Piriformospora indica alleviates soda saline-alkaline stress in Glycine max by modulating plant metabolism.

Soil salinization is one of the major factors limiting agricultural production. Utilizing beneficial microorganisms like Piriformospora indica (P. indica) to enhance plant tolerance to abiotic stresses is a highly effective method, but the influence of P. indica on the growth of soybean in natural saline-alkaline soil remains unclear. Therefore, we investigated the effects of non-inoculation, P. indica inoculation, and fertilization on the growth, antioxidant defense, osmotic adjustment, and photosynthetic gas exchange parameters of soybean under two different levels of saline-alkaline stress in non-sterilized natural saline-alkaline soil. The study found that: 1) P. indica inoculation significantly promoted soybean growth, increasing plant height, root length, and biomass. Under mildly saline-alkaline stress, the increases were 11.5%, 16.0%, and 14.8%, respectively, compared to non-inoculated treatment. Under higher stress, P. indica inoculation achieved the same level of biomass increase as fertilization, while fertilization only significantly improved stem diameter. 2) Under saline-alkaline stress, P. indica inoculation significantly increased antioxidant enzyme activities and reduced malondialdehyde (MDA) content. Under mildly stress, MDA content was reduced by 47.1% and 43.3% compared to non-inoculated and fertilized treatments, respectively. Under moderate stress, the MDA content in the inoculated group was reduced by 29.9% and 36.6% compared to non-inoculated and fertilized treatments, respectively. Fertilization only had a positive effect on peroxidase (POD) activity. 3) P. indica inoculation induced plants to produce more osmotic adjustment substances. Under mildly stress, proline, soluble sugars, and soluble proteins were increased by 345.7%, 104.4%, and 6.9%, respectively, compared to non-inoculated treatment. Under higher stress, the increases were 75.4%, 179.7%, and 12.6%, respectively. Fertilization had no significant positive effect on proline content. 4) With increasing stress, soybean photosynthetic capacity in the P. indica-inoculated treatment was significantly higher than in the non-inoculated treatment, with net photosynthetic rate increased by 14.8% and 37.0% under different stress levels. These results indicate that P. indica can enhance soybean's adaptive ability to saline-alkaline stress by regulating ROS scavenging capacity, osmotic adjustment substance content, and photosynthetic capacity, thereby promoting plant growth. This suggests that P. indica has great potential in improving soybean productivity in natural saline-alkaline soils.

Formed in context: A mixed-methods study of medical students' mindsets in an Eastern culture.

INTRODUCTION: Mindsets have become an important focus in the fields of social and cognitive psychology. When holding a growth mindset, people appear more likely to engage in hard work and effort to foster success, seeing setbacks as necessary for learning. When holding a fixed mindset, in contrast, people tend to believe success comes from innate ability, seeing setbacks as evidence of inability. As such, mindsets affect students' learning, resilience and personal development. There is little empirical evidence, however, regarding how medical students perceive mindsets and the fundamental determinants of mindset formation, especially in non-Western contexts. This study investigated medical students' mindsets and perceptions of mindset formation with the aim of broadening the cross-cultural understanding of self-theories. METHODS: Using a convergent mixed-methods approach at a medical school in China, the authors conducted a survey and four focus groups with medical students in first to third years. Quantitatively, we used the Dweck Mindset Scale to describe medical students' mindsets in the domains of intelligence and talent. Qualitatively, we analysed focus group data using a grounded theory approach to develop a descriptive model. RESULTS: Survey results included 464 responses for quantitative analysis. Multivariable regression found that Year 3 students had more fixed mindsets for intelligence and talent (p < 0.05) compared with Year 1 students. Rural students reported a more mixed mindset for intelligence compared to urban students (p < 0.05). Qualitative analysis of focus group data yielded four major categories: beliefs about mindsets, conceptualization of mindsets, achievement motivation and source of mindset formation. We developed a Mindset Basis Model to depict connections among the factors students perceived to influence mindset formation-intra- and interindividual factors; contextual factors; and micro-, meso- and macro-system factors-and students' motivation regarding achievement. CONCLUSION: The study describes medical students' mindsets for the domains of intelligence and talent and explores how they conceptualised these mindsets. The findings indicate that factors influencing mindsets do not operate in isolation but through intricate interactions among multilevel factors embedded within a context.

