bluuebunny/crossref_metadata_2025_split huggingface dataset:
Serum angiopoietin-2: a promising biomarker for early diabetic kidney disease in children and adolescents with type 1 diabetes
Authors: Salem Nanees Abdel-Badie , Ismail Wafaa M. , Hendawy Shimaa R. , Abdelrahman Ashraf M. , El-Refaey Ahmed M. 
Published: June 2024

AbstractAlbuminuria has been considered the golden standard biomarker for diabetic kidney disease (DKD), but appears once significant kidney damage has already occurred. Angiopoietin-2 (Angpt-2) has been implicated in the development and progression of DKD in adults. We aimed to explore the association of serum Angpt-2 levels with DKD in children and adolescents with type 1 diabetes mellitus (T1DM) of short duration (3–5 years) and to evaluate the predictive power of serum Angpt-2 in the early detection of DKD prior to the microalbuminuric phase. The current cross-sectional study included 90 children divided into three age and sex-matched groups based on urinary albumin-to-creatinine ratio (UACR): microalbuminuric diabetic group (n = 30), non-albuminuric diabetic group (n = 30), and control group (n = 30). All participants were subjected to anthropometric measurements, serum Angpt-2 and fasting lipid profile (total cholesterol, triglycerides, LDL-C, HDL-C, and Non-HDL-C) assessment. Glomerular filtration rate was estimated based on serum creatinine (eGFR-Cr). Higher serum Angpt-2 levels were detected in both diabetic groups compared to controls and in microalbuminuric compared to non-albuminuric diabetic group. There was no detected significant difference in eGFR-Cr values across the study groups. Serum Angpt-2 was positively correlated with triglycerides, LDL, Non-HDL-C, HbA1c, and UACR, while UACR, HbA1c, and Non-HDL-C were independent predictors for serum Angpt-2. Serum Angpt-2 at level of 137.4 ng/L could discriminate between microalbuminuric and non-albuminuric diabetic groups with AUC = 0.960 and at level of 115.95 ng/L could discriminate between the non-albuminuric diabetic group and controls with AUC = 0.976.Conclusion: Serum Angpt-2 is a promising potent biomarker for the detection of early stage of DKD in childhood T1DM before albuminuria emerges.

What is Known?• Urine albumin-to-creatinine ratio (UACR) and glomerular filtration rate (GFR) are the golden standard but late biomarkers for DKD.• Angiopoietin-2 has been implicated in the development and progression of DKD in adults with diabetes, but has not been explored in T1DM children with DKD.

What is New?• Higher serum angiopoietin-2 was detected in diabetic groups compared to controls and in microalbuminuric compared to non-albuminuric group.• Angiopoietin-2 correlated positively with triglycerides, LDL, Non-HDL-C, HbA1c, and UACR.• Serum angiopoietin-2 is a promising early diagnostic biomarker for DKD in children with T1DM.
Physics-informed data-driven discovery of constitutive models with application to strain-rate-sensitive soft materials
Authors: Upadhyay Kshitiz , Fuhg Jan N. , Bouklas Nikolaos , Ramesh K. T. 
Published: June 2024

AbstractA novel data-driven constitutive modeling approach is proposed, which combines the physics-informed nature of modeling based on continuum thermodynamics with the benefits of machine learning. This approach is demonstrated on strain-rate-sensitive soft materials. This model is based on the viscous dissipation-based visco-hyperelasticity framework where the total stress is decomposed into volumetric, isochoric hyperelastic, and isochoric viscous overstress contributions. It is shown that each of these stress components can be written as linear combinations of the components of an irreducible integrity basis. Three Gaussian process regression-based surrogate models are trained (one per stress component) between principal invariants of strain and strain rate tensors and the corresponding coefficients of the integrity basis components. It is demonstrated that this type of model construction enforces key physics-based constraints on the predicted responses: the second law of thermodynamics, the principles of local action and determinism, objectivity, the balance of angular momentum, an assumed reference state, isotropy, and limited memory. The three surrogate models that constitute our constitutive model are evaluated by training them on small-size numerically generated data sets corresponding to a single deformation mode and then analyzing their predictions over a much wider testing regime comprising multiple deformation modes. Our physics-informed data-driven constitutive model predictions are compared with the corresponding predictions of classical continuum thermodynamics-based and purely data-driven models. It is shown that our surrogate models can reasonably capture the stress–strain-strain rate responses in both training and testing regimes and improve prediction accuracy, generalizability to multiple deformation modes, and compatibility with limited data.
The effect of early remdesivir administration in COVID-19 disease progression in hospitalised patients
Authors: Platzer Moritz , Totschnig David , Karolyi Mario , Clodi-Seitz Tamara , Wenisch Christoph , Zoufaly Alexander 
Published: June 2024

