Background: Treating hypertensive patients by integrating the patient-centered approach would influence the practice and outcome of treatment. Our purpose was to determine whether the implementation of a patientcentered approach in health care delivery can improve adhering to guidelines and the quality-of-care. Methods: A retrospective study was conducted using secondary data from the electronic medical records of the patients treated in the two primary care outpatient settings at the Family Medicine (FM) and Social Security (SS) clinics. A key feature of the FM clinic is the incorporation of a patient-centered approach in its service delivery. Individual information regarding initial assessment and treatment at the follow-up visits was reviewed for 1 year. Comparison of adherence to treatment guidelines between the two primary care clinics was performed by using chi-square, Fisher's exact test or a t-test. To explore the difference in blood pressure and BP control between the two clinics, linear and logistic regression analysis respectively were performed with an adjustment for CV risk score in 2016 as a key confounder. Results: The evidence included 100 records from each clinic, showed variation between the two primary care sites. The FM clinic had more complete records regarding family history of hypertension, assessment for secondary causes, prescription for lifestyle modification and appropriate adjustment of medication. Higher levels of blood pressure control were recorded in the FM clinic, specifically systolic pressure 2.92 mmHg (p = 0.073) and diastolic pressure 5.38 mmHg (p < 0.001) lower than those recorded in the SS clinic. There was a 2.96 times higher chance for BP goals to be achieved in patients in receipt of hypertensive care at the FM clinic (p = 0.004). Conclusions: Adopting a patient-centered approach in service delivery could improve the quality of care for hypertension patients in primary care in Thailand.

The coronavirus disease 2019 (COVID-19) pandemic causes many morbidity and mortality cases. Despite several developed vaccines and antiviral therapies, some patients experience severe conditions that need intensive care units (ICU); therefore, precision medicine is necessary to predict and treat these patients using novel biomarkers and targeted drugs. In this study, we proposed a multi-level biological network analysis framework to identify key genes via protein–protein interaction (PPI) network analysis as well as survival analysis based on differentially expressed genes (DEGs) in leukocyte transcriptomic profiles, discover novel biomarkers using microRNAs (miRNA) from regulatory network analysis, and provide candidate drugs targeting the key genes using drug–gene interaction network and structural analysis. The results show that upregulated DEGs were mainly enriched in cell division, cell cycle, and innate immune signaling pathways. Downregulated DEGs were primarily concentrated in the cellular response to stress, lysosome, glycosaminoglycan catabolic process, and mature B cell differentiation. Regulatory network analysis revealed that hsa-miR-6792-5p, hsa-let-7b-5p, hsa-miR-34a-5p, hsa-miR-92a-3p, and hsa-miR-146a-5p were predicted biomarkers. CDC25A, GUSB, MYBL2, and SDAD1 were identified as key genes in severe COVID-19. In addition, drug repurposing from drug–gene and drug–protein database searching and molecular docking showed that camptothecin and doxorubicin were candidate drugs interacting with the key genes. In conclusion, multi-level systems biology analysis plays an important role in precision medicine by finding novel biomarkers and targeted drugs based on key gene identification.

Coronavirus disease 2019 (COVID-19) continues to spread globally despite the discovery of vaccines. Many people die due to COVID-19 as a result of catastrophic consequences, such as acute respiratory distress syndrome, pulmonary embolism, and disseminated intravascular coagulation caused by a cytokine storm. Immunopathology and immunogenetic research may assist in diagnosing, predicting, and treating severe COVID-19 and the cytokine storm associated with COVID-19. This paper reviews the immunopathogenesis and immunogenetic variants that play a role in COVID-19. Although various immune-related genetic variants have been investigated in relation to severe COVID-19, the NOD-like receptor protein 3 (NLRP3) and interleukin 18 (IL-18) have not been assessed for their potential significance in the clinical outcome. Here, we a) summarize the current understanding of the immunogenetic etiology and pathophysiology of COVID-19 and the associated cytokine storm; and b) construct and analyze protein-protein interaction (PPI) networks (using enrichment and annotation analysis) based on the NLRP3 and IL18 variants and all genes, which were established in severe COVID-19. Our PPI network and enrichment analyses predict a) useful drug targets to prevent the onset of severe COVID-19 including key antiviral pathways such as Toll-Like-Receptor cascades, NOD-like receptor signaling, RIG-induction of interferon (IFN) α/β, and interleukin (IL)-1, IL-6, IL-12, IL-18, and tumor necrosis factor signaling; and b) SARS-CoV-2 innate immune evasion and the participation of MYD88 and MAVS in the pathophysiology of severe COVID-19. The PPI network genetic variants may be used to predict more severe COVID-19 outcomes, thereby opening the door for targeted preventive treatments.