Background Knee osteoarthritis (OA) is a major cause of disability in the elderly, however, there are few studies to estimate the global prevalence, incidence, and risk factors of knee OA. Methods For this study, we searched PUBMED, EMBASE and SCOPUS from inception to April 4, 2020, without language restriction. We identified eligible studies with information on the prevalence or incidence of knee OA in population-based observational studies and extracted data from published reports. We did random-effects meta-analysis to generate estimates. This study was registered with PROSPERO (CRD42020181035). Findings Out of 9570 records identified, 88 studies with 10,081,952 participants were eligible for this study. The pooled global prevalence of knee OA was 16⋅0% (95% CI, 14⋅3%-17⋅8%) in individuals aged 15 and over and was 22⋅9% (95% CI, 19⋅8%-26⋅1%) in individuals aged 40 and over. Correspondingly, there are around 654⋅1 (95% CI, 565⋅6–745⋅6) million individuals (40 years and older) with knee OA in 2020 worldwide. The pooled global incidence of knee OA was 203 per 10,000 person-years (95% CI, 106–331) in individuals aged 20 and over. Correspondingly, there are around annual 86⋅7 (95% CI, 45⋅3–141⋅3) million individuals (20 years and older) with incident knee OA in 2020 worldwide. The prevalence and incidence varied substantially between individual countries and increased with age. The ratios of prevalence and incidence in females and males were 1⋅69 (95% CI, 1⋅59–1⋅80, p <0⋅00) and 1⋅39 (95% CI, 1⋅24–1⋅56, p <0⋅00), respectively. Interpretation Our study provides the global prevalence (16⋅0% [95% CI, 14⋅3%-17⋅8%]) and incidence (203 per 10,000 person-years [95% CI, 106–331]) of knee OA. These findings can be used to better assess the global health burden of knee OA. Further prospective cohort studies are warranted to identify modifiable risk factors for providing effectively preventive strategies in the early stages of the disease. Funding This work was supported by grants from the (nos. 81772384 and 81572174).

Nanoparticles (NPs) have demonstrated great potential for the oral delivery of protein drugs that have very limited oral bioavailability. Orally administered NPs could be absorbed by the epithelial tissue only if they successfully permeate through the mucus that covers the epithelium. However, efficient epithelial absorption and mucus permeation require very different surface properties of a nanocarrier. We herein report self-assembled NPs for efficient oral delivery of insulin by facilitating both of these two processes. The NPs possess a nanocomplex core composed of insulin and cell penetrating peptide (CPP), and a dissociable hydrophilic coating of N-(2-hydroxypropyl) methacrylamide copolymer (pHPMA) derivatives. After systematic screening using mucus-secreting epithelial cells, NPs exhibit excellent permeation in mucus due to the "mucus-inert" pHPMA coating, as well as high epithelial absorption mediated by CPP. The investigation of NP behavior shows that the pHPMA molecules gradually dissociate from the NP surface as it permeates through mucus, and the CPP-rich core is revealed in time for subsequent transepithelial transport through the secretory endoplasmic reticulum/Golgi pathway and endocytic recycling pathway. The NPs exhibit 20-fold higher absorption than free insulin on mucus-secreting epithelium cells, and orally administered NPs generate a prominent hypoglycemic response and an increase of the serum insulin concentration in diabetic rats. Our study provides the evidence of using pHPMA as dissociable "mucus-inert" agent to enhance mucus permeation of NPs, and validates a strategy to overcome the multiple absorption barriers using NP platform with dissociable hydrophilic coating and drug-loaded CPP-rich core.

Major depressive disorder (MDD) is a widespread and debilitating mental disorder. However, there are no biomarkers available to aid in the diagnosis of this disorder. In this study, a nuclear magnetic resonance spectroscopy-based metabonomic approach was employed to profile urine samples from 82 first-episode drug-naïve depressed subjects and 82 healthy controls (the training set) in order to identify urinary metabolite biomarkers for MDD. Then, 44 unselected depressed subjects and 52 healthy controls (the test set) were used to independently validate the diagnostic generalizability of these biomarkers. A panel of five urinary metabolite biomarkers-malonate, formate, N-methylnicotinamide, mhydroxyphenylacetate, and alanine-was identified. This panel was capable of distinguishing depressed subjects from healthy controls with an area under the receiver operating characteristic curve ( Major depressive disorder (MDD)1 is a debilitating mental disorder affecting up to 15% of the general population and accounting for 12.3% of the global burden of disease (1, 2). Currently, the diagnosis of MDD still relies on the subjective identification of symptom clusters rather than empirical laboratory tests. The current diagnostic modality results in a considerable error rate (3), as the clinical presentation of MDD is highly heterogeneous and the current symptombased method is not capable of adequately characterizing this heterogeneity (4). An approach that can be used to circumvent these limitations is to identify disease biomarkers to support objective diagnostic laboratory tests for MDD.Metabonomics, which can measure the small molecules in given biosamples such as plasma and urine without bias (5), has been extensively used to characterize the metabolic changes of diseases and thus facilitate the identification of novel disease-specific signatures as putative biomarkers (6 -10). Nuclear magnetic resonance (NMR) spectroscopy-based metabonomic approaches characterized by sensitive, highthroughput molecular screening have been employed previously in identifying novel biomarkers for a variety of neuropsychiatric disorders, including stroke, bipolar disorder, and schizophrenia (11-13).Specifically with regard to MDD, several animal studies have already characterized the metabolic changes in the blood and urine (14 -19). These studies provide valuable clues as to the pathophysiological mechanism of MDD. However, no study has been designed with the aim of diagnosing this disease. Recently, using an NMR-based metabonomic approach, this research group identified a unique plasma metabolic signature that enables the discrimination of MDD from healthy controls with both high sensitivity and specificity (20). These findings motivated further study on urinary diagnostic metabolite biomarkers for MDD, which would be more valuable from a clinical applicability standpoint, as urine can be more non-invasively collected. Moreover, previous studies have also demonstrated the feasibility of identifying diagnostic metabolite biomarkers of psyc...

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