METHODOLOGY: A total of 571 healthcare workers at COVID-19 and non-COVID-19 wards as well as the emergency department and laboratory staff at COVID-19 testing labs were recruited. The presence of novel human coronavirus (SARS-CoV-2) and IgM/IgG antibodies were confirmed in all healthcare workers. The healthcare workers responded to an online Google Forms questionnaire that evaluates demographic information and comorbidities, exposure and adherence to infection prevention and control measures against COVID-19. Descriptive analysis was performed using Statistical Package for the Social Sciences 24.0.

RESULTS: Three healthcare workers (0.5%) tested positive for SARS-CoV-2, while the remaining 568 (99.5%) were negative. All were negative for IgM and IgG antibodies during recruitment (day 1) and follow-up (day 15). More than 90% of the healthcare workers followed infection prevention and control practices recommendations regardless of whether they have been exposed to occupational risk for COVID-19.

PURPOSE: The aim was to determine the metabolic fingerprint that predicts warfarin response based on the international normalized ratio (INR) in patients who are already receiving warfarin (phase I: identification) and to ascertain the metabolic fingerprint that discriminates stable from unstable INR in patients starting treatment with warfarin (phase II: validation).

EXPERIMENTAL APPROACH: A total of 94 blood samples were collected for phase I: 44 patients with stable INR and 50 with unstable INR. Meanwhile, 23 samples were collected for phase II: nine patients with stable INR and 14 with unstable INR. Data analysis was performed using multivariate analysis including principal component analysis and partial least square-discriminate analysis (PLS-DA), followed by univariate and multivariate logistic regression (MVLR) to develop a model to identify unstable INR biomarkers.

KEY RESULTS: For phase I, the PLS-DA model showed the following results: sensitivity 93.18%, specificity 91.49% and accuracy 92.31%. In the MVLR analysis of phase I, ten regions were associated with unstable INR. For phase II, the PLS-DA model showed the following results: sensitivity 66.67%, specificity 61.54% and accuracy 63.64%.

Materials and Methods: In two tertiary care selected hospitals, the included diabetic patients were randomly divided into two study arms. In the control group, 200 patients who were receiving usual treatment from hospitals were included. However, in the intervention group, those 200 patients who were receiving usual treatment along with counseling sessions from pharmacists under the Diabetes Medication Therapy Adherence Clinic (DMTAC) program were included. The study continued for 1 year, and there were four follow-up visits for both study arms. A prevalidated data collection form was used to measure the improvement in predictors of diabetic foot in included patients. Data were analyzed by using the Statistical Package for the Social Sciences (SPSS) software program, version 24.0.

Results: With the average decrease of 1.97% of HbA1c values in the control group and 3.43% in the intervention group, the univariate and multivariate analysis showed a statistically significant difference between both of the study arms in the improvement of predictors belonging to the diabetic foot (P < 0.05). The proportion of patients without any signs and symptoms of the diabetic foot in the intervention group was 91.7%, which increased from 42.3% at baseline (P < 0.05). However, this proportion in the control group was 76.9% at the fourth follow-up, from 48.3% at baseline (P < 0.05).

MATERIALS AND METHODS: This retrospective cohort study included only COVID-19 positive patients hospitalized in a Private Hospital in West Jakarta between March and September 2020. All patients were not vaccinated during this period and treatment was based on the guidelines by the Ministry of Health Indonesia. A convenience sampling method was used and all patients who met the inclusion criteria were enrolled.

RESULTS: The clinical outcome of COVID-19 patients following medical therapy was either cured (85.7%) or died (14.3%), with 14.3% patients reported to have cytokine storm, from which 23.1% led to fatalities. A plasma immunoglobulin (Gammaraas®) and/or tocilizumab (interleukin-6 receptor antagonist; Actemra®) injection was utilised to treat the cytokine storm while remdesivir and oseltamivir were administered to ameliorate COVID-19. Most (61.5%) patients who experienced the cytokine storm were male; mean age 60 years. Interestingly, all patients who experienced the cytokine storm had hypertension or/ and diabetes complication (100%). Fever, cough and shortness of breath were also the common symptoms (100.0%). Almost all (92.3%) patients with cytokine storm had to be treated in the intensive care unit (ICU). Most (76.9%) patients who had cytokine storm received hydroxychloroquine and all had antibiotics [1) azithromycin + levofloxacin or 2) meropenam for critically ill patients] and vitamins such as vitamins C and B-complex as well as mineral. Unfortunately, from this group, 23.1% patients died while the remaining 70% of patients recovered. A significant (p<0.05) correlation was established between cytokine storms and age, the presence of comorbidity, diabetes, hypertension, fever, shortness of breath, having oxygen saturation (SPO2) less than 93%, cold, fatigue, ward of admission, the severity of COVID-19 disease, duration of treatment as well as the use of remdesivir, Actemra® and Gammaraas®. Most patients recovered after receiving a combination treatment (oseltamivir + remdesivir + Antibiotics + Vitamin/Mineral) for approximately 11 days with a 90% survival rate. On the contrary, patients who received oseltamivir + hydroxychloroquine + Gammaraas® + antibiotics +Vitamin/Mineral, had a 83% survival rate after being admitted to the hospital for about ten days.