Quantitative regulation of the thermal stability of enveloped virus vaccines by surface charge engineering to prevent the self-aggregation of attachment glycoproteins.

The development of thermostable vaccines can relieve the bottleneck of existing vaccines caused by thermal instability and subsequent poor efficacy, which is one of the predominant reasons for the millions of deaths caused by vaccine-preventable diseases. Research into the mechanism of viral thermostability may provide strategies for developing thermostable vaccines. Using Newcastle disease virus (NDV) as model, we identified the negative surface charge of attachment glycoprotein as a novel determinant of viral thermostability. It prevented the temperature-induced aggregation of glycoprotein and subsequent detachment from virion surface. Then structural stability of virion surface was improved and virus could bind to and infect cells efficiently after heat-treatment. Employing the approach of surface charge engineering, thermal stability of NDV and influenza A virus (IAV) vaccines was successfully improved. The increase in the level of vaccine thermal stability was determined by the value-added in the negative surface charge of the attachment glycoprotein. The engineered live and inactivated vaccines could be used efficiently after storage at 37°C for at least 10 and 60 days, respectively. Thus, our results revealed a novel surface-charge-mediated link between HN protein and NDV thermostability, which could be used to design thermal stable NDV and IAV vaccines rationally.

Automated machine learning for the identification of asymptomatic COVID-19 carriers based on chest CT images.

BACKGROUND: Asymptomatic COVID-19 carriers with normal chest computed tomography (CT) scans have perpetuated the ongoing pandemic of this disease. This retrospective study aimed to use automated machine learning (AutoML) to develop a prediction model based on CT characteristics for the identification of asymptomatic carriers. METHODS: Asymptomatic carriers were from Yangzhou Third People's Hospital from August 1st, 2020, to March 31st, 2021, and the control group included a healthy population from a nonepizootic area with two negative RT‒PCR results within 48 h. All CT images were preprocessed using MATLAB. Model development and validation were conducted in R with the H2O package. The models were built based on six algorithms, e.g., random forest and deep neural network (DNN), and a training set (n = 691). The models were improved by automatically adjusting hyperparameters for an internal validation set (n = 306). The performance of the obtained models was evaluated based on a dataset from Suzhou (n = 178) using the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and F1 score. RESULTS: A total of 1,175 images were preprocessed with high stability. Six models were developed, and the performance of the DNN model ranked first, with an AUC value of 0.898 for the test set. The sensitivity, specificity, PPV, NPV, F1 score and accuracy of the DNN model were 0.820, 0.854, 0.849, 0.826, 0.834 and 0.837, respectively. A plot of a local interpretable model-agnostic explanation demonstrated how different variables worked in identifying asymptomatic carriers. CONCLUSIONS: Our study demonstrates that AutoML models based on CT images can be used to identify asymptomatic carriers. The most promising model for clinical implementation is the DNN-algorithm-based model.

Identification of Asymptomatic COVID-19 Patients on Chest CT Images Using Transformer-Based or Convolutional Neural Network-Based Deep Learning Models.

Novel coronavirus disease 2019 (COVID-19) has rapidly spread throughout the world; however, it is difficult for clinicians to make early diagnoses. This study is to evaluate the feasibility of using deep learning (DL) models to identify asymptomatic COVID-19 patients based on chest CT images. In this retrospective study, six DL models (Xception, NASNet, ResNet, EfficientNet, ViT, and Swin), based on convolutional neural networks (CNNs) or transformer architectures, were trained to identify asymptomatic patients with COVID-19 on chest CT images. Data from Yangzhou were randomly split into a training set (n = 2140) and an internal-validation set (n = 360). Data from Suzhou was the external-test set (n = 200). Model performance was assessed by the metrics accuracy, recall, and specificity and was compared with the assessments of two radiologists. A total of 2700 chest CT images were collected in this study. In the validation dataset, the Swin model achieved the highest accuracy of 0.994, followed by the EfficientNet model (0.954). The recall and the precision of the Swin model were 0.989 and 1.000, respectively. In the test dataset, the Swin model was still the best and achieved the highest accuracy (0.980). All the DL models performed remarkably better than the two experts. Last, the time on the test set diagnosis spent by two experts-42 min, 17 s (junior); and 29 min, 43 s (senior)-was significantly higher than those of the DL models (all below 2 min). This study evaluated the feasibility of multiple DL models in distinguishing asymptomatic patients with COVID-19 from healthy subjects on chest CT images. It found that a transformer-based model, the Swin model, performed best.

Vacuolar Sugar Transporter TMT2 Plays Crucial Roles in Germination and Seedling Development in Arabidopsis.

