bluuebunny/crossref_metadata_2025_split huggingface dataset:
Association of Cardiovascular Outcomes With Low‐Dose Glucocorticoid Prescription in Patients With Rheumatoid Arthritis
Authors: Coburn Brian W. , Baker Joshua F. , Hsu Jesse Y. , Wu Qufei , Xie Fenglong , Curtis Jeffrey R. , George Michael D. 
Published: June 2024

ObjectiveMany guidelines recommend limiting glucocorticoids in patients with rheumatoid arthritis (RA), but 40% of patients remain on glucocorticoids long term. We evaluated the cardiovascular risk of long‐term glucocorticoid prescription by studying patients on stable disease‐modifying antirheumatic drugs (DMARDs).MethodsUsing two claims databases, we identified patients with RA on stable DMARD therapy for >180 days. Proportional hazards models with inverse‐probability weights and clustering to account for multiple observations were used to estimate the effect of glucocorticoid dose on composite cardiovascular outcomes (stroke or myocardial infarction [MI]).ResultsThere were 135,583 patients in Medicare and 39,272 in Optum's de‐identified Clinformatics Data Mart (CDM) database. Medicare and CDM patients had an incidence of 1.3 and 0.8 composite cardiovascular outcomes per 100 person‐years, respectively. In the older, comorbid Medicare cohort, glucocorticoids were associated with a dose‐dependent increase in composite cardiovascular outcomes in adjusted models with predicted one‐year incidence of 1.4% (95% confidence interval [CI] 1.2%–1.6%) for ≤5 mg, 1.6% (95% CI 1.4%–1.9%) for >5 to 10 mg, and 1.8% (95% CI 1.2%–2.5%) for >10 mg versus 1.1% (95% CI 1.1%–1.2%) among patients not receiving glucocorticoids. There was no significant association among the CDM cohort. However, in the subgroup of younger patients with RA and higher cardiovascular risk, glucocorticoids were associated with a dose‐dependent increase in composite cardiovascular outcomes.ConclusionAmong older patients with more comorbidities and younger patients with higher cardiovascular risk with RA on stable DMARD therapy, glucocorticoids were associated with a dose‐dependent increased risk of MI and stroke, even at doses ≤5 mg/day. By contrast, no association was noted among younger, healthier patients with RA.image
Cooperative output regulation of heterogeneous multi‐agent systems under false data injection attacks
Authors: Geng Yanru , Zhao Zhiyun 
Published: June 2024

AbstractThis paper studies the cooperative output regulation problem for heterogeneous multi‐agent systems (MASs) subject to false data injection (FDI) attacks, which means the malicious cyber‐attackers may inject false data into the communication topology randomly. To ensure that each follower agent can track the leader agent accurately, we construct both an observer and a control law for each follower agent. Since the FDI attacks may exist in the communication network, a filter with an adaptive compensator, which can estimate attack signals dynamically, is equipped for each observer, such that the filter can neutralize attacks when malicious attackers launch attacks. Therefore, the cooperative output regulation can be achieved under the control law based on the observer no matter whether there exist attacks or not. Finally, a numerical example is provided for validating the theoretical results.
Balance and motor skills differences between children and teenagers with autism spectrum disorder and neurotypically developing
Authors: Martín‐Díaz Paloma , Cuesta‐Gómez Alicia , Fernádez‐González Pilar , Carratlá‐Tejada María 
Published: June 2024

AbstractThis study examined the differences between children and adolescents with autism spectrum disorder (ASD) and neurotypically developing (NTD) in terms of balance, postural control, and motor skills. It also examined which motor skills are most affected and whether scores on different assessment tests in ASD children are correlated. A cross‐sectional observational study with two research groups was conducted. Timed up and go test (TUG), short form of Bruininks‐Oseretsky test of Motor Proficiency version 2 (SFBOT‐2), and pediatric balance scale (PBS) were used. A total of 100 participants 50 with ASD and 50 with NTD engaged in the research. Statistically significant differences were obtained between control group and ASD group in TUG test and in SFBOT‐2 standard score and total score (p‐value = <0.01). A statistically significant difference (p‐value = <0.01) was seen between ASD group's and control group's PBS scores. Poor correlation was noted between TUG and SFBOT‐2, as well as between PBS and TUG. A moderate correlation was also found between SFBOT‐2 and PBS. Children with ASD present difficulties in motor skills and in static and dynamic balance compared to children with NTD. Differences were observed in the motor skills of strength followed by manual dexterity, running speed and agility, fine motor precision, fine motor integration, and balance. The PBS item that showed the greatest difference between the ASD group and control group was maintaining monopodial support with hands on hips. Finally, poor to moderate correlations were obtained between the different tests with statistically significant differences.
Penalized Regression Methods With Modified Cross‐Validation and Bootstrap Tuning Produce Better Prediction Models
Authors: Pavlou Menelaos , Omar Rumana Z. , Ambler Gareth 
Published: June 2024