Summary
Background
Antiviral drugs have become crucial in managing COVID-19, reducing complications and mortality. Remdesivir has emerged as an effective therapeutic drug for hospitalized patients at risk of disease progression, especially when alternative treatments are infeasible. While the recommended treatment duration of remdesivir extends up to 7 days post-symptom onset, this study examines how early remdesivir administration impacts clinical outcomes.

Methods
We conducted a retrospective analysis using clinical data from consecutively PCR confirmed SARS-CoV‑2 adult patients (≥ 18 years) who received remdesivir during their hospitalization at the department of infectious diseases, Klinik Favoriten in Vienna. The data covered the period from July 1, 2021, to April 31, 2022. Patients were divided into two groups based on the timing of remdesivir administration: an early group (0–3 days since symptom onset) and a late group (≥ 4 days since symptom onset). The primary outcome was in-hospital disease progression, assessed using the WHO COVID-19 Clinical Progression Scale (≥ 1 point increase). Multivariable logistic regression, adjusted for age, sex, SARS-CoV‑2 variant, and COVID-19 vaccination status, was used to assess clinical outcomes.

Results
In total 219 patients were included of whom 148 (67.6%) were in the early group and 71 (32.4%) were in the late group. The average age was 66.5 (SD: 18.0) years, 68.9% of the patients were vaccinated, and 72.6% had the Omicron virus variant. Late remdesivir administration was associated with a significantly higher probability of needing high-flow oxygen therapy (OR 2.52, 95% CI 1.40–4.52, p = 0.002) and ICU admission (OR 4.34, 95% CI 1.38–13.67, p = 0.012) after adjusting for confounders. In the late group there was a trend towards a higher risk of clinical worsening (OR 2.13, 95% CI 0.98–4.64, p = 0.056) and need for any oxygen therapy (OR 1.85, 95% CI 0.94–3.64, p = 0.074).

Conclusion
Compared to patients who received remdesivir within the first 3 days after symptom onset, administering remdesivir after day 3 in hospitalized COVID-19 patients is associated with higher risk for complications, such as the need for high-flow oxygen therapy and ICU admission.
This or not that: select and reject control of relational responding in rats using a blank comparison procedure with odor stimuli
Authors: Wolff Bobbie Faith , Galizio Mark , Bruce Katherine 
Published: June 2024

Abstract
The blank comparison (BLC) task was developed to assess stimulus relations in discrimination learning; that is, are subjects learning to “select” the correct stimulus (S+) or “reject” the incorrect stimulus (S-) or both? This task has been used to study exclusion learning, mostly in humans and monkeys, and the present study extends the procedure to rats. The BLC task uses an ambiguous stimulus (BLC+/-) that replaces S+ (in the presence of S-) and replaces S- (in the presence of S+). In the current experiment, four rats were trained to remove session-novel scented lids from sand-filled cups in a two-choice, simultaneous presentation procedure called the Odor Span Task (OST) before being trained on the BLC procedure using odors as the discriminative stimuli. The BLC training procedure utilized simple discrimination training (S+ and S-) and added select (S+ and BLC-) and reject (BLC+ and S-) trial types. All rats demonstrated accurate performance in sessions with both select and reject type trials. Next, BLC probe trials were interspersed in standard OST sessions to assess the form of stimulus control in the OST. Rats performed accurately on select type probe trials (similar to baseline OST performance) and also showed above chance accuracy on reject type trials. Thus, we demonstrated that rats could acquire an odor-based version of the BLC task and that both select and exclusion-based (reject) relations were active in the OST. The finding of exclusion in rats under the rigorous BLC task conditions confirms that exclusion-based responding is not limited to humans and non-human primates.
The False Recognition Test, a new tool for the assessment of false memories, with normative data from an Italian sample
Authors: Panico Francesco , Catalano Laura , Sagliano Laura , Trojano Luigi 
Published: June 2024

