The Role of the Intestinal Flora and Its Derivatives in Neurocognitive Disorders: A Narrative Review from Surgical Perspective.

Perioperative neurocognitive dysfunction is a significant concern for population health, impacting postoperative recovery and increasing the financial burden on patients. With an increasing number of surgical procedures being performed, the prevention and management of perioperative neurocognitive dysfunction have garnered significant attention. While factors such as age, lifestyle, genetics, and education are known to influence the development of cognitive dysfunction, recent research has highlighted the role of the gut microbiota in neurological health. An increased abundance of pro-inflammatory gut microbiota can trigger and worsen neuroinflammation, neuronal cell damage, and impaired cellular autophagy. Moreover, the inflammation-promoting gut microbiota can disrupt immune function, impair neuroautophagy, and affect the production and circulation of extracellular vesicles and neurotransmitters. These factors collectively play a role in the onset and advancement of cognitive impairment. This narrative review delves into the molecular mechanisms through which gut microbiota and their derivatives contribute to cognitive impairment, focusing on the impact of anesthesia surgery, changes in gut microbial populations, and perioperative cognitive impairment associations. The study suggests that alterations in the abundance of various bacterial species and their metabolites pre- and post-surgery may be linked to postoperative cognitive impairment. Furthermore, the potential of probiotics or prebiotics in addressing cognitive impairment is discussed, offering a promising avenue for investigating the treatment of perioperative neurocognitive disorders.

Assessment of Drivers' Mental Workload by Multimodal Measures during Auditory-Based Dual-Task Driving Scenarios.

Assessing drivers' mental workload is crucial for reducing road accidents. This study examined drivers' mental workload in a simulated auditory-based dual-task driving scenario, with driving tasks as the main task, and auditory-based N-back tasks as the secondary task. A total of three levels of mental workload (i.e., low, medium, high) were manipulated by varying the difficulty levels of the secondary task (i.e., no presence of secondary task, 1-back, 2-back). Multimodal measures, including a set of subjective measures, physiological measures, and behavioral performance measures, were collected during the experiment. The results showed that an increase in task difficulty led to increased subjective ratings of mental workload and a decrease in task performance for the secondary N-back tasks. Significant differences were observed across the different levels of mental workload in multimodal physiological measures, such as delta waves in EEG signals, fixation distance in eye movement signals, time- and frequency-domain measures in ECG signals, and skin conductance in EDA signals. In addition, four driving performance measures related to vehicle velocity and the deviation of pedal input and vehicle position also showed sensitivity to the changes in drivers' mental workload. The findings from this study can contribute to a comprehensive understanding of effective measures for mental workload assessment in driving scenarios and to the development of smart driving systems for the accurate recognition of drivers' mental states.

NR0B1 suppresses ferroptosis through upregulation of NRF2/c-JUN-CBS signaling pathway in lung cancer cells.

Ferroptosis has demonstrated significant potential in treating radiochemotherapy-resistant cancers, but its efficacy can be affected by recently discovered ferroptosis suppressors. In this study, we discovered that NR0B1 protects against erastin- or RSL3-induced ferroptosis in lung cancer cells. Transcriptomic analysis revealed that NR0B1 significantly interfered with the expression of 12 ferroptosis-related genes, and the expression level of NR0B1 positively correlated with that of c-JUN, NRF2, and CBS. We further revealed that NR0B1 suppression of ferroptosis depended on the activities of c-JUN, NRF2, and CBS. NR0B1 directly promoted the expression of NRF2 and c-JUN and indirectly upregulated CBS expression through enhancing NRF2 and/or c-JUN transcription. Moreover, we showed that NR0B1 depletion restrained xenograft tumor growth and facilitated RSL3-induced ferroptosis in the tumors. In conclusion, our findings uncover that NR0B1 suppresses ferroptosis by activating the c-JUN/NRF2-CBS signaling pathway in lung cancer cells, providing new evidence for the involvement of NR0B1 in drug resistance during cancer therapy.

Influence of Individual-technology-task-environment Fit on University Student Online Learning Performance: The Mediating Role of Behavioral, Emotional, and Cognitive Engagement.