A spatio-temporal graph convolutional network for ultrasound echocardiographic landmark detection.

Landmark detection is a crucial task in medical image analysis, with applications across various fields. However, current methods struggle to accurately locate landmarks in medical images with blurred tissue boundaries due to low image quality. In particular, in echocardiography, sparse annotations make it challenging to predict landmarks with position stability and temporal consistency. In this paper, we propose a spatio-temporal graph convolutional network tailored for echocardiography landmark detection. We specifically sample landmark labels from the left ventricular endocardium and pre-calculate their correlations to establish structural priors. Our approach involves a graph convolutional neural network that learns the interrelationships among landmarks, significantly enhancing landmark accuracy within ambiguous tissue contexts. Additionally, we integrate gate recurrent units to grasp the temporal consistency of landmarks across consecutive images, augmenting the model's resilience against unlabeled data. Through validation across three echocardiography datasets, our method demonstrates superior accuracy when contrasted with alternative landmark detection models.

Multi-residual 2D network integrating spatial correlation for whole heart segmentation.

Whole heart segmentation (WHS) has significant clinical value for cardiac anatomy, modeling, and analysis of cardiac function. This study aims to address the WHS accuracy on cardiac CT images, as well as the fast inference speed and low graphics processing unit (GPU) memory consumption required by practical clinical applications. Thus, we propose a multi-residual two-dimensional (2D) network integrating spatial correlation for WHS. The network performs slice-by-slice segmentation on three-dimensional cardiac CT images in a 2D encoder-decoder manner. In the network, a convolutional long short-term memory skip connection module is designed to perform spatial correlation feature extraction on the feature maps at different resolutions extracted by the sub-modules of the pre-trained ResNet-based encoder. Moreover, a decoder based on the multi-residual module is designed to analyze the extracted features from the perspectives of multi-scale and channel attention, thereby accurately delineating the various substructures of the heart. The proposed method is verified on a dataset of the multi-modality WHS challenge, an in-house WHS dataset, and a dataset of the abdominal organ segmentation challenge. The dice, Jaccard, average symmetric surface distance, Hausdorff distance, inference time, and maximum GPU memory of the WHS are 0.914, 0.843, 1.066 mm, 15.778 mm, 9.535 s, and 1905 MB, respectively. The proposed network has high accuracy, fast inference speed, minimal GPU memory consumption, strong robustness, and good generalization. It can be deployed to clinical practical applications for WHS and can be effectively extended and applied to other multi-organ segmentation fields. The source code is publicly available at https://github.com/nancy1984yan/MultiResNet-SC.

Moral distress from professionalism dilemmas and its association with self-rated professionalism behaviors among Chinese residents.

OBJECTIVES: Residents inevitably witness or participate in a diverse range of professionalism dilemmas. However, few studies have focused on residents' moral distress from professionalism dilemmas and its relationship with residents' professionalism. This study aimed to understand the moral distress that Chinese residents may face after exposure to professionalism dilemmas and to examine the associations between moral distress and residents' perceived fulfillment of professionalism behaviors. METHODS: We conducted a cross-sectional survey of residents from four standardized residency training bases in Liaoning Province, China, using stratified cluster sampling. A checklist of professionalism dilemmas, the Moral Distress Scale, and the Behavior-based Medical Professionalism Inventory were used to assess residents' moral distress from professionalism dilemmas and their perceived fulfillment of professionalism behaviors. Descriptive statistics, non-parametric tests, multiple linear regressions, and binary logistic regressions were used to analyze the data. RESULTS: A total of 647 (81.1%) residents effectively completed the survey. The proportion of residents suffering from moral distress ranged from 58.4 to 90.6% for different professionalism dilemmas. As the number of professionalism dilemmas associated with moral distress increased, residents reported lower fulfillment of professionalism behaviors (ß < 0, p < 0.05). Compared with residents with no distress, residents suffering from distress reported lower fulfillment of professionalism behaviors (OR < 1, p < 0.05). Among residents suffering from distress, as the distress intensity increased, residents reported higher fulfillment of professionalism behaviors (OR > 1, p < 0.05). CONCLUSIONS: Residents suffered a wide range of moral distress from professionalism dilemmas, and residents with moral distress reported lower fulfillment of professional behaviors. A responsive reporting system for residents and reflection on role modeling may help residents cope with the negative effects of moral distress and professionalism dilemmas.

