bluuebunny/crossref_metadata_2025_split huggingface dataset:
(Re-)shaping learning experiences in supply chain management and logistics education under disruptive uncertain situations
Authors: Salinas-Navarro David Ernesto , Pacheco-Velazquez Ernesto , da Silva-Ovando Agatha Clarice 
Published: February 2024

IntroductionDisruptive uncertain events often necessitate universities to abruptly shift from face-to-face to remote online learning, posing challenges to instructional quality and learning outcomes. Remote online instruction may lack social interaction, educational resources, and practical activities, hindering effective learning.MethodsThis study presents an experiential learning framework designed to foster active learning experiences amidst disruptive uncertain events. The framework incorporates such events as study situations to enhance student motivation and engagement. A methodology for designing learning experiences is proposed, with a focus on incorporating real-world disruptions into educational settings.ResultsA case study is presented, detailing the implementation of the framework in supply chain management and logistics education at a private university in Bolivia, amidst the COVID-19 pandemic. Results indicate that the novel learning experience positively impacted student motivation and engagement, facilitating the achievement of learning outcomes.DiscussionThe initial implementation suggests that the novel learning experience positively impacted student motivation and engagement while achieving their intended learning outcomes. However, further instances of utilizing the proposed framework are necessary to ascertain its generalizability and effectiveness across diverse contexts and situations. Identifying similarities or invariances will help validate the applicability of this approach in different educational settings affected by disruptive uncertain events.
Tree–litter–soil system C:N:P stoichiometry and tree organ homeostasis in mixed and pure Chinese fir stands in south subtropical China
Authors: Zhang Han , Li Xian , Wang Shengqiang , Jiang Chenyang , Cui Yuhong , Fan Rongyuan , Lan Yahui , Zhang Qianchun , Ye Shaoming 
Published: February 2024

IntroductionCultivation of Chinese fir (Cunninghamia lanceolata) have alleviated timber shortages and mixed stands with Chinese fir and indigenous species represent a sustainable forestry model. Studying system nutrient balance and tree nutrient homeostasis can provide insights into the ecological advantages of Chinese fir mixed stands and guide the management of plantations.MethodsMixed Chinese fir plantations with two native broadleaf species (Michelia macclurei and Mytilaria laosensis) and pure Chinese fir stands were examined for our study. The responses in carbon (C), nitrogen (N), and phosphorus (P) distribution and their stoichiometric characterization in the tree–litter–soil system to stand changes were evaluated. In addition, the ecological stoichiometric homeostasis of leaves, branches, trunks, bark and roots was used to measure the trees’ adaptive capacity to stand changes.ResultsThe results showed that the mixed stands of Michelia macclurei and Chinese fir significantly increased soil OC, TN, and TP, and improved the carbon sequestration and nutrient storage functions of the plantations. The mixed stands improved the litter mass and C:N and C:P to different degrees. The soil N and P imbalance reduced the leaf N:P, resulting in N limitation of different trees, while the principal component analysis showed that the improvement of soil TN in the mixed plantation alleviated the N limitation. In addition, mixed stands reduced N, P, and N:P homeostasis in branch, trunk, and bark of some Chinese fir trees, whereas mixed species showed flexibility in leaf N:P homeostasis.DiscussionTherefore, the selection of mixed species for mixed forests is a critical factor to consider when creating mixed plantations. These results contribute to our understanding of the ecological stoichiometry of fir plantations and are of considerable importance for the sustainable development of plantations as well as for the response to global climate change.
Causal relationship between gut microbiota and risk of gastroesophageal reflux disease: a genetic correlation and bidirectional Mendelian randomization study
Authors: Wang Kui , Wang Suijian , Chen Yuhua , Lu Xinchen , Wang Danshu , Zhang Yao , Pan Wei , Zhou Chunhua , Zou Duowu 
Published: February 2024