The present study aimed to examine whether and to what extent university student online learning performance was influenced by individual-technology fit (ITF), task-technology fit (TTF), environment-technology fit (ETF), and whether the influence was mediated by their behavioral, emotional, and cognitive engagement. A theoretical research model was developed by integrating the extended TTF theory and student engagement framework. The validity of the model was assessed using a partial least squares structural equation modeling approach based on data collected from 810 university students. Student learning performance was influenced by TTF (ß = 0.25, p < 0.001), behavioral engagement (ß = 0.25, p < 0.001), and emotional engagement (ß = 0.27, p < 0.001). Behavioral engagement was affected by TTF (ß = 0.31, p < 0.001) and ITF (ß = 0.41, p < 0.001). TTF, ITF, and ETF were observed as significant antecedents of emotional engagement (ß = 0.49, p < 0.001; ß = 0.19, p < 0.001; ß = 0.12, p = 0.001, respectively) and cognitive engagement (ß = 0.28, p < 0.001; ß = 0.34, p < 0.001; ß = 0.16, p < 0.001, respectively). Behavioral and emotional engagement served as mediators between fit variables and learning performance. We suggest the need for an extension to the TTF theory by introducing ITF and ETF dimensions and demonstrate the important role of these fit variables in facilitating student engagement and learning performance. Online education practitioners should carefully consider the fit between the individual, task, environment, and technology to facilitate student learning outcomes.

Modeling Consumer Acceptance and Usage Behaviors of m-Health: An Integrated Model of Self-Determination Theory, Task-Technology Fit, and the Technology Acceptance Model.

Although mobile health (m-health) has great potential to reduce the cost of medical care and improve its quality and efficiency, it is not widely accepted by consumers. In addition, there is still a lack of comprehensive insight into m-health acceptance, especially among consumers with different demographic characteristics. This study aimed to explore the factors affecting consumers' acceptance and usage behaviors of m-health and to examine whether their roles differ by demographic characteristics. A comprehensive m-health acceptance model was proposed by integrating factors from the Self-Determination Theory, Task-Technology Fit, and Technology Acceptance Model. Survey data were collected from 623 Chinese adults with at least 6 months of m-health usage experience and analyzed using structural equation modeling techniques. Multi-group analyses were performed to assess whether the model relationships were different across gender, age, and usage experience. The results indicated that relatedness and competence were significant motivational antecedents of perceived ease of use. Task-technology fit and the perceived ease of use significantly affected the perceived usefulness. The perceived ease of use and perceived usefulness were significant determinants of consumer usage behaviors of m-health and together explained 81% of its variance. Moreover, the relationships among autonomy, perceived usefulness, and usage behaviors of m-health were moderated by gender. Consumer usage behaviors of m-health were affected by factors such as self-motivation (i.e., relatedness and competence), technology perceptions (i.e., perceived ease of use and perceived usefulness), and task-technology fit. These findings provide a theoretical underpinning for future research on m-health acceptance and provide empirical evidence for practitioners to promote the better design and use of m-health for healthcare activities.

The predictive accuracy of CT radiomics combined with machine learning in predicting the invasiveness of small nodular lung adenocarcinoma.