Vacuolar sugar transporters transport sugar across the tonoplast, are major players in maintaining sugar homeostasis, and therefore play vital roles in plant growth, development, and biomass yield. In this study, we analyzed the physiological roles of the tonoplast monosaccharide transporter 2 (TMT2) in Arabidopsis. In contrast to the wild type (WT) that produced uniform seedlings, the tmt2 mutant produced three types of offspring: un-germinated seeds (UnG), seedlings that cannot form true leaves (tmt2-S), and seedlings that develop normally (tmt2-L). Sucrose, glucose, and fructose can substantially, but not completely, rescue the abnormal phenotypes of the tmt2 mutant. Abnormal cotyledon development, arrested true leaf development, and abnormal development of shoot apical meristem (SAM) were observed in tmt2-S seedlings. Cotyledons from the WT and tmt2-L seedlings restored the growth of tmt2-S seedlings through micrografting. Moreover, exogenous sugar sustained normal growth of tmt2-S seedlings with cotyledon removed. Finally, we found that the TMT2 deficiency resulted in growth defects, most likely via changing auxin signaling, target of rapamycin (TOR) pathways, and cellular nutrients. This study unveiled the essential functions of TMT2 for seed germination and initial seedling development, ensuring cotyledon function and mobilizing sugars from cotyledons to seedlings. It also expanded the current knowledge on sugar metabolism and signaling. These findings have fundamental implications for enhancing plant biomass production or seed yield in future agriculture.

In Silico Prediction of Chemicals Binding to Aromatase with Machine Learning Methods.

Environmental chemicals may affect endocrine systems through multiple mechanisms, one of which is via effects on aromatase (also known as CYP19A1), an enzyme critical for maintaining the normal balance of estrogens and androgens in the body. Therefore, rapid and efficient identification of aromatase-related endocrine disrupting chemicals (EDCs) is important for toxicology and environment risk assessment. In this study, on the basis of the Tox21 10K compound library, in silico classification models for predicting aromatase binders/nonbinders were constructed by machine learning methods. To improve the prediction ability of the models, a combined classifier (CC) strategy that combines different independent machine learning methods was adopted. Performances of the models were measured by test and external validation sets containing 1336 and 216 chemicals, respectively. The best model was obtained with the MACCS (Molecular Access System) fingerprint and CC method, which exhibited an accuracy of 0.84 for the test set and 0.91 for the external validation set. Additionally, several representative substructures for characterizing aromatase binders, such as ketone, lactone, and nitrogen-containing derivatives, were identified using information gain and substructure frequency analysis. Our study provided a systematic assessment of chemicals binding to aromatase. The built models can be helpful to rapidly identify potential EDCs targeting aromatase.

How technical and situational cues affect impulse buying behavior in social commerce? Evidence from bored consumers.

Introduction: With the rise of social media and web technologies, users are increasingly spending time on browsing and purchasing on social commerce, particularly during idle moments of casual scrolling. Social commerce applications with sophisticated social features and security measures may tend to attract a significant number of highly engaged users. The purpose of this study is to find out whether customers will be interested in the content posted on the applications and generate impulse consumption when they are bored. Methods: Drawing on stimulus-organism-response framework, this paper explores how technical cues and situational cues affect impulse buying behavior in social commerce applications and the mediating impact of consumer-perceived values. Data were gathered from 395 respondents who frequently utilize and have shopping experience on social commerce applications. The PLS-SEM and fsQCA were applied to formulate and test the proposed hypotheses. Results: The results of PLS-SEM reveal technical cues (ease of use, visual appeal and security) and situational cues (passing time and serendipity) positively influenced impulse buying. The results of fsQCA offer six solutions of different combinations of constructs which can lead to high impulse buying. Discussion: These findings may extend existing research on impulse buying behavior and consumer psychology, offering valuable insights for marketers. They also point towards strategies for more effectively encouraging impulse purchase in digital retail environments, particularly among consumers who are browsing out of boredom.

Adaptive penalty-based neurodynamic approach for nonsmooth interval-valued optimization problem.

The complex and diverse practical background drives this paper to explore a new neurodynamic approach (NA) to solve nonsmooth interval-valued optimization problems (IVOPs) constrained by interval partial order and more general sets. On the one hand, to deal with the uncertainty of interval-valued information, the LU-optimality condition of IVOPs is established through a deterministic form. On the other hand, according to the penalty method and adaptive controller, the interval partial order constraint and set constraint are punished by one adaptive parameter, which is a key enabler for the feasibility of states while having a lower solution space dimension and avoiding estimating exact penalty parameters. Through nonsmooth analysis and Lyapunov theory, the proposed adaptive penalty-based neurodynamic approach (APNA) is proven to converge to an LU-solution of the considered IVOPs. Finally, the feasibility of the proposed APNA is illustrated by numerical simulations and an investment decision-making problem.