STGA-MS: AI diagnosis model of regional wall motion abnormality based on 2D transthoracic echocardiography.

Regional wall motion abnormality (RWMA) is a common manifestation of ischemic heart disease detected through echocardiography. Currently, RWMA diagnosis heavily relies on visual assessment by doctors, leading to limitations in experience-based dependence and suboptimal reproducibility among observers. Several RWMA diagnosis models were proposed, while RWMA diagnosis with more refined segments can provide more comprehensive wall motion information to better assist doctors in the diagnosis of ischemic heart disease. In this paper, we proposed the STGA-MS model which consists of three modules, the spatial-temporal grouping attention (STGA) module, the segment feature extraction module, and the multiscale downsampling module, for the diagnosis of RWMA for multiple myocardial segments. The STGA module captures global spatial and temporal information, enhancing the representation of myocardial motion characteristics. The segment feature extraction module focuses on specific segment regions, extracting relevant features. The multiscale downsampling module analyzes myocardial motion deformation across different receptive fields. Experimental results on a 2D transthoracic echocardiography dataset show that the proposed STGA-MS model achieves better performance compared to state-of-the-art models. It holds promise in improving the accuracy and reproducibility of RWMA diagnosis, assisting clinicians in diagnosing ischemic heart disease more reliably.

Social Support and Daytime Sleepiness Among Chinese Medical Students: Mediating Roles of Loneliness and Problematic Smartphone Use.

Background: Sleep problems have become a serious threat to public health worldwide. Daytime sleepiness is an important indicator of many sleep problems, which have a significant impact on academic performance, physical and mental health, and wellbeing among medical students. We aimed to explore the factors associated with daytime sleepiness among medical students. Methods: We conducted a cross-sectional survey of 466 medical students recruited via convenience sampling. Self-rated psychometric scales, including the Social Support Rate Scale, the Epworth Sleepiness Scale, the Chinese version of the UCLA loneliness scale, and the Mobile Phone Addiction Index (MPAI), were utilized to evaluate social support, daytime sleepiness, loneliness, and problematic smartphone use. A sequential mediation model was constructed with daytime sleepiness as the dependent variable, social support as the independent variable, and loneliness and problematic smartphone use as mediating variables. Age, gender, and grade were taken as covariates. Results: Social support among medical students was negatively associated with loneliness, problematic smartphone use, and daytime sleepiness (p < 0.01). A sequential mediation analysis showed that loneliness (the first mediator) and problematic smartphone use (the second mediator) sequentially mediated the path from social support to daytime sleepiness among medical students (ß = -0.008; 95% CI = -0.016, -0.002). Moreover, there were also significant mediating roles observed in the relationship between social support and daytime sleepiness via loneliness only (ß = -0.037; 95% CI = -0.062, -0.015) and problematic smartphone use only (ß = -0.022; 95% CI = -0.041, -0.008). Conclusion: Social support, loneliness, and problematic smartphone use among medical students influenced their daytime sleepiness. Interventions targeting medical student loneliness and problematic smartphone use behaviors might attenuate the effects from low levels of social support and further improve daytime sleepiness.

