Engineering circular RNA for enhanced protein production.

Circular RNAs (circRNAs) are stable and prevalent RNAs in eukaryotic cells that arise from back-splicing. Synthetic circRNAs and some endogenous circRNAs can encode proteins, raising the promise of circRNA as a platform for gene expression. In this study, we developed a systematic approach for rapid assembly and testing of features that affect protein production from synthetic circRNAs. To maximize circRNA translation, we optimized five elements: vector topology, 5' and 3' untranslated regions, internal ribosome entry sites and synthetic aptamers recruiting translation initiation machinery. Together, these design principles improve circRNA protein yields by several hundred-fold, provide increased translation over messenger RNA in vitro, provide more durable translation in vivo and are generalizable across multiple transgenes.

Comparative Study on International Research Hotspots and National-Level Policy Keywords of Dynamic Disaster Monitoring and Early Warning in China (2000-2021).

For more than 20 years, disaster dynamic monitoring and early warning have achieved orderly and sustainable development in China, forming a systematic academic research system and top-down policy design, which are inseparable from the research of China's scientific community and the promotion of government departments. In the past, most of the research on dynamic disaster monitoring and early warning focused on specific research in a certain field, scene, and discipline, while a few studies focused on research review or policy analysis, and few studies combined macro and meso research reviews in academia with national policy analysis for comparative analysis. It is necessary and urgent to explore the interaction between scholars' research and policy deployment, which can bring theoretical contributions and policy references to the top-down design, implementation promotion, and academic research of China's dynamic disaster monitoring and early warning. Based on 608 international research articles on dynamic disaster monitoring and early warning published by Chinese scholars from 2000-2021 and 187 national policy documents published during this period, this paper conducts a comparative analysis between the knowledge maps of international research hotspots and the co-occurrence maps of policy keywords on dynamic disaster monitoring and early warning. The research shows that in the stage of initial development (2000-2007), international research articles are few and focused, and research hotspots are somewhat alienated from policy keywords. In the stage of rising development (2008-2015), after the Wenchuan earthquake, research hotspots are closely related to policy keywords, mainly in the fields of geology, engineering disasters, meteorological disasters, natural disasters, etc. Meanwhile, research hotspots also focus on cutting-edge technologies and theories, while national-level policy keywords focus more on overall governance and macro promotion, but the two are gradually closely integrated. In the stage of rapid development (2016-2021), with the continuous attention and policy promotion of the national government, the establishment of the Ministry of Emergency Management, and the gradual establishment and improvement of the disaster early warning and monitoring system, research hotspots and policy keywords are integrated and overlapped with each other, realizing the organic linkage and mutual promotion between academic research and political deployment. The motivation, innovation, integration, and transformation of dynamic disaster monitoring and early warning are promoted by both policy and academic research. The institutions that issue policies at the national level include the State Council and relevant departments, the Ministry of Emergency Management, the Ministry of Water Resources, and other national ministries and commissions. The leading affiliated institutions of scholars' international research include China University of Mining and Technology, Chinese Academy of Sciences, Wuhan University, Shandong University of Science and Technology, and other institutions. The disciplines involved are mainly multidisciplinary geosciences, environmental sciences, electrical and electronic engineering, remote sensing, etc. It is worth noting that in the past two to three years, research and policies focusing on COVID-19, public health, epidemic prevention, environmental governance, and emergency management have gradually increased.

Cooperative Evolution of China's Excellent Innovative Research Groups from the Perspective of Innovation Ecosystem: Taking an "Environmental Biogeochemistry" Research Innovation Group as a Case Study.

Research, understanding, and prediction of complex systems is an important starting point for human beings to tackle major problems and emergencies such as global warming and COVID-19. Research on innovation ecosystem is an important part of research on complex systems. With the rapid development of sophisticated industries, the rise of innovative countries, and the newly developed innovation theory, innovation ecosystem has become a new explanation and new paradigm for adapting to today's global innovation cooperation network and the scientific development of complex systems, which is also in line with China's concept of building an innovative country and promoting comprehensive innovation and international cooperation with scientific and technological innovation as the core. The Innovative Research Group at Peking University is the most representative scientific and technological innovation team in the frontier field of basic research in China. The characteristics of its organization mechanism and dynamic evolution connotation are consistent with the characteristics and evolution of innovation ecosystem. An excellent innovative research group is regarded as a small innovation ecosystem. We selected the "Environmental Biogeochemistry" Innovation Research Group at Peking University as a typical case in order to understand and analyze the evolution of cooperation among scientific and technological innovation teams, improve the healthy development as well as internal and external governance of this special small innovation ecosystem, promote the expansion of an innovation team cooperation network and the improvement of cooperation quality, promote the linkage supports of funding and management departments, and improve their scientific and technological governance abilities. Through scientometrics, visual analysis of knowledge maps, and an exploratory case study, we study the evolution process and development law of team cooperation. It is found that the main node authors of the cooperation network maintain strong cooperation frequency and centrality, and gradually strengthen with the expansion of the cooperation network and the evolution of time. Driven by the internal cooperative governance of the team and the external governance of the funding and management departments, this group has gradually formed a healthy, orderly, open, and cooperative special innovation ecosystem, which is conducive to the stability and sustainable development of the national innovation ecosystem and the global innovation ecosystem.

