Many university students have been struggling with multiple challenges that may cause mental fatigue. Exposure to the natural environment is found to have restorative effects on mental fatigue, which ...can be explained by its benefits in physiological, psychological, and cognitive aspects. While the natural environment contains both visual and auditory elements, research on the effects of auditory elements, such as nature sounds, is underdeveloped and limited to laboratory settings. It remains unclear what are the effects of exposure to nature sounds in daily life settings. The study was conducted with 71 students from a university, who were randomly assigned to the experimental group using a nature-sound mobile application in daily life and the control group not using the application. After a 4-week exposure to the intervention, the students in the experimental group outperformed their counterparts in the control group on psychological well-being reflected in positive affect, as well as cognitive performance reflected in flow state, attention (in terms of alerting) and working memory (in terms of accuracy and reaction time). The findings reveal the positive impact of exposure to relaxing nature sounds on university students' psychological well-being and cognitive performance, as well as the potential of mobile applications to provide easy exposure to nature sounds.
Concept mapping has been employed as a promising tool to facilitate group learning and interaction mainly in the classroom. Computer- and web-based applications provide plenty of opportunities to ...extend concept mapping mediated group learning to online environments. Nevertheless, the effects of concept mapping on group learning and interaction may vary with the nature of group tasks and other factors. This study investigated the effects of web-based collaborative concept mapping on group interaction in two task situations: concept-oriented task and design-oriented task. Web-based collaborative concept mapping was used to mediate small group learning among college students in an online environment. The findings revealed that there were differences in the effects of collaborative concept mapping on group interaction between the two task situations; collaborative concept mapping functioned more effectively in facilitating group interaction in the concept-oriented task situation than in the design-oriented task situation in several ways.
•Web-based collaborative concept mapping supports interaction in group learning.•Concept mapping is effective in mediating group interaction in a conceptual task.•Concept mapping is ineffective in facilitating group interaction in a design task.
Sarcopenia greatly reduces the quality of life of the elderly, and iron metabolism plays an important role in muscle loss. This study aimed to investigate the association between iron status and ...sarcopenia. A total of 286 adult patients hospitalized between 2019 and 2021 were included in this study, of which 117 were diagnosed with sarcopenia. Serum iron, total iron binding capacity (TIBC), transferrin, and transferrin saturation levels were compared between groups with and without sarcopenia and were included in the logistic analyses, with significant variables further included in the logistic regression model for the prediction of sarcopenia. Serum iron, TIBC, and transferrin levels decreased significantly in the sarcopenia group (p < 0.05), and were negatively associated with handgrip strength, relative skeletal muscle index, and multiple test performances (p < 0.05). Multivariate logistic analysis showed that sex, age, body mass index (BMI), and serum iron level were independent risk factors for sarcopenia. In the final logistic regression model, male sex (odds ratio OR 3.65, 95% confidence interval CI 1.67-7.98), age > 65 years (OR 5.40, 95% CI 2.25-12.95), BMI < 24 kg/m
(OR 0.17, 95% CI 0.08-0.36), and serum iron < 10.95 μmol/L (OR 0.39, 95% CI 0.16-0.93) were included. Our study supported the impact of iron metabolism on muscle strength and performance.
The high-fidelity and interactivity afforded by head-mounted displays (HMD) has a potential to improve learning in problem-solving contexts. However, there is a lack of studies with mixed findings on ...the efficacy of HMD in the development of problem-solving competence. Moreover, the integration of learning strategies with HMD supported learning is often overlooked. This study aimed to address the gap by investigating whether the simulation media (HMD or 2D simulation environments) and learning strategy (with or without planning) may influence student learning with problem-solving tasks. The results show that the HMD and planning groups outperformed other groups in simulated problem-solving tasks, and in transferring the competence to real-world tasks. Students using the HMD perceived a higher level of sense of presence, self-efficacy, and simulator acceptance; but they reported a higher level of mental workload and simulator sickness than those using the 2D simulation. Implications of the findings are also discussed.
The aim of this study is to prospectively evaluate whole-brain CT perfusion (CTP) on admission to predict delayed cerebral ischemia (DCI) in patients with aneurysmal subarachnoid hemorrhage (aSAH).
...All 252 consecutive patients with aSAH enrolled in this study underwent one-stop whole-brain CTP scan within 24 h after aneurysm rupture. The qualitative and quantitative CTP parameters and clinical data were compared between patients with and without DCI. Diagnostic performance of clinical data and mean and lowest CTP parameters were evaluated by receiver-operating characteristic (ROC) analyses. Logistic regression analysis was employed to determine predictors of DCI.
The study evaluated 191 of 252 consecutive patients, 57 of whom (29.8%) developed DCI during hospitalization. Patients with diffused hypoperfusion had the highest incidence rate of DCI (43%, 46/107). Mean TMax produced the largest area under the curve of 0.726 (95% confidence interval CI 0.638–0.814), and a cutoff value of 2.240 s provided sensitivity of 73.7% and specificity of 71.6% for early prediction of developing DCI. Glasgow Coma Scale score (odds ratio OR = 0.716, 95% CI 0.565–0.908, P = 0.006), cerebral vasospasm (OR = 6.117, 95% CI 1.427–26.223, P = 0.015), hydrocephalus (OR = 3.795, 95% CI 1.327–10.858, P = 0.013), and qualitative CTP analysis (OR = 3.383, 95% CI 1.686–6.789, P = 0.001) were all significant independent predictors of DCI.