The Relationship Between Social Media Use and Negative Emotions Among Chinese Medical College Students: The Mediating Role of Fear of Missing Out and the Moderating Role of Resilience.

Objective: This study aimed to examine the mediating role of fear of missing out (FoMO) and the moderating role of resilience in the association between social media use and negative emotions among medical college students in China during the COVID-19 pandemic. Participants and Methods: A cross-sectional study design was conducted, medical college students from Qiqihar Medical University, China participated in the study. A total of 470 medical college students were recruited voluntarily to complete questionnaires on social media use, FoMO, resilience, and negative emotions. Results: Social media use was positively associated with FoMO and negative emotions among medical students (p < 0.01). The total effect of social media use on negative emotions was significant (ß = 1.101, SE = 0.079, p < 0.001). Social media use had no significant direct effect on negative emotions (ß = 0.168, SE = 0.096, p > 0.05), but it had an indirect effect on negative emotions via mediation of FoMO (ß = 0.933, SE = 0.106, 95% CI: 0.731 to 1.149). Moreover, resilience had a significant moderating effect on the relationship between FoMO and negative emotions (ß = -0.021, SE = 0.005, 95% CI: -0.032 to -0.012). FoMO had a significant positive predictive effect on negative emotions at both levels of resilience (low resilience individuals: ß = 1.079, SE = 0.076, p < 0.001; high resilience individuals: ß = 0.212, SE = 0.094, p < 0.05). Conclusion: Social media use and FoMO may be risk factors for increased negative emotions, and resilience should be considered in prevention and intervention strategies designed to mitigate negative emotions among medical college students.

Evaluation of left ventricular function in patients with coronary slow flow: A systematic review and meta-analysis.

BACKGROUND: Coronary slow flow (CSF) is an angiographic finding defined as delayed distal vessel perfusion without severe stenosis of the epicardial coronary arteries. However, definite alterations in left ventricular (LV) function in patients with CSF remains inconsistent. This study aimed to clarify the changes in LV function in patients with CSF and explore the factors that may influence LV function. METHODS: PubMed, Embase, and Cochrane Library databases were systematically searched. Standardized mean differences and 95% confidence intervals (CI) for the LV function parameters were calculated. Subgroup analysis, meta-regression analysis, and correlation analysis were performed to explore the factors influencing LV function. RESULTS: Twenty-two studies (1101 patients with CSF) were included after searching three databases. In patients with CSF, LV ejection function in patients with CSF was marginally lower (61.8%; 95% CI: 61.0%, 62.7%), global longitudinal strain was decreased (-18.2%; 95% CI: -16.7%, -19.7%). Furthermore, left atrial diameter, left atrial volume index, and E/e' were significantly increased, while E/A and e' were significantly decreased. The mean thrombolysis in myocardial infarction frame count (TFC) was linearly associated with LV function; the larger the mean TFC, the greater the impairment of LV function. CONCLUSIONS: Left ventricular systolic and diastolic functions were impaired in patients with CSF, and this impairment was aggravated with increasing mean TFC.

The mediating role of psychological capital in the association between life satisfaction and depressive and anxiety symptoms among Chinese medical students during the COVID-19 pandemic: a cross-sectional study.

