Rheumatoid Arthritis Disease Activity Index-5: an easy and effective way of monitoring patients with rheumatoid arthritis

Objective To study the utility of the Rheumatoid Arthritis Disease Activity Index-5 (RADAI-5) as a valid tool for daily rheumatoid arthritis (RA) monitoring and to compare its predictability to assess RA activity with respect to Disease Activity Score 28 (DAS28) and Clinical Disease Activity Index (CDAI). Patients and methods A total of 100 patients with RA (diagnosed as per American College of Rheumatology 1987 criteria) were enrolled in the study group. Each patient was assessed two times with 3-month interval for disease activity (DA) using DAS28, CDAI, and RADAI-5. Spearman’s correlation coefficient (ρ) for correlation and kappa for agreement between different activity measures were assessed. Results In our study group, 19% patients were men and 81% patients were women, with male to female ratio of 1 : 4.3. Their mean age was 44.4±11.8 years, and their mean disease duration was 67.5±59.8 months. On initial visit, that is, baseline, mean DA as per RADAI-5, DAS28, and CDAI were 5.14±2.17, 5.58±1.55, and 27.96±15.46, respectively, and on follow-up visit, the readings were 3.76±1.92, 4.54±1.41, and 17.67±12.46, respectively. The mean changes in DA at follow-up visit were −1.37±2.15 by RADAI-5, −1.04±1.58 by DAS28, and −10.29±15.75 by CDAI. Changes in DA indices correlated significantly with each other with ρ ranging from 0.8 to 0.9 (P<0.001). An average agreement was found among all three measures at different DA level. Conclusion RADAI-5 seems to be an effective tool with high tendency to assess the changes in RA DA in routine patient care in hospital settings as well as in home-based settings

Global Open Health Data Cooperatives Cloud in an Era of COVID-19 and Planetary Health

Big data in both the public domain and the health care industry are growing rapidly, for example, with broad availability of next-generation sequencing and large-scale phenomics datasets on patient-reported outcomes. In parallel, we are witnessing new research approaches that demand sharing of data for the benefit of planetary society. Health data cooperatives (HDCs) is one such approach, where health data are owned and governed collectively by citizens who take part in the HDCs. Data stored in HDCs should remain readily available for translation to public health practice but at the same time, governed in a critically informed manner to ensure data integrity, veracity, and privacy, to name a few pressing concerns. As a solution, we suggest that data generated from high-throughput omics research and phenomics can be stored in an open cloud platform so that researchers around the globe can share health data and work collaboratively. We describe here the Global Open Health Data Cooperatives Cloud (GOHDCC) as a proposed cloud platform-based model for the sharing of health data between different HDCCs around the globe. GOHDCC's main objective is to share health data on a global scale for robust and responsible global science, research, and development. GOHDCC is a citizen-oriented model cooperatively governed by citizens. The model essentially represents a global sharing platform that could benefit all stakeholders along the health care value chain

Global Open Health Data Cooperatives Cloud in an Era of COVID-19 and Planetary Health

Big data in both the public domain and the health care industry are growing rapidly, for example, with broad availability of next-generation sequencing and large-scale phenomics datasets on patient-reported outcomes. In parallel, we are witnessing new research approaches that demand sharing of data for the benefit of planetary society. Health data cooperatives (HDCs) is one such approach, where health data are owned and governed collectively by citizens who take part in the HDCs. Data stored in HDCs should remain readily available for translation to public health practice but at the same time, governed in a critically informed manner to ensure data integrity, veracity, and privacy, to name a few pressing concerns. As a solution, we suggest that data generated from high-throughput omics research and phenomics can be stored in an open cloud platform so that researchers around the globe can share health data and work collaboratively. We describe here the Global Open Health Data Cooperatives Cloud (GOHDCC) as a proposed cloud platform-based model for the sharing of health data between different HDCCs around the globe. GOHDCC's main objective is to share health data on a global scale for robust and responsible global science, research, and development. GOHDCC is a citizen-oriented model cooperatively governed by citizens. The model essentially represents a global sharing platform that could benefit all stakeholders along the health care value chain

Curative remedies for rheumatoid arthritis: Herbal informatics approach for rational based selection of natural plant products

128-133Rheumatoid arthritis is a chronic inflammatory and systemic autoimmune disease characterized by joint pain, swelling and stiffness. Inflammatory mediators such as pro-inflammatory cytokines, IL-6, COX-2, TNF- alpha, etc. attack joints upon activation and generate reactive oxygen/nitrogen species, causing oxidative stress. High risk of opportunistic infections has also been reported in patients associated with the use of TNF-α inhibitors, corticosteroids and methotrexate drugs. Use of herbal medicine is becoming popular due to their negligible toxicity and rare side effects. The present study emphasizes the use of a bioprospection model which includes random search, priority indexing and rationale based selection of natural plant products targeting key descriptors of the disease. The identified plants could be used as nutraceuticals to ameliorate the vulnerable condition in inflammatory disorder(s). Further in vitro and in vivo analysis is also warranted, so as to validate the findings of the in silico Bioprospection model

Herbal informatics approach for the selection of natural compounds targeting diabetes mellitus

270-275Diabetes mellitus is a chronic metabolic disorder where the Beta-cells present on the islets of Langerhans of pancreas fail to produce enough insulin resulting in unstable blood glucose level that pave the way for insulin resistance by tissues. According to recent WHO (2016) report, 422 million populations were suffering from this metabolic disorder worldwide. Inflammatory mediators such as Alpha-glucosidase, Glucose Transporter Type-4, Sodium Glucose Transporter Type-2, Glucagon like peptide-1, 11 beta-Hydroxysteroid dehydrogenases, etc., are known to play an important role in pathogenesis of this disease and have been considered as potential therapeutic targets.  However, despite the great progress achieved in synthesis of drugs designed to act on targeted molecules, treatment to diabetes is still a challenge because of the common side effects associate with these medicines. Medicinal plants and their biomolecules may be feasible alternative for treatment of diabetes. In the present study, we have utilized in silico herbal informatics model to develop a natural remedies with minimal or no side effects which can inhibit the mediators of disease. In this study bio-prospection model were used where random search is included, followed by the index and rationale based selection of plant products targeting the diabetic factors. These models provide Nigella sativa L., Momordica charantia L. and Camellia sinensis (L.) Kuntze as identified plants that can be used to target the disease at both in vitro and in vivo level