bluuebunny/crossref_metadata_2025_split huggingface dataset:
Physiological and behavioral variation by urbanization and climate in an urban-tolerant toad
Authors: Monroe Dillon J. , Bókony Veronika , Gabor Caitlin R. 
Published: October 2024

The distribution of a species is best understood by examining the organism-environment interaction. Climate and anthropogenic habitat degradation, including urbanization, are salient features of the environment that can limit species distributions, especially for ectotherms. Comparative studies of the capacity of individuals to cope with rapid environmental change can help us understand the future success or failure of local populations or even the species. Studies of the glucocorticoid stress response are commonly used to understand how species cope with environmental stressors. Glucocorticoids modulate many aspects of physiological homeostasis including changes in energetic allocation and behavior. In a time of global amphibian decline the Gulf Coast Toad (Incilius nebulifer) is increasing its distribution and abundance. To understand how this species deals with thermal and urban stressors, we studied glucocorticoid regulation, hop performance, and lipid storage in I. nebulifer juveniles across nine populations that differed in average annual temperature and level of imperviousness (as an indication of urbanization). We measured corticosterone release rates at baseline, during agitation stress, and during recovery; then measured locomotor performance and whole-body lipids. We tested if I. nebulifer in hotter temperatures and more urbanized habitats exhibits elevated baseline corticosterone levels and either a reduced corticosterone stress response (“stress resistance” hypothesis) or quick post-stress recovery by negative feedback (“on again, off again” hypothesis). We also tested whether they exhibit reduced fat stores and decreased locomotor performance as costs of dealing with thermal and urban stressors. We found that I. nebulifer showed elevated baseline and agitation (stressed-induced) corticosterone release rates, and higher lipid storage with increasing urbanization. Climate had quadratic effects on these traits, such that populations living at the lowest and highest temperatures had the lowest corticosterone release rates and lipid stores, and the highest hop performance was observed in the least urbanized site at the warmest climate. Additionally, the rate of glucocorticoid recovery after agitation (negative feedback) decreased with increasing temperature and increased with increasing urbanization. These results indicate that I. nebulifer follows the “on again, off again” hypothesis in an adaptive pattern, which may help them cope with environmental change in terms of urbanization and climatic differences.
VP-net: an end-to-end deep learning network for elastic wave velocity prediction in human skin in vivo using optical coherence elastography
Authors: Zhang Yilong , Liao Jinpeng , Feng Zhengshuyi , Yang Wenyue , Perelli Alessandro , Wang Zhiqiong , Li Chunhui , Huang Zhihong 
Published: October 2024

IntroductionAcne vulgaris, one of the most common skin conditions, affects up to 85% of late adolescents, currently no universally accepted assessment system. The biomechanical properties of skin provide valuable information for the assessment and management of skin conditions. Wave-based optical coherence elastography (OCE) quantitatively assesses these properties of tissues by analyzing induced elastic wave velocities. However, velocity estimation methods require significant expertise and lengthy image processing times, limiting the clinical translation of OCE technology. Recent advances in machine learning offer promising solutions to simplify velocity estimation process.MethodsIn this study, we proposed a novel end-to-end deep-learning model, named velocity prediction network (VP-Net), aiming to accurately predict elastic wave velocity from raw OCE data of in vivo healthy and abnormal human skin. A total of 16,424 raw phase slices from 1% to 5% agar-based tissue-mimicking phantoms, 28,270 slices from in vivo human skin sites including the palm, forearm, back of the hand from 16 participants, and 580 slices of facial closed comedones were acquired to train, validate, and test VP-Net.ResultsVP-Net demonstrated highly accurate velocity prediction performance compared to other deep-learning-based methods, as evidenced by small evaluation metrics. Furthermore, VP-Net exhibited low model complexity and parameter requirements, enabling end-to-end velocity prediction from a single raw phase slice in 1.32 ms, enhancing processing speed by a factor of ∼100 compared to a conventional wave velocity estimation method. Additionally, we employed gradient-weighted class activation maps to showcase VP-Net’s proficiency in discerning wave propagation patterns from raw phase slices. VP-Net predicted wave velocities that were consistent with the ground truth velocities in agar phantom, two age groups (20s and 30s) of multiple human skin sites and closed comedones datasets.DiscussionThis study indicates that VP-Net could rapidly and accurately predict elastic wave velocities related to biomechanical properties of in vivo healthy and abnormal skin, offering potential clinical applications in characterizing skin aging, as well as assessing and managing the treatment of acne vulgaris.
Biocatalytic degradation of environmental endocrine disruptor chlorobenzene via surfactant-optimized laccase-mediator system
Authors: Wang Dan , Huang Guifang , Yu Chunming , Wang Yawen , Baek Nawon , Zhu Ruofei 
Published: October 2024

