bluuebunny/crossref_metadata_2025_split huggingface dataset:
Map plasticity following noise exposure in auditory cortex of rats: implications for disentangling neural correlates of tinnitus and hyperacusis
Authors: Wake Naoki , Shiramatsu Tomoyo I. , Takahashi Hirokazu 
Published: May 2024

IntroductionBoth tinnitus and hyperacusis, likely triggered by hearing loss, can be attributed to maladaptive plasticity in auditory perception. However, owing to their co-occurrence, disentangling their neural mechanisms proves difficult. We hypothesized that the neural correlates of tinnitus are associated with neural activities triggered by low-intensity tones, while hyperacusis is linked to responses to moderate- and high-intensity tones.MethodsTo test these hypotheses, we conducted behavioral and electrophysiological experiments in rats 2 to 8 days after traumatic tone exposure.ResultsIn the behavioral experiments, prepulse and gap inhibition tended to exhibit different frequency characteristics (although not reaching sufficient statistical levels), suggesting that exposure to traumatic tones led to acute symptoms of hyperacusis and tinnitus at different frequency ranges. When examining the auditory cortex at the thalamocortical recipient layer, we observed that tinnitus symptoms correlated with a disorganized tonotopic map, typically characterized by responses to low-intensity tones. Neural correlates of hyperacusis were found in the cortical recruitment function at the multi-unit activity (MUA) level, but not at the local field potential (LFP) level, in response to moderate- and high-intensity tones. This shift from LFP to MUA was associated with a loss of monotonicity, suggesting a crucial role for inhibitory synapses.DiscussionThus, in acute symptoms of traumatic tone exposure, our experiments successfully disentangled the neural correlates of tinnitus and hyperacusis at the thalamocortical recipient layer of the auditory cortex. They also suggested that tinnitus is linked to central noise, whereas hyperacusis is associated with aberrant gain control. Further interactions between animal experiments and clinical studies will offer insights into neural mechanisms, diagnosis and treatments of tinnitus and hyperacusis, specifically in terms of long-term plasticity of chronic symptoms.
Carbohydrate beliefs and practices of ultra-endurance runners in Ireland for gastrointestinal symptom management
Authors: Ryan Tansy , Daly Ed , Ryan Lisa 
Published: May 2024

PurposeThis study aimed to investigate the carbohydrate beliefs and practices of ultra-endurance runners in Ireland to gain an understanding of their gastrointestinal symptom (GIS) management strategies.MethodologyAn adapted version of a previously developed and validated questionnaire was distributed online to ultra-endurance runners, recognized as those who completed a single bout of exercise lasting 4 or more hours. The questionnaire asked about carbohydrate awareness and sourcing, and dietary practices, particularly in relation to GIS management.ResultsA total of n = 68 individuals completed the adapted questionnaire. Of these, n = 1 was excluded due to their reporting of an ulcerative colitis diagnosis. The remaining participants included 46 men and 21 women. Personal previous experience was the main source directing participants’ nutrition practices (n = 30), while only 3 participants quoted sourcing information from qualified professionals. Forty-two participants experienced GIS, usually equally around training and competition times. Many participants had not previously implemented any specific dietary or non-dietary strategies to alleviate exercise-induced GIS. Supplementing with nitrates (n = 9) and probiotics (n = 4) were the most common dietary practices to alleviate GIS, while other practices (n = 14) and portion control (n = 13) were the most reported non-dietary practices.DiscussionSimilar to previous studies, these findings suggest that GISs are prevalent in the ultra-endurance running community, occurring regardless of whether during training or an event. Similarly, this research highlights the vast range of GISs experienced by this population. However, the absence of both dietary and non-dietary-related practices used for GIS management alludes to a current deficit in the availability of nutrition information specific to this problem. Further research is required to understand the mechanisms behind ultra-endurance-associated GISs and its various management strategies as well as best practices for communicating these to the target audience to reduce individuals’ risks of developing long-term, chronic health complications.
Assessing the prognostic value of KRAS mutation combined with tumor size in stage I-II non-small cell lung cancer: a retrospective analysis
Authors: Eklund Ella A. , Mourad Ali , Wiel Clotilde , Sayin Sama I. , Fagman Henrik , Hallqvist Andreas , Sayin Volkan I. 
Published: May 2024

