Development and validation of a risk nomogram to estimate risk of hyponatremia after spinal cord injury: A retrospective single-center study.

OBJECTIVE: This study aimed to establish a nomogram-based assessment for predicting the risk of hyponatremia after spinal cord injury (SCI). DESIGN: The study is a retrospective single-center study. PARTICIPANTS: SCI patients hospitalized in the First Affiliated Hospital of Guangxi Medical University. SETTING: The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China. METHODS: We performed a retrospective clinical study to collect SCI patients hospitalized in the First Affiliated Hospital of Guangxi Medical University from 2016 to 2020. Based on their clinical scores, the SCI patients were grouped as either hyponatremic or non-hyponatremic, SCI patients in 2016-2019 were identified as the training set, and patients in 2020 were identified as the test set. A nomogram was generated, the calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were used to validate the model. RESULTS: A total of 895 SCI patients were retrieved. After excluding patients with incomplete data, 883 patients were finally included in this study and used to construct the nomograms. The indicators used in the nomogram included sex, completeness of SCI, pneumonia, urinary tract infection, fever, constipation, white blood cell (WBC), albumin and serum Ca2+. These indices were determined by the least absolute shrinkage and selection operator (LASSO) regression analysis. The C-index of the model was 0.81, the area under the curve (AUC) of the training set was 0.82(Cl:0.79-0.85), and the validation set was 0.79(Cl:0.73-0.85). CONCLUSIONS: Nomogram has good predictive ability, sex, completeness of SCI, pneumonia, urinary tract infection, fever, constipation, WBC, albumin and serum Ca2+ were predictors of hyponatremia after SCI.

Examining the normality assumption of a design-comparable effect size in single-case designs.

What Works Clearinghouse (WWC, 2022) recommends a design-comparable effect size (D-CES; i.e., gAB) to gauge an intervention in single-case experimental design (SCED) studies, or to synthesize findings in meta-analysis. So far, no research has examined gAB's performance under non-normal distributions. This study expanded Pustejovsky et al. (2014) to investigate the impact of data distributions, number of cases (m), number of measurements (N), within-case reliability or intra-class correlation (ρ), ratio of variance components (λ), and autocorrelation (ϕ) on gAB in multiple-baseline (MB) design. The performance of gAB was assessed by relative bias (RB), relative bias of variance (RBV), MSE, and coverage rate of 95% CIs (CR). Findings revealed that gAB was unbiased even under non-normal distributions. gAB's variance was generally overestimated, and its 95% CI was over-covered, especially when distributions were normal or nearly normal combined with small m and N. Large imprecision of gAB occurred when m was small and ρ was large. According to the ANOVA results, data distributions contributed to approximately 49% of variance in RB and 25% of variance in both RBV and CR. m and ρ each contributed to 34% of variance in MSE. We recommend gAB for MB studies and meta-analysis with N ≥ 16 and when either (1) data distributions are normal or nearly normal, m = 6, and ρ = 0.6 or 0.8, or (2) data distributions are mildly or moderately non-normal, m ≥ 4, and ρ = 0.2, 0.4, or 0.6. The paper concludes with a discussion of gAB's applicability and design-comparability, and sound reporting practices of ES indices.

Beyond the waves: Unraveling pandemic outcomes with genomic insights and immunity analysis - Evidence from 14 countries.

OBJECTIVE: Although the World Health Organization and many governments have recategorized COVID-19 as a generally mild to moderately severe disease, consecutive pandemic waves driven by immune escape variants have underscored the need for timely and accurate prediction of the next outbreak. Nevertheless, little attention has been paid to translating genomic data and infection- and vaccine-induced immunity into direct estimates. METHODS: We retrieved epidemiologic and genomic data shortly before pandemic waves across 14 developed countries from late 2021 to mid-2022 and examined associations between early-stage variant competition, infection- and vaccine-induced immunity, and the time intervals between wave peaks. We applied regression analysis and the generalized estimating equation method to construct an inferential model. RESULTS: Each per cent increase in the proportion of a new variant was associated with a 1.0% reduction in interpeak intervals on average. Curvilinear associations between vaccine-induced immunity and outcome variables were observed, suggesting that reaching a critical vaccine distribution rate may decrease the caseload of the upcoming wave. CONCLUSIONS: By leveraging readily accessible pre-outbreak genomic and epidemiologic data, our results not only substantiate the predictive potential of early variant fractions but also propose that immunity acquired through infection alone may not sufficiently mitigate transmission. Conversely, a rapid and widespread vaccination initiative appears to be correlated with a decrease in disease incidence.