ABSTRACTRisk prediction models fitted using maximum likelihood estimation (MLE) are often overfitted resulting in predictions that are too extreme and a calibration slope (CS) less than 1. Penalized methods, such as Ridge and Lasso, have been suggested as a solution to this problem as they tend to shrink regression coefficients toward zero, resulting in predictions closer to the average. The amount of shrinkage is regulated by a tuning parameter, commonly selected via cross‐validation (“standard tuning”). Though penalized methods have been found to improve calibration on average, they often over‐shrink and exhibit large variability in the selected and hence the CS. This is a problem, particularly for small sample sizes, but also when using sample sizes recommended to control overfitting. We consider whether these problems are partly due to selecting using cross‐validation with “training” datasets of reduced size compared to the original development sample, resulting in an over‐estimation of and, hence, excessive shrinkage. We propose a modified cross‐validation tuning method (“modified tuning”), which estimates from a pseudo‐development dataset obtained via bootstrapping from the original dataset, albeit of larger size, such that the resulting cross‐validation training datasets are of the same size as the original dataset. Modified tuning can be easily implemented in standard software and is closely related to bootstrap selection of the tuning parameter (“bootstrap tuning”). We evaluated modified and bootstrap tuning for Ridge and Lasso in simulated and real data using recommended sample sizes, and sizes slightly lower and higher. They substantially improved the selection of , resulting in improved CS compared to the standard tuning method. They also improved predictions compared to MLE.
Chinese hamster ovary cell line engineering strategies for modular production of custom extracellular vesicles
Authors: Carrillo Sanchez Braulio , Hinchliffe Matthew , Ellis Mark , Simpson Catherine , Humphreys David , Sweeney Bernie , Bracewell Daniel G. 
Published: June 2024

AbstractContinuously secreted by all cell types, extracellular vesicles (EVs) are small membrane‐bound structures which shuttle bioactive cargo between cells across their external environment. Their central role as natural molecular messengers and ability to cross biological barriers has garnered significant attention in the use of EVs as therapeutic delivery vehicles. Still, harnessing the potential of EVs is faced with many obstacles. A cell line engineering approach can be used to exploit EVs to encapsulate a bespoke cargo of interest. However, full details regarding native EV‐loading mechanisms remain under debate, making this a challenge. While Chinese hamster ovary (CHO) cells are well known to be the preferred host for recombinant therapeutic protein production, their application as an EV producer cell host has been largely overlooked. In this study, we engineered CHO DG44 cells to produce custom EVs with bespoke cargo. To this end, genetic constructs employing split green fluorescent protein technology were designed for tagging both CD81 and protein cargoes to enable EV loading via self‐assembling activity. To demonstrate this, NanoLuc and mCherry were used as model reporter cargoes to validate engineered loading into EVs. Experimental findings indicated that our custom EV approach produced vesicles with up to 15‐fold greater cargo compared with commonly used passive loading strategies. When applied to recipient cells, we observed a dose‐dependent increase in cargo activity, suggesting successful delivery of engineered cargo via our custom CHO EVs.
An explainable machine learning model to solid adnexal masses diagnosis based on clinical data and qualitative ultrasound indicators
Authors: Fanizzi Annarita , Arezzo Francesca , Cormio Gennaro , Comes Maria Colomba , Cazzato Gerardo , Boldrini Luca , Bove Samantha , Bollino Michele , Kardhashi Anila , Silvestris Erica , Quarto Pietro , Mongelli Michele , Naglieri Emanuele , Signorile Rahel , Loizzi Vera , Massafra Raffaella 
Published: June 2024