Background: Conventionally, the judgment of whether small pulmonary nodules are invasive is mainly made by thoracic surgeons according to the chest computed tomography (CT) features of patients. However, there are limits to how much useful information can be obtained from this approach. A large number of feature information was extracted from CT images by CT radiomics. The machine learning algorithm was used to construct models based on radiomic characteristics to predict the invasiveness of lung adenocarcinoma (LUAD) with a good prediction accuracy. Methods: A total of 416 patients with pathologically confirmed preinvasive lesions and LUAD after video-assisted thoracoscopic surgery (VATS) in the Department of Thoracic Surgery of the First People's Hospital of Yunnan Province from February 2020 to February 2022 were retrospectively analyzed. According to random classification, patients were divided into 2 groups. The RadCloud platform was used to extract radiomics features, and the most relevant radiomics features were selected by continuous dimension reduction method. Then, 6 machine learning algorithms were used to establish and verify the prediction model of small lung nodular adenocarcinoma invasiveness. Receiver operating characteristic (ROC) curve and area under curve (AUC) were used to evaluate the predictive performance. Results: There were 78 cases of pre-invasive lesions and 226 cases of invasive lesions in the training group, and 34 cases of pre-invasive lesions and 78 cases of invasive lesions in the validation group. In the training group, the AUC values of the 6 models were all more than 0.914, the 95% confidence interval (CI) was 0.857-1.00, the sensitivity was equal or more than 0.87, and the specificity was equal or more than 0.85. In the validation group, the AUC values of the 6 models were all equal or more than 0.732, the 95% CI was 0.651-1.00, the sensitivity was equal or more than 0.7, and the specificity was more than 0.77. Conclusions: Machine learning algorithms were used to construct models to predict the invasiveness of small nodular LUAD based on radiomics features, which it could provide more evidence for doctors to make diagnoses and more personalized treatment plans for patients.

In robot we trust? The effect of emotional expressions and contextual cues on anthropomorphic trustworthiness.

Following the evolution of technology and its application in various daily contexts, social robots work as an advanced artificial intelligence (AI) system to interact with humans. However, limited research has been done to discuss the role of emotional expressions and contextual cues in influencing anthropomorphic trustworthiness, especially from the design perspective. To address this research gap, the current study designed a specific robot prototype and conducted two lab experiments to explore the effect of emotional expressions and contextual cues on trustworthiness via a combination of subjective ratings and physiological measures. Results showed that: 1) positive (vs. negative) emotional expressions enjoyed a higher level of anthropomorphic trustworthiness and visual attention; 2) regulatory fit was expanded in parasocial interaction and worked as a prime to activate anthropomorphic trustworthiness for social robots. Theoretical contributions and design implications were also discussed in this study.

The Predictors of Unsafe Behaviors among Nuclear Power Plant Workers: An Investigation Integrating Personality, Cognitive and Attitudinal Factors.

Unsafe behaviors, such as violations and human errors, have long been recognized as the main causes of accidents in nuclear power plants (NPPs). However, personal factors that influence unsafe behaviors among NPP workers have not been well examined, especially in an integrated model. This study proposes an integrated contextual mediated model to examine personality, cognitive and attitudinal predictors of unsafe behaviors among commissioning workers at NPPs. The model was verified using structural equation modeling technique with survey data from 177 commissioning workers in two Chinese NPPs. Results show that personality traits (i.e., conscientiousness, neuroticism, openness and agreeableness), executive function and safety attitudes exerted significant effects on unsafe behaviors. In addition, the effects of agreeableness and executive function were partly mediated by safety attitudes. Both conscientiousness and neuroticism indirectly influenced unsafe behaviors through the mediating role of executive function. The findings shed light on the design of evidence-based interventions for safety performance in NPPs.

Input modality matters: A comparison of touch, speech, and gesture based in-vehicle interaction.

Innovative input devices are being available for in-vehicle information systems (IVISs). While they have the potential to provide enjoyable driving by enabling drivers to perform non-driving related tasks (NDRTs) in more natural ways, the associated distracting effects should be paid with more attention. The purpose of this exploratory study was to compare the effects of three novel input modalities, i.e., touchscreen-based interaction (TBI), speech-based interaction (SBI), and gesture-based interaction (GBI), on driving performance and driver visual behaviors. Moreover, we examined if the influence of different modalities would be moderated by the difficulty level of NDRTs. A total of 36 participants were invited to a simulated driving experiment where they were randomly assigned to one of the four groups (TBI, GBI, SBI or baseline) and completed three driving trials. The results showed that TBI led to the worse driving performance, as indicated by the significantly prolonged reaction time, reduced minimum time-to-collision, and increased variations in both longitudinal and lateral vehicle control. The deteriorated driving performance could be attributed, at least partially, to the intense visual demand induced by looking towards the touchscreen, as indicated by more and longer off-the-road glances. The adverse impacts of GBI were relatively smaller, but it still posed great crash risk by leading to a shorter minimum time-to-collision and less stable vehicle control compared to the baseline. SBI, although not completely equivalent to the baseline group, showed the minimum influence on driving and visual performance. Only very few interaction effects were found, suggesting that the effects of modality were quite robust across different NDRTs. It was concluded that SBI and GBI provided safer alternatives to in-vehicle interaction than TBI.