The emergence of environmental endocrine disruptor chlorobenzene (CB) in surface water and its potential environmental impacts have attracted serious global attention. It is still very difficult to achieve effective degradation of it by catalytic oxidation process under mild conditions. Here, an optimized method for degrading CB in aqueous solution using Trametes versicolor laccase and surfactant-assisted laccase-mediator (SALM) system was investigated. The use of a Tween 80 surfactant enhanced the solubility of CB and promoted its efficient degradation. Under favorable conditions, the SALM system yielded a degradation efficiency of 43.5% and a dechlorination efficiency of 41.55% for CB (25 mg/L) within 24 h. The possible degradation pathway of CB by this system was speculated by detecting the intermediates produced during the reaction. The outcome of the proliferation assays on MCF-7 human breast cancer cells demonstrated a reduction in the estrogenic activity of the CB solution following treatment with the SALM system. Furthermore, the influence of the quantity and positional variation of chlorine substituents on the degradation process was methodically investigated. Moreover, molecular analyses were employed to study the detailed interaction mechanism between laccase and CB, which revealed that the hydrophobic interaction contributed dominantly to binding process. These findings provide an efficient and environmentally friendly degradation system for the development of purification strategies for halogenated pollutants.
Evaluation of four machine learning methods in predicting orthodontic extraction decision from clinical examination data and analysis of feature contribution
Authors: Huang Jialiang , Chan Ian-Tong , Wang Zhixian , Ding Xiaoyi , Jin Ying , Yang Congchong , Pan Yichen 
Published: October 2024

IntroductionThe study aims to predict tooth extraction decision based on four machine learning methods and analyze the feature contribution, so as to shed light on the important basis for experts of tooth extraction planning, providing reference for orthodontic treatment planning.MethodsThis study collected clinical information of 192 patients with malocclusion diagnosis and treatment plans. This study used four machine learning strategies, including decision tree, random forest, support vector machine (SVM) and multilayer perceptron (MLP) to predict orthodontic extraction decisions on clinical examination data acquired during initial consultant containing Angle classification, skeletal classification, maxillary and mandibular crowding, overjet, overbite, upper and lower incisor inclination, vertical growth pattern, lateral facial profile. Among them, 30% of the samples were randomly selected as testing sets. We used five-fold cross-validation to evaluate the generalization performance of the model and avoid over-fitting. The accuracy of the four models was calculated for the training set and cross-validation set. The confusion matrix was plotted for the testing set, and 6 indicators were calculated to evaluate the performance of the model. For the decision tree and random forest models, we observed the feature contribution.ResultsThe accuracy of the four models in the training set ranges from 82% to 90%, and in the cross-validation set, the decision tree and random forest had higher accuracy. In the confusion matrix analysis, decision tree tops the four models with highest accuracy, specificity, precision and F1-score and the other three models tended to classify too many samples as extraction cases. In the feature contribution analysis, crowding, lateral facial profile, and lower incisor inclination ranked at the top in the decision tree model.ConclusionAmong the machine learning models that only use clinical data for tooth extraction prediction, decision tree has the best overall performance. For tooth extraction decisions, specifically, crowding, lateral facial profile, and lower incisor inclination have the greatest contribution.
Development of a transformer-based deep learning algorithm for diabetic peripheral neuropathy classification using corneal confocal microscopy images
Authors: Chen Wenqu , Liao Danling , Deng Yuyang , Hu Jianzhang 
Published: October 2024

