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Introduction The outbreak of Corona Virus Disease (COVID- 19) in 2019 has continued until now, posing a huge threat to the public’s physical and mental health, resulting in different degrees of mental health problems. Previous studies have shown that insomnia is one of the main influences on anxiety. However, due to the specificity of the disease and the situation of centralized treatment of COVID- 19 patients in mobile cabin hospitals, insomnia interventions are limited. Therefore, it is necessary to find the complex mediating variables between insomnia and anxiety to provide new ideas for the prevention and intervention of anxiety caused by insomnia in COVID- 19 patients. Methods The measurement tools were the Athens Insomnia Scale, Psychological Capital Questionna-ire, and Self-Rating Anxiety Scale. Statistical analysis was performed using SPSS 25.0 software, and a mediator model was established and corrected using Amos software. Results More than one in five patients in this study had anxiety, and the total score was significantly higher than the Chinese standard total score. Insomnia was positively correlated with anxiety, but negatively correlated with psychological capital; and psychological capital was negatively correlated with anxiety. Psychological capital can act as a mediating factor in the relationship between insomnia and anxiety. Psychological capital played a mediating role in the relationship between insomnia and anxiety among COVID- 19 patients, that is, insomnia not only directly affected anxiety, but also indirectly affected anxiety through the mediating role of psychological capital. Conclusion It is recommended that patients and health care professionals increase the psychological capital of COVID- 19 patients through various methods to counter the effects of insomnia on anxiety.

The spatial proteomic profiling of complex tissues is essential for investigating cellular function in physiological and pathological states. However, the imbalance among resolution, protein coverage, and expense precludes their systematic application to analyze whole tissue sections in an unbiased manner and with high resolution. Here, we introduce panoramic spatial enhanced resolution proteomics (PSERP), a method that combines tissue expansion, automated sample segmentation, and tryptic digestion with high-throughput proteomic profiling. The PSERP approach facilitates rapid quantitative profiling of proteomic spatial variability in whole tissue sections at sub-millimeter resolution. We demonstrated the utility of this method for determining the streamlined large-scale spatial proteomic features of gliomas. Specifically, we profiled spatial proteomic features for nine glioma samples across three different mutation types ( IDH1 -WT/ EGFR -mutant, IDH1 -mutant, and IDH1 / EGFR -double-WT gliomas) at sub-millimeter resolution (corresponding to a total of 2,230 voxels). The results revealed over 10,000 proteins identified in a single slide, which helps us to portray the diverse proteins and pathways with spatial abundance patterns in the context of tumor heterogeneity and cellular features. Our spatial proteomic data revealed distinctive proteomic features of malignant and non-malignant tumor regions and depicted the distribution of proteins from tumor centers to tumor borders and non-malignant tumor regions. Through integrative analysis with single-cell transcriptomic data, we elucidated the cellular composition and cell–cell communications in a spatial context. Our PSERP also includes a spatially resolved tumor-specific peptidome identification workflow that not only enables us to elucidate the spatial expression patterns of tumor-specific peptides in glioma samples with different genomic types but also provides us with opportunities to select combinations of tumor-specific mutational peptides whose expression could cover the maximum tumor regions for future immune therapies. We further demonstrated that combining tumor-specific peptides might enhance the efficacy of immunotherapy in both patient-derived cell (PDC) and patient-derived xenograft (PDX) models. PSERP efficiently retains precise spatial proteomic information within the tissue context and provides a deeper understanding of tissue biology and pathology at the molecular level.

