The relationship between family cohesion and bedtime procrastination among Chinese college students: the chain mediating effect of coping styles and mobile phone addiction.

BACKGROUND: Bedtime procrastination refers to an individual's inability to go to bed at a predetermined time without external obstacles. Previous researchers have found that the bedtime procrastination is harmful to human physical and mental health, but these research on bedtime procrastination have mostly focused on exploring individual factors, while ignoring the external environmental factors. Therefore, this is the first study to investigate bedtime procrastination from the perspective of family environments. METHODS: The study was conducted using a convenient sampling method and online questionnaires. Family Cohesion Scale, Coping Styles Questionnaire, Mobile Phone Addiction Tendency Scale and Bedtime Procrastination Scale were used to measure sleep and psychological condition of 1,048 college students. RESULTS: Family cohesion negatively predicted bedtime procrastination. Additionally, positive coping style and mobile phone addiction had significant independent mediating effects. Furthermore, positive coping style and mobile phone addiction had chain mediating effects between family cohesion and bedtime procrastination. CONCLUSION: This study revealed the effect of coping styles and mobile phone addiction on the relationship between family cohesion and bedtime procrastination among Chinese college students. These findings explained the mechanisms of bedtime procrastination from the perspective of environment, so as to effectively intervene the bedtime procrastination of college students from the perspective of external environment.

Heterojunction-Mediated Co-Adjustment of Band Structure and Valence State for Achieving Selective Regulation of Semiconductor Nanozymes.

Improving reaction selectivity is the next target for nanozymes to mimic natural enzymes. Currently, the majority of strategies in this field are exclusively applicable to metal-organic-based or organic-based nanozymes, while limited in regulating metal oxide-based semiconductor nanozymes. Herein, taking semiconductor Co3O4 as an example, a heterojunction strategy to precisely regulate nanozyme selectivity by simultaneously regulating three vital factors including band structure, metal valence state, and oxygen vacancy content is proposed. After introducing MnO2 to form Z-scheme heterojunctions with Co3O4 nanoparticles, the catalase (CAT)-like and peroxidase (POD)-like activities of Co3O4 can be precisely regulated since the introduction of MnO2 affects the position of the conduction bands, preserves Co in a higher oxidation state (Co3+), and increases oxygen vacancy content, enabling Co3O4-MnO2 exhibit improved CAT-like activity and reduced POD-like activity. This study proposes a strategy for improving reaction selectivity of Co3O4, which contributes to the development of metal oxide-based semiconductor nanozymes.

Cell-in-cell-mediated intercellular communication exacerbates the pro-inflammatory progression in asthma.

Cell-in-cell (CIC) structures have been suggested to mediate intracellular substance transport between cells and have been found widely in inflammatory lung tissue of asthma. The aim of this study was to investigate the significance of CIC structures in inflammatory progress of asthma. CIC structures and related inflammatory pathways were analyzed in asthmatic lung tissue and normal lung tissue of mouse model. In vitro, the activation of inflammatory pathways by CIC-mediated intercellular communication was analyzed by RNA-Seq and verified by Western blotting and immunofluorescence. Results showed that CIC structures of lymphocytes and alveolar epithelial cells in asthmatic lung tissue mediated intercellular substance (such as mitochondria) transfer and promoted pro-inflammation in two phases. At early phase, internal lymphocytes triggered inflammasome-dependent pro-inflammation and cell death of itself. Then, degraded lymphocytes released cellular contents such as mitochondria inside alveolar epithelial cells, further activated multi-pattern-recognition receptors and NF-kappa B signaling pathways of alveolar epithelial cells, and thereby amplified pro-inflammatory response in asthma. Our work supplements the mechanism of asthma pro-inflammation progression from the perspective of CIC structure of lymphocytes and alveolar epithelial cells, and provides a new idea for anti-inflammatory therapy of asthma.

Branched chemically modified poly(A) tails enhance the translation capacity of mRNA.

