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The aim of the present research is to develop economic, fast, and versatile method for the removal of toxic organic pollutant phenol from wastewater using eggshell. The batch experiments are conducted to evaluate the effect of pH, phenol concentration, dosage of adsorbent, and contact time on the removal of phenol. The paper includes in-depth kinetic studies of the ongoing adsorption process. Attempts have also been made to verify Langmuir and Freundlich adsorption isotherms. The morphology and characteristics of eggshell have also been studied using scanning electron microscopy, Fourier transform infrared spectroscopy, X-ray diffraction, and X-ray fluorescence analysis. At ambient temperature, the maximum adsorption of phenol onto eggshells has been achieved at pH 9 and the contact time, 90 min. The experimental data give best-fitted straight lines for pseudo-first-order as well as pseudo-second-order kinetic models. Furthermore, the adsorption process verifies Freundlich and Langmuir adsorption isotherms, and on the basis of mathematical expressions of these models, various necessary adsorption constants have been calculated. Using adsorption data, various thermodynamic parameters like change in enthalpy (∆H(0)), change in entropy (∆S(0)), and change in free energy ∆G(0) have also been evaluated. Results clearly reveal that the solid waste material eggshell acts as an effective adsorbent for the removal of phenol from aqueous solutions.

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Background: Diabetes mellitus (DM) and cardiovascular disease (CVD) are present in a large number of patients with novel Coronavirus disease 2019 (COVID-19). We aimed to determine the risk and predictors of in-hospital mortality from COVID-19 in patients with DM and CVD. Methods: This retrospective cohort study included hospitalized patients aged ≥ 18 years with confirmed COVID-19 in Alborz province, Iran, from 20 February 2020 to 25 March 2020. Data on demographic, clinical and outcome (in-hospital mortality) data were obtained from electronic medical records. Self-reported comorbidities were classified into the following groups: "DM" (having DM with or without other comorbidities), "only DM" (having DM without other comorbidities), "CVD" (having CVD with or without other comorbidities), "only CVD" (having CVD without other comorbidities), and "having any comorbidity". Multivariate logistic regression models were fitted to quantify the risk and predictors of in-hospital mortality from COVID-19 in patients with these comorbidities. Results: Among 2957 patients with COVID-19, 2656 were discharged as cured, and 301 died. In multivariate model, DM (OR: 1.62 (95% CI 1.14-2.30)) and only DM (1.69 (1.05-2.74)) increased the risk of death from COVID-19; but, both CVD and only CVD showed non-significant associations (p > 0.05). Moreover, "having any comorbidities" increased the risk of in-hospital mortality from COVID-19 (OR: 2.66 (95% CI 2.09-3.40)). Significant predictors of mortality from COVID-19 in patients with DM were lymphocyte count, creatinine and C-reactive protein (CRP) level (all P-values < 0.05). Conclusions: Our findings suggest that diabetic patients have an increased risk of in-hospital mortality following COVID-19; also, lymphocyte count, creatinine and CRP concentrations could be considered as significant predictors for the death of COVID-19 in these patients.

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