Breath-, air- and surface-borne SARS-CoV-2 in hospitals

The COVID-19 pandemic has brought an unprecedented crisis to the global health sector. When discharging COVID-19 patients in accordance with throat or nasal swab protocols using RT-PCR, the potential risk of reintroducing the infection source to humans and the environment must be resolved. Here, 14 patients including 10 COVID-19 subjects were recruited; exhaled breath condensate (EBC), air samples and surface swabs were collected and analyzed for SARS-CoV-2 using reverse transcription-polymerase chain reaction (RT-PCR) in four hospitals with applied natural ventilation and disinfection practices in Wuhan. Here we discovered that 22.2% of COVID-19 patients (n = 9), who were ready for hospital discharge based on current guidelines, had SARS-CoV-2 in their exhaled breath (~10⁵ RNA copies/m³). Although fewer surface swabs (3.1%, n = 318) tested positive, medical equipment such as face shield frequently contacted/used by healthcare workers and the work shift floor were contaminated by SARS-CoV-2 (3–8 viruses/cm²). Three of the air samples (n = 44) including those collected using a robot-assisted sampler were detected positive by a digital PCR with a concentration level of 9–219 viruses/m³. RT-PCR diagnosis using throat swab specimens had a failure rate of more than 22% in safely discharging COVID-19 patients who were otherwise still exhaling the SARS-CoV-2 by a rate of estimated ~1400 RNA copies per minute into the air. Direct surface contact might not represent a major transmission route, and lower positive rate of air sample (6.8%) was likely due to natural ventilation (1.6–3.3 m/s) and regular disinfection practices. While there is a critical need for strengthening hospital discharge standards in preventing re-emergence of COVID-19 spread, use of breath sample as a supplement specimen could further guard the hospital discharge to ensure the safety of the public and minimize the pandemic re-emergence risk

Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors

Background Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. Methods We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors. Results Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged. Conclusions Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders.Peer reviewe

A hybrid strategy of AutoML and SHAP for automated and explainable concrete strength prediction

The precise prediction of concrete compressive strength is essential for ensuring safe and reliable infrastructure design and construction. However, traditional empirical models often struggle to accurately predict compressive strength due to the complex nonlinear relationship between concrete properties and target strength. This study introduces an AutoML-SHAP (Automatic Machine Learning - SHapley Additive exPlanations) strategy, designed to automatically predict the compressive strength of concrete and provide insightful interpretations of the predictive outcomes. The AutoML model uses K-fold bagging and multilayer stacking to automate model selection and hyperparameter tuning. The integration of AutoML and SHAP offers synergistic benefits, facilitating the development of a precise, efficient, and comprehensively interpretable model. Results demonstrate that AutoML-SHAP model outperforms other machine learning models for predicting compressive strength without human intervention. The AutoML model is automatically established within 174 s and exhibits comparable predictive performance with R2 = 0.96, RMSE = 3.63, and MAE = 2.41. SHAP provides a global explanation of the impact of mixing parameters on compressive strength, and a local explanation of feature contribution to each prediction, making the process transparent and reliable. Feature dependence analysis reveals the influence tendency of mixing parameters on strength

Ghrelin Protects against Dexamethasone-Induced INS-1 Cell Apoptosis via ERK and p38MAPK Signaling

Glucocorticoid excess induces apoptosis of islet cells, which may result in diabetes. In this study, we investigated the protective effect of ghrelin on dexamethasone-induced INS-1 cell apoptosis. Our data showed that ghrelin (0.1 μM) inhibited dexamethasone-induced (0.1 μM) apoptosis of INS-1 cells and facilitated cell proliferation. Moreover, ghrelin upregulated Bcl-2 expression, downregulated Bax expression, and decreased caspase-3 activity. The protective effect of ghrelin against dexamethasone-induced INS-1 cell apoptosis was mediated via growth hormone secretagogue receptor 1a. Further studies revealed that ghrelin increased ERK activation and decreased p38MAPK expression after dexamethasone treatment. Ghrelin-mediated protection of dexamethasone-induced apoptosis of INS-1 cells was attenuated using the ERK inhibitor U0126 (10 μM), and cell viability increased using the p38MAPK inhibitor SB203580 (10 μM). In conclusion, ghrelin could protect against dexamethasone-induced INS-1 cell apoptosis, at least partially via GHS-R1a and the signaling pathway of ERK and p38MAPK

