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SARS-CoV-2 is the underlying cause for the COVID-19 pandemic. Like most enveloped RNA viruses, SARS-CoV-2 uses a homotrimeric surface antigen to gain entry into host cells. Here we describe S-Trimer, a native-like trimeric subunit vaccine candidate for COVID-19 based on Trimer-Tag technology. Immunization of S-Trimer with either AS03 (oil-in-water emulsion) or CpG 1018 (TLR9 agonist) plus alum adjuvants induced high-level of neutralizing antibodies and Th1-biased cellular immune responses in animal models. Moreover, rhesus macaques immunized with adjuvanted S-Trimer were protected from SARS-CoV-2 challenge compared to vehicle controls, based on clinical observations and reduction of viral loads in lungs. Trimer-Tag may be an important platform technology for scalable production and rapid development of safe and effective subunit vaccines against current and future emerging RNA viruses. Vaccines for SARS-CoV-2 are needed to fight the pandemic. Here the authors show immunogenicity of an adjuvanted subunit vaccine, SARS-CoV-2 spike protein trimerized with trimer-tag technology, in small animal models and protection from SARS-CoV-2 challenge in non-human primates.

Irritable bowel syndrome (IBS) is the most common functional bowel disorder worldwide and is associated with visceral hypersensitivity, gut motility, immunomodulation, gut microbiota alterations, and dysfunction of the brain-gut axis; however, its pathophysiology remains poorly understood. Gut microbiota and its metabolites are proposed as possible etiological factors of IBS. The aim of our study was to investigate specific types of microbiota-derived metabolites, especially bile acids, short-chain fatty acids, vitamins, amino acids, serotonin and hypoxanthine, which are all implicated in the pathogenesis of IBS. Metabolites-focused research has identified multiple microbial targets relevant to IBS patients, important roles of microbiota-derived metabolites in the development of IBS symptoms have been established. Thus, we provide an overview of gut microbiota and their metabolites on the different subtypes of IBS (constipation-predominant IBS-C, diarrhea-predominant IBS-D) and present controversial views regarding the role of microbiota in IBS.

Energy loss within organic solar cells (OSCs) is undesirable as it reduces cell efficiency1–4. In particular, non-radiative recombination loss3 and energetic disorder5, which are closely related to the tail states below the band edge and the overall photon energy loss, need to be minimized to improve cell performance. Here, we report how the use of a small-molecule acceptor with torsion-free molecular conformation can achieve a very low degree of energetic disorder and mitigate energy loss in OSCs. The resulting single-junction OSC has an energy loss due to non-radiative recombination of just 0.17 eV and a high power conversion efficiency of up to 16.54% (certified as 15.89% by the National Renewable Energy Laboratory). The findings take studies of organic photovoltaics deeper into a new regime, beyond the limits of energetic disorder and large energy offset for charge generation.An organic solar cell designed with minimal energetic disorder exhibits very low energy loss due to non-radiative recombination and highly efficient operation.

Luteolin is a flavonoid in a variety of fruits, vegetables, and herbs, which has shown anti-inflammatory, antioxidant, and anti-cancer neuroprotective activities. In this study, we investigated the potential beneficial effects of luteolin on memory deficits and neuroinflammation in a triple-transgenic mouse model of Alzheimer’s disease (AD) (3 × Tg-AD). The mice were treated with luteolin (20, 40 mg · kg −1  · d −1 , ip) for 3 weeks. We showed that luteolin treatment dose-dependently improved spatial learning, ameliorated memory deficits in 3 × Tg-AD mice, accompanied by inhibiting astrocyte overactivation (GFAP) and neuroinflammation (TNF-α, IL-1β, IL-6, NO, COX-2, and iNOS protein), and decreasing the expression of endoplasmic reticulum (ER) stress markers GRP78 and IRE1α in brain tissues. In rat C6 glioma cells, treatment with luteolin (1, 10 µM) dose-dependently inhibited LPS-induced cell proliferation, excessive release of inflammatory cytokines, and increase of ER stress marker GRP78. In conclusion, luteolin is an effective agent in the treatment of learning and memory deficits in 3 × Tg-AD mice, which may be attributable to the inhibition of ER stress in astrocytes and subsequent neuroinflammation. These results provide the experimental basis for further research and development of luteolin as a therapeutic agent for AD.