Although messenger RNA (mRNA) has proved effective as a vaccine, its potential as a general therapeutic modality is limited by its instability and low translation capacity. To increase the duration and level of protein expression from mRNA, we designed and synthesized topologically and chemically modified mRNAs with multiple synthetic poly(A) tails. Here we demonstrate that the optimized multitailed mRNA yielded ~4.7-19.5-fold higher luminescence signals than the control mRNA from 24 to 72 h post transfection in cellulo and 14 days detectable signal versus <7 days signal from the control in vivo. We further achieve efficient multiplexed genome editing of the clinically relevant genes Pcsk9 and Angptl3 in mouse liver at a minimal mRNA dosage. Taken together, these results provide a generalizable approach to synthesize capped branched mRNA with markedly enhanced translation capacity.

Deep learning-based diffusion tensor cardiac magnetic resonance reconstruction: a comparison study.

In vivo cardiac diffusion tensor imaging (cDTI) is a promising Magnetic Resonance Imaging (MRI) technique for evaluating the microstructure of myocardial tissue in living hearts, providing insights into cardiac function and enabling the development of innovative therapeutic strategies. However, the integration of cDTI into routine clinical practice poses challenging due to the technical obstacles involved in the acquisition, such as low signal-to-noise ratio and prolonged scanning times. In this study, we investigated and implemented three different types of deep learning-based MRI reconstruction models for cDTI reconstruction. We evaluated the performance of these models based on the reconstruction quality assessment, the diffusion tensor parameter assessment as well as the computational cost assessment. Our results indicate that the models discussed in this study can be applied for clinical use at an acceleration factor (AF) of × 2 and × 4 , with the D5C5 model showing superior fidelity for reconstruction and the SwinMR model providing higher perceptual scores. There is no statistical difference from the reference for all diffusion tensor parameters at AF × 2 or most DT parameters at AF × 4 , and the quality of most diffusion tensor parameter maps is visually acceptable. SwinMR is recommended as the optimal approach for reconstruction at AF × 2 and AF × 4 . However, we believe that the models discussed in this study are not yet ready for clinical use at a higher AF. At AF × 8 , the performance of all models discussed remains limited, with only half of the diffusion tensor parameters being recovered to a level with no statistical difference from the reference. Some diffusion tensor parameter maps even provide wrong and misleading information.

Exploring the role and mechanism of hyperoside against cardiomyocyte injury in mice with myocardial infarction based on JAK2/STAT3 signaling pathway.

BACKGROUND: Myocardial infarction (MI) is one of the most deadly diseases in the world. Hyperoside (Hyp) has been shown to have a protective effect on cardiovascular function through various signaling pathways, but whether it can protect myocardial infarction by regulating JAK2/STAT3 signaling pathway is unknown. AIM OF THE STUDY: To investigate whether Hyp could protect the heart against myocardial infarction injury in mice by modulating JAK2/STAT3 signaling pathway and its potential mechanism. METHODS: In vivo experiments, the myocardial infarction model was established by ligating the left anterior descending coronary artery (LAD) of male C57BL/6 mice permanently. The mice were divided into seven groups: sham group, MI group, MI+Hyp (9 mg/kg), MI+Hyp (18 mg/kg) group, MI+Hyp (36 mg/kg) group, MI+Captopril group (15 mg/kg) group and MI+Hyp (36 mg/kg)+AG490 (7.5 mg/kg) group. Each group of animals were given different concentrations of hyperoside, positive control drug or inhibitor of JAK2/STAT3 singaling. After 14 days of administration, the electrocardiogram (ECG), echocardiography and serum myocardial injury markers were examined; Slices of mouse myocardial tissue were assessed for histopathological changes by HE, Masson and Sirius Red staining. TTC and TUNEL staining were used to evaluate the myocardial infarction area and cardiomyocytes apoptosis respectively. The expression of JAK2/STAT3 signaling pathway, apoptosis and autophagy-related proteins were detected by western blot. In vitro experiments, rat H9c2 cardiomyocytes were deprived of oxygen and glucose (OGD) to stimulate myocardial ischemia. The experiment was divided into seven groups: Control group, OGD group, OGD+Hyp (20 µM) group, OGD+Hyp (40 µM) group, OGD+Hyp (80 µM), OGD+Captopril (10 µM) group and OGD+Hyp (80 µM)+AG490 (100 µM) group. Myocardial cell damage and redox index were measured 12 h after OGD treatment. ROS content in cardiomyocytes was detected by immunofluorescence. Cardiomyocytes apoptosis was detected by flow cytometry. The expressions of JAK2/STAT3 signaling pathway-related proteins, apoptosis and autophagy related proteins were detected by western blot. RESULTS: In vivo, hyperoside could ameolirate ECG abnormality, increase cardiac function, reduce myocardial infarction size and significantly reduce myocardial fibrosis level and oxidation level. The experimental results in vitro showed that Hyp could reduce the ROS content in cardiomyocytes, decrease the level of oxidative stress and counteract the apoptosis induced by OGD injury . Both in vivo and in vitro experiments showed that hyperoside could increase phosphorylated JAK2 and STAT3, indicating that hyperoside could play a cardioprotective role by activating JAK2/STAT3 signaling pathway. It was also shown that hyperoside could increase the autophagy level of cardiomyocytes in vivo and in vitro. However the cardiomyocyte-protective effect of Hyp was abolished in combination with JAK2/ STAT3 signaling pathway inhibitor AG490. These results indicated that the protective effect of Hyp on cardiomyocyte injury was at least partially achieved through the activation of the JAK2/STAT3 signaling pathway. CONCLUSION: Hyp can significantly improve cardiac function, ameliorate myocardial hypertrophy and myocardial remodeling in MI mice. The mechanism may be related to improving mitochondrial autophagy of cardiomyocytes to maintain the advantage of autophagy, and blocking apoptosis pathway through phagocytosis, thus suppressing apoptosis level of cardiomyocytes. These effects of Hyp are achieved, at least in part, by activating the JAK2/STAT3 signaling pathway.