BackgroundKRAS mutation status is a well-established independent prognostic factor in advanced non-small cell lung cancer (NSCLC), yet its role in early-stage disease is unclear. Here, we investigate the prognostic value of combining survival data on KRAS mutation status and tumor size in stage I-II NSCLC.MethodsWe studied the combined impact of KRAS mutational status and tumor size on overall survival (OS) in patients with stage I-II NSCLC. We performed a retrospective study including 310 diagnosed patients with early (stage I-II) NSCLCs. All molecularly assessed patients diagnosed with stage I-II NSCLC between 2016–2018 in the Västra Götaland Region of western Sweden were screened in this multi-center retrospective study. The primary study outcome was overall survival.ResultsOut of 310 patients with stage I-II NSCLC, 37% harbored an activating mutation in the KRAS gene. Our study confirmed staging and tumor size as prognostic factors. However, KRAS mutational status was not found to impact OS and there was no difference in the risk of death when combining KRAS mutational status and primary tumor size.ConclusionsIn our patient cohort, KRAS mutations in combination with primary tumor size did not impact prognosis in stage I-II NSCLC.
Celastrol alleviates diabetic vascular injury via Keap1/Nrf2-mediated anti-inflammation
Authors: An Ning , Wang Rixiang , Li Lin , Wang Bingyu , Wang Huiting , Peng Ganyu , Zhou Hua , Chen Gen 
Published: May 2024

Introduction: Celastrol (Cel) is a widely used main component of Chinese herbal medicine with strong anti-inflammatory, antiviral and antitumor activities. In the present study, we aimed to elucidate the cellular molecular protective mechanism of Cel against diabetes-induced inflammation and endothelial dysfunction.Methods: Type 2 diabetes (T2DM) was induced by db/db mice, and osmotic pumps containing Cel (100 μg/kg/day) were implanted intraperitoneally and were calibrated to release the drug for 28 days. In addition, human umbilical vein endothelial cells (HUVECs) were cultured in normal or high glucose and palmitic acid-containing (HG + PA) media in the presence or absence of Cel for 48 h.Results: Cel significantly ameliorated the hyperglycemia-induced abnormalities in nuclear factor (erythroid-derived 2)-like protein 2 (Nrf2) pathway activity and alleviated HG + PA-induced oxidative damage. However, the protective effect of Cel was almost completely abolished in HUVECs transfected with short hairpin (sh)RNA targeting Nrf2, but not by nonsense shRNA. Furthermore, HG + PA reduced the phosphorylation of AMP-activated protein kinase (AMPK), the autophagic degradation of p62/Kelch-like ECH-associated protein 1 (Keap1), and the nuclear localization of Nrf2. However, these catabolic pathways were inhibited by Cel treatment in HUVECs. In addition, compound C (AMPK inhibitors) and AAV9-sh-Nrf2 reduced Cel-induced Nrf2 activation and angiogenesis in db/db mice.Discussion: Taking these findings together, the endothelial protective effect of Cel in the presence of HG + PA may be at least in part attributed to its effects to reduce reactive oxygen species (ROS) and inflammation through p62/Keap1-mediated Nrf2 activation.
Emotional sociology applied: predictive influence of affective neuroscience personality traits on Chinese preschool teachers’ performance and wellbeing
Authors: Lu Ling , Jian Lu 
Published: May 2024