BackgroundNumerous observational studies have identified a linkage between the gut microbiota and gastroesophageal reflux disease (GERD). However, a clear causative association between the gut microbiota and GERD has yet to be definitively ascertained, given the presence of confounding variables.MethodsThe genome-wide association study (GWAS) pertaining to the microbiome, conducted by the MiBioGen consortium and comprising 18,340 samples from 24 population-based cohorts, served as the exposure dataset. Summary-level data for GERD were obtained from a recent publicly available genome-wide association involving 78 707 GERD cases and 288 734 controls of European descent. The inverse variance-weighted (IVW) method was performed as a primary analysis, the other four methods were used as supporting analyses. Furthermore, sensitivity analyses encompassing Cochran’s Q statistics, MR-Egger intercept, MR-PRESSO global test, and leave-one-out methodology were carried out to identify potential heterogeneity and horizontal pleiotropy. Ultimately, a reverse MR assessment was conducted to investigate the potential for reverse causation.ResultsThe IVW method’s findings suggested protective roles against GERD for the Family Clostridiales Vadin BB60 group (P = 0.027), Genus Lachnospiraceae UCG004 (P = 0.026), Genus Methanobrevibacter (P = 0.026), and Phylum Actinobacteria (P = 0.019). In contrast, Class Mollicutes (P = 0.037), Genus Anaerostipes (P = 0.049), and Phylum Tenericutes (P = 0.024) emerged as potential GERD risk factors. In assessing reverse causation with GERD as the exposure and gut microbiota as the outcome, the findings indicate that GERD leads to dysbiosis in 13 distinct gut microbiota classes. The MR results’ reliability was confirmed by thorough assessments of heterogeneity and pleiotropy.ConclusionsFor the first time, the MR analysis indicates a genetic link between gut microbiota abundance changes and GERD risk. This not only substantiates the potential of intestinal microecological therapy for GERD, but also establishes a basis for advanced research into the role of intestinal microbiota in the etiology of GERD.
The fear of spiders: perceptual features assessed in augmented reality
Authors: Frumento Sergio , Frumento Paolo , Laurino Marco , Menicucci Danilo , Gemignani Angelo 
Published: February 2024

BackgroundPersons with specific phobias typically generalize the dangerousness of the phobic animal to all members of its species, possibly as a result of malfunctioning brain circuitry normally providing quick and dirty identification of evolutionary-relevant stimuli. An objective assessment of which perceptual features make an animal more or less scary to phobic and non-phobic people would help overcome the limitations of the few studies available so far, based on self-reports.ObjectiveTo achieve this aim, we built an augmented reality setting where volunteers with different levels of fear of spiders were asked to make holographic spiders that look either dangerous or harmless. To reach this goal, a computerized interface allowed participants to modify the spider’s perceptual features (hairiness, body/leg size, and locomotion) in real time.ResultsOn average, the dangerous spiders were made hairy, thick, and moving according to spider-like locomotion; coherently, the harmless spiders were made hairless, slim, and moving according to a butterfly-like locomotion. However, these averaged preferences could not fully describe the complex relationship between perceptual preferences with each other and with arachnophobia symptoms. An example of a key finding revealed by cluster analysis is the similarity in perceptual preferences among participants with little or no fear of spiders, whereas participants with more arachnophobia symptoms expressed more varying preferences.ConclusionPerceptual preferences toward the spider’s features were behaviorally assessed through an observational study, objectively confirming a generalization effect characterizing spider-fearful participants. These results advance our knowledge of phobic preferences and could be used to improve the acceptability of exposure therapies.
Therapeutic inertia in the management of neuromyelitis optica spectrum disorder
Authors: Cobo-Calvo Álvaro , Gómez-Ballesteros Rocío , Orviz Aida , Díaz Sánchez María , Boyero Sabas , Aguado-Valcarcel Marta , Sepúlveda María , Rebollo Pablo , López-Laiz Paloma , Maurino Jorge , Téllez Lara Nieves 
Published: February 2024