Thermal-Responsive Gel-Based Overheat Limiter Enabled Intelligent Photothermal Therapy.

Uncontrolled and excessive photothermal heating in photothermal therapy (PTT) inevitably causes thermal damage to surrounding normal tissues, severely limiting the universality and safety of PTT. To address this issue, an intelligent cooling thermal-responsive (ICTR) gel containing poly(N-isopropylacrylamide-co-acrylamide) (P(NIPAM-AM))microgel is applied onto the skin to realize intelligent PTT, which can avoid excessive heating and accidental injury. The high near-infrared (NIR) light transmittance (> 95%) of the ICTR gel ensures effective light delivery at low temperatures, while the refractive index of the P(NIPAM-AM) microgel increases remarkably when the temperature exceeds a predetermined threshold, resulting in progressively enhanced light scattering and weakened photothermal conversion. In animal studies, the negative feedback regulation of ICTR gel on light transmittance and photothermal heating allows the photothermal temperature in the lesion site to be stabilized within the effective therapeutic range (45 °C) while ensuring that the skin surface temperature does not exceed 35 °C. Compared with the severe skin thermal damage found in the histological staining of mice skin receiving conventional PTT, the mice skin receiving the ICTR gel-enabled intelligent PTT remains in good condition. This study establishes an intelligent and universal paradigm for PTT thermal regulation, which is of great significance for achieving safe and effective PTT.

Ambient Monitoring Portable Sensor Node for Robot-Based Applications.

The leakage of gases and chemical vapors is a common accident in laboratory processes that requires a rapid response to avoid harmful effects if humans and instruments are exposed to this leakage. In this paper, the performance of a portable sensor node designed for integration with mobile and stationary robots used to transport chemical samples in automated laboratories was tested and evaluated. The sensor node has four main layers for executing several functions, such as power management, control and data preprocessing, sensing gases and environmental parameters, and communication and data transmission. The responses of three metal oxide semiconductor sensors, BME680, ENS160, and SGP41, integrated into the sensing layer have been recorded for various volumes of selected chemicals and volatile organic compounds, including ammonia, pentane, tetrahydrofuran, butanol, phenol, xylene, benzene, ethanol, methanol, acetone, toluene, and isopropanol. For mobile applications, the sensor node was attached to a sample holder on a mobile robot (ASTI ProBOT L). In addition, the sensor nodes were positioned close to automation systems, including stationary robots. The experimental results revealed that the tested sensors have a different response to the tested volumes and can be used efficiently for hazardous gas leakage detection and monitoring.