BackgroundThe interplay between teaching engagement and performance has garnered attention in both theoretical and empirical research, primarily due to its influence on student academic achievement, teacher well-being, and the realization of institutional goals. This is especially pertinent in the realm of preschool education, where the scope of learning extends beyond academic content to encompass the broader socialization of children. Drawing from Affective Neuroscience research, this study investigates the role of affective tendencies as mediators in the relationship between work engagement and job performance.ObjectiveThe primary aim of this research is to examine a chain mediation model that hypothesizes the predictive role of teacher engagement. This model posits the intermediary influence of four basic emotions—CARING, SEEKING, ANGER, and FEAR—followed by the mediating effect of job satisfaction on teacher job performance.MethodThe study utilized a sample of 842 Chinese preschool teachers. Data were collected through an online questionnaire, employing a time-lagged design. The analysis was conducted using Model 80 of the PROCESS Macros.ResultsThe findings reveal that both positive and negative emotions significantly predict teachers’ job satisfaction. However, job satisfaction does not influence job performance. The analysis confirmed the direct and total effects of teacher engagement, as well as the indirect effects, particularly through the positive emotion of Caring.ImplicationsThe results are instrumental in informing and refining interventions designed to enhance teacher engagement and performance, underscoring the importance of emotional factors in the educational environment.
A meta-analysis of the intervention effect of mindfulness training on athletes’ performance
Authors: Si Xing Wei , Yang Zhen Kun , Feng Xia 
Published: May 2024

ObjectiveTo explore the intervention effect of mindfulness training on athletes’ performance using meta-analysis method.MethodsA total of 11 articles and 23 effect sizes were included through retrieval of Chinese and English databases, with a total sample size of 582.ResultMindfulness training improves the level of mindfulness [SMD =1.08, 95%CI (0.30, 1.86), p &lt; 0.01], fluency (The optimal competitive psychological state of the athlete, the athlete’s attention is all focused on the task, and other things no longer attract their attention) [SMD =1.47, 95%CI (0.87, 2.08), p &lt; 0.001] and performance [SMD =0.92, 95% CI (0.40, 1.43), p &lt; 0.01], reduced psychological anxiety [SMD = -0.87, 95% CI (−1.54, −0.20), p &lt; 0.05], and all reached the level of large effect size.ConclusionThe effect of mindfulness training on athletes’ sports performance is effective, and it can be used as an effective psychological skill intervention method to improve athletes’ sports performance. In the future, we should further expand the sample size, strengthen the comparative study of different sports and intervention modes, and pay attention to the difference between the time effect and trait mindfulness level in fluency state.
Elevated TyG index associated with increased prevalence of gallstones in a United States cross-sectional study
Authors: Feng Xueyi , Wu Shenwei , Ke Bin , Liang Yongkang 
Published: May 2024

ObjectiveThis study aimed to investigate the correlation between the triglyceride-glucose (TyG) index and the incidence of cholelithiasis.Research approachIn this investigation, a cross-sectional analysis was undertaken utilizing data from the US National Health and Nutrition Examination Survey (NHANES) spanning the years 2017 to 2020. The TyG index served as an independent predictor, while gallstone prevalence was considered the dependent variable of interest. We employed a multivariate logistic regression model to evaluate the interplay between these independent and dependent variables. To assess the presence of potential non-linear associations, sensitivity analysis was executed, utilizing inverse probability weighted validation, smooth curve fitting, and threshold effect analysis. In cases where non-linear relationships were observed, likelihood ratios were utilized to pinpoint potential inflection points. Ultimately, subgroup analyses were conducted to identify specific populations demonstrating heightened susceptibility to gallstone prevalence.ResultsEncompassing 838 patients who self-reported gallstones, a total of 7,794 participants were included in the analytical cohort. A statistically significant disparity in the TyG index was observed when all individuals were categorized into gallstone patients and non-patients (p &lt; 0.05). Logistic regression findings indicated a positive correlation between the TyG index and gallstone disease prevalence (OR = 1.28, 95% CI: 1.12, 1.47), with a strengthening association as the TyG index increased (p trend &lt;0.01). The results were corroborated by the use of inverse probability weighting. Additionally, a non-linear connection between the TyG index and gallstone prevalence was identified (log-likelihood ratio p &lt; 0.01), with the optimal inflection point for TyG calculated at 8.96. In subgroup analysis, the positive relationship between the TyG index and gallstone prevalence was notably pronounced among black Americans under the age of 40 and female participants.ConclusionAlterations in the TyG index may potentially correlate with shifts in the prevalence of gallstones among adult populations in the United States. Elevated TyG index values may coincide with an augmented likelihood of gallstone occurrence.
Middle school students’ psychological health on suicide ideation: based on latent profile analysis
Authors: Wei Meiting , Liu Jiang 
Published: May 2024