Preventive effect of salmon sperm DNA on acute carbon tetrachloride-induced liver injury in mice through Nrf2/ARE and mitochondrial apoptosis pathway.

Liver injury refers to the damage of liver function, which will seriously harm the body's health if it is not prevented and treated in time. Sporadic researches have reported that ingestion of DNA has a hepatoprotective effect, but its effect and mechanism were not clarified. The purpose of this study was to explore the preventive effect and mechanism of salmon sperm DNA on acute liver injury in mice induced by carbon tetrachloride (CCl4). Six-week-old ICR (Institute of Cancer Research) male mice were used to establish a liver injury model by injecting with 4% CCl4, silymarin, and three different concentrations of DNA solutions were given to mice by gavage for 14 days. The histological and pathological changes in the liver were observed. The levels of alanine aminotransferase (ALT) and aspartate aminotransferase (AST) in serum and the levels of oxidative and antioxidant markers such as malondialdehyde (MDA), superoxide dismutase (SOD), glutathione peroxidase (GSH-Px), and glutathione (GSH) in liver tissue were determined. The levels of interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α) were detected by enzyme-linked immunosorbent assay (ELISA), and hepatic oxidative stress and apoptosis-related markers were determined by western blotting. The results showed that compared with the model group, the DNA test group significantly improved the liver pathological changes and the level of liver function, regulated liver oxidative stress, reduced hepatocyte apoptosis, and decreased the levels of inflammatory factors such as TNF-α and IL-6. Compared with the silymarin group, the high dose of DNA was even more effective in preventing liver injury. In conclusion, salmon sperm DNA has a potential protective effect against acute liver injury induced by CCl4, which is achieved by regulating the Nrf2/ARE (nuclear factor erythroid 2 (NF-E2)-related factor 2/antioxidant responsive element) oxidative stress pathway and mitochondrial apoptosis pathway.

Targeted delivery of doxorubicin to liver used a novel biotinylated ß-cyclodextrin grafted pullulan nanocarrier.

A series of biotinylated ß-cyclodextrin grafted pullulan (Bio-CDPu) for liver-specific drug delivery were synthesized and characterized by Fourier transform infrared (FTIR) and hydrogen nuclear magnetic resonance (1H NMR) spectroscopy, and then their self-assembled spherical nanoparticles (NPs) with 110-200 nm diameter were prepared. Doxorubicin (DOX) was selected as an anti-cancer model drug to prepare the drug-loaded NPs, and the drug loading efficiency (LE%) and loading content (LC%) were determined. The drug release behavior in vitro of DOX/Bio-CDPu NPs exhibited sustained release. The results of methyl thiazolyl tetrazolium (MTT) assays, confocal laser scanning microscopy (CLSM) observations and flow cytometric (FCM) analysis indicated that the designed Bio-CDPu nanocarriers showed good cytocompatibility with Bel-7404 cells and high cellular uptake. Due to their rich biotin /or pullulan ligands, Bio-CDPu NPs could promote DOX to enter Bel-7404 cells and inhibit the tumor cell growth. After intravenous injection to rats, DOX/Bio-CDPu NPs solution exhibited increased area under the curve (AUC) and prolonged half life time (t1/2), meanwhile, the drug concentration in the liver was significantly increased, and the cardio-renal toxicity was reduced. Furthermore, DOX/Bio-CDPu NPs exhibited a better anti-tumor therapeutic effect on tumor-bearing mice. Therefore, Bio-CDPu NPs can be chosen as a potential high-efficiency liver targeted carrier.

Salmon sperm DNA prevents acute liver injury by regulating alcohol-induced steatosis and restores chronic hepatosis via alleviating inflammation and apoptosis.

Current medications used to treat alcoholic liver injury (ALD) can cause secondary damage to the liver. Therefore, it is important to improve alcoholic liver injury from the perspective of dietary and nutritional supplementation. Nucleic acids, as functional biomolecules, are present in almost all foods, especially in aquatic products, but their edible research has been neglected for a long time. Hence, the effects of a typical aquatic nucleic acid, namely, salmon sperm DNA, in acute, and chronic alcoholic liver injury model of male ICR mice were studied. The results showed that salmon sperm DNA significantly attenuated the accumulation of cholesterol (TC) and triglycerides (TG) in acute alcoholic liver injury, and it was further demonstrated to mainly regulate lipid metabolism by fluorescent quantitative PCR and immunoblotting experiments. In addition, nucleic acid intervention alleviated inflammation and apoptosis in mice with chronic alcoholic liver injury. PRACTICAL APPLICATIONS: These results suggest that salmon sperm DNA can prevent and ameliorate alcoholic liver injury and can be used as an effective dietary and nutritional supplement for the prevention and treatment of ALD. Moreover, this study provided some new ideas for the development and utilization of large aquatic nucleic acid resources, promoted the comprehensive use of fish processing waste, such as fish sperm, and provided new directions for reducing emissions.