A MoS2 nanosheet-based CRISPR/Cas12a biosensor for efficient miRNA quantification for acute myocardial infarction.

Acute myocardial infarction (AMI) represents the leading cause of cardiovascular death worldwide, and it is thus pivotal to develop effective approaches for the timely detection of AMI markers, especially possessing the characteristics of antibody-free, signal amplification, and manipulation convenience. We herein construct a MoS2 nanosheet-powered CRISPR/Cas12a sensing strategy for sensitive determination of miR-499, a superior AMI biomarker to protein markers. The presence of miR-499 at a trace level is able to induce a significantly enhanced fluorescence signal in a DNA-based molecular engineering platform, which consists of CRISPR/Cas12a enzymatic reactions and MoS2 nanosheet-controllable signal reporting components. The MoS2 nanosheets were characterized by using atomic force microscopy (AFM) and transmission electron microscope (TEM). The detection feasibility was verified by using polyacrylamide gel electrophoresis (PAGE) analysis and fluorescence measurements. The detection limit is determined as 381.78 pM with the linear range from 0.1 ⅹ 10-9 to 13.33 ⅹ 10-9 M in a fast manner (about 30 min). Furthermore, miRNA detection in real human serum is also conducted with desirable recovery rates (89.5 %-97.6 %), which may find potential application for the clinic diagnosis. We describe herein the first example of MoS2 nanosheet-based signal amplified fluorescence sensor for effective detection of AMI-related miRNA.

Dichotomic Role of Low-Concentration EGCG in the Oxaliplatin Sensitivity of Colorectal Cancer Cells.

Although epigallocatechin-3-gallate (EGCG) can potentiate chemotherapeutic drugs at high concentrations, its clinical translation is hampered by exceeding possible concentration thresholds. This study proposes a dichotomous use of low-concentration EGCG in chemotherapy. During the first cycle of combined treatment with oxaliplatin (OXA), low-concentration EGCG antagonized the cytotoxic effect of OXA on colorectal cancer (CRC) cells. However, when OXA was subsequently administered, the sensitivity of CRC cells markedly increased. Although low-concentration EGCG counteracted OXA, it reduced the OXA-induced secretion of vascular endothelial growth factor by tumor cells, thereby contributing to the increase in the sensitivity of tumor cells to the second round of OXA treatment. Therefore, low-concentration EGCG showed potential as a viable adjunct to modulate chemosensitivity in CRC.

Influence of Cuticular Waxes from Triticale on Rumen Fermentation: A Metabolomic and Microbiome Profiling Study.

Cuticular wax, a critical defense layer for plants, remains a relatively unexplored factor in rumen fermentation. We investigated the impact of cuticular wax on rumen fermentation using triticale as a model. In total, six wax classes were identified, including fatty acids, aldehydes, alkane, primary alcohol, alkyresorcinol, and ß-diketone, with low-bloom lines predominated by 46.05% of primary alcohols and high-bloom lines by 35.64% of ß-diketone. Low-wax addition (2.5 g/kg DM) increased the gas production by 19.25% (P < 0.05) and total volatile fatty acids by 6.34% (P > 0.05), and enriched key carbohydrate-fermenting rumen microbes like Saccharofermentans, Ruminococcus, and Prevotellaceae, when compared to non-wax groups. Metabolites linked to nucleotide metabolism, purine metabolism, and protein/fat digestion in the rumen showed a positive correlation with low-wax, benefiting rumen microbes. This study highlights the intricate interplay among cuticular wax, rumen microbiota, fermentation, and metabolomics in forage digestion, providing insights into livestock nutrition and forage utilization.

GCY-20 signaling controls suppression of Caenorhabditis elegans egg laying by moderate cold.