Background Professional identity has a positive impact on the career development, job satisfaction, and job performance of undergraduate nursing students, and it is also an important foundation for their future nursing careers. Therefore, it is necessary to explore the influencing factors of professional identity and the inherent relationships between these factors. Objective This study aims to investigate the serial multiple mediating effect of psychological capital and self-directed learning ability in the relationship between perceived stress and professional identity among undergraduate nursing students. Methods All participants ( n  = 596) were collected using an online survey between October 2023 to January 2024 through cluster sampling. Measures of perceived stress, psychological capital, self-directed learning ability, and professional identity were assessed. The serial multiple mediation of psychological capital and self-directed learning ability in the relationship between perceived stress and professional identity was examined using the SPSS PROCESS 3.4 macro by Hayes. Data analysis included descriptive analysis, independent samples t-tests, one-way ANOVA, Pearson's correlation, ordinary least-squares regression, and the bootstrap method. Results Professional identity was negatively correlated with perceived stress ( r  = -0.414, p  < 0.001), and was positively correlated with psychological capital ( r  = 0.495, p  < 0.001) and self-directed learning ability ( r  = 0.443, p  < 0.001). In the model of perceived stress → psychological capital → self-directed learning ability → professional identity, the overall mediated effect was -0.567 (95% CI: -0.707 ~ -0.443), and the mediating effect of psychological capital was -0.365 (95% CI: -0.501 ~ -0.237), accounting for 46.09% of total effect. The mediating effect of self-directed learning ability was -0.083 (95% CI: -0.150 ~ -0.028), accounting for 10.48% of total effect. The serial mediating effect of psychological capital and self-directed learning ability was -0.120 (95% CI: -0.184 ~ -0.065), accounting for 15.15% of total effect. Conclusions Psychological capital and self-directed learning ability serve as chain mediators between perceived stress and professional identity among undergraduate nursing students. This indicates that nursing educators can enhance the psychological capital and self-directed learning ability of nursing students to reduce the adverse effects of perceived stress on their professional identity.

Building height and footprint are two fundamental urban morphological features required by urban climate modelling. Although some statistical methods have been proposed to estimate average building height and footprint from publicly available satellite imagery, they often involve tedious feature engineering which makes it hard to achieve efficient knowledge discovery in a changing urban environment with ever-increasing earth observations. In this work, we develop a deep-learning-based (DL) Python package – SHAFTS (Simultaneous building Height And FootprinT extraction from Sentinel imagery) to extract such information. Multi-task deep-learning (MTDL) models are proposed to automatically learn feature representation shared by building height and footprint prediction. Besides, we integrate digital elevation model (DEM) information into developed models to inform models of terrain-induced effects on the backscattering displayed by Sentinel-1 imagery. We set conventional machine-learning-based (ML) models and single-task deep-learning (STDL) models as benchmarks and select 46 cities worldwide to evaluate developed models’ patch-level prediction skills and city-level spatial transferability at four resolutions (100, 250, 500 and 1000 m). Patch-level results of 43 cities show that DL models successfully produce discriminative feature representation and improve the coefficient of determination (R2) of building height and footprint prediction more than ML models by 0.27–0.63 and 0.11–0.49, respectively. Moreover, stratified error assessment reveals that DL models effectively mitigate the severe systematic underestimation of ML models in the high-value domain: for the 100 m case, DL models reduce the root mean square error (RMSE) of building height higher than 40 m and building footprint larger than 0.25 by 31 m and 0.1, respectively, which demonstrates the superiority of DL models on refined 3D building information extraction in highly urbanized areas. For the evaluation of spatial transferability, when compared with an existing state-of-the-art product, DL models can achieve similar improvement on the overall performance and high-value prediction. Furthermore, within the DL family, comparison in building height prediction between STDL and MTDL models reveals that MTDL models achieve higher accuracy in all cases and smaller bias uncertainty for the prediction in the high-value domain at the refined scale, which proves the effectiveness of multi-task learning (MTL) on building height estimation.