Abstract
Introduction
False memory can be defined as remembering something that did not happen. To a certain extent it is a normal phenomenon, but its occurrence seems to increase in healthy and pathological aging, possibly providing relevant clues on some clinical conditions in the spectrum of dementia. We adapted a well-established Deed-Roediger-McDermott paradigm, frequently used in experimental contexts, to devise a new neuropsychological assessment tool, the False Recognition Test (FRT), that can investigate classical facets of episodic memory performance (i.e. free recall and recognition), and assess proneness to produce semantically related and non-semantic false memories. Here we describe the FRT and provide normative data and correction grids to consider the possible effects of age, gender, and education on the FRT scores.

Method
Two-hundred and thirty-two Italian healthy individuals (99 male) aged 18–91 years, with different educational levels (from primary to university) underwent the FRT, together with validated tests for cognitive screening and episodic memory assessment and one scale for depression.

Results
Multiple linear regression analysis revealed that age and education significantly influenced performance on FRT. From the derived linear equations, we provide correction grids for the raw scores of the FRT, and equivalent scores estimated using a nonparametric method. Correlational analysis showed significant associations between FRT subscores and cognitive, executive and memory functions, and depression.

Conclusion
The FRT may constitute a useful instrument for both clinical and research purposes.
The role of transforming growth factor-β (TGF-β) in the formation of exhausted CD8 + T cells
Authors: Ma Rong , Sun Jin-Han , Wang Yan-Yang 
Published: June 2024

AbstractCD8 + T cells exert a critical role in eliminating cancers and chronic infections, and can provide long-term protective immunity. However, under the exposure of persistent antigen, CD8 + T cells can differentiate into terminally exhausted CD8 + T cells and lose the ability of immune surveillance and disease clearance. New insights into the molecular mechanisms of T-cell exhaustion suggest that it is a potential way to improve the efficacy of immunotherapy by restoring the function of exhausted CD8 + T cells. Transforming growth factor-β (TGF-β) is an important executor of immune homeostasis and tolerance, inhibiting the expansion and function of many components of the immune system. Recent studies have shown that TGF-β is one of the drivers for the development of exhausted CD8 + T cells. In this review, we summarized the role and mechanisms of TGF-β in the formation of exhausted CD8 + T cells and discussed ways to target those to ultimately enhance the efficacy of immunotherapy.
Applications of Machine Learning in Periodontology and Implantology: A Comprehensive Review
Authors: Șalgău Cristiana Adina , Morar Anca , Zgarta Andrei Daniel , Ancuța Diana-Larisa , Rădulescu Alexandros , Mitrea Ioan Liviu , Tănase Andrei Ovidiu 
Published: June 2024

AbstractMachine learning (ML) has led to significant advances in dentistry, easing the workload of professionals and improving the performance of various medical processes. The fields of periodontology and implantology can profit from these advances for tasks such as determining periodontally compromised teeth, assisting doctors in the implant planning process, determining types of implants, or predicting the occurrence of peri-implantitis. The current paper provides an overview of recent ML techniques applied in periodontology and implantology, aiming to identify popular models for different medical tasks, to assess the impact of the training data on the success of the automatic algorithms and to highlight advantages and disadvantages of various approaches. 48 original research papers, published between 2016 and 2023, were selected and divided into four classes: periodontology, implant planning, implant brands and types, and success of dental implants. These papers were analyzed in terms of aim, technical details, characteristics of training and testing data, results, and medical observations. The purpose of this paper is not to provide an exhaustive survey, but to show representative methods from recent literature that highlight the advantages and disadvantages of various approaches, as well as the potential of applying machine learning in dentistry.
“Safer to plant corn and beans”? Navigating the challenges and opportunities of agricultural diversification in the U.S. Corn Belt
Authors: Traldi Rebecca , Asprooth Lauren , M. Usher Emily , Floress Kristin , Arbuckle J. Gordon , Baskerville Megan , Church Sarah P. , Genskow Ken , Harden Seth , Maynard Elizabeth T. , Thompson Aaron William , Torres Ariana P. , Prokopy Linda S. 
Published: June 2024