Rapid observation of dispersion curves in system-on-chip devices.

We have developed a rapid detection instrument to characterize the behavior of light on the surface of devices during light-matter interactions. The equipment enables the non-destructive and real-time observations of the dispersion curves for microstructures, providing the basis for a large number of new planar photonic chip applications. The method is based on the traditional prismatic reflection and makes full use of the grating dispersion capabilities, enabling simultaneous multi-wavelength and multi-angle reflectance measurements over a wide range. This method is beneficial for designing new microstructure devices and brings convenience to delicate microstructure processing. The instruments do not require any mechanical scanning, allowing for rapid acquisition, and the integrated and reusable optics make them easily miniaturized. Additionally, the functionalized design allows for spectral analysis applications, such as far-field spectral measurements. The instrument can also be easily integrated into established microscopic imaging systems, extending their observational characterization capabilities as well as accomplishing dynamic monitoring in proven system-on-a-chip devices.

"Afterglow" Photodynamic Therapy Based on Carbon Dots Embedded Silica Nanoparticles for Nondestructive Teeth Whitening.

Teeth staining is a common dental health challenge in many parts of the world. Traditional teeth whitening techniques often lead to enamel damage and soft tissue toxicity due to the use of bioincompatible whitening reagents and continuous strong light irradiation. Herein, an "afterglow" photodynamic therapy (aPDT) for teeth whitening is proposed, which is realized by energy transition pathways of intersystem crossing. The covalent and hydrogen bonds formed by carbon dots embedded in silica nanoparticles (CDs@SiO2) facilitate the passage of energy through intersystem crossing (ISC), thereby extending the half-life of reactive oxygen species (ROS). The degradation efficiency of aPDT on dyes was higher than 95% in all cases. It can thoroughly whiten teeth by eliminating stains deep in the enamel without damaging the enamel structure and causing any tissue toxicity. This study illustrates the superiority of aPDT for dental whitening and the approach to exploring carbon-dots-based nanostructures in the treatment of oral diseases.

A switch in N-terminal capping of ß-peptides creates novel self-assembled nanoparticles.

Small tripeptides composed entirely of ß3-amino acids have been shown to self-assemble into fibres following acylation of the N-terminus. Given the use of Fmoc as a strategy to initiate self-assembly in α-peptides, we hypothesized that the acyl cap can be replaced by an Fmoc without perturbation to the self-assembly and enable simpler synthetic protocols. We therefore replaced the N-acyl cap for an Fmoc group and herein we show that these Fmoc-protected ß3-peptides produce regular spherical particles, rather than fibrous structures, that are stable and capable of encapsulating cargo. We then demonstrated that these particles were able to deliver cargo to cells without any obvious signs of cytotoxicity. This is the first description of such regular nanoparticles derived from Fmoc-protected ß3-peptides.

Pathogenicity, transmissibility, and immunogenicity of recombinant H9N2 avian influenza viruses based on representative viruses of Southeast China.

