Background China has a large population; however, medical resources are unevenly distributed and extremely limited, and more medical services are needed. With the development and ever-increasing popularity of mobile internet communication, China has created a mode of mobile health (mHealth) care to resolve this problem. Objective The aim of this study was (1) to describe the problems associated with China’s medical care practice, (2) explore the need for and the feasibility of internet-based medical care in China, and (3) analyze the functionality of and services offered by internet-based health care platforms for the management of chronic diseases. Methods Data search was performed by searching national websites, the popular search engine Baidu, the App Store, and websites of internet medical care institutions, using search terms like “mobile health,” “Internet health,” “mobile medical,” “Internet medical,” “digital medical,” “digital health,” and “online doctor.” A total of 6 mobile apps and websites with the biggest enrollment targeting doctors and end users with chronic diseases in China were selected. Results We recognized the limitations of medical and health care providers and unequal distribution of medical resources in China. An mHealth care platform is a novel and efficient way for doctors and patients to follow up and manage chronic diseases. Services offered by these platforms include reservation and payment, medical consultation, medical education assessment, pharmaceutical and medical instruments sales, electronic medical records, and chronic disease management. China’s health policies are now strongly promoting the implementation of mHealth solutions, particularly in response to the increasing burden of chronic diseases and aging in the population. Conclusions China's internet-based medical and health care mode can benefit the populace by providing people with high-quality medical resources. This can help other countries and regions with high population density and unevenly distributed medical resources manage their health care concerns.

Background To describe demographic and clinical factors associated with the presence and incidence of depression and explore the temporal relationship between depression and joint symptoms in patients with symptomatic knee osteoarthritis (OA). Methods Three hundred ninety-seven participants were selected from a randomized controlled trial in people with symptomatic knee OA and vitamin D deficiency (age 63.3 ± 7.1 year, 48.6% female). Depression severity and knee joint symptoms were assessed using the patient health questionnaire (PHQ-9) and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), respectively, at baseline and 24 months. Results The presence and incidence of depression was 25.4 and 11.2%, respectively. At baseline, having younger age, a higher body mass index (BMI), greater scores of WOMAC pain (PR: 1.05, 95%CI:1.03, 1.07), dysfunction (PR: 1.02, 95%CI:1.01, 1.02) and stiffness (PR: 1.05, 95%CI: 1.02, 1.09), lower education level, having more than one comorbidity and having two or more painful body sites were significantly associated with a higher presence of depression. Over 24 months, being female, having a higher WOMAC pain (RR: 1.05, 95%CI: 1.02, 1.09) and dysfunction score (RR: 1.02, 95%CI: 1.01, 1.03) at baseline and having two or more painful sites were significantly associated with a higher incidence of depression. In contrast, baseline depression was not associated with changes in knee joint symptoms over 24 months. Conclusion Knee OA risk factors and joint symptoms, along with co-existing multi-site pain are associated with the presence and development of depression. This suggests that managing common OA risk factors and joint symptoms may be important for prevention and treatment depression in patients with knee OA. Trial registration ClinicalTrials.gov identifier: NCT01176344. Anzctr.org.au identifier: ACTRN12610000495022.

Objective Gout is the most common inflammatory arthritis and the worldwide incidence is increasing. By revealing the metabolic alterations in serum and urine of gout patients, the first aim of our study was to discover novel molecular biomarkers allowing for early diagnosis. We also aimed to investigate the underlying pathogenic pathways. Methods Serum and urine samples from gout patients (n = 30) and age-matched healthy controls (n = 30) were analysed by ultra-performance liquid chromatography–mass spectrometry (UPLC-MS) to screen the differential metabolites and construct a diagnostic model. Next, the model was verified and optimized in the second validation cohort (n = 100). The pathways were illustrated to understand the underlying pathogenesis of gout. Results In general, serum metabolomics demonstrated a clearer distinction than urine metabolomics. In the discovery cohort, 40 differential serum metabolites were identified that could distinguish gout patients from healthy controls. Among them, eight serum metabolites were verified in the validation cohort. Through regression analysis, the final model consisted of three serum metabolites—pyroglutamic acid, 2-methylbutyryl carnitine and Phe-Phe—that presented optimal diagnostic power. The three proposed metabolites produced an area under the curve of 0.956 (95% CI 0.911, 1.000). Additionally, the proposed metabolic pathways were primarily involved in purine metabolism, branched-chain amino acids (BCAAs) metabolism, the tricarboxylic acid cycle, synthesis and degradation of ketone bodies, bile secretion and arachidonic acid metabolism. Conclusion The metabolomics signatures could serve as an efficient tool for early diagnosis and provide novel insights into the pathogenesis of gout.

