The number of confirmed COVID-19 cases admitted in hospitals is continuously increasing in the Philippines. Frontline health care workers are faced with imminent risks of getting infected. In this study, we formulate a theoretical model to calculate the risk of being infected in health care facilities considering the following factors: the average number of encounters with a suspected COVID-19 patient per hour; interaction time for each encounter; work shift duration or exposure time; crowd density, which may depend on the amount of space available in a given location; and availability and effectiveness of protective gears and facilities provided for the frontline health care workers. Based on the simulation results, a set of risk assessment criteria is proposed to classify risks as 'low', 'moderate', or 'high'. We recommend the following: (1) decrease the rate of patient encounter per frontline health care worker, e.g., maximum of three encounters per hour in a 12-h work shift duration; (2) decrease the interaction time between the frontline health care worker and the patients, e.g., less than 40 min for the whole day; (3) increase the clean and safe space for social distancing, e.g., maximum of 10% crowd density, and if possible, implement compartmentalization of patients; and/or (4) provide effective protective gears and facilities, e.g., 95% effective, that the frontline health care workers can use during their shift. Moreover, the formulated model can be used for other similar scenarios, such as identifying infection risk in public transportation, school classroom settings, offices, and mass gatherings.

Unripe calamansi peels were prepared and used as a bioadsorbent in the removal of congo red from an aqueous solution using batch adsorption studies. The efficiency of adsorption was evaluated by varying adsorbent dose and contact time. The removal of congo red increased at higher adsorbent dose and longer contact time. The overall rate of adsorption processes appeared to be in accordance with the pseudo-second order reaction mechanism. Higher initial adsorption rate, extent of surface coverage, and activation energy were favored at a lower adsorbent dose, while the intraparticle diffusion was relatively faster at a higher adsorbent dose. The intraparticle diffusion, Elovich, and MacArthur-Wilson models were adequate in describing the chaotic behavior of the kinetic processes involved in the removal of congo red dye onto unripe calamansi peels.

Although most patients recover from COVID-19, it has been linked to cardiac, pulmonary, and neurologic complications. Despite not having formal criteria for its diagnosis, COVID-19 associated cardiomyopathy has been observed in several studies through biomarkers and imaging. This study aims to estimate the proportion of COVID-19 patients with cardiac abnormalities and to determine the association between the cardiac abnormalities in COVID-19 patients and disease severity and mortality. Observational studies published from December 1, 2019 to September 30, 2020 were obtained from electronic databases (PubMed, Embase, Cochrane Library, CNKI) and preprint servers (medRxiv, bioRxiv, ChinaXiv). Studies that have data on prevalence were included in the calculation of the pooled prevalence, while studies with comparison group were included in the calculation of the odds ratio. If multiple tests were done in the same study yielding different prevalence values, the largest one was used as the measure of prevalence of that particular study. Metafor using R software package version 4.0.2 was used for the meta-analysis. A total of 400 records were retrieved from database search, with 24 articles included in the final analysis. Pooled prevalence of cardiac abnormalities in 20 studies was calculated to be 0.31 [95% Confidence Intervals (CI) of (0.23; 0.41)], with statistically significant heterogeneity (percentage of variation or I-squared statistic I2 = 97%, p < 0.01). Pooled analysis of 19 studies showed an overall odds ratio (OR) of 6.87 [95%-CI (3.92; 12.05)] for cardiac abnormalities associated with disease severity and mortality, with statistically significant heterogeneity (I2 = 85%, between-study variance or tau-squared statistic τ2 = 1.1485, p < 0.01). Due to the high uncertainty in the pooled prevalence of cardiac abnormalities and the unquantifiable magnitude of risk (although an increased risk is certain) for severity or mortality among COVID-19 patients, much more long-term prognostic studies are needed to check for the long-term complications of COVID-19 and formalize definitive criteria of “COVID-19 associated cardiomyopathy”.

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The number of confirmed COVID-19 cases admitted in hospitals is continuously increasing in the Philippines. Frontline health care workers are faced with imminent risks of getting infected. In this study, we formulate a theoretical model to calculate the risk of being infected in health care facilities considering the following factors: the average number of encounters with a suspected COVID-19 patient per hour; interaction time for each encounter; work shift duration or exposure time; crowd density, which may depend on the amount of space available in a given location; and availability and effectiveness of protective gears and facilities provided for the frontline health care workers. Based on the simulation results, a set of risk assessment criteria is proposed to classify risks as low, moderate, or high. We recommend the following: (i) decrease the rate of patient encounter per frontline health care worker, e.g., maximum of three encounters per hour in a 12-hour work shift duration; (ii) decrease the interaction time between the frontline health care worker and the patients, e.g., less than 40 minutes for the whole day; (iii) increase the clean and safe space for social distancing, e.g., maximum of 10% crowd density, and if possible, implement compartmentalization of patients; and/or (iv) provide effective protective gears and facilities, e.g., 95% effective, that the frontline health care workers can use during their shift. Moreover, the formulated model can be used for other similar scenarios, such as identifying infection risk in public transportation, school classroom settings, offices, and mass gatherings.

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