Characteristics of Visual Fixation in Chinese Children with Autism During Face-to-Face Conversations.

Few eye tracking studies have examined how people with autism spectrum disorder (ASD) visually attend during live interpersonal interaction, and none with the Chinese population. This study used an eye tracker to record the gaze behavior in 20 Chinese children with ASD and 23 children with typical development (TD) when they were engaged in a structured conversation. Results demonstrated that children with ASD looked significantly less at the interlocutor's mouth and whole-face, and more at background. Additionally, gaze behavior was found to vary with the conversational topic. Given the great variability in eye tracking findings in existing literature, future explorations might consider investigating how fundamental factors (i.e., participant's characteristics, tasks, and context) influence the gaze behavior in people with ASD.

The Determinants of User Acceptance of Mobile Medical Platforms: An Investigation Integrating the TPB, TAM, and Patient-Centered Factors.

Mobile medical platforms (MMPs) can make medical services more accessible and effective. However, the patient-centered factors that influence patients' acceptance of MMPs are not well understood. Our study examined the factors affecting patients' acceptance of MMPs by integrating the theory of planned behavior (TPB), the technology acceptance model (TAM), and three patient-centered factors (i.e., perceived convenience, perceived credibility, and perceived privacy risk). Three hundred and eighty-nine Chinese respondents were recruited in this study and completed a self-administered online questionnaire that included items adapted from validated measurement scales. The partial least squares structural equation modeling results revealed that perceived privacy risk, perceived credibility, and perceived ease of use directly determined the perceived usefulness of an MMP. Perceived convenience, perceived credibility, and perceived usefulness significantly affected the patients' attitudes toward MMPs. Perceived usefulness, attitude, perceived privacy risk, and perceived behavioral control were important determinants of the patients' behavioral intentions to use MMPs. Behavioral intention and perceived behavioral control significantly influenced perceived effective use. Perceived credibility and perceived ease of use significantly affected perceived convenience. However, social influence had no significant effect on attitude and behavioral intention. The study provides important theoretical and practical implications, which could help practitioners enhance the patients' use of MMPs for their healthcare activities.

The complete chloroplast genome sequence of Begonia arachnoidea (Begoniaceae).

There are more than 2035 Begonia species (Begoniaceae) reported currently in the world. Begonia arachnoidea was found as a new species within a small area in Southern China. In this study, we are reporting for the first time its chloroplast genome for the purpose to compare with the chloroplast genomic data from other plant taxa which were closely related to this new species. Our results show that the circular chloroplast genome of B. arachnoidea is 169,725 bp in length, with 35.49% GC content. The whole structure of the genome has 76,431 bp in a large single-copy (LSC) region, 18,146 bp in a small single-copy (SSC) region, and the two inverted repeat (IRs) regions are both 37,574 bp. There are 90 protein-coding genes, 8 rRNA genes, and 42 tRNA genes encoded in this genome. Final phylogenetic analysis revealed that B. arachnoidea is genetically closest to B. pulchrifolia and B. coptidifolia.

Typing with mobile devices: A comparison of upper limb and shoulder muscle activities, typing performance and perceived workload under varied body postures, typing styles and device types.

This study aimed to examine the effects of body posture, typing style and device type on upper limb and shoulder muscle activities, typing performance and perceived workload while typing with mobile devices. Participants were asked to type with two mobile devices (i.e., a tablet and a smartphone) under three postures and in two typing styles. Muscle activity was recorded for four upper limb and shoulder muscles on both sides with surface electromyography. Results showed that body posture and typing style yielded significant effects on tying performance, perceived workload, and muscle activities in the forearm, upper arm and shoulder. Typing with a tablet was more accurate and had greater muscle activities in the upper arm and forearm on both sides than typing with a smartphone. The findings may be useful in developing evidence-based guidelines for the wise use of mobile devices and for the prevention of risks for musculoskeletal disorders.