Organisms sensing environmental cues and internal states and integrating the sensory information to control fecundity are essential for survival and proliferation. The present study finds that a moderate cold temperature of 11°C reduces egg laying in Caenorhabditis elegans. ASEL and AWC neurons sense the cold via GCY-20 signaling and act antagonistically on egg laying through the ASEL and AWC/AIA/HSN circuits. Upon cold stimulation, ASEL and AWC release glutamate to activate and inhibit AIA interneurons by acting on highly and lowly sensitive ionotropic GLR-2 and GLC-3 receptors, respectively. AIA inhibits HSN motor neuron activity via acetylcholinergic ACR-14 receptor signaling and suppresses egg laying. Thus, ASEL and AWC initiate and reduce the cold suppression of egg laying. ASEL's action on AIA and egg laying dominates AWC's action. The biased opposite actions of these neurons on egg laying provide animals with a precise adaptation of reproductive behavior to environmental temperatures.

Value function assessment to different RL algorithms for heparin treatment policy of patients with sepsis in ICU.

Heparin is a critical aspect of managing sepsis after abdominal surgery, which can improve microcirculation, protect organ function, and reduce mortality. However, there is no clinical evidence to support decision-making for heparin dosage. This paper proposes a model called SOFA-MDP, which utilizes SOFA scores as states of MDP, to investigate clinic policies. Different algorithms provide different value functions, making it challenging to determine which value function is more reliable. Due to ethical restrictions, we cannot test all policies on patients. To address this issue, we proposed two value function assessment methods: action similarity rate and relative gain. We experimented with heparin treatment policies for sepsis patients after abdominal surgery using MIMIC-IV. In the experiments, TD(0) shows the most reliable performance. Using the action similarity rate and relative gain to assess AI policy from TD(0), the agreement rates between AI policy and "good" physician's actual treatment are 64.6% and 73.2%, while the agreement rates between AI policy and "bad" physician's actual treatment are 44.1% and 35.8%, the gaps are 20.5% and 37.4%, respectively. External validation using action similarity rate and relative gain based on eICU resulted in agreement rates of 61.5% and 69.1% with the "good" physician's treatment, and 45.2% and 38.3% with the "bad" physician's treatment, with gaps of 16.3% and 30.8%, respectively. In conclusion, the model provides instructive support for clinical decisions, and the evaluation methods accurately distinguish reliable and unreasonable outcomes.

Nanozyme-Based Regulation of Cellular Metabolism and Their Applications.

Metabolism is the sum of the enzyme-dependent chemical reactions, which produces energy in catabolic process and synthesizes biomass in anabolic process, exhibiting high similarity in mammalian cell, microbial cell, and plant cell. Consequently, the loss or gain of metabolic enzyme activity greatly affects cellular metabolism. Nanozymes, as emerging enzyme mimics with diverse functions and adjustable catalytic activities, have shown attractive potential for metabolic regulation. Although the basic metabolic tasks are highly similar for the cells from different species, the concrete metabolic pathway varies with the intracellular structure of different species. Here, the basic metabolism in living organisms is described and the similarities and differences in the metabolic pathways among mammalian, microbial, and plant cells and the regulation mechanism are discussed. The recent progress on regulation of cellular metabolism mainly including nutrient uptake and utilization, energy production, and the accompanied redox reactions by different kinds of oxidoreductases and their applications in the field of disease therapy, antimicrobial therapy, and sustainable agriculture is systematically reviewed. Furthermore, the prospects and challenges of nanozymes in regulating cell metabolism are also discussed, which broaden their application scenarios.

Investigation into white matter microstructure differences in visual training by using an automated fiber tract subclassification segmentation quantification method.

Visual training has emerged as a useful framework for investigating training-related brain plasticity, a highly complex task involving the interaction of visual orientation, attention, reasoning, and cognitive functions. However, the effects of long-term visual training on microstructural changes within white matter (WM) is poorly understood. Therefore, a set of visual training programs was designed, and automated fiber tract subclassification segmentation quantification based on diffusion magnetic resonance imaging was performed to obtain the anatomical changes in the brains of visual trainees. First, 40 healthy matched participants were randomly assigned to the training group or the control group. The training group underwent 10 consecutive weeks of visual training. Then, the fiber tracts of the subjects were automatically identified and further classified into fiber clusters to determine the differences between the two groups on a detailed scale. Next, each fiber cluster was divided into segments that can analyze specific areas of a fiber cluster. Lastly, the diffusion metrics of the two groups were comparatively analyzed to delineate the effects of visual training on WM microstructure. Our results showed that there were significant differences in the fiber clusters of the cingulate bundle, thalamus frontal, uncinate fasciculus, and corpus callosum between the training group compared and the control group. In addition, the training group exhibited lower mean fractional anisotropy, higher mean diffusivity and radial diffusivity than the control group. Therefore, the long-term cognitive activities, such as visual training, may systematically influence the WM properties of cognition, attention, memory, and processing speed.