Association between urinary VEGFA and renal pathology of IgA nephropathy patients.

BACKGROUND: Renal biopsy remains the golden standard for diagnosing and monitoring IgA nephropathy (IgAN). Vascular endothelial growth factor A (VEGFA) was crucial for the survival of glomerular cells. Our aim was to screen the expression pattern of urinary, circulating and renal VEGFA in IgAN patients to reveal their relationship with renal pathology and outcomes. METHODS: Baseline VEGFA levels were determined with ELISA, real-time PCR and immunohistochemistry. Associations between VEGFA expression and clinical-pathological parameters, and renal outcomes were evaluated. RESULTS: Compared with healthy controls, urinary VEGFA level was obviously elevated in IgAN patients (76.19 ± 63.67 pg/mg Cr vs 146.67 ± 232.71 pg/mg Cr, p = 0.0291) and not correlated with serum VEGFA level. Baseline urinary VEGFA was significantly associated with gender and tubular atrophy/interstitial fibrosis by stepwise multivariate regression analysis. Urinary VEGFA was higher in male patients accompanied with higher serum creatinine, larger proportion of hypertension and recurrent hematuria than in female patients. In the kidney of IgAN patients, VEGFA were robustly expressed in the parietal epithelial cells, podocytes, mesangial cells and tubular epithelial cells. After a follow-up duration of 38.53 ± 27.14 months, IgAN patients with higher urinary VEGFA level were found to have a poorer renal outcome of renal replacement therapy (HR = 1.027, p = 0.037) or composite outcome (HR = 1.023, p = 0.039) after adjusting for confounders. CONCLUSIONS: Increased urinary VEGFA might reflect certain renal pathology and, although not fully specific, still could be served as a valuable noninvasive indicator in predicting renal progression of IgAN.

Severity-associated markers and assessment model for predicting the severity of COVID-19: a retrospective study in Hangzhou, China.

BACKGROUND: The severity of COVID-19 associates with the clinical decision making and the prognosis of COVID-19 patients, therefore, early identification of patients who are likely to develop severe or critical COVID-19 is critical in clinical practice. The aim of this study was to screen severity-associated markers and construct an assessment model for predicting the severity of COVID-19. METHODS: 172 confirmed COVID-19 patients were enrolled from two designated hospitals in Hangzhou, China. Ordinal logistic regression was used to screen severity-associated markers. Least Absolute Shrinkage and Selection Operator (LASSO) regression was performed for further feature selection. Assessment models were constructed using logistic regression, ridge regression, support vector machine and random forest. The area under the receiver operator characteristic curve (AUROC) was used to evaluate the performance of different models. Internal validation was performed by using bootstrap with 500 re-sampling in the training set, and external validation was performed in the validation set for the four models, respectively. RESULTS: Age, comorbidity, fever, and 18 laboratory markers were associated with the severity of COVID-19 (all P values < 0.05). By LASSO regression, eight markers were included for the assessment model construction. The ridge regression model had the best performance with AUROCs of 0.930 (95% CI, 0.914-0.943) and 0.827 (95% CI, 0.716-0.921) in the internal and external validations, respectively. A risk score, established based on the ridge regression model, had good discrimination in all patients with an AUROC of 0.897 (95% CI 0.845-0.940), and a well-fitted calibration curve. Using the optimal cutoff value of 71, the sensitivity and specificity were 87.1% and 78.1%, respectively. A web-based assessment system was developed based on the risk score. CONCLUSIONS: Eight clinical markers of lactate dehydrogenase, C-reactive protein, albumin, comorbidity, electrolyte disturbance, coagulation function, eosinophil and lymphocyte counts were associated with the severity of COVID-19. An assessment model constructed with these eight markers would help the clinician to evaluate the likelihood of developing severity of COVID-19 at admission and early take measures on clinical treatment.