Whole-brain CTP within 24 h of admission can qualitatively and quantitatively detect abnormal cerebral perfusion. It is possible to predict the risk of developing DCI after aSAH when the TMax value is larger than 2.240 s.
Higher-order thinking is crucial to inquiry learning. It is important to investigate how students think in inquiry contexts. Given the tacit nature of higher-order thinking, cognitive maps (e.g., ...concept maps, reasoning maps) have been used to externalize thinking and have shown promising effects in terms of improving inquiry task performance. However, few studies have analyzed student-constructed maps that reflect the thinking underpinning students’ inquiry task performance. This study aimed to address this gap. Sixty-nine 11th grade students worked in small groups to explain a fish die-off phenomenon in a virtual ecosystem and collaboratively constructed an integrative cognitive map to facilitate thinking during the task. The map comprised a concept map (representing conceptual thinking about relevant subject knowledge) and a reasoning map (representing the reasoning process). Regression analyses showed that the quality of the student-constructed maps, particularly the reasoning maps, was a significant predictor of inquiry task performance assessed based on students’ written explanations of the phenomenon. Although the quality of the concept maps was not a significant predictor of inquiry task performance, it did predict the quality of the reasoning maps. Student thinking reflected in concept mapping and that reflected in reasoning mapping play different roles in inquiry learning.
Our goal was to establish and verify a radiomics risk grading model for gastrointestinal stromal tumors (GISTs) and to identify the optimal algorithm for risk stratification.
We conducted a ...retrospective analysis of 324 patients with GISTs, the presence of which was confirmed by surgical pathology. Patients were treated at three different hospitals. A training cohort of 180 patients was collected from the largest center, while an external validation cohort of 144 patients was collected from the other two centers. To extract radiomics features, regions of interest (ROIs) were outlined layer by layer along the edge of the tumor contour on CT images of the arterial and portal venous phases. The dimensionality of radiomic features was reduced, and the top 10 features with importance value above 5 were selected before modeling. The training cohort used three classifiers logistic regression, support vector machine (SVM), and random forest to establish three GIST risk stratification prediction models. The receiver operating characteristic curve (ROC) was used to compare model performance, which was validated by external data.
In the training cohort, the average area under the curve (AUC) was 0.84 ± 0.07 of the logistic regression, 0.88 ± 0.06 of the random forest, and 0.81 ± 0.08 of the SVM. In the external validation cohort, the AUC was 0.85 of the logistic regression, 0.90 of the random forest, and 0.80 of the SVM. The random forest model performed the best in both the training and the external validation cohorts and could be generalized.
Based on CT radiomics, there are multiple machine-learning models that can predict the risk of GISTs. Among them, the random forest algorithm had the highest prediction efficiency and could be readily generalizable. Through external validation data, we assume that the random forest model may be used as an effective tool to guide preoperative clinical decision-making.
E-learning is now being used by many organizations as an approach for enhancing the skills of knowledge workers. However, most applications have performed poorly in motivating employee learning, ...being perceived as less effective due to a lack of alignment of learning with work performance. To help solve this problem, we developed a performance-oriented approach using design science research methods. It uses performance measurement to clarify organizational goals and individual learning needs and links them to e-learning applications. The key concept lies in a Key Performance Indicator model, where organizational mission and vision are translated into a set of targets that drive learning towards a goal of improving work performance. We explored the mechanisms needed to utilize our approach and examined the necessary conceptual framework and implementation details. To demonstrate the effectiveness of the approach, a prototype workplace e-learning system was developed and used to evaluate the effectiveness of our approach.
To establish and verify a computed tomography (CT)-based multi-class prediction model for discriminating the risk stratification of gastrointestinal stromal tumors (GISTs).
A total of 381 patients ...with GISTs were confirmed by surgery and pathology. Information on 213 patients were obtained from one hospital and used as training cohort, whereas the details of 168 patients were collected from two other hospitals and used as independent validation cohort. Regions of interest on CT images of arterial and venous phases were drawn, radiomics features were extracted, and dimensionality reduction processing was performed. Using a one-vs-rest method, a Random Forest-based GISTs risk three-class prediction model was established, and the receiver operating characteristic curve (ROC) was used to evaluate the performance of the multi-class classification model, and the generalization ability was verified using external data.
The training cohort included 96 very low-risk and low-risk, 60 intermediate-risk and 57 high-risk patients. External validation cohort included 82 very low-risk and low-risk, 48 intermediate-risk and 38 high-risk patients. The GISTs risk three-class radiomics model had a macro/micro average area under the curve (AUC) of 0.84 and an accuracy of 0.78 in the training cohort. It had a stable performance in the external validation cohort, with a macro/micro average AUC of 0.83 and an accuracy of 0.80.
CT radiomics can discriminate GISTs risk stratification. The performance of the three-class radiomics prediction model is good, and its generalization ability has also been verified in the external validation cohort, indicating its potential to assist stratified and accurate treatment of GISTs in the clinic.
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