H9N2 is currently the main subtype of avian influenza in China. In order to use reverse genetics to rapid preparation of seed strains for vaccine production, and intend to prevent and control the H9N2 subtype epidemic strains of avian influenza virus (AIV). In this study, we successfully rescued 2 H9N2 recombinant viruses based on the representative viruses of Southeast China and confirmed by RT-PCR and sequencing. Genetic stability, pathogenicity, transmissibility, and antigenicity of 2 recombinant viruses were evaluated. Compared to the FZ1, the growth kinetics of H9N2(HA+NA)/PR8 showed no significant difference, H9N2(HA+NA+M+PB1)/PR8 was slightly lower. Our study also confirmed 2 recombinant viruses had good genetic stability after 10 passages but possessed lower pathogenicity than FZ1. Although both recombinant viruses led to seroconversion in all inoculated birds on 14 dpi, they complete loss of viral transmission of the virus to contact birds. In addition, birds were immunized via hypodermic route by inactivated vaccines of H9N2(HA+NA)/PR8, H9N2(HA+NA+M+PB1)/PR8 and wild-type virus with a single dose, and the results showed that the hemagglutination inhibition titers on 21 dpv were 10.5, 9.6, and 10.5 log2, respectively. And recombinant viruses both provided a certain protection against wild-type virus challenge. In conclusion, these data indicated that 2 recombinant viruses will be expected to be used as inactivated vaccines to controlling the spread of H9N2 subtype AIV even have potential application for attenuated viral vaccines, which provides a reference for the prevention and control of influenza virus pandemics.

Dysregulation of ribosome-associated quality control elicits cognitive disorders via overaccumulation of TTC3.

Ribosome-associated quality control (RQC) pathway is responsible for degradation of nascent polypeptides in aberrantly stalled ribosomes, and its defects may lead to neurological diseases. However, the underlying molecular mechanism of how RQC dysfunction elicits neurological disorders remains poorly understood. Here we revealed that neurons with knockout (KO) of ubiquitin ligase LTN1, a key gene in the RQC pathway, show developmental defects in neurons via upregulation of TTC3 and UFMylation signaling proteins. The abnormally enhanced TTC3 protein in Ltn1 KO neurons reduced further accumulation of translationally arrested products by preventing translation initiation of selective genes. However, the overaccumulated TTC3 protein in turn caused dendritic abnormalities and reduced surface-localized GABAA receptors during neuronal development. Ltn1 KO mice showed behavioral deficits associated with cognitive disorders, a subset of which were restored by TTC3 knockdown in medial prefrontal cortex. Together, the overactivated cellular compensatory mechanism against defective RQC through TTC3 overaccumulation induced synaptic and cognitive deficits. More broadly, these findings represent a novel cellular mechanism underlying neuronal dysfunctions triggered by exaggerated cellular stress response to accumulated abnormal translation products in neurons.

Treatments and regulatory mechanisms of acoustic stimuli on mood disorders and neurological diseases.

Acoustic stimuli such as music or ambient noise can significantly affect physiological and psychological health in humans. We here summarize positive effects of music therapy in premature infant distress regulation, performance enhancement, sleep quality control, and treatment of mental disorders. Specifically, music therapy exhibits promising effects on treatment of neurological disorders such as Alzheimer's disease (AD) and Parkinson's disease (PD). We also highlight regulatory mechanisms by which auditory intervention affects an organism, encompassing modulation of immune responses, gene expression, neurotransmitter regulation and neural circuitry. As a safe, cost-effective and non-invasive intervention, music therapy offers substantial potential in treating a variety of neurological conditions.

The impact of teacher feedback on students' decisions to stay on or change course after math failure in a Confucian cultural context.

Previous research indicated that instructors holding entity belief tended to judge students to have low ability and provided ability-comforting feedback following math failure. Students receiving such feedback tended to quit and change course, creating a potential decrease in the pool of students pursuing math related fields. In Confucian heritage cultures (CHCs), the ideal society is primarily based on fulfillment of duties. Thus, the ability-based findings, derived from WEIRD samples, may not apply to duty-based CHCs. We hypothesized that CHC's teachers holding obligation belief tend to attribute students' failure to lack of duty fulfillment and provide duty-based feedback, including duty-comforting and duty-advising feedback, which motivates students to stay on rather than change course. To validate our hypothesis, we conducted three scenario experiments with 160 college students with teaching experiences, 273 high school students, and 369 pre-service teachers in Taiwan. Results showed that while ability-based paradigm may be culture-free, duty-based paradigm seems to be culture-bound. Consistent with previous research, teachers with entity belief tended to give ability-comforting feedback, pushing students to pursue non-math related fields. In contrast, teachers with obligation belief were likely to offer duty-comforting and duty-advising feedback, contributing to students' persistent pursuit in math. Furthermore, three fifths of teachers were inclined to provide ability-comforting, duty-comforting and duty-advising feedback concurrently, thus putting students in an unpleasant predicament that might be detrimental to their psychological well-being. Theoretical and practical implications are discussed.