Background: Studies into ankylosing spondylitis (AS) and its relationship with immune imbalance are controversial, and the correlation between the efficacy of TNF-α inhibitor and changes in immune imbalance is unclear. Methods: A total of 40 immune cells were tested with flow cytometry, and the results of 105 healthy control (HC) subjects, 177 active-stage AS patients, and 23 AS cases before and after 12 weeks of TNF-α inhibitor therapy (Anbainuo) were analyzed. Results: Compared with the HC group, the proportion of immune cells, such as naïve and central memory CD4+T cells, in AS increased (P < 0.0001), but effector memory and terminally differentiated CD4+T cells were decreased (P < 0.01 and 0.0001, respectively). Naïve, central memory, and effector memory CD8+T cells were increased (P < 0.0001, 0.001, and 0.01, respectively), but terminally differentiated CD8+T cells were decreased (P < 0.0001). Th1 cells (helper T cells-1), Tfh1 cells (follicular helper T cells-1), Tc1 cells (cytotoxic T cells-1), and Tregs (regulatory T cells) were lower (P < 0.01, 0.05, 0.0001, and 0.001, respectively), but Th17 cells, Tfh17 cells, and Tc cells were higher (P < 0.001, 0.0001, and 0.001, respectively). The proportions of total B cells and class-switched B cells were increased (P < 0.05), but non-switched B cells, plasma cells, memory B cells, and immature Bregs (regulatory B cells) were lower (P < 0.01, 0.0001, 0.0001, and 0.0001, respectively). After Anbainuo therapy, the percentage of naïve CD4+ T cells had decreased (P < 0.05) but Tregs and B10 cells (IL-10-producing regulatory B cells) had increased (P < 0.01 and 0.05, respectively), and the increase in Tregs was positively correlated with the decrease in C-reactive protein (CRP) (r = 0.489, P = 0.018). Conclusions: We found that active-stage AS patients have an immunity imbalance of frequency involving multiple types of immune cells, including CD4+T cells, CD8+T cells, Th cells, Tfh cells, Tc cells, Tregs, Bregs, and B cells. TNF-α inhibitor Anbainuo can not only help to inhibit disease activity but can also improve the immune imbalance of CD4+ T cells and negative regulatory cells in frequency. But CD8+ T cells have not been rescued.

Ankylosing spondylitis (AS) is a type of spondyloarthropathies, the diagnosis of which is often delayed. The lack of early diagnosis tools often delays the institution of appropriate therapy. This study aimed to investigate the systemic metabolic shifts associated with AS and TNF inhibitors treatment. Additionally, we aimed to define reliable serum biomarkers for the diagnosis. We employed an untargeted technique, ultra-performance liquid chromatography-mass spectroscopy (LC-MS), to analyze the serum metabolome of 32 AS individuals before and after 24-week TNF inhibitors treatment, as well as 40 health controls (HCs). Multivariate and univariate statistical analyses were used to profile the differential metabolites associated with AS and TNF inhibitors. A diagnostic panel was established with the least absolute shrinkage and selection operator (LASSO). The pathway analysis was also conducted. A total of 55 significantly differential metabolites were detected. We generated a diagnostic panel comprising five metabolites (L-glutamate, arachidonic acid, L-phenylalanine, PC (18:1(9Z)/18:1(9Z)), 1-palmitoylglycerol), capable of distinguishing HCs from AS with a high AUC of 0.998, (95%CI: 0.992–1.000). TNF inhibitors treatment could restore the equilibrium of 21 metabolites. The most involved pathways in AS were amino acid biosynthesis, glycolysis, glutaminolysis, fatty acids biosynthesis and choline metabolism. This study characterized the serum metabolomics signatures of AS and TNF inhibitor therapy. We developed a five-metabolites-based panel serving as a diagnostic tool to separate patients from HCs. This serum metabolomics study yielded new knowledge about the AS pathogenesis and the systemic effects of TNF inhibitors.