The Effects of Smart Home Interface Touch Button Design Features on Performance among Young and Senior Users.

Touch technology-based smart homes have become increasingly prevalent, as they can help people with independent daily life, especially for the elderly. The aim of this study was to investigate the effects of button features (i.e., button size, graphics/text ratio, and icon style) in smart home interfaces on user performance across two age groups. Participants in the young group (n = 15) and senior group (n = 15) completed a clicking task. Button size ranged from 10 mm to 25 mm with 5 mm increments. The three levels of graphics/text ratio were 3:1, 1:1, and 1:3, while icon style was either flat or skeuomorphic. Results showed that button size and graphics/text ratio had significant effects on user performance in both groups, whereas icon style only had an effect in the senior group. It was observed that the elderly were fond of buttons with a larger size of 20 mm with larger texts and skeuomorphic icons, whereas the young preferred a button size of 15 mm with equal-sized graphics and text. These results may help to improve the accessibility and usability of smart home interface design.

Effects of Text Enhancement on Reduction of Look-Alike Drug Name Confusion: A Systematic Review and Meta-analysis.

BACKGROUND AND OBJECTIVES: Drug name confusion induced by look-alike drug names represents a serious health care management problem in practice. Text enhancement by changing visual attributes of look-alike drug names has been proposed and widely applied in practice to mitigate drug name confusion. However, the effectiveness of text enhancement on reducing drug name confusion is yet to be determined. This study aimed to explore the effects of text enhancement on reduction of confusion caused by look-alike drug names through systematic review and meta-analysis. METHODS: We searched 5 databases (from database inception to January 2020) for empirical studies that examined the effects of text enhancement on reduction of look-alike drug name-induced drug name confusion. The pooled outcome data were analyzed using either meta-analysis or a narrative synthesis approach. RESULTS: From the 351 identified articles, 11 articles representing 20 individual trials were included. Five basic text enhancement methods were revealed, including Tall Man, red, boldface, contrast, and size enhancement, from which 4 Tall Man variants and 6 text enhancement combinations were derived. The meta-analysis results showed significant reduction in omission errors when using Tall Man (standardized mean difference [SMD] = -0.628, 95% confidence interval [CI]: -1.018 to -0.238, P = .002), red (SMD = -0.516, 95% CI: -1.002 to -0.030, P = .038), boldface (SMD = -1.027, 95% CI: -1.240 to -0.814, P < .001), and contrast (SMD = -0.437, 95% CI: -0.869 to -0.004, P = 0.048), as compared with lowercase. This finding was also supported in our subgroup analysis by task type for name differentiation tasks. No other significant effects of text enhancement were found for either commission errors or response time. CONCLUSIONS: Using Tall Man, red, boldface, or contrast could help reduce omission errors (ie, wrong medication selection) caused by look-alike drug names, particularly in name differentiation tasks. However, no text enhancement could shorten name search and/or differentiation time. Our findings could facilitate the understanding of the effects of text enhancement in the prevention of confusion errors caused by look-alike drug names and promote the application of text enhancement in practice.

What drives people to use automated vehicles? A meta-analytic review.

Lack of consumer acceptance is a prominent barrier to the large-scale adoption of automated vehicles (AVs). This study investigated the underlying mechanisms for AV acceptance and how the mechanisms differed across subgroups by reviewing and synthesizing existing literature. We proposed AV acceptance models by extending the basic Technology Acceptance Model (TAM) with trust and perceived risk factors. Data from 36 studies were extracted to fit the models using meta-analytic structural equation modeling technique. The results suggested that trust contributed most in determining AV acceptance, followed by perceived usefulness and perceived risk, and perceived ease of use makes the least contribution. The subgroup analyses showed that the model parameters differed across the levels of three variables, i.e., sample origin (Europe/Asia/America), automation level (full/partial), and age (young/middle-aged). Specifically, trust was unanimously identified as the most important determinant of AV acceptance across all subgroups. Perceived risk only remained significant in America, fully AVs, and middle-aged subgroups. Perceived ease of use was insignificant in the above-mentioned three subgroups while remained significant in the rest subgroups. Building trust could be the most useful and universal way to improve AV acceptance, and policy makers should consider the characteristics of consumers when making AV promotion strategies.