AbstractAgricultural diversification in the Midwestern Corn Belt has the potential to improve socioeconomic and environmental outcomes by buffering farmers from environmental and economic shocks and improving soil, water, and air quality. However, complex barriers related to agricultural markets, individual behavior, social norms, and government policy constrain diversification in this region. This study examines farmer perspectives regarding the challenges and opportunities for both corn and soybean production and agricultural diversification strategies. We analyze data from 20 focus groups with 100 participants conducted in Indiana, Illinois, and Iowa through a combined inductive and deductive approach, drawing upon interpretive grounded theory. Our results suggest that when identifying challenges and opportunities, participants center economics and market considerations, particularly income, productivity, and market access. These themes are emphasized both as benefits of the current corn-soybean system, as well as challenges for diversification. Additionally, logistical, resource and behavioral hurdles– including the comparative difficulty and time required to diversify, and constraints in accessing land, labor, and technical support– are emphasized by participants as key barriers to diversification. Agricultural policies shape these challenges, enhancing the comparative advantage and decreasing the risk of producing corn and soybeans as compared to diversified products. Meanwhile, alternative marketing arrangements, farmer networks, family relationships, and improved soil health are highlighted as important opportunities for diversification. We contextualize our findings within the theories of reasoned action and diffusion of innovation, and explore their implications for farmer engagement, markets, and agricultural policy, and the development of additional resources for business and technical support.
Travel Burden and Timely Linkage to Care Among People Newly Diagnosed with HIV Infection in South Carolina from 2005 to 2020
Authors: Shi Fanghui , Zhang Jiajia , Hung Peiyin , Sun Xiaowen , Yang Xueying , Olatosi Bankole , Weissman Sharon , Li Xiaoming 
Published: June 2024

AbstractThis retrospective study explored the association between travel burden and timely linkage to care (LTC) among people with HIV (PWH) in South Carolina. HIV care data were derived from statewide all-payer electronic health records, and timely LTC was defined as having at least one viral load or CD4 count record within 90 days after HIV diagnosis before the year 2015 and 30 days after 2015. Travel burden was measured by average driving time (in minutes) to any healthcare facility visited within six months before and one month after the initial HIV diagnosis. Multivariable logistic regression models with the least absolute shrinkage and selection operator were employed. From 2005 to 2020, 81.2% (3,547 out of 4,366) of PWH had timely LTC. Persons who had longer driving time (adjusted Odds Ratio (aOR): 0.37, 95% CI: 0.14–0.99), were male versus female (aOR: 0.73, 95% CI: 0.58–0.91), had more comorbidities (aOR: 0.73, 95% CI: 0.57–0.94), and lived in counties with a higher percentage of unemployed labor force (aOR: 0.21, 95% CI: 0.06–0.71) were less likely to have timely LTC. However, compared to those aged between 18 and 24 years old, those aged between 45 and 59 (aOR:1.47, 95% CI: 1.14–1.90) or older than 60 (aOR:1.71, 95% CI: 1.14–2.56) were more likely to have timely LTC. Concentrated and sustained interventions targeting underserved communities and the associated travel burden among newly diagnosed PWH who are younger, male, and have more comorbidities are needed to improve LTC and reduce health disparities.
Establishment of the Diagnostic Signature of Ferroptosis Genes in Multiple Sclerosis
Authors: Yang Yang , Bai Qianqian , Liu Fangfei , Zhang Shumin , Tang Wenchao , Liu Ling , Xing Zhehua , Wang Hao , Zhang Chi , Yang Yanhui , Fan Hua 
Published: June 2024