Introduction and objectiveLimited information is available on how neurologists make therapeutic decisions in neuromyelitis optica spectrum disorder (NMOSD), especially when new treatments with different mechanisms of action, administration, and safety profile are being approved. Decision-making can be complex under this uncertainty and may lead to therapeutic inertia (TI), which refers to lack of treatment initiation or intensification when therapeutic goals are not met. The study aim was to assess neurologists’ TI in NMOSD.MethodsAn online, cross-sectional study was conducted in collaboration with the Spanish Society of Neurology. Neurologists answered a survey composed of demographic characteristics, professional background, and behavioral traits. TI was defined as the lack of initiation or intensification with high-efficacy treatments when there is evidence of disease activity and was assessed through five NMOSD aquaporin-4 positive (AQP4+) simulated case scenarios. A multivariate logistic regression analysis was used to determine the association between neurologists’ characteristics and TI.ResultsA total of 78 neurologists were included (median interquartile range [IQR] age: 36.0 [29.0–46.0] years, 55.1% male, median [IQR] experience managing demyelinating conditions was 5.2 [3.0–11.1] years). The majority of participants were general neurologists (59.0%) attending a median (IQR) of 5.0 NMOSD patients (3.0–12.0) annually. Thirty participants (38.5%) were classified as having TI. Working in a low complexity hospital and giving high importance to patient’s tolerability/safety when choosing a treatment were predictors of TI.ConclusionTI is a common phenomenon among neurologists managing NMOSD AQP4+. Identifying TI and implementing specific intervention strategies may be critical to improving therapeutic decisions and patient care.
Feasibility of video-based real-time nystagmus tracking: a lightweight deep learning model approach using ocular object segmentation
Authors: Cho Changje , Park Sejik , Ma Sunmi , Lee Hyo-Jeong , Lim Eun-Cheon , Hong Sung Kwang 
Published: February 2024

BackgroundEye movement tests remain significantly underutilized in emergency departments and primary healthcare units, despite their superior diagnostic sensitivity compared to neuroimaging modalities for the differential diagnosis of acute vertigo. This underutilization may be attributed to a potential lack of awareness regarding these tests and the absence of appropriate tools for detecting nystagmus. This study aimed to develop a nystagmus measurement algorithm using a lightweight deep-learning model that recognizes the ocular regions.MethodThe deep learning model was used to segment the eye regions, detect blinking, and determine the pupil center. The model was trained using images extracted from video clips of a clinical battery of eye movement tests and synthesized images reproducing real eye movement scenarios using virtual reality. Each eye image was annotated with segmentation masks of the sclera, iris, and pupil, with gaze vectors of the pupil center for eye tracking. We conducted a comprehensive evaluation of model performance and its execution speeds in comparison to various alternative models using metrics that are suitable for the tasks.ResultsThe mean Intersection over Union values of the segmentation model ranged from 0.90 to 0.97 for different classes (sclera, iris, and pupil) across types of images (synthetic vs. real-world images). Additionally, the mean absolute error for eye tracking was 0.595 for real-world data and the F1 score for blink detection was ≥ 0.95, which indicates our model is performing at a very high level of accuracy. Execution speed was also the most rapid for ocular object segmentation under the same hardware condition as compared to alternative models. The prediction for horizontal and vertical nystagmus in real eye movement video revealed high accuracy with a strong correlation between the observed and predicted values (r = 0.9949 for horizontal and r = 0.9950 for vertical; both p &lt; 0.05).ConclusionThe potential of our model, which can automatically segment ocular regions and track nystagmus in real time from eye movement videos, holds significant promise for emergency settings or remote intervention within the field of neurotology.
Case report: Mirror paraclinoid aneurysm associated with mirror clinoidal meningioma
Authors: Paitán Alexander Feliciano Vilcahuamán , Gibbon Frederico de Lima , Korotkov Dmitriy , Castillo Ileane Camallery , Moguel Ambar E. Riley , Salvagni Felipe Pereira , Chaddad-Neto Feres 
Published: February 2024

