Journal of Environmental Health and Sustainable Development
Volume:7 Issue: 3, Sep 2022

Predicting acute respiratory insufficiency due to coronavirus disease 2019 (COVID-19) can diminish the severe complications and mortality associated with the disease. This study aimed to develop an intelligent system based on machine learning (ML) models for frontline clinicians to effectively triage high-risk patients and prioritize who needs mechanical intubation (MI).

In this retrospective-design study, the data regarding 482 COVID-19 hospitalized patients from February 9, 2020, to July 20, 2021, was analyzed by six ML classifiers. The most critical clinical variables were identified by a minimal-redundancy-maximal-relevance (mRMR) feature selection technique. In the next step, the models' performance was assessed using confusion matrix criteria and, finally, the best model was adopted.

Proposed models were implemented using 23 confirmed variables. Results of comparing six selected ML algorithms indicated the extreme gradient boosting (XGBoost) classifier with 84.7% accuracy, 76.5 % specificity, 90.7% sensitivity, 85.1% f-measure, 87.4% Kappa statistic, and 85.3% for receiver operating characteristic (ROC) had the best performance in the intubation prediction.

It is found that ML enables a satisfactory accuracy level in calculating intubation risk in COVID-19 patients. Therefore, using the ML-based intelligent models, notably the XGBoost algorithm, actually enables recognizing high-risk cases and advising correct therapeutic and supportive care by the clinicians.

SARS CoV-2 pandemic has caused illness and death in millions of people worldwide. Extensive studies are being conducted on the effect of meteorological parameters on the number of patients and clinical symptoms of COVID-19.

This cross-sectional descriptive study was conducted in Abarkouh city with a population of 51199 people during 215 days (from April 20, 2020 to November 20, 2020).  The present study aimed to investigate the effect of meteorological parameters, such as temperature, humidity, sunshine hours, evaporation, and maximum wind speed on the number of cases with definite diagnosis of COVID-19. Moreover, symptoms in the infected patients were assessed using Pearson correlation coefficient and regression coefficient.

During the 215 days of the study, a total of 2526 symptoms were diagnosed in 1298 outpatients. Among which, fever and body aches were the most common symptoms in the subjects. The results of examining the relationship between meteorological parameters and the prevalence of symptoms showed that there was a negative correlation between the minimum and maximum temperature, sunshine hours, evaporation, and wind speed with the number of cases and the prevalence of symptoms. The results of regression coefficient also showed that among the meteorological parameters, the minimum temperature variable had the most negative effect on the prevalence of symptoms as well as the number of cases.

The results showed that changing the meteorological parameters in cold weather can increase both the number of patients and clinical symptoms of outpatients with COVID-19.

Heavy metals (HMs) accumulated in the road dust may be transferred to the surrounding land and water bodies and pose ecological risks. Hence, such pollution should be considered, especially in coastal roads. This study focuses on the HMs pollution of dust settled on the surface of coastal roads along the Persian Gulf in Bandar Abbas city.

In this study, road dust samples were collected from 13 points in coastal roads and analyzed by ICP-OES for the measurement of As, Cd, Co, Cr, Cu, Mn, Ni, and Pb. Based on the HMs contents of dust, pollution, and ecological risk indices, including contamination factor (CF), modified pollution index (MPI), and modified ecological risk index (MRI) were calculated.

Among the studied HMs, Cu (4.73) showed the highest CF value. The levels of MPI showed that the road dust was strongly polluted by HMs (4.02), but it was mainly contributed by a low toxic metal (Cu). Therefore, the mean value of MRI was 132.3, indicating the low ecological risk of such pollution. Note that the MRI value was > 150 in some sampling points, indicating moderate ecological risk.

This study showed that the road dust along the Persian Gulf coastline is polluted to different levels of the studied HMs and may pose various levels of ecological risks. The current level of HMs pollution in the study area was not significantly high. However, high pollution levels in the west roads should not be overlooked.

Although various liquid, solid, and gaseous streams of wastewater treatment plants (WWTPs) have been analyzed in many studies for the presence of SARS-CoV-2 RNA, no study was found to sample and detect SARS-CoV-2 RNA in screenings and grit samples separated from primary treatment units of WWTP. Hence, this study aims to provide an experimental protocol for sampling and extracting SARS-CoV-2 RNA from screenings and grits separated from WWTPs.

First, sampling was conducted to extract SARS-CoV-2 RNA from screenings and grit samples. After sample processing and viral RNA extraction, SARS-CoV-2 RNA detection was performed by one-step reverse transcription quantitative polymerase chain reaction (RT-qPCR).

Based on the results, SARS-CoV-2 RNA was successfully extracted from screenings and grit samples of the studied WWTP with concentrations of (1.54 –3.9 × 104) and (0.8 – 2.3 × 104) genomic copies/L, respectively.

Considering the successfully isolation and detection of SARS-CoV-2 viral RNA in solid phase samples of WWTP,  this method can be applied for extracting SARS-CoV-2 RNA and maybe other viruses from the screenings and grit samples of WWTPs in related studies.

According to high volumes of water used in hemodialysis, quality of water entering the dialysis machine is very important. The current study aims to analyze microbial and chemical quality of water used for hemodialysis in hospitals of Kashan city in 2019.