AbstractBackgroundAccurate characterization of newly diagnosed a solid adnexal lesion is a key step in defining the most appropriate therapeutic approach. Despite guidance from the International Ovarian Tumor Analyzes Panel, the evaluation of these lesions can be challenging. Recent studies have demonstrated how machine learning techniques can be applied to clinical data to solve this diagnostic problem. However, ML models can often consider as black‐boxes due to the difficulty of understanding the decision‐making process used by the algorithm to obtain a specific result.AimsFor this purpose, we propose an Explainable Artificial Intelligence model trained on clinical characteristics and qualitative ultrasound indicators to predict solid adnexal masses diagnosis.Materials & MethodsSince the diagnostic task was a three‐class problem (benign tumor, invasive cancer, or ovarian metastasis), we proposed a waterfall classification model: a first model was trained and validated to discriminate benign versus malignant, a second model was trained to distinguish nonmetastatic versus metastatic malignant lesion which occurs when a patient is predicted to be malignant by the first model. Firstly, a stepwise feature selection procedure was implemented. The classification performances were validated on Leave One Out scheme.ResultsThe accuracy of the three‐class model reaches an overall accuracy of 86.36%, and the precision per‐class of the benign, nonmetastatic malignant, and metastatic malignant classes were 86.96%, 87.27%, and 77.78%, respectively. Discussion: SHapley Additive exPlanations were performed to visually show how the machine learning model made a specific decision. For each patient, the SHAP values expressed how each characteristic contributed to the classification result. Such information represents an added value for the clinical usability of a diagnostic system.ConclusionsThis is the first work that attempts to design an explainable machine‐learning tool for the histological diagnosis of solid masses of the ovary.
Who's willing to lay on the virtual couch? Attitudes, anthropomorphism and need for human interaction as factors of intentions to use chatbots for psychotherapy
Authors: Huțul Tudor‐Daniel , Popescu Alexandru , Karner‐Huțuleac Adina , Holman Andrei Corneliu , Huțul Andreea 
Published: June 2024

AbstractBackgroundThe rising demand for psychotherapy exceeds therapist availability, often due to financial or physical proximity constraints. Artificial intelligence (AI), particularly through chatbots, presents a promising solution, having shown efficacy in enhancing mental health.AimsThis study investigated the psychotherapeutic relationship in the context of using chatbots for psychotherapy, in which the intimate and emotionally involving nature of the relationship might render the need for human interaction a strong deterrent to people's acceptance of anthropomorphised chatbots.MethodsThis study included four hundred ninety‐four participants, aged 15 to 69 years (M = 22.77; SD = 8.43); 75.1% of them were females. They completed scales that measure attitudes towards the use of chatbots in psychotherapy, AI and seeking professional psychological help, need for human interaction in psychotherapy, importance of anthropomorphic qualities and intentions to use chatbots in psychotherapy.ResultsIndividuals' engagement in using chatbots in psychotherapy is related to their attitudes towards chatbots and towards AI in general, not only to the importance that they place on chatbots having anthropomorphic traits, but also to the strength of their needs for human interaction in the psychotherapeutic relationship.ConclusionTechnology can enhance the psychotherapeutic process, but should not replace therapists entirely for people who seek psychological assistance.
Antimicrobial Potential of Chitosan Films Incorporated with Alcoholic Extract from Mimosa tenuiflora Leaves
Authors: dos Santos Arnaud Victor , Santos Rodrigo N. , Santana Aiane N. , Santos de Jesus Andreza , Torquato Suzimone de Jesus C. , Pereira Madson de G. , Santos Luis Filipe G. , Lassarote Lavall Rodrigo , Malta Marcos , dos Santos Gustavo Souza , Ferraz Caline G. , Ribeiro Paulo R. , de Souza Neta Lourdes C. 
Published: June 2024