Purpose This study aims to explore the incidence of hypoglycemia in patients with type 2 diabetes mellitus (T2DM) and the influence of hypoglycemia on the specific quality of life in T2DM patients. Methods It was a comparative cross-sectional study consisting of 519 T2DM patients in Xi'an, China and patients were investigated by self-reported hypoglycemia and specific quality of life questionnaires from September 2019 to January 2020. Descriptive analysis, t -test, Chi-square test, hierarchical regression analysis and stepwise multiple regression analysis were applied to assess the influence of hypoglycemia on the specific quality of life. Results The incidence of hypoglycemia in T2DM patients was 32.18%. The mean score of specific quality of life in diabetes without hypoglycemia was 57.33 ± 15.36 and was 61.56 ± 17.50 in those with hypoglycemia, which indicated that hypoglycemia had a serious impact on the quality of life of diabetics ( t  = − 5.172, p  = 0.000). In the Univariate analysis of specific quality of life, age, education background, marital status, living status, duration of diabetes, monthly income per capita were independent and significant factors associated with specific quality of life of two groups of T2DM patients ( p  < 0.05). In the hierarchical regression analysis, the duration of the diabetes more than 11 years and the frequency of hypoglycemia more than 6 times in half a year entered the equation of specific quality of life of 519 diabetics respectively ( p  < 0.001). In multiple linear regression analysis, age, marital status and income all entered the regression equation of quality of life of the two groups ( p  < 0.05). Conclusion Hypoglycemia will have a serious impact on the quality of life of T2DM patients. In order to improve the living quality in diabetics, effective measurements should be taken to strengthen the management of blood glucose and to avoid hypoglycemia.

Background The outbreak of Corona Virus Disease (COVID-19) in 2019 has continued until now, posing a huge threat to the public’s physical and mental health, resulting in different degrees of mental health problems. As a vulnerable segment of the public, anxiety is one of the most common mental health problems among COVID-19 patients. Excessive anxiety aggravates the physical and psychological symptoms of COVID-19 patients, which is detrimental to their treatment and recovery, increases financial expenditure, affects family relations, and adds to the medical burden. Objective This study aimed to explore the role of psychological capital and self-esteem in the relationship between insomnia and anxiety, thereby shedding light on the mechanism of the effect of insomnia on anxiety in COVID-19 patients. Methods A cross-sectional study was conducted from April to May 2022 in Fangcang hospital in Shanghai, China. The self-administered questionnaires were distributed to 718 COVID-19 patients via cell phone using the Internet platform “Questionnaire Star”, which included Athens Insomnia Scale, Psychological Capital Questionnaire, Self-esteem Scale, Self-Rating Anxiety Scale, gender, age, marital status, education. Data analysis was performed using descriptive analysis, independent-samples t-test, one-way analysis of variance, Pearson correlation analysis, ordinary least-squares regression, and bootstrap method. Results Education background had significant impact on anxiety in COVID-19 patients (F = 7.70, P  < 0.001). Insomnia, psychological capital, self-esteem and anxiety were significantly correlated, respectively ( P  < 0.001). And Regression analysis showed that insomnia had a direct negative predictive effect on psychological capital (β = -0.70, P  < 0.001) and self-esteem (β = -0.13, P  < 0.001). Psychological capital had a direct positive predictive effect on self-esteem (β = 0.12, P  < 0.001). Insomnia had a direct positive predictive effect on anxiety (β = 0.61, P  < 0.001). Both psychological capital and self-esteem had significant negative predictive effects on anxiety (β = -0.06, P  < 0.05; β = -0.72, P  < 0.001). The results showed that the mediating effect of psychological capital and self-esteem was significant, and the mediating effect value was 0.21. First, the indirect effect consisting of insomnia - psychological capital - anxiety was 0.04, showing that psychological capital had a significant mediating effect. Second, the indirect effect consisting of insomnia-self-esteem-anxiety had a value of 0.10, indicating that self-esteem had a significant mediating effect. Third, the indirect effect consisting of insomnia-psychological capital-self-esteem-anxiety had a value of 0.06, suggesting that psychological capital and self-esteem had a significant chain mediating effect between insomnia and anxiety. Conclusions Insomnia had a significant positive predictive effect on anxiety. Insomnia was first associated with a decrease in psychological capital, followed by a sequential decrease in self-esteem, which in turn was associated with increased anxiety symptoms in COVID-19 patients. Therefore, focusing on improving the psychological capital and self-esteem of patients can help alleviate the anxiety caused by insomnia in COVID-19 patients. It is recommended that patients and health care professionals increase the psychological capital and Self-esteem of COVID-19 patients through various methods to counter the effects of insomnia on anxiety.