BACKGROUND: Although life satisfaction is a predictor of depressive and anxiety symptoms, the mechanisms underlying this association are poorly understood. This study examined how psychological capital (PsyCap), a positive psychological state, mediated the association between life satisfaction and depressive and anxiety symptoms among Chinese medical students during the COVID-19 pandemic. METHODS: A cross-sectional survey was conducted at three medical universities in China. A self-administered questionnaire was distributed to 583 students. Depressive symptoms, anxiety symptoms, life satisfaction, and PsyCap were measured anonymously. A hierarchical linear regression analysis was performed to explore the effects of life satisfaction on depressive and anxiety symptoms. Asymptotic and resampling strategies were used to examine how PsyCap mediates the association between life satisfaction and depressive and anxiety symptoms. RESULTS: Life satisfaction was positively associated with PsyCap and its four components. There were significant negative associations between life satisfaction, psychological capital, resilience, optimism, and depressive and anxiety symptoms among medical students. Self-efficacy was negatively associated with depressive and anxiety symptoms. Psychological capital (a×b = -0.3201, BCa 95% CI: -0.3899, -0.2446; a×b = -0.2749, BCa 95% CI: -0.3817, -0.1996), resilience (a×b = -0.2103, BCa 95% CI: -0.2727, -0.1580; a×b = -0.1871, BCa 95% CI: -0.2520, -0.1414), optimism (a×b = -0.2100, BCa 95% CI: -0.3388, -0.1150; a×b = -0.1998, BCa 95% CI: -0.3307, -0.0980), and self-efficacy (a×b = -0.0916, BCa 95% CI: 0.0048, 0.11629; a×b = 0.1352, BCa 95% CI: 0.0336, 0.2117) significantly mediated the association between life satisfaction and depressive and anxiety symptoms. LIMITATIONS: This was a cross-sectional study, and causal relationships between the variables could not be ascertained. Self-reported questionnaire instruments were used for data collection, which may have recall bias. CONCLUSIONS: Life satisfaction and PsyCap can be used as positive resources to reduce depressive and anxiety symptoms among third-year Chinese medical students during the COVID-19 pandemic. Psychological capital and its components (self-efficacy, resilience, and optimism) partially mediated the relationship between life satisfaction and depressive symptoms, and completely mediated the relationship between life satisfaction and anxiety symptoms. Therefore, improving life satisfaction and investing in psychological capital (especially self-efficacy, resilience, and optimism) should be included in the prevention and treatment of depressive and anxiety symptoms among third-year Chinese medical students. Additional attention is needed to pay for self-efficacy in such disadvantageous contexts.

Learning what and where to segment: A new perspective on medical image few-shot segmentation.

Traditional medical image segmentation methods based on deep learning require experts to provide extensive manual delineations for model training. Few-shot learning aims to reduce the dependence on the scale of training data but usually shows poor generalizability to the new target. The trained model tends to favor the training classes rather than being absolutely class-agnostic. In this work, we propose a novel two-branch segmentation network based on unique medical prior knowledge to alleviate the above problem. Specifically, we explicitly introduce a spatial branch to provide the spatial information of the target. In addition, we build a segmentation branch based on the classical encoder-decoder structure in supervised learning and integrate prototype similarity and spatial information as prior knowledge. To achieve effective information integration, we propose an attention-based fusion module (AF) that enables the content interaction of decoder features and prior knowledge. Experiments on an echocardiography dataset and an abdominal MRI dataset show that the proposed model achieves substantial improvements over state-of-the-art methods. Moreover, some results are comparable to those of the fully supervised model. The source code is available at github.com/warmestwind/RAPNet.

EchoEFNet: Multi-task deep learning network for automatic calculation of left ventricular ejection fraction in 2D echocardiography.

Left ventricular ejection fraction (LVEF) is essential for evaluating left ventricular systolic function. However, its clinical calculation requires the physician to interactively segment the left ventricle and obtain the mitral annulus and apical landmarks. This process is poorly reproducible and error prone. In this study, we propose a multi-task deep learning network EchoEFNet. The network use ResNet50 with dilated convolution as the backbone to extract high-dimensional features while maintaining spatial features. The branching network used our designed multi-scale feature fusion decoder to segment the left ventricle and detect landmarks simultaneously. The LVEF was then calculated automatically and accurately using the biplane Simpson's method. The model was tested for performance on the public dataset CAMUS and private dataset CMUEcho. The experimental results showed that the geometrical metrics and percentage of correct keypoints of EchoEFNet outperformed other deep learning methods. The correlation between the predicted LVEF and true values on the CAMUS and CMUEcho datasets was 0.854 and 0.916, respectively.

