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.

Effect of Zataria multiflora Boiss. essential oil on colony morphology and ultrastructure of Aspergillus flavus.

The mode of inhibitory action of Zataria multiflora Boiss. essential oil (EO) on the fungus, Aspergillus flavus, was studied by colony morphology examination, light microscopy, scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The EO at concentrations used in this study suppressed the size of the colony as well as sporulation. SEM of mycelia treated with given concentrations of EO showed morphological alterations ranging from loss of turgidity and uniformity of mycelia at low concentrations of EO to evident destruction of the hyphae at higher concentration of EO. Semi-thin sections of mycelia exposed to different concentrations of EO were analysed by light microscopy and revealed that the major change at level as low as 50 ppm of EO was limited to vacuolisation of cytoplasm resulting in cell swelling, while at higher concentrations, detachment of the cell membrane from the cell wall, deformation of mycelia and shedding the cytoplasm from the cell were the main alterations. These damages were well documented by TEM, which showed that the main sites of action of EO were the plasma membrane and cell wall. In conclusion, morphological and structural changes observed in this study may be one of the mechanisms involved in growth inhibition of the fungi and reducing aflatoxin production.

Comparison of flame atomic absorption spectrometry (FAAS) and Cerimetric methods for the determination of ferrous sulfate content of some pharmaceutical products.

The iron content of ferrous sulphate syrup and tablet, ferroglobin, and Fefol were determined by both titration with cerium (Cerimetric method) proposed by united state pharmacopeias and flame atomic absorption spectrometry (FAAS). In FAAS both external calibration and standard addition method were used to evaluate the matrix effects. In the determination of iron content of ferrous sulphate tablet and syrup FAAS using external standard give approximately the same results as cerimetric methods of analysis. But in the case of ferroglobin syrup and Fefol capsule the external calibration results had large deviation from cerimetric method of analysis. So flame atomic absorption spectrometric involving standard addition is proposed for the analysis of iron content of these pharmaceutical products. The coefficient of variation for the FAAS determinations were in the range of 0.73-5.85 in one day and 2.39-7.51 between different days. Statistical evaluation showed good correlation between two methods.