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0% in the elderly group and 78.4% in the younger group achieved sustained virologic response 12 (P = 0.30). In the modified intention-to-treat population, all patients achieved sustained virologic response 12. A total of 27.5% of patients experienced adverse events. The most frequently observed adverse events was pruritus, and was significantly associated with female sex, the presence of hemodialysis and serum albumin at baseline less then 4.0 g/dL. CONCLUSION Glecaprevir/pibrentasvir therapy was effective and well tolerated, even in elderly patients with hepatitis C virus infection aged ≥75 years. Geriatr Gerontol Int 2020; •• ••-••. © 2020 Japan Geriatrics Society.Universal health care (UHC) is primarily a financing concern, whereas primary health care (PHC) is primarily concerned with providing the right care at the right time to achieve the best possible health outcomes for individuals and communities. A recent call for contributions by the WHO emphasized that UHC can only be achieved through PHC, and that to achieve this goal will require the strengthening of the three pillars of PHC - (a) enabling primary care and public health to integrate health services, (b) empowering people and communities to create healthy living conditions, and (c) integrating multisectoral policy decisions to ensure UHC that achieves the goal of "health for all." "Pillars" - as a static metaphor - sends the wrong signal to the research and policy-making community. It, in fact, contradicts the WHO's own view, namely that there is "the need to strengthen comprehensive primary health care systems based on local priorities, needs and contexts … [that are] co-developed by people who are engaged in their own health." What we really need to develop PHC as the basis to achieve the goal of UHC is a dynamic agency to drive a "system-as-a-whole framework" that simultaneously takes into account finance, individual, and local needs. Health systems are socially constructed organizational systems that are "functionally layered" in a hierarchical fashion - governments and/or funders at the top-level not only promote the goals of the system (policies) but also constrain the system (rules, regulations, resources) in its ability to deliver. check details Hence, there is a need to focus on two key system features - political leadership and dynamic bottom-up agency that maintains everyone's focus on the goal to be achieved, and a limitation of system constraints so that communities can shape best adapted primary care services that truly meet the needs of their individuals, families, and community. © 2020 John Wiley & Sons, Ltd.Flavonoids represent a diversified family of phenylpropanoid-derived plant secondary metabolites. They are widely found in fruits, vegetables and medicinal herbs and plants. There has been increasing interest on flavonoids because of their proven bioactivity associated with anti-obesity, anti-cancer, anti-inflammatory, anti-diabetic activity and the prevention of aging-related chronic conditions, such as nervous and cardiovascular disease. Low bioavailability of flavonoids is a major challenge restricting their wide applications. Due to safety and economic issues, traditional plant extraction or chemical synthesis could not provide a scalable route for large-scale production of flavonoids. Alternatively, reconstruction of biosynthetic gene clusters in plants and industrially relevant microbes offer significant promise for discovery and scalable synthesis of flavonoids. This review provides an update on biotechnological production of flavonoids. We summarized the recent advances on plant metabolic engineering, microbial host and genetically encoded biosensors. Plant metabolic engineering holds the promise to improve the yield of specific flavonoids and expand the chemical space of novel flavonoids. The choice of microbial host provides the cellular chassis that could be tailored for various stereo- or regio-selective chemistries that are crucial for their bioactivities. When coupled with transcriptional biosensing, genetically encoded biosensors could be welded into cellular metabolism to achieve high throughput screening or dynamic carbon flux re-allocation to deliver efficient and robust microbial workhorse. The convergence of these technologies will translate the vast majority of plant genetic resources into valuable flavonoids with pharmaceutical/nutraceutical values in the foreseeable future. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.BACKGROUND Pediatric pneumonia remains a significant health challenge, while the viral risk factors for adverse outcomes in pediatric pneumonia are not yet fully clear. METHODS A matched case-control study of pediatric patients with pneumonia was carried out in Beijing, China, between 2007 and 2015. The study enrolled 334 intensive care unit patients who developed life-threatening diseases and 522 controls matched to the sex, age, ethnicity, admission dates, and residing district of the cases suffered from pneumonia. Nasopharyngeal aspirates were taken from all participants and tested by PCR for 18 common respiratory viruses. RESULTS At least, one virus was detected in 257 (77%) of the cases and 409 (78%) of the controls. We observed no difference in the prevalence of 17 respiratory viruses between cases and controls but found a higher frequency of influenza A virus (IFV-A) in the cases than in the controls (7% vs 4%, P = .036). After adjusting for comorbid conditions and a history of reactive airway diseases, IFV-A was associated with an increase in life-threatening pneumonia (adjusted odds ratio = 2.55, 95% CI = 1.24-5.24). Young age and congenital heart disease (aOR = 10.16-10.27, P  less then  .001) were also independent risk factors. CONCLUSIONS The prevention of IFV infection is critical in decreasing the risk of life-threatening pneumonia in children. © 2020 The Authors. Influenza and Other Respiratory Viruses Published by John Wiley & Sons Ltd.A lot of energy in the field of proteomics is dedicated to the application of challenging experimental workflows, which include metaproteomics, proteogenomics, data independent acquisition (DIA), non-specific proteolysis, immunopeptidomics, and open modification searches. These workflows are all challenging because of ambiguity in the identification stage; they either expand the search space and thus increase the ambiguity of identifications, or, in the case of DIA, they generate data that is inherently more ambiguous. In this context, machine learning-based predictive models are now generating considerable excitement in the field of proteomics because these predictive models hold great potential to drastically reduce the ambiguity in the identification process of the above-mentioned workflows. Indeed, the field has already produced classical machine learning and deep learning models to predict almost every aspect of a liquid chromatography-mass spectrometry (LC-MS) experiment. Yet despite all the excitement, thorough integration of predictive models in these challenging LC-MS workflows is still limited, and further improvements to the modeling and validation procedures can still be made.