IntroductionClinoidal meningiomas and paraclinoid aneurysms are individually uncommon, with the coexistence of mirror paraclinoid aneurysms and clinoidal meningiomas presenting an even rarer scenario. While the association between meningiomas and aneurysms is documented, the simultaneous presence of mirror lesions for both pathologies is not reported in the literature.Clinical presentationWe report a 62-year-old female with a three-month history of moderate bifrontal headaches. Magnetic Resonance Angiography (MRA) revealed mirror paraclinoid aneurysms, prompting surgical intervention. During the procedure, mirror clinoidal meningiomas were incidentally discovered. The left aneurysm was addressed first due to higher rupture risk, followed by the right aneurysm 3 months later. Both meningiomas were confirmed as Transitional Meningiomas (Grade 1; OMS, 2021). The aneurysms were successfully clipped, and the patient had an excellent postoperative outcome.ConclusionThis case represents a unique occurrence of mirror ophthalmic segment internal carotid artery aneurysms associated with mirror clinoidal meningiomas, a combination not previously reported. Despite the limitations of MRA in detecting small meningiomas, it remains a valuable non-invasive screening tool for neurovascular diseases. The case underscores the need for further research to elucidate the association between cerebral aneurysms and meningiomas.
Long-range temporal correlations in resting state alpha oscillations in major depressive disorder and obsessive-compulsive disorder
Authors: Proshina Ekaterina , Martynova Olga , Portnova Galina , Khayrullina Guzal , Sysoeva Olga 
Published: February 2024

IntroductionMental disorders are a significant concern in contemporary society, with a pressing need to identify biological markers. Long-range temporal correlations (LRTC) of brain rhythms have been widespread in clinical cohort studies, especially in major depressive disorder (MDD). However, research on LRTC in obsessive-compulsive disorder (OCD) is severely limited. Given the high co-occurrence of OCD and MDD, we conducted a comparative LRTC investigation. We assumed that the LRTC patterns will allow us to compare measures of brain cortical balance of excitation and inhibition in OCD and MDD, which will be useful in the area of differential diagnosis.MethodsIn this study, we used the 64-channel resting state EEG of 29 MDD participants, 26 OCD participants, and a control group of 37 volunteers. Detrended fluctuation analyzes was used to assess LRTC.ResultsOur results indicate that all scaling exponents of the three subject groups exhibited persistent LRTC of EEG oscillations. There was a tendency for LRTC to be higher in disorders than in controls, but statistically significant differences were found between the OCD and control groups in the entire frontal and left parietal occipital areas, and between the MDD and OCD groups in the middle and right frontal areas.DiscussionWe believe that these results indicate abnormalities in the inhibitory and excitatory neurotransmitter systems, predominantly affecting areas related to executive functions.
Machine learning-based evaluation of spontaneous pain and analgesics from cellular calcium signals in the mouse primary somatosensory cortex using explainable features
Authors: Bak Myeong Seong , Park Haney , Yoon Heera , Chung Geehoon , Shin Hyunjin , Shin Soonho , Kim Tai Wan , Lee Kyungjoon , Nägerl U. Valentin , Kim Sang Jeong , Kim Sun Kwang 
Published: February 2024

IntroductionPain that arises spontaneously is considered more clinically relevant than pain evoked by external stimuli. However, measuring spontaneous pain in animal models in preclinical studies is challenging due to methodological limitations. To address this issue, recently we developed a deep learning (DL) model to assess spontaneous pain using cellular calcium signals of the primary somatosensory cortex (S1) in awake head-fixed mice. However, DL operate like a “black box”, where their decision-making process is not transparent and is difficult to understand, which is especially evident when our DL model classifies different states of pain based on cellular calcium signals. In this study, we introduce a novel machine learning (ML) model that utilizes features that were manually extracted from S1 calcium signals, including the dynamic changes in calcium levels and the cell-to-cell activity correlations.MethodWe focused on observing neural activity patterns in the primary somatosensory cortex (S1) of mice using two-photon calcium imaging after injecting a calcium indicator (GCaMP6s) into the S1 cortex neurons. We extracted features related to the ratio of up and down-regulated cells in calcium activity and the correlation level of activity between cells as input data for the ML model. The ML model was validated using a Leave-One-Subject-Out Cross-Validation approach to distinguish between non-pain, pain, and drug-induced analgesic states.Results and discussionThe ML model was designed to classify data into three distinct categories: non-pain, pain, and drug-induced analgesic states. Its versatility was demonstrated by successfully classifying different states across various pain models, including inflammatory and neuropathic pain, as well as confirming its utility in identifying the analgesic effects of drugs like ketoprofen, morphine, and the efficacy of magnolin, a candidate analgesic compound. In conclusion, our ML model surpasses the limitations of previous DL approaches by leveraging manually extracted features. This not only clarifies the decision-making process of the ML model but also yields insights into neuronal activity patterns associated with pain, facilitating preclinical studies of analgesics with higher potential for clinical translation.
Prediction of risk factors for linezolid-induced thrombocytopenia based on neural network model
Authors: Zhao Xian , Peng Qin , Hu Dongmei , Li Weiwei , Ji Qing , Dong Qianqian , Huang Luguang , Piao Miyang , Ding Yi , Wang Jingwen 
Published: February 2024