This descriptive cross-sectional study was performed on 54 water samples used in dialysis machines in hospitals of Kashan city during 3 months of the fall season in 2019. Microbial tests of the samples were done, and also heavy metals were assessed using inductively coupled plasma optical emission spectrometry. Statistical tests, sample t-test, and ANOVA were used to compare the mean results with standards.

Based on the results, the mean concentrations of magnesium (Mg) (2.7 ± 2.22 mg/L), sulfate (13.09 ± 21.06 mg/L), sodium (Na) (17.27 ± 24.47 mg/L), and potassium (K) (0.09 ± 0.17 mg/L) in all samples were based on the standard levels. However, the mean concentrations of nitrate (3.22 ± 1.21 mg/L), aluminum (Al) (0.26 ± 0.16 mg/L), silver (Ag) (0.52 ± 0.85 mg/L), lead (Pb) (0.08 ± 0.13 mg/L), and zinc (Zn) (0.91 ± 0.71 mg/L) were above standard levels in all the samples. Thallium (Tl) ion was reported to be zero. Moreover, heterotrophic bacteria were not observed in any of the samples.

Given the high concentration of chemicals and heavy metals in dialysis machines water input, it is necessary to plan for periodic monitoring of water treatment systems and heavy metals and regular replacement of reverse osmosis filters.

Accidents in the workplace hurt people and sometimes cause death. One of the ways to prevent occupational accidents is to change the behavior and attitude of people towards safety. The present study was conducted to investigate the effect of the lean approach on the promotion of safety culture in Sarv combined cycle power plant of MAPNA exploitation.

In this study, to evaluate the safety culture before performing the lean approach interventions, a safety culture questionnaire was distributed among 110 people who were randomly selected from 294 employees of the company. The results were analyzed as pre-intervention data. Then, with the introduction of lean tools that can be implemented in the power plant and their selection by a group of managers who came together for this purpose, the necessary measures were defined and performed in line with the lean approach. After six months from the beginning of the intervention, the questionnaire was distributed and collected among the selected individuals in the second stage.

Based on the findings, safety culture had no significant relationship with the demographic data of the sample. The results of paired t-tests showed that the safety culture score increased in all dimensions after the intervention. As a result, the total safety culture score showed a significant increase compared to the safety culture score before the intervention.

The findings indicated that the pure approach has a great impact on safety culture and it is important to pay attention to it.

Natural products can alleviate oxidative stress induced by toxic metals. This study evaluated antioxidant properties of clove essential oil (CEO), compared to oxidative deterioration of Cd. It was carried out by measuring growth performance parameters (BW, FI, and FCR), oxidative indices (TBARS, CP, CAT, SOD, and GPx), lipid profile (TG, TC, LDL-C, and HDL-C), and Cd bioaccumulation in quails.

In this study, 480 Japanese quails were fed with diets for 35 days. The diets included basal diet, basal diet + VC (500 mg/kg), basal diet + CEO (450 mg/kg), basal diet + CEO (100 mg/kg), basal diet + VC (500 mg/kg) + Cd (40 mg/l), basal diet + CEO (450 mg/kg) + Cd (40 mg/l), basal diet + CEO (100 mg/kg) + Cd (40 mg/l), and basal diet + Cd (40 mg/l). Oxidative indices and Cd bioaccumulation (ICP-OES) were measured.

The major ingredients of CEO included Eugenol (77.63%) and β-Caryophyllene (9.55%).  Quails exposed to Cd and treated with CEO had a reduced amount of oxidative stress as evidenced by lower concentrations of TBARS and CP, higher activities of SOD, GPx, and CAT, an improved lipid profile, and lower accumulation of Cd compared to the positive control. However, FI and FCR did not change.

Antioxidant properties of CEO were dose-dependent. CEO (450 mg/kg) was potentially as effective as, or even more potent than VC (500 mg/kg) in reducing the adverse effects of Cd. However, further studies are required to determine the minimum concentration of COE.

The effects of dietary nitrate on health are controversial. The current study aims to investigate the relationship between dietary intake of nitrate and liver enzymes among Iranian adolescent girls.

This cross sectional study was conducted on 733 adolescent girls. They were recruited from several schools in different areas in the cities of Mashhad and Sabzevar, northeast region of Iran, by random cluster sampling method. The dietary intake of nitrate was assessed using a validated food-frequency questionnaire (FFQ). Levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP) and gamma-glutamyl transferase (GGT) were measured by auto-analyzer. Linear regression was applied to investigate the correlation between nitrate intake and liver enzymes in crude and adjusted models.

There was a direct association between dietary intake of nitrate and serum levels of ALT in crude [β = 0.117; 95% CI (0.003-0.016); P < 0.01] and adjusted models for energy intake, age, BMI percentile, physical activity, menstruation, father's education, and mother's education [β = 0.128; 95% CI (0.003-0.016); P < 0.01]. No significant associations were found between dietary intake of nitrate and levels of ALP, AST, and GGT in crude or adjusted models.

There was a direct relationship between dietary intake of nitrate and serum concentration of ALT. Longitudinal studies are required to examine the association between dietary nitrate intake and liver functional tests.