Computational insights into the different catalytic activities of CYP3A4 and CYP3A5 toward schisantherin E.

The cytochromes CYP3A4 and CYP3A5 share 84% sequence identity, but they exhibit different catalytic activities toward some substrates. Schisantherin E (SE) was recently identified as a selective substrate of CYP3A5, which exhibited catalytic efficiency that was more than 23 times higher than CYP3A4. At present, however, the structural determinants responsible for the different catalytic activities of the two enzymes toward SE have not been fully understood. In this study, a combination of molecular docking, molecular dynamic simulations, and binding free energy calculation was performed on the CYP3A4/CYP3A5-SE systems to investigate the issue. The results demonstrate that Ser119 in CYP3A4 and Glu374 in CYP3A5 formed direct hydrogen bonding with SE, respectively. Additionally, one water molecule located between the B-C loop and the I helix mediated different hydrogen-bonding networks between CYP3A4/3A5 and SE. The residue differences (Phe/Leu108 and Leu/Phe210) triggered the distinct conformational changes of the Phe-cluster residues, especially Phe213 and Phe215, which formed stronger hydrophobic interactions with SE in CYP3A5. The calculated binding free energies were consistent with the experimental results.

The preparation of hyaluronic acid grafted pullulan polymers and their use in the formation of novel biocompatible wound healing film.

A series of hyaluronic acid grafted pullulan (HA-g-Pu) polymers with different hyaluronic acid (HA) moieties degrees of substitution (DS) were synthesized and characterized by fourier transform infrared (FT-IR), proton nuclear magnetic resonance (1H NMR) and differential scanning calorimetry measurement (DSC). Compared to pure HA, HA-g-Pu polymers obtained better anti-enzymatic degradation ability in vitro, and the degradation rate of HA-g-Pu polymers depended on their different DS of HA moieties. The HA-g-Pu films were made of leaf-shape cascading arrangement with many small porous ranging from 0 to 100 µm in diameter when observed by scanning electron microscopy (SEM). Therefore, HA-g-Pu films have a higher swelling ratio than that of the pullulan/or HA films. HA-g-Pu films could absorb much liquid, effectively protect the wound bed from accumulation of exudates and reduce the frequency of replacement. Moreover, the good biocompatibility of HA-g-Pu polymers were confirmed by skin irritation, cytotoxicity, cell proliferation and hemolysis test. Compared with the natural healing, the HA-g-Pu films promoted the wound healing. HA of HA-g-Pu polymers played an important role in the wound healing response. Furthermore, the HA-g-Pu polymers appeared a certain coagulation function and obtained a relative rapid hemostasis ability which might be attribute to heal wound.

Subglottic Squamous Cell Carcinoma: A Population-Based Study of 889 Cases.

OBJECTIVE: Subglottic squamous cell carcinoma (SCCa) is a rare malignancy representing <5% of all laryngeal cancers. Patients often present with late-stage disease, and survival outcomes are reportedly worse than those for SCCa in other regions of the larynx. STUDY DESIGN: Analysis of a population-based tumor registry. SETTING: Academic medical center. SUBJECTS AND METHODS: The US National Cancer Institute's Surveillance, Epidemiology, and End Results database was queried for cases of subglottic SCCa from 1973 to 2011 (889 cases). Resulting data were analyzed, including patient demographics, therapeutic measures, and survival outcomes. RESULTS: Subglottic SCCa most frequently occurred in the fifth to seventh decade of life, with a mean age at diagnosis of 65.7 ± 11.3 years. There was a strong male predilection, with a male:female ratio of 3.83:1. Most patients were stage III and IV (64.4%) per the American Joint Committee on Cancer. The most common treatment modality was a combination of radiotherapy and surgery (38.8%), followed by radiotherapy alone (33.9%), and surgery alone (17.0%). Overall 5-year disease-specific survival rate was 53.7%. When stratified by treatment modality, 5-year disease-specific survival was 62.4% for surgery alone, 56.7% for radiotherapy alone, and 55.1% for surgery with adjuvant radiotherapy (P = .3892). CONCLUSION: This study represents the largest cohort of subglottic SCCa. It shows a strong predilection for men in the US population. Surgery with adjuvant radiotherapy was the most commonly employed treatment modality. No statistically significant differences were observed in 5-year DSS by treatment modality.