Educational values and challenges of i-STEM project-based learning: A mixed-methods study with data-transformation design.

Integrated science, technology, engineering, and mathematics (STEM) embedding project-based learning (i-STEM PjBL) is still faced with challenges, and its educational values have not been revealed, which is what the study aimed to explore. Participants consisted of 48 freshmen from a senior high school, including 27 male students and 21 female students. The open-ended questionnaire and the interview for the students were administrated after the i-STEM PjBL. The qualitative data were converted into quantitative data counted by the occurrence frequencies of the codes. The results based on the integration and comparison of the open-ended questionnaire and interview outcomes showed that i-STEM PjBL provided students with positive educational values (including learning acquisition, performance, and perception), but there were also learning challenges in the process. Learning acquisition focused on the basic structure and components of a robot, principles of robot motion, hull structure, principles of sailboat navigation, and skills of designing and assembling sailboats. Learning performance referred that students were satisfied with their hands-on performances and confident of their abilities to perform better in similar disciplines, but did not learn well on programming. Learning perception indicated that students felt interested in i-STEM PjBL materials could acquire knowledge and skills from various fields, PjBL could be helpful to complete works, and principles could be helpful in practice, while programming design learning materials were not enough. Learning challenges indicated that students were unfamiliar with the usage of tools and hands-on operation, and they also felt challenged by programming. Students' feedback can be taken as references to modify and improve i-STEM PjBL and the materials in the future.

Mapping of Spatiotemporal Auricular Electrophysiological Signals Reveals Human Biometric Clusters.

Underneath the ear skin there are rich vascular network and sensory nerve branches. Hence, the 3D mapping of auricular electrophysiological signals can provide new biomedical perspectives. However, it is still extremely challenging for current sensing techniques to cover the entire ultra-curved auricle. Here, a 3D graphene-based ear-conformable sensing device with embedded and distributed 3D electrodes for full-auricle physiological monitoring is reported. As a proof-of-concept, spatiotemporal auricular electrical skin resistance (AESR) mapping is demonstrated for the first time, and human subject-specific AESR distributions are observed. From the data of more than 30 ears (both right and left ears), the auricular region-specific AESR changes after cycling exercise are observed in 98% of the tests and are clustered into four groups via machine learning-based data analyses. Correlations of AESR with heart rate and blood pressure are also studied. This 3D electronic platform and AESR-based biometrical findings show promising biomedical applications.

Dynamic moist air monitor in a micro area with extremely high figure-of-merit.

In the rapidly changing moisture air, conventional relative humidity (RH) sensors are often difficult to respond in time and accurately due to the limitation of flow rate and non-uniform airflow distribution. In this study, we numerically demonstrate that humidity changes on micro-zones can be monitored in real time using a Bloch surface wave (BSW) ubiquitous in one-dimensional photonic crystals (1DPC). This phenomenon can be observed by leakage radiation microscope (LRM). After theoretically deriving the angular resolution limit of LRM, we obtained the minimum BSW angular change on a practical scheme that can be observed in the momentum space to complete the detection, and realized the dynamic real-time monitoring of small-scale humidity change in experiment for the first time. This monitoring method has extremely high figure of merit (FOM) without hysteresis, which can be used in humidity sensing and refractive index sensing as well as the research on turbulence.

Refractive index sensing based on surface plasmon-coupled emission excited by reverse Kretschmann or Tamm structure.

Surface plasmon coupled emission (SPCE) is the directional emission of surface plasmon polaritons (SPPs) through the reverse channels of focused surface plasmon excitation to the far field, which has shown significant possibilities in bioanalysis, medical diagnosis, and so on. We carried out a theoretical study of SPCE to analyze its mechanisms and proposed a new structure to improve the emission intensity of SPCE. We proposed a method for refractive index sensing based on SPCE, consisting of a reverse Kretschmann (RK) or a Tamm structure for the first time, to the best of our knowledge. The corresponding sensing sensitivity reaches 87.61 deg/RIU and 67.44 deg/RIU, respectively. Compared with that in the RK, the far-field radiation intensity of SPCE in the Tamm structure is enhanced by two orders of magnitude. Furthermore, compared with surface plasmon resonance (SPR) sensing, SPCE sensing can improve the signal-to-noise ratio (SNR) and excitation efficiency. Our structures enable refractive index sensing with a high SNR, high spatial resolution, and without the requirement of angular alignment using complex mechanics, which are suitable for practical applications such as quantitative biomolecular detection and medical diagnosis.