Type 2 diabetes mellitus (T2DM) is a significant risk factor for mild cognitive impairment (MCI) and the acceleration of MCI to dementia. The high glucose level induce disturbance of neurovascular (NV) coupling is suggested to be one potential mechanism, however, the neuroimaging evidence is still lacking. To assess the NV decoupling pattern in early diabetic status, 33 T2DM without MCI patients and 33 healthy control subjects were prospectively enrolled. Then, they underwent resting state functional MRI and arterial spin labeling imaging to explore the hub-based networks and to estimate the coupling of voxel-wise cerebral blood flow (CBF)-degree centrality (DC), CBF-mean amplitude of low-frequency fluctuation (mALFF) and CBF- mean regional homogeneity (mReHo). We further evaluated the relationship between NV coupling pattern and cognitive performance (false discovery rate corrected). T2DM without MCI patients displayed significant decrease in the absolute CBF-mALFF, CBF-mReHo coupling of CBFnetwork and in the CBF-DC coupling of DCnetwork. Besides, networks which involved CBF and DC hubs mainly located in the default mode network (DMN). Furthermore, less severe disease and better cognitive performance in T2DM patients were significantly correlated with higher coupling of CBF-DC, CBF-mALFF or CBF-mReHo, especially for the cognitive dimensions of general function and executive function. Thus, coupling of CBF-DC, CBF-mALFF and CBF-mReHo may serve as promising indicators to reflect NV coupling state and to explain the T2DM related early cognitive impairment. [Display omitted]

A positive professional identity is key for nursing students in determining career direction and predicting future engagement in the profession. Despite its complexity and susceptibility to various influences, the factors shaping nursing students' professional identity remain poorly understood. This study aims to investigate how perceived stress can directly and indirectly influence professional identity among nursing students, with self-control and self-directed learning ability as mediators. A cross-sectional survey was conducted from October to December 2023, collecting data from 675 nursing students across five tertiary hospitals in Xi'an, Shaanxi Province, China. The survey captured detailed data on sociodemographic characteristics, perceived stress, self-control, self-directed learning ability, and professional identity among the participants. Descriptive analysis and correlation matrices were used to analyze participant characteristics and assess bivariate correlations. The mediation model was analyzed using the PROCESS macro for SPSS. Perceived stress showed a direct and negative influence on professional identity among nursing students; self-control was shown to play a mediating role between perceived stress and professional identity; self-directed learning ability was shown to play a mediating role between perceived stress and professional identity; and self-control and self-directed learning ability were shown to play a chain mediating role between perceived stress and professional identity. Self-control and self-directed learning ability have a chain mediating role in between perceived stress and professional identity among nursing students. It suggests that nursing managers and educators can improve the self-control and self-directed learning ability of nursing students to mitigate the negative impact of perceived stress on professional identity.

When the urban heat island (UHI) effect coincides with a heat wave (HW), thermal stress in cities is exacerbated. Understanding the surface energy balance (SEB) responses to HWs is critical for improving predictions of the synergies between UHIs and HWs. This study evaluates observed SEB characteristics in four cities (Beijing, Łódź, London and Swindon), along with their ambient meteorological conditions, for both HW and background summer climate scenarios. Using the Analytical Objective Hysteresis Model (AnOHM), particular emphasis is on the heat storage. The results demonstrate that in London and Swindon the amount of daytime heat storage and its fraction relative to the net all-wave radiation increase under HWs. Results further demonstrate that such increases are strongly tied to lower wind speeds. The effects of different UHI mitigation measures on heat storage are assessed using AnOHM. Results reveal that use of reflective materials and maintaining higher soil moisture availability can offset the adverse effects of increased heat storage.