AbstractFerroptosis is a novel form of membrane-dependent cell death that differs from other cell death modalities such as necrosis, apoptosis, and autophagy. Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system primarily affecting brain and spinal cord neurons. Although the pathogenesis of these two conditions may seem unrelated, recent studies have indicated a connection between ferroptosis and multiple sclerosis. In fact, ferroptosis plays a significant role in the development of MS, as evidenced by the presence of elevated iron levels and iron metabolism abnormalities in the brains, spinal cords, and other neurons of MS patients. These abnormalities disrupt iron homeostasis within cells, leading to the occurrence of ferroptosis. However, there is currently a lack of research on the diagnostic value of ferroptosis-related genes in multiple sclerosis. In this study, we employed bioinformatics methods to identify ferroptosis-related genes (ATM, GSK3B, HMGCR, KLF2, MAPK1, NFE2L1, NRAS, PCBP1, PIK3CA, RPL8, VDAC3) associated with the diagnosis of multiple sclerosis and constructed a diagnostic model. The results demonstrated that the diagnostic model accurately identified the patients’ condition. Subsequently, subgroup analysis was performed based on the expression levels of ferroptosis-related genes, dividing patients into high and low expression groups. The results showed differences in immune function and immune cell infiltration between the two groups. Our study not only confirms the correlation between ferroptosis and multiple sclerosis but also demonstrates the diagnostic value of ferroptosis-related genes in the disease. This provides guidance for clinical practice and direction for further mechanistic research.
Receiving Social Support Motivates Long-Term Prosocial Behavior
Authors: Trombini Chiara , Jiang Winnie , Kinias Zoe 
Published: June 2024

AbstractProsocial behavior—actions aimed to benefit other individuals, groups, or communities—are important for promoting and maintaining a healthy society. Extant research on the factors driving prosocial behavior has mainly addressed short-term effects, overlooking the factors that motivate long-term prosocial behavior. Building on attachment theory, we theorize that an interpersonal factor, receiving social support, can foster prosocial behavior in the long-term, both in the environment where the support was received and beyond it. We argue that receiving social support positively predicts felt security—a sense of being safe, cared for, and loved—which in turn associates with higher motivation to engage in behaviors that benefit others. We test our hypotheses with cross-sectional, longitudinal, retrospective, and experimental data. In Study 1, data from a sample of international business school alumni validate past research and show a significant positive relationship between receiving social support and engaging in prosocial behavior both within and beyond the environment in which support was received. Study 2 leverages data of US adults in a multi-wave study to show that receiving social support predicts prosocial activities several years later. Study 3 uses a retrospective survey to show that receiving social support relates positively to long-term prosocial behavior through higher felt security. Study 4 experimentally manipulates social support and further demonstrates that receiving social support fosters prosocial behavior through boosting felt security. Overall, our findings show that receiving social support motivates long-term prosociality through its positive association with felt security.
Energy-efficient buildings with energy-efficient optimized models: a case study on thermal bridge detection
Authors: Fişne Alparslan , Yurtsever M. Mücahit Enes , Eken Süleyman 
Published: June 2024

AbstractThermographic inspection is particularly effective in identifying thermal bridges because it visualizes temperature differences on the building’s surface. The focus of this work is on energy-efficient computing for deep learning-based thermal bridge (anomaly) detection models. In this study, we concentrate on object detection-based models such as Mask R-CNN_FPN_50, Swin-T Transformer, and FSAF. We do benchmark tests on TBRR dataset with varying input sizes. To overcome the energy-efficient design, we apply optimizations such as compression, latency reduction, and pruning to these models. After our proposed improvements, the inference of the anomaly detection model, Mask R-CNN_FPN_50 with compression technique, is approximately 7.5% faster than the original. Also, more acceleration is observed in all models with increasing input size. Another criterion we focus on is total energy gain for optimized models. Swin-T transformer has the most inference energy gains for all input sizes ($$\approx$$
≈
27 J for 3000 x 4000 and $$\approx$$
≈
14 J for 2400 x 3400). In conclusion, our study presents an optimization of size, weight, and power for vision-based anomaly detection for buildings.