To mask or not to mask: Debunking the myths of mask-wearing during COVID-19 across cultures.

Mask-wearing is the simplest yet most effective preventive behavior during COVID-19. However, it has sparked great controversy, particularly in America. Little is known about what psychosocial factors predict people's decision to mask. This research challenges three myths about mask-wearing. First, does mask-wearing provide a false sense of security? Second, is knowledge of COVID-19 a more robust predictor than political ideology of mask-wearing behavior? Third, does resistance to masks reflect anti-authoritarianism or a lack of trust in government? With nationally representative samples across two cultures (N = 1,121), findings reveal a significant positive correlation between mask-wearing and other preventive behaviors. Moreover, knowledge of COVID-19 and trust in government significantly predicted mask-wearing. Implications of the results are also discussed in the cross-cultural context. Critically, findings could provide practical implications for public education and policymaking by uncovering how to more effectively promote compliance with recommended preventive behaviors during our ongoing struggle with COVID-19.

Temporal-spatial evolution analysis of severe traffic violations using three functional forms of models considering the diurnal variation of meteorology.

Traffic violations and crashes are inherently associated. Analysis of traffic violation frequency is a prerequisite for improvements in crash prevention and corresponding countermeasures. One of the essential works in the field of traffic violations relates to the exploration of the correlations between a certain violation type (e.g., speeding or safety belt use) and its causal factors (e.g., demographics and road types). Till now, the effects of spatiotemporal and meteorological factors on severe traffic violations, a general term for dangerous driving behaviors, have not been fully considered. Using the dataset consisting of daily severe traffic violations and meteorological conditions during 12 months in Jiangsu Province, China, violation performance functions were developed for three violation types (total violations, driving under the influence, and speeding) based on three models (Poisson regression, zero-inflated Poisson regression, and negative binomial model). The findings indicate that the negative binomial model has a better performance for traffic violation frequency estimation. Additionally, elastic analysis for three violation types relying on the negative binomial model was conducted to present the relationships between the explanatory variables and the expected violation frequency. The effects of spatiotemporal factors have revealed that the violation situations are significantly different in varying cities and the frequency of drunk driving shows a significant time instability. It is also found that rainy days will generate a decrease in the possibility of violation occurrence. With regard to temperature, a significant negative effect is found and the decrease in temperature will bring about an increase in violation frequency. Besides, traffic violation frequency is significantly increased during holidays with comfortable weather conditions. The conclusion of this study can provide insightful suggestions for the department of traffic enforcement to adjust the patrol plans according to the specified periods (weeks, months, or holidays) and weather conditions. Special rectification actions and targeted educational activities are also advised to be put forward simultaneously.

Controlling Chemoselectivity of Catalytic Hydroboration with Light.

The ability to selectively react one functional group in the presence of another underpins efficient reaction sequences. Despite many designer catalytic systems being reported for hydroboration reactions, which allow introduction of a functional handle for cross-coupling or act as mild method for reducing polar functionality, these platforms rarely deal with more complex systems where multiple potentially reactive sites exist. Here we demonstrate, for the first time, the ability to use light to distinguish between ketones and carboxylic acids in more complex molecules. By taking advantage of different activation modes, a single catalytic system can be used for hydroboration, with the chemoselectivity dictated only by the presence or absence of visible light.

Spatiotemporal analysis of crash severity on rural highway: A case study in Anhui, China.