Objectives To investigate the potential of dual-energy computed tomography (DECT) parameters in identifying metastatic cervical lymph nodes in oral squamous cell carcinoma (OSCC) patients and to explore the relationships between DECT and pathological features. Methods Clinical and DECT data were collected from patients who underwent radical resection of OSCC and cervical lymph node dissection between November 2019 and June 2021. Microvascular density was assessed using the Weidner counting method. The electron density (ED) and effective atomic number ( Z eff ) in non - contrast phase and iodine concentration (IC), normalized IC, slope of the energy spectrum curve ( λ HU ), and dual-energy index (DEI) in parenchymal phase were compared between metastatic and non - metastatic lymph nodes. Student’s t -test, Pearson’s rank correlation, and receiver operating characteristic curves were performed. Results The inclusion criteria were met in 399 lymph nodes from 103 patients. Metastatic nodes ( n = 158) displayed significantly decreased ED, IC, normalized IC, λ HU , and DEI values compared with non-metastatic nodes ( n = 241) (all p < 0.01). Strong correlations were found between IC ( r = 0.776), normalized IC ( r = 0.779), λ HU ( r = 0.738), DEI ( r = 0.734), and microvascular density. Area under the curve (AUC) for normalized IC performed the highest (0.875) in diagnosing metastatic nodes. When combined with the width of nodes, AUC increased to 0.918. Conclusion DECT parameters IC, normalized IC, λ HU , and DEI reflect pathologic changes in lymph nodes to a certain extent, and aid for detection of metastatic cervical lymph nodes from OSCC. Key Points • Electron density, iodine concentration, normalized iodine concentration, λ HU , and dual-energy index values showed significant differences between metastatic and non-metastatic nodes. • Strong correlations were found between iodine concentration, normalized iodine concentration, slope of the spectral Hounsfield unit curve, dual-energy index, and microvascular density. • DECT qualitative parameters reflect the pathologic changes in lymph nodes to a certain extent, and aid for the detection of metastatic cervical lymph nodes from oral squamous cell carcinoma.

Proteolysis-targeting chimera (PROTAC) and other targeted protein degradation (TPD) molecules that induce degradation by the ubiquitin-proteasome system (UPS) offer new opportunities to engage targets that remain challenging to be inhibited by conventional small molecules. One fundamental element in the degradation process is the E3 ligase. However, less than 2% amongst hundreds of E3 ligases in the human genome have been engaged in current studies in the TPD field, calling for the recruiting of additional ones to further enhance the therapeutic potential of TPD. To accelerate the development of PROTACs utilizing under-explored E3 ligases, we systematically characterize E3 ligases from seven different aspects, including chemical ligandability, expression patterns, protein-protein interactions (PPI), structure availability, functional essentiality, cellular location, and PPI interface by analyzing 30 large-scale data sets. Our analysis uncovers several E3 ligases as promising extant PROTACs. In total, combining confidence score, ligandability, expression pattern, and PPI, we identified 76 E3 ligases as PROTAC-interacting candidates. We develop a user-friendly and flexible web portal ( https://hanlaboratory.com/E3Atlas/ ) aimed at assisting researchers to rapidly identify E3 ligases with promising TPD activities against specifically desired targets, facilitating the development of these therapies in cancer and beyond. Proteolysis-targeting chimeras (PROTACs) offer new avenues for drug development. Here the authors investigate E3 ligases—key to PROTAC function—and identify candidate targets for cancer drivers such as KRAS and EGFR.

Medicinal plants are globally valuable sources of herbal products, and they are disappearing at a high speed. This article reviews global trends, developments and prospects for the strategies and methodologies concerning the conservation and sustainable use of medicinal plant resources to provide a reliable reference for the conservation and sustainable use of medicinal plants. We emphasized that both conservation strategies (e.g. in situ and ex situ conservation and cultivation practices) and resource management (e.g. good agricultural practices and sustainable use solutions) should be adequately taken into account for the sustainable use of medicinal plant resources. We recommend that biotechnical approaches (e.g. tissue culture, micropropagation, synthetic seed technology, and molecular marker-based approaches) should be applied to improve yield and modify the potency of medicinal plants.