Background: Based on real-world medical data, the artificial neural network model was used to predict the risk factors of linezolid-induced thrombocytopenia to provide a reference for better clinical use of this drug and achieve the timely prevention of adverse reactions.Methods: The artificial neural network algorithm was used to construct the prediction model of the risk factors of linezolid-induced thrombocytopenia and further evaluate the effectiveness of the artificial neural network model compared with the traditional Logistic regression model.Results: A total of 1,837 patients receiving linezolid treatment in a hospital in Xi ‘an, Shaanxi Province from 1 January 2011 to 1 January 2021 were recruited. According to the exclusion criteria, 1,273 cases that did not meet the requirements of the study were excluded. A total of 564 valid cases were included in the study, with 89 (15.78%) having thrombocytopenia. The prediction accuracy of the artificial neural network model was 96.32%, and the AUROC was 0.944, which was significantly higher than that of the Logistic regression model, which was 86.14%, and the AUROC was 0.796. In the artificial neural network model, urea, platelet baseline value and serum albumin were among the top three important risk factors.Conclusion: The predictive performance of the artificial neural network model is better than that of the traditional Logistic regression model, and it can well predict the risk factors of linezolid-induced thrombocytopenia.
Evaluation of the hepatotoxicity of Psoralea corylifolia L. based on a zebrafish model
Authors: Gao Shu-Yan , Zhao Jing-Cheng , Xia Qing , Sun Chen , Aili Maimaiti , Talifu Ainiwaer , Huo Shi-Xia , Zhang Yun , Li Zhi-Jian 
Published: February 2024

Objective:Psoralea corylifolia L. (FP) has received increasing attention due to its potential hepatotoxicity.Methods: In this study, zebrafish were treated with different concentrations of an aqueous extract of FP (AEFP; 40, 50, or 60 μg/mL), and the hepatotoxic effects of tonicity were determined by the mortality rate, liver morphology, fluorescence area and intensity of the liver, biochemical indices, and pathological tissue staining. The mRNA expression of target genes in the bile acid metabolic signaling pathway and lipid metabolic pathway was detected by qPCR, and the mechanism of toxicity was initially investigated. AEFP (50 μg/mL) was administered in combination with FXR or a peroxisome proliferator-activated receptor α (PPARα) agonist/inhibitor to further define the target of toxicity.Results: Experiments on toxic effects showed that, compared with no treatment, AEFP administration resulted in liver atrophy, a smaller fluorescence area in the liver, and a lower fluorescence intensity (p &lt; 0.05); alanine transaminase (ALT), aspartate transaminase (AST), and γ-GT levels were significantly elevated in zebrafish (p &lt; 0.01), and TBA, TBIL, total cholesterol (TC), TG, low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) levels were elevated to different degrees (p &lt; 0.05); and increased lipid droplets in the liver appeared as fatty deposits. Molecular biological validation revealed that AEFP inhibited the expression of the FXR gene, causing an increase in the expression of the downstream genes SHP, CYP7A1, CYP8B1, BSEP, MRP2, NTCP, peroxisome proliferator-activated receptor γ (PPARγ), ME-1, SCD-1, lipoprotein lipase (LPL), CPT-1, and CPT-2 and a decrease in the expression of PPARα (p &lt; 0.05).Conclusion: This study demonstrated that tonic acid extracts are hepatotoxic to zebrafish through the inhibition of FXR and PPARα expression, thereby causing bile acid and lipid metabolism disorders.