IntroductionThis study identifies potential categories of mental health for adolescents in different school years and further analyzes the relationship between these categories and suicidal ideation.MethodsA total of 1944 middle school students completed SCL-90 and Self-rating Idea of Suicide Scale on November 29, 2022, selecting via a whole-group sampling method. Latent profile analysis was used to analyze the psychological health subtypes of students from a middle school in Southwest China. The R3step method and the DU3step method were conducted to analyze the predictive role of demographic variables and the effects of different profiles on suicidal ideation.ResultsDifferent potential categories of psychological health were observed among middle school students. Junior middle school students can be classified into three types: Psychological Health Type (62.3%), Low-risk Type (27.1%) and High-risk Type (10.7%). Senior middle school students can be classified into four types: Psychological Health Type (43.3%), Low-risk Type (33.9%), Medium -risk Type (16.8%), and High-risk Type (6.0%). Gender and subjective family atmosphere are predictors of psychological health, and they also influence the population distribution of psychological health patterns in different sections of middle school students. Girls and students with poor subjective family atmosphere are more prone to experiencing psychological problems. There were significant differences in suicidal ideation among different potential categories of psychological health of different sections middle school students (χ2 = 1178.71, 1174.85, p&lt;0.001). Among senior high school students classified as High-risk Type, they exhibited the highest score for suicidal ideation.ConclusionThere is obvious group heterogeneity in psychological health of different sections middle school students. Older students are more likely to have suicidal thoughts.
Using Google web search to analyze and evaluate the application of ChatGPT in femoroacetabular impingement syndrome
Authors: Chen Yifan , Zhang Shengqun , Tang Ning , George Daniel M. , Huang Tianlong , Tang JinPing 
Published: May 2024

BackgroundChat Generative Pre-trained Transformer (ChatGPT) is a new machine learning tool that allows patients to access health information online, specifically compared to Google, the most commonly used search engine in the United States. Patients can use ChatGPT to better understand medical issues. This study compared the two search engines based on: (i) frequently asked questions (FAQs) about Femoroacetabular Impingement Syndrome (FAI), (ii) the corresponding answers to these FAQs, and (iii) the most FAQs yielding a numerical response.PurposeTo assess the suitability of ChatGPT as an online health information resource for patients by replicating their internet searches.Study designCross-sectional study.MethodsThe same keywords were used to search the 10 most common questions about FAI on both Google and ChatGPT. The responses from both search engines were recorded and analyzed.ResultsOf the 20 questions, 8 (40%) were similar. Among the 10 questions searched on Google, 7 were provided by a medical practice. For numerical questions, there was a notable difference in answers between Google and ChatGPT for 3 out of the top 5 most common questions (60%). Expert evaluation indicated that 67.5% of experts were satisfied or highly satisfied with the accuracy of ChatGPT’s descriptions of both conservative and surgical treatment options for FAI. Additionally, 62.5% of experts were satisfied or highly satisfied with the safety of the information provided. Regarding the etiology of FAI, including cam and pincer impingements, 52.5% of experts expressed satisfaction or high satisfaction with ChatGPT’s explanations. Overall, 62.5% of experts affirmed that ChatGPT could serve effectively as a reliable medical resource for initial information retrieval.ConclusionThis study confirms that ChatGPT, despite being a new tool, shows significant potential as a supplementary resource for health information on FAI. Expert evaluations commend its capacity to provide accurate and comprehensive responses, valued by medical professionals for relevance and safety. Nonetheless, continuous improvements in its medical content’s depth and precision are recommended for ongoing reliability. While ChatGPT offers a promising alternative to traditional search engines, meticulous validation is imperative before it can be fully embraced as a trusted medical resource.
Detection of forest disturbance across California using deep-learning on PlanetScope imagery
Authors: Carter Griffin , Wagner Fabien H. , Dalagnol Ricardo , Roberts Sophia , Ritz Alison L. , Saatchi Sassan 
Published: May 2024

