Predicting COVID-19 fatality rate based on age group using LSTM

Objective: To predict the daily incidence and fatality rates based on long short-term memory (LSTM) in 4 age groups of COVID-19 patients in Mazandaran Province, Iran. Methods: To predict the daily incidence and fatality rates by age groups, this epidemiological study was conducted based on the LSTM model. All data of COVID-19 disease were collected daily for training the LSTM model from February 22, 2020 to April 10, 2021 in the Mazandaran University of Medical Sciences. We defined 4 age groups, i.e., patients under 29, between 30 and 49, between 50 and 59, and over 60 years old. Then, LSTM models were applied to predict the trend of daily incidence and fatality rates from 14 to 40 days in different age groups. The results of different methods were compared with each other. Results: This study evaluated 5 0826 patients and 5 109 deaths with COVID-19 daily in 20 cities of Mazandaran Province. Among the patients, 25 240 were females (49.7%), and 25 586 were males (50.3%). The predicted daily incidence rates on April 11, 2021 were 91.76, 155.84, 150.03, and 325.99 per 100 000 people, respectively; for the fourteenth day April 24, 2021, the predicted daily incidence rates were 35.91, 92.90, 83.74, and 225.68 in each group per 100 000 people. Furthermore, the predicted average daily incidence rates in 40 days for the 4 age groups were 34.25, 95.68, 76.43, and 210.80 per 100 000 people, and the daily fatality rates were 8.38, 4.18, 3.40, 22.53 per 100 000 people according to the established LSTM model. The findings demonstrated the daily incidence and fatality rates of 417.16 and 38.49 per 100 000 people for all age groups over the next 40 days. Conclusions: The results highlighted the proper performance of the LSTM model for predicting the daily incidence and fatality rates. It can clarify the path of spread or decline of the COVID-19 outbreak and the priority of vaccination in age groups.

Predicting COVID-19 fatality rate based on age group using LSTM

Objective: To predict the daily incidence and fatality rates based on long short-term memory (LSTM) in 4 age groups of COVID-19 patients in Mazandaran Province, Iran. Methods: To predict the daily incidence and fatality rates by age groups, this epidemiological study was conducted based on the LSTM model. All data of COVID-19 disease were collected daily for training the LSTM model from February 22, 2020 to April 10, 2021 in the Mazandaran University of Medical Sciences. We defined 4 age groups, i.e., patients under 29, between 30 and 49, between 50 and 59, and over 60 years old. Then, LSTM models were applied to predict the trend of daily incidence and fatality rates from 14 to 40 days in different age groups. The results of different methods were compared with each other. Results: This study evaluated 5 0826 patients and 5 109 deaths with COVID-19 daily in 20 cities of Mazandaran Province. Among the patients, 25 240 were females (49.7%), and 25 586 were males (50.3%). The predicted daily incidence rates on April 11, 2021 were 91.76, 155.84, 150.03, and 325.99 per 100 000 people, respectively; for the fourteenth day April 24, 2021, the predicted daily incidence rates were 35.91, 92.90, 83.74, and 225.68 in each group per 100 000 people. Furthermore, the predicted average daily incidence rates in 40 days for the 4 age groups were 34.25, 95.68, 76.43, and 210.80 per 100 000 people, and the daily fatality rates were 8.38, 4.18, 3.40, 22.53 per 100 000 people according to the established LSTM model. The findings demonstrated the daily incidence and fatality rates of 417.16 and 38.49 per 100 000 people for all age groups over the next 40 days. Conclusions: The results highlighted the proper performance of the LSTM model for predicting the daily incidence and fatality rates. It can clarify the path of spread or decline of the COVID-19 outbreak and the priority of vaccination in age groups.

Urinary Trans, Trans-Muconic Acid is Not a Reliable Biomarker for Low-level Environmental and Occupational Benzene Exposures

BACKGROUND: Benzene is a known occupational and environmental pollutant. Its urinary metabolite trans, trans-muconic acid (tt-MA) has been introduced by some environmental and occupational health regulatory associations as a biological index for the assessment of benzene exposure; however, recently, doubts have been raised about the specificity of tt-MA for low-level benzene exposures. In the present study, we investigated the association between urinary levels of tt-MA and inhalational exposure to benzene in different exposure groups. METHODS: Benzene exposure was assessed by personal air sampling. Collected benzene on charcoal tube was extracted by carbon disulfide and determined by a gas chromatograph (gas chromatography with a flame ionization detector). Urinary tt-MA was extracted by a strong anion-exchange column and determined with high-performance liquid chromatography–UV. RESULTS: Urinary levels of tt-MA in intensive benzene exposure groups (chemical workers and police officers) were significantly higher than other groups (urban and rural residents), but its levels in the last two groups with significant different exposure levels (mean = 0.081 ppm and 0.019 ppm, respectively) showed no significant difference (mean = 388 μg/g creatinine and 282 μg/g, respectively; p < 0.05). Before work shift, urine samples of workers and police officers showed a high amount of tt-MA and its levels in rural residents’ samples were not zero. CONCLUSION: Our results suggest that tt-MA may not be a reliable biomarker for monitoring low-level (below 0.5 ppm) benzene exposures.

Determination of Pb and cd in garlic herb [allium sativum] planted in gilan and khuzestan provinces using graphite furnace atomic absorption spectrometry

Foods are enriched with variety of chemical elements. Some of these elements are necessary for human health. These elements enter in liquid, the cells and other organs, certain amount of these elements are essential for body organs to work properly. This research was conducted to compare the level of lead and cadmium contamination in garlic species planted in Ramhormoz and Rasht and discuss about the source of this contaminations. Twenty composite samples were collected from each province, Khozestan [Ramhormoz] and Gilan [Rasht]. Each sample was burned according to instruction reported in AOAC. Then the white residue was dissolved in diluted nitric acid then the Pb and Cd contents were determined using graphite furnace atomic absorption spectrometer at 217 and 228.8 nm, respectively. No Pb level was detected in garlic cultured in both cities. The mean concentration of cadmium was 0.40 and 0.58 mg/kg in garlics planted in Ramhormoz and Rasht, respectively. Statistical analysis showed significant difference between mean concentration of cadmium in Rasht garlic and the permitted value [P < 0.00l]. The cadmium level in Rasht garlic is much higher than the standard level. In order to test the reliability of the data obtained using this method, randomly selected samples were spiked with Pb and Cd standard solutions. The cadmium and lead recovery were reported 93.93 and 90.18 percent

Monitoring airborne iron oxide fumes in beam rolling mills factory, in Ahwaz, Iran

To determine the indoor iron Oxide [FeO] concentrations in two production lines 630 and 650 and compare them with REL- NIOSH. National Institute for Occupational Safety and Hygiene [NIOSH] No 550 is used for the determination of FeO concentrations in Beam Rolling Mills Factory. The total size sample was 80 for indoor FeO in each production Line 630 and 650 respectively. Samples [FeO] were collected using Low volume sampling pump operated at flow rate of 2 L/min-1 on membrane fitters with pore size 0.5 micrometer and 27mm diameter. CTA 3000 Atomic Absorption Spectrophotometer was used. There is a significant difference between mean FeO concentrations in Lines 650, 630 and NIOSH [5 mg/m3] [Pc 0.05]. It is not a serious health problem for workers. 100% Mean value of Iron oxide concentrations i.e., 20 stations in production Lines 650 and 630 are at a safe level