"It really puts me in a bind", professionalism dilemmas reported by Chinese residents.

Residents play a pivotal role in the healthcare system. However, few tools have systematically revealed the dilemmas and challenges faced by residents. This study aimed to develop a checklist for professionalism dilemmas based on a behavior-based professionalism framework and to examine the range and proportion of professionalism dilemmas heard of, witnessed, or experienced by Chinese residents. Mixed methods were used, comprising qualitative (document analysis and focus group interviews) and quantitative (a small-scale questionnaire survey) data. Document analysis summarized professionalism dilemma items from previous publications. For focus group interviews, we used narrative inquiry to explore and make sense of residents' experiences and perceptions of professionalism dilemmas. A small-scale questionnaire survey was conducted during each focus group to investigate the proportion of professionalism dilemma items that residents reported to have heard of, witnessed, or experienced. Through document analysis and focus group interviews, we developed a checklist of professionalism dilemmas based on a behavior-based professionalism framework. The checklist included 58 items over four domains, with 10 sub-domains (compassion, respect, communication, collaboration, integrity, duty, pursuit of excellence, fair stewardship of health care resources, patient confidentiality, and informed consent). We also sought a preliminarily subjective impression by exploring the proportion of residents who have heard of, witnessed, and experienced each of the professionalism dilemma items and residents' perspectives when faced with professionalism dilemmas. Residents inevitably encounter or experience a diverse range of professionalism dilemmas. This checklist of professionalism dilemmas that was developed could prove to be a significant reference for targeted professionalism education, both for the resident as well as for faculty. It can also act as a helpful tool for improving hospital management guidelines and patient education.

The mediating role of general academic emotions in burnout and procrastination among Chinese medical undergraduates during the COVID-19 pandemic: A cross-sectional study.

Background: Academic procrastination has become more prevalent during the COVID-19 pandemic due to teaching/learning changes. This phenomenon induces academic burnout, which is already serious among medical students. However, the academic emotion, which is the factor most vulnerable to changes in the academic environment, is still unknown. Therefore, the current study aimed to investigate the mediating role of general academic emotions in procrastination and burnout among Chinese medical students during the COVID-19 pandemic. Methods: This cross-sectional study enrolled 995 medical students from China Medical University. We applied the Chinese version of the Maslach Burnout Inventory Student Survey (MBI-SS), the Aitken Procrastination Inventory (API) and the General Academic Emotion Questionnaire for College Students (GAEQ) to evaluate the variables of interest. We examined the mediation effects of GAEs by hierarchical linear regression analysis. Results: Correlation analyses showed a significant positive correlation between procrastination and burnout. Procrastination and burnout positively and negatively correlated with negative academic emotions, respectively. Hierarchical linear regression analyses showed that procrastination had positive associations with negative academic emotions, while it had negative associations with positive academic emotions. The contributions (as mediators) of GAEs to burnout and procrastination were 21.16% (NAEs), 29.75% (PAEs), 54.25% (NDEs) and 23.69% (PDEs). Conclusions: The results indicate that academic emotions had mediating effects on procrastination and burnout. Medical students' worries about the uncertainty of the learning environment may have exacerbated academic burnout. Targeted improvements in the teaching environment to communicate encouragement and reduce anxiety and helplessness among medical undergraduates for implementing medical education while preventing and controlling the infection.

Learning burnout and its association with perceived stress, social support, and the Big Five personality traits in Chinese medical students during the COVID-19 pandemic: a cross-sectional study.