AbstractAntimicrobial films were prepared with chitosan containing the methanolic extract of M. tenuiflora leaves (FECT20 %, FECT30 %, and FECT40 %), and their antimicrobial activities were evaluated by agar diffusion. The films were characterized by IR spectroscopy, scanning electron microscopy (SEM) and TG/DTG curves. TG/DTG curves showed thermal stability of chitosan‐extract films up to 166 °C. Micrographs of chitosan‐extract films revealed an increase in porosity with the addition of extract. The FECT40 % film showed inhibition zone diameters (IZ) against Micrococcus luteus, Staphylococcus aureus, Bacillus subtilis, and B. cereus, ranging from 1.0±0.02 to 0.72±0.09 cm. Only FECT30 % and FECT40 % inhibited the P. aeruginosa with IZs of 0.68±0.02 and 0.77±0.06 cm, respectively. In turn, the extract showed inhibition against B. subtilis and B. cereus, with IZs values of 0.92±0.2 cm and 0.72±0.05 cm, respectively. Additionally, the crude extract presented antioxidant potential with inhibition percentages of 32.74 %±0.90 for ABTS and 27.04 %±1.36 for DPPH. The antimicrobial and antioxidant activities of the crude extract, as well as the antimicrobial property of chitosan‐extract films, suggests the potential of these biopolymers for the development of wound healing bandages and new food packaging alternatives.
Successful treatment of corneal hypertrophic scar in Hurler syndrome
Authors: Koosha Nima , Irajpour Matin , Rostamiyan Zeynab , Shahsavari Ali , Forouhari Ali , Pourazizi Mohsen 
Published: June 2024

Key Clinical MessageIn Hurler syndrome, corneal opacification is a common finding but rarely manifests as hypertrophic scars. A 6‐year‐old boy with Hurler syndrome had a hypertrophic scar on his left eye, which was successfully treated with superficial keratectomy.
Expanded‐access use of elamipretide in a patient with membrane protein‐associated neurodegeneration
Authors: Patino Jorge , Clearman Anna Haertling , Miller Lindsey , Koenig Mary Kay 
Published: June 2024

Key Clinical MessageThis case report presents a progressively declining 17‐year‐old patient with membrane protein‐associated neurodegeneration who demonstrated symptomatic improvements in her dysarthria, dysphagia, and gait, and objective improvements in her 6‐minute walk test and 5 times sit‐to‐stand test during elamipretide treatment.
Small Organic Molecule‐Assisted Mesoscopic Self‐Assembly of SnO2 Nanoparticles: An Efficient Catalyst for the Synthesis of β‐Nitroaldol
Authors: Das Swapan K. , Abdul Matheen I. , Kathiravan Arunkumar , Asha Jhonsi Mariadoss , Bhaumik Asim 
Published: June 2024

AbstractOver the past few decades, huge number of mesoporous materials have been synthesized by using supramolecular assembly of ionic/non‐ionic surfactants as structure‐directing agents (SDAs). Here, we report a facile synthetic strategy to fabricate self‐assembled SnO2 nanoparticles with well‐defined nanocrystalline spherical morphology and mesoporosity using sodium salicylate as a SDA and stabilizing agent. The mesophases of the materials were investigated by powder X‐ray diffraction, TEM and N2 sorption studies. TEM results show that the mesopores are formed by the assembly of ultrasmall SnO2 NPs with broad range of interparticle voids and N2 sorption studies agreed well with this TEM data. This synthesis strategy facilitated the generation of SnO2 NPs with increased surface area and pores of nanoscale dimensions with a large number of exposed surface‐active sites. The photoinduced electron transfer in these materials was investigated by the fluorescence quenching study with pyranine, which suggested the presence of surface Lewis basicity. The materials showed excellent catalytic activity for the synthesis of β‐nitroaldol through the reaction of benzaldehyde and nitromethane. The heterogeneous nature of the catalyst could be attributed to the high surface area, presence of numerous surface active sites, and structural robustness of the self‐assembled SnO2 nanostructure. The catalysts exhibited negligible loss in activity after several catalytic cycles.
Developing an asynchronous LGBTQ+ affirmative counseling training: A mixed‐methods study
Authors: Pope Amber L. , St. Germain‐Sehr Noelle , Augustine Bianca R. , St. Germain‐Sehr Amanda , Lexumé Tai , Moe Jeff , Snowden‐Gregg Senttra , Jackson Tamika N. 
Published: June 2024