BackgroundDiabetic peripheral neuropathy (DPN) is common and can go unnoticed until it is firmly developed. This study aims to establish a transformer-based deep learning algorithm (DLA) to classify corneal confocal microscopy (CCM) images, identifying DPN in diabetic patients.MethodsOur classification model differs from traditional convolutional neural networks (CNNs) using a Swin transformer network with a hierarchical architecture backbone. Participants included those with (DPN+, n = 57) or without (DPN−, n = 37) DPN as determined by the updated Toronto consensus criteria. The CCM image dataset (consisting of 570 DPN+ and 370 DPN− images, with five images selected from each participant’s left and right eyes) was randomly divided into training, validation, and test subsets at a 7:1:2 ratio, considering individual participants. The effectiveness of the algorithm was assessed using diagnostic accuracy measures, such as sensitivity, specificity, and accuracy, in conjunction with Grad-CAM visualization techniques to interpret the model’s decisions.ResultsIn the DPN + group (n = 12), the transformer model successfully predicted all participants, while in the DPN− group (n = 7), one participant was misclassified as DPN+, with an area under the curve (AUC) of 0.9405 (95% CI 0.8166, 1.0000). Among the DPN + images (n = 120), 117 were correctly classified, and among the DPN− images (n = 70), 49 were correctly classified, with an AUC of 0.8996 (95% CI 0.8502, 0.9491). For single-image predictions, the transformer model achieved a superior AUC relative to the ResNet50 model (0.8761, 95% CI 0.8155, 0.9366), the Inception_v3 model (0.8802, 95% CI 0.8231, 0.9374), and the DenseNet121 model (0.8965, 95% CI 0.8438, 0.9491).ConclusionTransformer-based networks outperform CNN-based networks in rapid binary DPN classification. Transformer-based DLAs have clinical DPN screening potential.
The impact of physician communication skills on glycemic control in patients with diabetes mellitus
Authors: Alotaibi Faisal Nahes , Aburisheh Khaled Hani , Alrasheed Abdulelah Ibrahim , Alazmi Alwaleed Khalaf , Alamri Khalid Dhaifallah , Alotaibi Nawaf Nahes , Alotaibi Anas Nahes , Alhoqail Abdulaziz Abdulkarem 
Published: October 2024

ObjectivesThe purpose of the study was to evaluate the impact of physicians’ communication skills on glycemic control in type I and II diabetes patients.MethodThis cross-sectional study was conducted at the diabetic center at King Saud University Medical City, Riyadh, Saudi Arabia. The Arabic version of the communication assessment tool was used to evaluate the patient’s perception of the physicians’ communication skills.ResultsA total of 348 patients were invited to participate in the study in which 332 filled the questionnaire completely and hence participation rate was 95.4%. The average age of the patients was 44.2 (±19.7) years with an almost equal gender ratio. The percentage of excellent ratings for most of the questions was varying between 70 and 80%. The overall percentage of excellent ratings was 75.3%. Glycated hemoglobin and other metabolic features except for diastolic blood pressure did not have any significant association with the overall mean communication assessment tool score and the percentage of excellent rating.ConclusionStudy findings provided high patient satisfaction with their physicians’ communication skills. However, diabetes and metabolic control were not affected by this satisfaction. Hence, improvement in the communication skills of the physicians is recommended.
Machine learning-based prediction of mortality in acute myocardial infarction with cardiogenic shock
Authors: Zhang Qitian , Xu Lizhen , Xie Zhiyi , He Weibin , Huang Xiaohong 
Published: October 2024