Traffic crashes are the result of the interaction between human activities and different socio-economic, geographical, and environmental factors, showing a temporal and spatial relationship. The temporal and spatial correlations must be characterized in crash severity studies, for which the geographically and temporally weighted ordered logistic regression (GTWOLR) model is an effective approach. However, existing studies using the GTWOLR model only subjectively selected a type of kernel function and kernel bandwidth, which cannot determine the best expression of the spatiotemporal relationship between crashes. This paper explores the optimal kernel function and kernel bandwidth considering the aforementioned problem to obtain the best GTWOLR model to analyze the crash data based on the crash data of rural highways in Anhui Province, China, from 2014 to 2017. First, the GTWOLR models with Gaussian or Bi-square kernel function and fixed (the spatiotemporal distance remains constant of local sample) or adaptive (the quantity of the local sample is constant) bandwidth are compared. Second, the log-likelihood and Akaike information criterion are used to compare the GTWOLR model with the ordered logistic regression (OLR) model. Finally, the spatial and temporal characteristics of the contributing factors in the best GTWOLR model are analyzed, and corresponding countermeasures for improving traffic safety on rural highways are proposed. Model comparison results reveal that although the difference was insignificant, the Bi-square kernel function with fixed bandwidth (BF)- GTWOLR model has a better goodness of fit than the GTWOLR models with other types of kernel function and bandwidth and the OLR model. The BF-GTWOLR model estimation results showed that eight factors, including pedestrian-vehicle crash, middle-aged driver, hit-and-run, truck, motorcycle, curve, slope and mountainous, passed the non-stationary test, indicating their varying effects on the crash severity across space and over time. As a crash severity modeling approach that effectively quantifies the spatiotemporal relationships in crashes, the BF-GTWOLR model, which adapts to crash data, may have implications for future research. In addition, the findings of this paper can help traffic management departments to propose progressive and targeted policies or countermeasures, so as to reduce the severity of rural highway crashes.

To approach or to avoid: The quadripolar model of achievement motivation revisited in a Confucian-heritage context.

Academic challenges and failure are inevitable in pursuit of higher education. According to the self-worth theory, trying hard but failing implies low ability that would be a threat to personal worth, thus preventing students from approaching academic challenges. Nevertheless, previous studies have shown that students in the Confucian-heritage contexts (CHCs) tend to persist rather than quit in the face of academic failure. According to the role obligation theory of self-cultivation (ROT), the CHC learners would perceive academic failure from personal and interpersonal perspectives. The former refers to personal obligations to exert oneself toward the ultimate good, and the latter refers to fulfilling filial obligations to parents by achieving academic excellence. Given the fundamental differences in learners' perceptions of academic failure between the CHCs and the Western, Educated, Industrialized, Rich, and Democratic (WEIRD) contexts, this study examined the applicability of the quadripolar model of achievement motivation based on the self-worth theory in a CHC higher education institution. Results of confirmatory factor analysis (CFA) supported a two-factor model of fear of failure, including a personal and an interpersonal subfactor. Latent class analysis (LCA) showed that apart from the four existing categories of the quadripolar model, two additional CHC categories emerged and constituted half of the sample. The two CHC categories demonstrated different learner characteristics compared with their corresponding quadripolar categories in terms of levels of emotional distress and academic risk-taking tendency. The results may help debunk the myth that learner characteristics in the CHCs are identical to those observed in the WEIRD contexts. The fundamental differences in fear of failure further indicated the inadequacy of the self-worth theory in explaining achievement motivation in the CHCs where relationalism and role obligations are significant parts of the cultural traditions.

Stable directional emission in active optical waveguides shielding external environmental influences.

The skillful confinement of light brought by the composite waveguide structure has shown great possibilities in the development of photonic devices. It has greatly expanded the application range of an on-chip system in dark-field imaging and confined the laser when containing an active medium. Here we experimentally proved a stable directional emission in an active waveguide composed of metal and photonic crystal, which is almost completely unaffected by the external environment and different from the common local light field that is seriously affected by the structure. When the refractive index of samples on the surface layer changes, it can ensure the constant emission intensity of the internal mode, while still retaining the external environmental sensitivity of the surface mode. It can also be used for imaging and sensing as a functional slide. This research of chip-based directional emission is very promising for various applications including quantitative detection of biological imaging, coupled emission intensity sensing, portable imaging equipment, and tunable micro lasers.