AbstractMaster's level counseling students completed a 5‐week online asynchronous LGBTQ+ affirmative counseling training. Using a mixed‐methods and quasi‐experimental design, results indicated that participants’ LGBTQ+ knowledge, clinical skills, and advocacy increased posttraining. Content analysis revealed four themes of how students experienced the training. Implications, limitations, and future directions are discussed.
The Excited‐State N−H Tautomerization Rate in Free‐Base Corroles
Authors: Schlachter Adrien , Karsenti Paul‐Ludovic , Harvey Pierre D. , Langlois Adam 
Published: June 2024

AbstractCorrole is a tetrapyrrolic dye with a structure that resembles porphyrin, apart from a single missing carbon. The absence of this carbon results in the re‐arrangement of the double bonds within the macrocycle, and the presence of three pyrrolic protons in the central cavity in its free‐base form. These protons lead to the existence of two distinct tautomeric structures that exist in a dynamic equilibrium. Although the ground‐state energies of the tautomers are similar, the excited states show a significant difference in energy which unbalances the equilibrium between the tautomers and results in rapid excited‐state tautomerization, favouring one tautomeric species over the other. Although the excited‐state tautomerization process has been known for a long time, very few studies have been performed on it, leaving many key aspects of the process poorly understood. Herein we show how ultrafast photoluminescence can be used to experimentally determine the rates of excited‐state tautomerization and activation energies of three free‐base corrole derivatives thus allowing us to completely describe the excited‐state dynamics of the unusual excited state of free‐base corrole and opening the door to the development of new materials that can exploit its unique characteristics.
All‐nonlinear static‐dynamic neural networks versus Bayesian machine learning for data‐driven modelling of chemical processes
Authors: Mukherjee Angan , Adeyemo Samuel , Bhattacharyya Debangsu 
Published: June 2024

AbstractIn recent decades, the utilization of machine learning (ML) and artificial intelligence (AI) approaches have been explored for process modelling applications. However, different types of ML models may have contrasting advantages and disadvantages, which become critical during the optimal selection of a specific data‐driven model for a particular application as well as estimation of parameters during model training. This paper compares and contrasts two different types of data‐driven modelling approaches, namely the series/parallel all‐nonlinear static‐dynamic neural network models and models from a Bayesian ML approach. Both types of AI modelling approaches considered in this work have shown to significantly outperform several state‐of‐the‐art steady‐state and dynamic data‐driven modelling techniques for various performance measures, specifically, model sparsity, predictive capabilities, and computational expense. The performances of the proposed model structures and algorithms have been evaluated for two nonlinear dynamic chemical engineering systems—a plug‐flow reactor for vapour phase cracking of acetone for production of acetic anhydride and a pilot‐plant for post‐combustion CO2 capture using monoethanolamine as the solvent. For the validation data from the CO2 capture pilot plant, root mean squared error (RMSE) for flue gas outlet temperature, flowrate and CO2 concentration is 0.05%, 1.07%, and 5.0%, respectively, for the all‐nonlinear static‐dynamic neural networks and 0.1%, 1.75%, and 14.14%, respectively, for the Bayesian ML models. For the plug flow reactor data, the Bayesian ML models yield superior RMSE compared to the all‐nonlinear static‐dynamic neural networks when the measurement data are corrupted with Gaussian, auto‐correlated, or cross‐correlated noise.
Low Energy (<10 eV) Electron Collision with Benzonitrile‐CCl4 Admixture: A Combined Theoretical and Experimental Study
Authors: Abdoul‐Carime H. , Thiam Guillaume , Rabilloud Franck 
Published: June 2024

AbstractBenzonitrile (BZN) and carbon tetrachloride (CCl4) are versatile solvents used as a precursor for the synthesis of many products. As multi‐usage molecules, these compounds may be involved in sustainable chemistry processes such as the cold plasma techniques for which the generated electrons are known to be responsible for reactions. Therefore, it is desirable to explore the interaction of low energy electrons with the co‐compounds in the gas phase. The production of chlorine and cyanine anions, initiated by the electron collision with CCl4 and BZN, respectively, undergo nucleophilic substitution SN2 reaction with the precursors molecules for the synthesis of chlorobenzene and tricholoacetonitrile. The mechanism of fragmentation of benzonitrile and the synthesis reactions are rationalized by DFT calculations. The yield of the cyanine anion produced from the ion reaction increases with the temperature of the admixture gas, probed in the 25–100 °C temperature range. The present work may contribute to a potential process for the production of chlorobenzene for instance via (cold) plasma techniques.