BackgroundIn the ICU, patients with acute myocardial infarction and cardiogenic shock (AMI-CS) often face high mortality rates, making timely and precise mortality risk prediction crucial for clinical decision-making. Despite existing models, machine learning algorithms hold the potential for improved predictive accuracy.MethodsIn this study, a predictive model was developed using the MIMIC-IV database, with external validation performed on the eICU-CRD database. We included ICU patients diagnosed with AMI-CS. Feature selection was conducted using the Boruta algorithm, followed by the construction and comparison of four machine learning models: Logistic Regression (LR), eXtreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Gaussian Naive Bayes (GNB). Model performance was evaluated based on metrics such as AUC (Area Under the Curve), accuracy, sensitivity, specificity, and so on. The SHAP method was employed to visualize and interpret the importance of model features. Finally, we constructed an online prediction model and conducted external validation in the eICU-CRD database.ResultsIn this study, a total of 570 and 391 patients with AMI-CS were included from the MIMIC-IV and eICU-CRD databases, respectively. Among all machine learning algorithms evaluated, LR exhibited the best performance with a validation set AUC of 0.841(XGBoost: 0.835, AdaBoost: 0.839, GNB: 0.826). The model incorporated five variables: prothrombin time, blood urea nitrogen, age, beta-blockers and Angiotensin-Converting Enzyme Inhibitors or Angiotensin II Receptor Blockers. SHAP plots are employed to visualize the importance of model features and to interpret the results. An online prediction tool was developed, externally validated with the eICU-CRD database, achieving an AUC of 0.755.ConclusionEmploying the LR algorithm, we developed a predictive model for assessing the mortality risk among AMI-CS patients in the ICU setting. Through model predictions, this facilitates early detection of high-risk individuals, ensures judicious allocation of healthcare resources.
Imminent risk of LVEF decline in asymptomatic patients with primary mitral regurgitation
Authors: Zheng Jingyi , Huang Shao-wei , Ahmed Mustafa I. , Pat Betty , Lloyd Steven G. , Sharifov Oleg F. , Denney Thomas S. , Dell’Italia Louis J. 
Published: October 2024

Background2020 American College of Cardiology/American Heart Association (ACC/AHA) Guidelines state that the ideal time for mitral valve surgery in primary mitral regurgitation (PMR) is when the LV approaches but has not yet reached echocardiographic LV ejection fraction (EF) &lt; 60% or LV end-systolic dimension (ESD) &gt; 40 mm. However, it is difficult to know the imminent risk of crossing this threshold when the surgical outcome is less optimal.ObjectiveUsing machine learning and statistical models, we have shown that cardiac magnetic resonance (CMR) LV sphericity index (SI) and LV mid circumferential strain rate (SRcirc) added to LVEF and LVESD predict LVEF &lt; 50% after mitral valve surgery. Here we test the hypothesis that these CMR features predict LVEF &lt; 60% in asymptomatic PMR patients at 18 months.Methods33 asymptomatic PMR patients with moderate to severe mitral regurgitation had CMR with tissue tagging at baseline and every 6 months for 18 months. Two types of models were employed to predict LVEF &lt; 60% at 18 months: a model using CMR features at a single time point (e.g., baseline) and a model utilizing repeated measurements over time.ResultsCMR LVEF decreased below 60% in 13 patients over 18 months. LVEF varied over time with an inverse relation to mean arterial pressure and mean end-systolic wall stress. Random Forest models utilizing LV SI, LV mid SRcirc, LVESD, and LVEF at a single time point (baseline) had a predictive accuracy of 64%. LV SI, LV mid SRcirc, LVESD and LVEF at baseline, 6, and 12 months achieved a higher predictive accuracy of 79%, improved sensitivity from 57% to 85% than baseline alone and identified a threshold of CMR LVEF 63%–64% signaling LVEF &lt; 60%.ConclusionThe variability of LVEF due to blood pressure dependence may require a longitudinal study that incorporates LVEF, LVESD, SRcirc at multiple time points to identify the threshold at which LVEF is at risk for decline to less than 60%.
Outcomes of HeartMate 3 in pediatric patients with end-stage heart failure: a single-center preliminary experience from Turkey
Authors: Doğan Eser , Ulger Tutar Zulal , Tuncer Osman Nuri , Levent Reşit E. , Engin Çağatay , Yağdı Tahir , Atay Yüksel , Özbaran Mustafa 
Published: October 2024