California forests have recently experienced record breaking wildfires and tree mortality from droughts, However, there is inadequate monitoring, and limited data to inform policies and management strategies across the state. Although forest surveys and satellite observations of forest cover changes exist at medium to coarse resolutions (30–500 m) annually, they remain less effective in mapping small disturbances of forest patches (&lt;5 m) occurring multiple times a year. We introduce a novel method of tracking California forest cover using a supervised U-Net deep learning architecture and PlanetScope’s Visual dataset which provides 3-band RGB (Red, Green, and Blue) mosaicked imagery. We created labels of forest and non-forest to train the U-Net model to map tree cover based on a semi-unsupervised classification method. We then detected changes of tree cover and disturbance with the U-Net model, achieving an overall accuracy of 98.97% over training data set, and 95.5% over an independent validation dataset, obtaining a precision of 82%, and a recall of 74%. With the predicted tree cover mask, we created wall to wall monthly tree cover maps over California at 4.77 m resolution for 2020, 2021, and 2022. These maps were then aggregated in a post-processing step to develop annual maps of disturbance, while accounting for the time of disturbance and other confounding factors such as topography, phenological and snow cover variability. We compared our high-resolution disturbance maps with wildfire GIS survey data from CALFIRE, and satellite-based forest cover changes and achieved an F-1 score of 54% and 88% respectively. The results suggest that high-resolution maps capture variability of forest disturbance and fire that wildfire surveys and medium resolution satellite products cannot. From 2020 to 2021, California maintained 30,923.5 sq km of forest while 5,994.9 sq km were disturbed. The highest observed forest loss rate was located at the Sierra Nevada mountains at 21.4% of the forested area being disturbed between 2020 and 2021. Our findings highlight the strong potential of deep learning and high-resolution RGB optical imagery for mapping complex forest ecosystems and their changes across California, as well as the application of these techniques on a national to global scale.
Quantifying effects of climate change and farmers' information demand on wheat yield in India: a deep learning approach with regional clustering
Authors: Godara Samarth , Birthal Pratap S. , Avinash G. , Ahmad Faiz Mukhtar , Bana Ram Swaroop , Jhajhria Abimanyu , Parsad Rajender , Marwaha Sudeep 
Published: May 2024

IntroductionWith increasing demand for food and changing environmental conditions, a better understanding of the factors impacting wheat yield is essential for ensuring food security and sustainable agriculture. By analyzing the effect of multiple factors on wheat yield, the presented research provides novel insights into the potential impacts of climate change on wheat production in India. In the present study, datasets consisting of countrywide environmental and agronomic factors and wheat yield were collected. In addition, the study also analyzes the effect of information demand of farmers on production.MethodologyThe study employs a regional analysis approach by dividing the country into five zonal clusters: Northern Hills, Central India, Indo-Gangetic Plains, North-Eastern India, and Peninsular India. Correlation and Principal Component Analysis (PCA) were performed to uncover the month-wise key factors affecting wheat yield in each zone. Furthermore, four Machine Learning/Deep Learning-based models, including XGBoost, Multi-layer Perceptron (MLP), Gated Recurrent Unit (GRU), and 1-D Convolutional Neural Network (CNN), were developed to estimate wheat yield. This study estimated partial derivatives for all factors using Newton's Quotient Technique, a numerical method-based approach.ResultsThe analysis focused on applying this technique to the best-performing wheat yield estimation model, which was the GRU-based model (with RMSE and MAE of 0.60 t/ha and 0.46 t/ha, respectively).DiscussionIn the later sections of the article, multiple policy recommendations are communicated based on the extracted insights. The results of the presented research help inform decision-making regarding the development of strategies and policies to mitigate the impacts of climate change on wheat production in India.