Three-Dimensional Printing of Large Objects with High Resolution by Dynamic Projection Scanning Lithography

Due to the development of printing materials, light-cured 3D printing is playing an increasingly important role in industrial and consumer markets for prototype manufacturing and conceptual design due to its advantages in high-precision and high-surface finish. Despite its widespread use, it is still difficult to achieve the 3D printing requirements of large volume, high resolution, and high speed. Currently, traditional light-cured 3D printing technologies based on stereolithography, such as regular DLP and SLA, can no longer meet the requirements of the processing size and processing rate. This paper introduces a dynamic projection of 3D printing technology utilizing a digital micro-mirror device (DMD). By projecting the ultraviolet light pattern in the form of “animation”, the printing resin is continuously cured in the exposure process to form the required three-dimensional structure. To print large-size objects, the three-dimensional model is sliced into high-resolution sectional images, and each layer of the sectional image is further divided into sub-regional images. These images are dynamically exposed to the light-curing material and are synchronized with the scanning motion of the projection lens to form a static exposure pattern in the construction area. Combined with the digital super-resolution, this system can achieve the layering and fine printing of large-size objects up to 400 × 400 × 200 mm, with a minimum feature size of 45 μm. This technology can achieve large-size, high-precision structural printing in industrial fields such as automobiles and aviation, promoting structural design, performance verification, product pre-production, and final part processing. Its printing speed and material bending characteristics are superior to existing DLP light-curing 3D printing methods

Experimental study and modeling of rubber joints for railway vehicles using magnetorheological shear stiffening elastomers

With the rapid development of transportation industry, advanced rail vehicle technology receives more attention than ever. The stiffness of the train\u27s rubber joint at the primary suspension system has a crucial influence on the operation stability and curve-passing performance. When the train is running on straight track at high speed, a high primary longitudinal stiffness in bogie design is required, whereas running on the curve track calls for a soft primary longitudinal stiffness. To solve this critical problem, a new rubber joint based on magnetorheological shear stiffening elastomer (MSSE) was proposed. Its stiffness can be adjusted by not only external magnetic field but also its inherent frequency-dependent property, ensuring the functionality of the rubber joint even when the controller fails. The prototype of the MSSE joint was fabricated and assembled. Stiffness controllability of the MSSE joint was evaluated using an material testing system (MTS) machine, with MTS testing performed under varying displacement amplitude at fixed frequency to investigate the influence of the varying displacement amplitude on the effective stiffness. The results revealed that the stiffness of this MSSE joint can be controlled effectively credited to the rate-dependent SSE and adjustable electromagnetics, exhibiting exceptional fail-safe characteristics. Lastly, a dynamic model was established to describe the dynamic performance of the rubber joint. All the above studies demonstrate the feasibility of the joint to satisfy the conflicting stiffness requirements to achieve high speed stability and curve trafficability simultaneously

Huaier Aqueous Extract Induces Hepatocellular Carcinoma Cells Arrest in S Phase via JNK Signaling Pathway

Huaier aqueous extract, the main active constituent of Huaier proteoglycan, has antihepatocarcinoma activity in experimental and clinical settings. However, the potential and associated antihepatoma mechanisms of Huaier extract are not yet fully understood. Therefore, in this study, we aimed to elucidate the inhibitory proliferation effect of Huaier extract on apoptosis and cycle of HepG2 and Bel-7402 cells. Our data demonstrated that incubation with Huaier extract resulted in a marked decrease in cell viability dose-dependently. Flow cytometric analysis showed that a 48 h treatment of Huaier extract caused cell apoptosis. Typical apoptotic nucleus alterations were observed with fluorescence microscope after Hoechst staining. Immunoblot analysis further demonstrated that Huaier extract activated caspase 3 and PARP. Additionally, Huaier extract inhibited the activity of p-ERK, p-p38, and p-JNK in terms of MAPK. Furthermore, Huaier extract induced HCC cells arrest in S phase and decreased the cycle related protein expression of β-catenin and cyclin D1. Studies with JNK specific inhibitor, SP600125, showed that Huaier extract induced S phase arrest and decreased β-catenin and cyclin D1 expression via JNK signaling pathway. In conclusion, we verify that Huaier extract causes cell apoptosis and induces hepatocellular carcinoma cells arrest in S phase via JNK pathway, which advances our understanding on the molecular mechanisms of Huaier extract in hepatocarcinoma management