ObjectivesWe aim to evaluate our initial experience with the HeartMate 3 (HM3) device (Abbott, USA) for palliating pediatric patients with end−stage heart failure (ESHF).MethodsWe conducted a retrospective review of clinical data from pediatric patients (aged 7–18 years) who underwent HM3 implantation for ESHF at our institution between 2022 and 2024. Patient demographics and follow−up data were comprehensively analyzed.ResultsWe identified 11 patients (45% males) with a median age of 14 years (IQR 11–17), a median weight of 47 kg (IQR 28–50), a median height of 159 cm (IQR 135–165), and a median body surface area of 1.36 m2 (IQR 1.07–1.53) at the time of the intervention. All patients were diagnosed with dilated cardiomyopathy and categorized with PEDIMACS profiles ranging from one to three. The median ICU stay was 14 days (IQR 6–32), with 11 patients receiving inotropic support for a median of four postoperative days (IQR 3–8). The median follow−up period was 150 days (IQR 90–210). Early complications included two cases of pleural effusion, 1 case of cardiac tamponade, 3 cases of polyuria, and one instance of positive blood cultures. One patient, who was non−compliant with warfarin therapy, developed a thrombus in the right atrium that was resolved with a revision of anticoagulant therapy, and did not experience pump thrombosis. During follow−up, one patient died after 28 days from sepsis, one underwent heart transplantation after 10 days, and nine patients remained alive on the device. Notably, there were no reported cases of pump thrombosis, ischemia, or stroke post− implantation.ConclusionsThe HM3 device appears to be a safe and effective palliative option for pediatric patients with ESHF.
The impact of inflammatory and oxidative stress biomarkers on the sympathetic nervous system in severe coronary atherosclerosis
Authors: Boieriu Alexandra Maria , Dumitrel Luca Cezar , Neculoiu Carmen Daniela , Ţînţ Diana 
Published: October 2024

ObjectiveThe present study aimed at evaluating the association between sympathetic nervous system activation (SNS) and the severity of coronary artery disease (CAD). In addition, we tested the hypothesis that inflammation and oxidative stress influence the SNS activation.MethodsAdult patients with severe CAD scheduled for coronary artery bypass graft (CABG) surgery were enrolled. SYNTAX I score was calculated based on coronary angiography. Systemic activation of the SNS was estimated through circulating levels of norepinephrine (NE). Plasma levels of pro-inflammatory cytokines (IL 1β, IL 6 and HIF 1α) and oxidative stress molecules (SOD-1 and LOX-1) were obtained prior to surgery.ResultsCirculating NE levels were significantly correlated with the severity of CAD, as assessed by the SYNTAX I score (p 0.002; r 0.329). Elevated levels of circulating pro-inflammatory markers were significantly correlated with increased NE concentrations (for IL-1β: p &lt; 0.001, r = 0.49; for IL-6 and NE: p = 0.003, r = 0.32; for HIF-1α and NE: p = 0.049, r = 0.21). Additionally, oxidative stress molecules were associated with circulating NE levels (for SOD-1 and NE: p = 0.016, r = 0.26; for LOX-1 and NE: p = 0.004, r = 0.31).ConclusionIn patients with CAD referred for CABG, SNS activation, indicated by plasma NE levels, was correlated with disease severity as assessed by the SYNTAX I score, as well as with markers of inflammation and oxidative stress. This suggests that inflammation, oxidative stress, and SNS activation form an interconnected network, with each component influencing the others. It might be of interest to develop a scoring system including inflammation and oxidative stress markers to identify patients that require a more aggressive approach to lower inflammation, oxidative stress and modulate the sympathetic nervous system. This could be of use especially in the setting of a scheduled intervention -such as CABG surgery.
Efficacy and safety of anakinra in radiation-induced acute pericarditis: a case report
Authors: Sicignano Ludovico Luca , Murace Celeste Ambra , Palazzo Antonella , Verrecchia Elena , Massaro Maria Grazia , Manna Raffaele , Gerardino Laura 
Published: October 2024

BackgroundAcute pericarditis represents an inflammatory disease affecting the pericardial layers. In developed countries more than 80% of pericarditis are defined as idiopathic, less frequently they are secondary to other conditions such as infections, rheumatic or systemic inflammatory diseases, cancer, post–cardiac injury syndromes and radiation therapy (RT).Case presentationWe reported a case of a patient with acute pericarditis that occurred a few hours after chest radiation therapy, performed for breast cancer. After the failure of first-line therapy, we introduced anakinra, an anti- interleukin-1 (IL-1) agent, observing an immediate clinical and instrumental response.ConclusionsOur experience highlights the efficacy and safety profile of anakinra in early stages of acute pericarditis with an inflammatory phenotype. To the best of our knowledge, there is no data supporting the use of anakinra in the treatment of RT induced acute pericarditis.