BACKGROUND: Owing to the coronavirus disease 2019, medical learning burnout has attracted increasing attention in educational research. It has a serious negative impact on medical students and their service quality. This could impair the professional development of medical students; weaken their personal and professional quality; and lead to problems such as increased medical errors and reduced patient care quality and satisfaction. This study aimed to examine the effects of perceived stress, social support, and the Big Five personality traits on learning burnout among medical students. METHODS: In November 2021, a cross-sectional survey was conducted at three medical universities in China. A self-administered questionnaire was distributed to 616 third- year students. Learning burnout, perceived stress, social support, and the Big Five personality traits (neuroticism, extroversion, openness, agreeableness, and conscientiousness) were anonymously measured. A total of 583 students were included in the final sample. Hierarchical linear regression was performed to explore the effects of perceived stress, social support, and Big Five personality traits on medical students' learning burnout. RESULTS: Perceived stress was positively associated with learning burnout (emotional exhaustion: ß = 0.577, p < 0.001; cynicism: ß = 0.543, p < 0.001; low professional efficacy: ß = 0.455, p < 0.001) whereas social support was negatively related with it (low professional efficacy: ß = -0.319, p < 0.001). Neuroticism had a positive effect on emotional burnout (ß = 0.152, p = 0.009). Extraversion (ß = -0.116, p = 0.006) and conscientiousness (ß = -0.363, p < 0.001) had a negative effect on low professional efficacy. Agreeableness negatively affected emotional exhaustion (ß = -0.181, p < 0.001) and cynicism (ß = -0.245, p < 0.001) and positively affected low professional efficacy (ß = 0.098, p = 0.008). The associated factors together accounted for an additional variance of learning burnout (emotional exhaustion: 39.0%; cynicism: 36.8%; low professional efficacy: 48.7%). CONCLUSIONS: Social support is a positive resource for fighting medical students' burnout. Perceived stress was the strongest indicator of learning burnout. In addition to reducing perceived stress, developing extraversion, agreeableness, and conscientiousness should be included in burnout prevention and treatment strategies, particularly for medical students.

Exploring the connection between parental bonding and smartphone addiction in Chinese medical students.

BACKGROUND: Smartphone addiction bodes adverse consequences, affecting different populations, including medical students. Parental bonding in childhood had been associated with addiction and recovery in later life. Therefore, this study aimed to examine the associations between parental bonding and smartphone addiction among Chinese medical students. METHOD: Binary logistic regressions were used to investigate the associations between parental bonding with mothers and fathers, respectively, and smartphone addiction. Interaction terms of care and protection were included in the models. RESULTS: A total of 517 medical students were included in the study. The prevalence of smartphone addiction was 48.16% (n = 249). The estimated effects of maternal and paternal parenting on smartphone addiction differed. Maternal protection was positively associated with smartphone addiction (OR, 1.046;95% CI, 1.005-1.087), and maternal care enhanced the estimated effect of protection on smartphone addiction. Paternal care was negatively associated with smartphone addiction (OR, 0.954;95% CI, 0.919-0.989). CONCLUSIONS: Chinese medical students with overprotective mothers or with indifferent fathers tended to exhibit traits of smartphone addiction. Further studies on factors influencing the associations between parental bonding and smartphone addiction may pave the way for potential family-oriented interventions for smartphone addiction.

Automatic identification of septal flash phenomenon in patients with complete left bundle branch block.

Complete left bundle branch block (cLBBB) is an electrical conduction disorder associated with cardiac disease. Septal flash (SF) involves septal leftward contraction during early systole followed by a lengthening motion toward the right ventricle and affects several patients with cLBBB. It has been revealed that cLBBB patients with SF may be at risk of cardiac function reduction and poor prognosis. Therefore, accurate identification of SF may play a vital role in counseling patients about their prognosis. Generally, Septal flash is identified by echocardiography using visual "eyeballing". However, this conventional method is subjective as it depends on operator experience. In this study, we build a linear attention cascaded net (LACNet) capable of processing echocardiography to identify SF automatically. The proposed method consists of a cascaded CNN-based encoder and an LSTM-based decoder, which extract spatial and temporal features simultaneously. A spatial transformer network (STN) module is employed to avoid image inconsistency and linear attention layers are implemented to reduce data complexity. Moreover, the left ventricle (LV) area-time curve calculated from segmentation results can be considered as a new independent disease predictor as SF phenomenon leads to transient left ventricle area enlargement. Therefore, we added the left ventricle area-time curve to LACNet to enrich input data diversity. The result shows the possibility of using echocardiography to diagnose cLBBB with SF automatically.