Gait initiation differences between overweight and normal weight individuals.

Accidental falls often occur during gait initiation. Excess body weight has been identified as a risk factor for accidental falls. This study aimed to examine the differences of gait initiation between overweight and normal-weight individuals. Fourteen overweight and 14 normal-weight young adults participated in the study. They were instructed to perform the gait initiation task under single-task and dual-task conditions. Dependent variables for the assessment of gait initiation included spatial-temporal measures and postural stability measures. The results showed that overweight could compromise postural stability during gait initiation, primarily by decreasing margin of stability in the anterior-posterior direction. Cognitive task interference with gait initiation was found to be similar between the overweight and normal weight groups. The findings from the present study can aid in better understanding the mechanisms associated with increased fall risks among overweight individuals. They also highlight the importance of overweight control in fall prevention. Practitioner summary: Overweight was found to compromise postural stability during gait initiation, primarily by decreasing margin of stability in the anterior-posterior direction. The findings highlight the importance of overweight control in fall prevention. Abbreviations: ANOVA: analyses of variance; AP: anterior-posterior; APA: anticipatory postural adjustment; BOS: base of support; BW: Body weight; COM: centre-of-mass; COP: center-of-pressure; CT: cognitive task; GI: Gait initiation; GRF: ground reaction force; HC: heel-contact; HO: heel-off; ML: medial-lateral; MOS: margin of stability; SD: standard deviation; SE: step execution; SL: step length; SW: step width; VEL_COM: velocity of the COM; XCOM: extrapolated center of mass.

Freehand interaction with large displays: Effects of body posture, interaction distance and target size on task performance, perceived usability and workload.

The past decade has seen increasing popularity of large display-based freehand interaction. This study examined the effects of body posture, interaction distance and target size on freehand interaction with a large display. Participants performed pointing and dragging tasks by freehand interaction with a large display under sitting and standing postures and at different interaction distances. Targets in both small and large sizes were examined. Results showed that interaction distance yielded a significant effect on error rate, but the effect differed by task type. Little measurable difference was found in interaction performance, perceived usability and workload between sitting and standing postures. There were significant interaction effects between posture and interaction distance on perceived workload. Larger target size led to higher efficiency and accuracy in pointing tasks, but reduced accuracy in dragging tasks. This study provided implications that are likely to improve the design and deployment of large display-based freehand interaction techniques.

Quantitative detection, sources exploration and reduction of in-cabin benzene series hazards of electric buses through climate chamber experiments.

In this study, a large-scale in-cabin benzene series hazard detection is firstly performed on 20 electric buses by a full-scale climate chamber. The sources of BTEX are analyzed deeply by parts detection, and a series of effective measures are performed to reduce BTEX. Firstly, the in-cabin BTEX pollution with considerations of a series of parameters, such as interior configuration, environment temperature, vehicle age, and ventilation mode, is analyzed. The result shows that: 1) The VOCs concentrations decrease with vehicle age, higher configuration level and better ventilation system (particularly, fresh wind mode reduce VOCs fastly), while increases with environment temperature; 2) BTEX in bus cabins occupy approximatively 70.1% of TVOC, thus the BTEX overproof is the main culprit which causes VOCs to exceed standard. Then, measurements on components/materials VOCs releases were performed in a small climate chamber to discriminate key species and their sources. Xylene released from glues materials is found as a key species that causes BTEX/VOC to exceed limitation. Lastly, some measures, such as optimizations of materials selection and manufacturing crafts, are adopted to improve in-cabin pollution, and positive effects are obtained. For example, ethylbenzene and xylene released from HL 125 (a polyurethane adhesive) decrease by 2456% and 1930% respectively after improvement. And in-cabin xylene and TVOC decrease by 2274% and 222%, respectively, and all of them are lower than limitation value.