–7 days) at admission. Outcomes included the Short Physical Performance Battery (SPPB) for functional capacity, Gait Velocity Test for gait speed, handgrip for muscle strength, and cognitive tests.

Results

Of the 570 patients included in the analysis, 298 were women (52.3%), and the mean (SD) age was 87.3 (4.8) years. Exercise groups increased SPPB scores compared with controls, with gains of 1.09 points after three days, 1.97 points after four days, and 2.02 points after 5–7 days (p < .001). The 4-day program showed the most significant benefit for functional capacity. Gait velocity increased by 0.11 m/s after 4 and 5–7 days (p = .032). Similar dose-response relationships were seen for handgrip strength and cognition, with 5–7 days showing more significant gains than three days (p < .05).

Discussion and Implications

Multicomponent exercise programs enhance physical and cognitive function in hospitalized older adults, regardless of exercise dosage. A 4-day program significantly boosts functional capacity, although 5–7 days improves handgrip strength and cognition, highlighting the importance of exercise dosage in countering functional decline. Implementing evidence-based inpatient exercise prescriptions can help reverse muscle weakness and improve cognitive and physical function.

Clinical Trial Registration: NCT04600453
Evaluating and validating 3-D simulated MASW and SPAC in situ tests in Argostoli, Greece
Authors: Riaño Andrea C , Lopez-Caballero Fernando , Hollender Fabrice
Published: June 2024

Geophysics and Geotechnical Engineering commonly use 1-D wave propagation analysis, simplifying complex scenarios by assuming flat and homogeneous soil layers, vertical seismic wave propagation and negligible pore water pressure effects (total stress analysis). These assumptions are commonly used in practice, providing the basis for applications like analysing site responses to earthquakes and characterizing soil properties through inversion processes. These processes involve various in situ tests to estimate the subsurface soil’s material profile, providing insights into its behaviour during seismic events. This study seeks to address the limitations inherent to 1-D analyses by using 3-D physics-based simulations to replicate in situ tests performed in the Argostoli basin, Greece. Active and passive source surveys are simulated, and their results are used to determine material properties at specific locations, using standard geophysical methods. Our findings underscore the potential of 3-D simulations to explore different scenarios, considering different survey configurations, source types and array sets.