Is Attention all You Need in Medical Image Analysis? A Review.

Medical imaging is a key component in clinical diagnosis, treatment planning and clinical trial design, accounting for almost 90% of all healthcare data. CNNs achieved performance gains in medical image analysis (MIA) over the last years. CNNs can efficiently model local pixel interactions and be trained on small-scale MI data. Despite their important advances, typical CNN have relatively limited capabilities in modelling "global" pixel interactions, which restricts their generalisation ability to understand out-of-distribution data with different "global" information. The recent progress of Artificial Intelligence gave rise to Transformers, which can learn global relationships from data. However, full Transformer models need to be trained on large-scale data and involve tremendous computational complexity. Attention and Transformer compartments ("Transf/Attention") which can well maintain properties for modelling global relationships, have been proposed as lighter alternatives of full Transformers. Recently, there is an increasing trend to co-pollinate complementary local-global properties from CNN and Transf/Attention architectures, which led to a new era of hybrid models. The past years have witnessed substantial growth in hybrid CNN-Transf/Attention models across diverse MIA problems. In this systematic review, we survey existing hybrid CNN-Transf/Attention models, review and unravel key architectural designs, analyse breakthroughs, and evaluate current and future opportunities as well as challenges. We also introduced an analysis framework on generalisation opportunities of scientific and clinical impact, based on which new data-driven domain generalisation and adaptation methods can be stimulated.

Multi-media distribution and risk assessment of per- and polyfluoroalkyl substances in the Huai River Basin, China.

The widespread existence, environmental persistence, and risks of per- and polyfluoroalkyl substances (PFASs) have attracted widespread attention. Herein, the distribution and risk assessment of PFASs were investigated from the Huai River Basin. The ranges in different media were 29.83-217.96 (average of 75.82 ± 35.64 ng/L) in water, 0.17-9.55 ng/g (2.56 ± 2.83 ng/g) in sediments, and 0.21-9.76 ng/g (3.43 ± 3.07 ng/g) in biota. Perfluoropentanoic acid (PFPeA) was the most prevalent PFAS in surface water, followed by perfluorooctanoic acid (PFOA) and perfluorobutanoic acid (PFBA), accounted for 42.62 %, 22.23 % and 17.72 % of the total concentrations of the PFASs analyzed, respectively. PFBA was dominant in sediments, accounting for 60.37 % of the total concentrations of the PFASs analyzed. Perfluorooctane sulfonate (PFOS) was the main pollutant in biota, and the highest concentration (5.09 ng/g) was found in Channa argus. Considering the measured concentrations in water, sediments and biota, the sediment-water partition coefficients (log Kd) and bioaccumulation factors (BAF) of PFASs were determined. The log Kd of the PFASs differed among those with a different carbon chain length, C7-C11 PFASs were more likely to be adsorbed onto sediments as the carbon chain length increases, and PFUnDA and PFDA showed the higher BAF value in Channa argus. PFASs in the Huai River Basin posed an acceptable ecological risk, and long-chain PFAS contamination provided green algae with a higher potential ecological risk. Compared to drinking water, aquatic products constituted a higher PFASs threat to human health, especially for children. The highest HQ was found in PFOS, with an HQmax of 0.97-4.32. Residents in the Huai River Basin should reduce their intake of Channa argus, Coilia nasus, and Carassius auratus, children aged 2 to 4 are limited to consuming no more than 6.9 g/d, 9.7 g/d, and 16.6 g/d, respectively.

Dynamic Nomogram for Predicting the Risk of Perioperative Neurocognitive Disorders in Adults.