A longitudinal evaluation on 3-year change of anxiety and depression, and their risk factors among parents of childhood and adolescence patients with resectable osteosarcoma: A cohort study.

Psychological disorders often occur among parents of children with cancer. The current study aimed to explore the longitudinal change of anxiety and depression and their related factors among parents of childhood and adolescence patients with osteosarcoma. A total of 56 childhood and adolescence patients with osteosarcoma who underwent tumor resection and corresponding 104 parents were enrolled. Hospital Anxiety and Depression Scale-Anxiety (HADS-A) and HADS-Depression (HADS-D) of parents were evaluated at baseline (the day of patients' hospital discharge), 0.5 year, 1 year, 2 years, and 3 years. From baseline to the 3rd year, HADS-A (from 8.3 ±â€…3.1 to 9.4 ±â€…3.1. P < .001), HADS-D score (from 7.7 ±â€…3.2 to 8.8 ±â€…2.9, P = .001), anxiety rate (from 45.2% to 60.6%, P = .038), depression rate (from 38.5% to 57.7%, P = .002) were elevated; meanwhile, anxiety severity (P = .001) and depression severity (P = .001) were also increased. Furthermore, multivariate logistic regression analysis presented that the role of mother, divorced/widowed marital status, declined family annual income, elevated Enneking stage, and amputation were independently correlated with elevated risk of parents' baseline anxiety or depression (all P < .05). Additionally, declined family annual income, elevated Enneking stage, and amputation were independently correlated with increased risk of parents' 3-year anxiety or depression (all P < .05). Anxiety and depression deteriorate with time in parents of childhood and adolescence patients with osteosarcoma, which are affected by parental role, marital status, family annual income, surgery type, and Enneking stage.

WeChat-platform-based education and care program as a candidate approach to relieve anxiety, depression, and post-traumatic stress disorder in parents of pediatric and adolescent patients with osteosarcoma.

Background: WeChat is the main social platform in China, characterized by its versatility and ease of communication. This study aimed to explore the effect of a WeChat-platform-based education and care (WBEC) program on relieving anxiety, depression, and post-traumatic stress disorder (PTSD) in parents of pediatric and adolescent patients with osteosarcoma. Methods: In total, 48 patients and 86 parents were enrolled in this randomized, controlled study and then assigned to the WBEC program (24 patients and 45 parents) and the usual education and care (UEC) program (22 patients and 41 parents) for 6 months as a 1:1 ratio. Results: Parents in the WBEC group had lower Hospital Anxiety and Depression Scale (HADS) for anxiety (HADS-A) scores at M3 (7.8 ± 2.2 vs. 9.1 ± 2.5; p = 0.010) and M6 (7.7 ± 2.5 vs. 8.9 ± 2.4; p = 0.027) when compared to the UEC group, while anxiety rate was only decreased at M3 (43.2% vs. 63.4%; p = 0.049) in the WBEC group. Meanwhile, parental HADS for depression (HADS-D) scores were reduced at M3 (7.0 ± 2.0 vs. 8.0 ± 2.1; p = 0.047) and M6 (7.1 ± 1.9 vs. 8.0 ± 2.4; p = 0.045) in the WBEC group when compared with the UEC group; while depression rate remained the same. Parental Impact of Event Scale-Revised (IES-R) scores were slightly reduced among the WBEC group at M6 when compared with the UEC group [12.0 (interquartile range (IQR): 10.0-20.8] vs. 15.0 (IQR: 9.5-25.0; p = 0.077)], but not statistically significant. Conclusion: WeChat-platform-based education and care is considered a feasible intervention to reduce anxiety and depression in parents of pediatric and adolescent patients with osteosarcoma, while also providing mild relief from PTSD.