Aims White matter hyperintensity (WMH) is the most common neuroimaging manifestation of cerebral small vessel disease and is related to cognitive dysfunction or dementia. This study aimed to investigate the mechanism and effective indicators to predict WMH‐related cognitive impairment. Methods We recruited 22 healthy controls (HC), 25 cases of WMH with normal cognition (WMH‐NC), and 23 cases of WMH with mild cognitive impairment (WMH‐MCI). All individuals underwent diffusion tensor imaging (DTI) and a standardized neuropsychological assessment. Automated Fiber Quantification was used to extract altered DTI metrics between groups, and partial correlation was performed to assess the associations between WM integrity and cognitive performance. Furthermore, machine learning analyses were performed to determine underlying imaging markers of WMH‐related cognitive impairment. Results Our study found that mean diffusivity (MD) values of several fiber bundles including the bilateral anterior thalamic radiation (ATR), the left inferior fronto‐occipital fasciculus (IFOF), the right inferior longitudinal fasciculus (ILF), and the right superior longitudinal fasciculus (SLF) were negatively correlated with memory function, while that of the anterior component of the right IFOF and the posterior and intermediate component of the right ILF showed significant negative correlation with MMSE and episodic memory, respectively. Furthermore, machine learning analyses showed that the accuracy of recognizing WMH‐MCI patients from the WMH populations was up to 80.5% and the intermediate and posterior components of the right ILF and the anterior component of the right IFOF contribute the most. Conclusions Changes in the properties of DTI may be the potential mechanism of WMH‐related MCI, especially the right IFOF and the right ILF, which may become imaging markers for predicting WMH‐related cognitive dysfunction.

Background Lipid-lowering therapy is important, and the distribution of lipid levels and the incidence of hyperlipidemia may vary in different subgroups of the population. We aimed to explore the distribution of lipid levels and the prevalence of hyperlipidemia in subpopulations with subgroup factors, including age, sex, race, and smoking status. Methods Our study used data from the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2018, ultimately enrolling and analyzing 15,499 participants. A cross-sectional analysis was performed to assess the distribution of lipids and prevalence of hyperlipidemia in subpopulations, and multifactorial logistic regression analyses were performed for the prevalence of hyperlipidemia, adjusted for age, sex, race and smoking status. Results Blacks had significantly lower mean serum total cholesterol and triglycerides and higher serum high-density lipoprotein cholesterol (HDL-C) than whites ( P  < 0.001). In contrast, Mexican Americans had markedly higher mean serum triglycerides and lower serum HDL-C than whites ( P  < 0.001). Furthermore, the prevalence of hypercholesterolemia and hypertriglyceridemia was lower in blacks than in whites ( P  = 0.003 and P  < 0.001, respectively), while the prevalence of hypertriglyceridemia was significantly higher in Mexican Americans than in whites ( P  = 0.002). In addition, total cholesterol and triglyceride levels were significantly higher in women aged 65 years or older and markedly higher than in men in the same age group ( P  < 0.001). In addition, overall mean total cholesterol, triglyceride, and low-density lipoprotein cholesterol (LDL-C) levels were higher in smokers than in nonsmokers ( P  = 0.01, P  < 0.001, and P  = 0.005, respectively). Conclusion Based on NHANES data, the mean lipid levels and prevalence of hyperlipidemia differed by sex, age, race, and smoking status.

The distribution and abundance of immune cells, particularly T‐cell subsets, play pivotal roles in cancer immunology and therapy. T cells have many subsets with specific function and current methods are limited in estimating them, thus, a method for predicting comprehensive T‐cell subsets is urgently needed in cancer immunology research. Here, Immune Cell Abundance Identifier (ImmuCellAI), a gene set signature‐based method, is introduced for precisely estimating the abundance of 24 immune cell types including 18 T‐cell subsets, from gene expression data. Performance evaluation on both the sequencing data with flow cytometry results and public expression data indicate that ImmuCellAI can estimate the abundance of immune cells with superior accuracy to other methods especially on many T‐cell subsets. Application of ImmuCellAI to immunotherapy datasets reveals that the abundance of dendritic cells, cytotoxic T, and gamma delta T cells is significantly higher both in comparisons of on‐treatment versus pre‐treatment and responders versus non‐responders. Meanwhile, an ImmuCellAI result‐based model is built for predicting the immunotherapy response with high accuracy (area under curve 0.80–0.91). These results demonstrate the powerful and unique function of ImmuCellAI in tumor immune infiltration estimation and immunotherapy response prediction. Immune Cell Abundance Identifier (ImmuCellAI) is a gene set signature‐based method for precisely estimating the abundance of 24 immune cell types including 18 T‐cell subsets. Application of ImmuCellAI to immunotherapy datasets reveals the dynamic change of immune cell abundance. An ImmuCellAI result‐based model for predicting the immunotherapy response achieves high accuracy with area under curve 0.80–0.91.