BACKGROUND: Simple and rapid tools for screening high-risk patients for perioperative neurocognitive disorders (PNDs) are urgently needed to improve patient outcomes. We developed an online tool with machine-learning algorithms using routine variables based on multicenter data. METHODS: The entire dataset was composed of 49,768 surgical patients from 3 representative academic hospitals in China. Surgical patients older than 45 years, those undergoing general anesthesia, and those without a history of PND were enrolled. When the patient's discharge diagnosis was PND, the patient was in the PND group. Patients in the non-PND group were randomly extracted from the big data platform according to the surgical type, age, and source of data in the PND group with a ratio of 3:1. After data preprocessing and feature selection, general linear model (GLM), artificial neural network (ANN), and naive Bayes (NB) were used for model development and evaluation. Model performance was evaluated by the area under the receiver operating characteristic curve (ROCAUC), the area under the precision-recall curve (PRAUC), the Brier score, the index of prediction accuracy (IPA), sensitivity, specificity, etc. The model was also externally validated on the multiparameter intelligent monitoring in intensive care (MIMIC) Ⅳ database. Afterward, we developed an online visualization tool to preoperatively predict patients' risk of developing PND based on the models with the best performance. RESULTS: A total of 1051 patients (242 PND and 809 non-PND) and 2884 patients (6.2% patients with PND) were analyzed on multicenter data (model development, test [internal validation], external validation-1) and MIMIC Ⅳ dataset (external validation-2). The model performance based on GLM was much better than that based on ANN and NB. The best-performing GLM model on validation-1 dataset achieved ROCAUC (0.874; 95% confidence interval [CI], 0.833-0.915), PRAUC (0.685; 95% CI, 0.584-0.786), sensitivity (72.6%; 95% CI, 61.4%-81.5%), specificity (84.4%; 95% CI, 79.3%-88.4%), Brier score (0.131), and IPA (44.7%), and of which the ROCAUC (0.761, 95% CI, 0.712-0.809), the PRAUC (0.475, 95% CI, 0.370-0.581), Brier score (0.053), and IPA (76.8%) on validation-2 dataset. Afterward, we developed an online tool (https://pnd-predictive-model-dynnom.shinyapps.io/ DynNomapp/) with 10 routine variables for preoperatively screening high-risk patients. CONCLUSIONS: We developed a simple and rapid online tool to preoperatively screen patients' risk of PND using GLM based on multicenter data, which may help medical staff's decision-making regarding perioperative management strategies to improve patient outcomes.

Examining the Association Between Boredom Proneness and Bedtime Procrastination Among Chinese College Students: A Sequential Mediation Model with Mobile Phone Addiction and Negative Emotions.

Purpose: The phenomenon of bedtime procrastination has become very common in China in recent years, especially among college students, and it can have numerous negative implications for both physical and mental health. However, the impacting factors and possible underlying mechanisms of bedtime procrastination have not yet been fully clarified. The present study aims to explore the relationship between boredom proneness and bedtime procrastination in Chinese college students, and propose a multiple mediation model to further explore the mediating effects of mobile phone addiction and negative emotions in this link. Participants and Methods: A cross-sectional study was conducted involving 668 Chinese college students aged 18 to 32 years old (M=20.36 years [SD=1.69]; 35.03% male). A series of self-rating questionnaires measuring degrees of boredom proneness, mobile phone addiction, bedtime procrastination, as well as negative emotions (depression, anxiety and stress) were administered. Results: The results showed that boredom proneness, mobile phone addiction, negative emotions and bedtime procrastination were significantly and positively correlated with each other in college students. Mobile phone addiction and negative emotions separately played partial mediating roles in the relationship between boredom proneness and bedtime procrastination. Furthermore, a sequential mediation pathway was significant whereby boredom proneness predicted mobile phone addiction, which was associated with higher levels of negative emotions, which were then associated with more bedtime procrastination behaviors. Conclusion: The present findings indicate that both mobile phone addiction and negative emotions are important risk-enhancing mediators in the association between boredom proneness and bedtime procrastination in Chinese college students. Therefore, intervention management that concentrating on reducing mobile phone addiction as well as improving negative emotions may be useful for decreasing bedtime procrastination among college students.