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.

Survival prediction of gastric cancer patients by Artificial Neural Network model

Aim: This study aims to predict survival rate of gastric cancer patients and identify the effective factors related to it, using artificial neural network model

Background: Gastric cancer is the most deadly disease in north and northeast provinces of Iran. A total of 430 patients with gastric cancer who referred to Baghban clinic in Sari, from early November 2006 to late October 2013 were followed

Methods: A historical cohort of patients who referred to Baghban Clinic, the cancer research center of Mazandaran University of Medical Sciences in Sari, from early November 2006 to late October 2013 was studied. Three groups of variables [demographic, biological and socio-economic] were studied. Survival rate and effective factors on survival time were calculated using Kaplan-Meier methods and artificial neural networks and the best network structure were chosen using the mean square error and ROC curve. All analyses were performed using SPSS v.18.0 and the level of significance was selected a=0.05

Results: In this research, the median survival time was 19+/-2.04 months. The 1 to 5-year survival rates for patients were 0.64, 0.44, 0.34, 0.24 and 0.19, respectively. The percentage of right predictions of the selected network and the area under the ROC curve were 92% and 94%, respectively. According to the results, the type of treatment, metastasis, stage of disease, histology grade, histology type and the age of diagnosis were effective factors on survival period

Conclusion: the 5 years survival rate of gastric cancer patients in Mazandaran is lower than other provinces which could be due to the delay in diagnosis or patient's referral. Therefore, the use of screening methods and early diagnosis could be influential for improving survival rate of these patients

Correlation between sun protection factor and antioxidant activity, phenol and flavonoid contents of some medicinal plants

Long exposure of UV radiation increases risk of skin diseases such as cancer and photoallergic reactions. UV-B [280-320 nm] radiation is mainly responsible for inducing skin problems. Skin protection is a suitable method in the fight against ultraviolet radiation induced damage. Various synthetic agents have been used as photo protective but because of their potential toxicity in humans, they have limited use. Natural substances have been recently considered as potential sunscreen resources because of their absorption in the UV region and their antioxidant activity. In the present study, the UV protective effects of 20 extracts from four common medicinal plants were evaluated. Their phenol and flavonoid contents and antioxidant activities were determined and correlation between SPF and these contents were evaluated. SPFs were between 0.102 and 24.470. The highest value was reached with ultrasonic extract of Crataegus pentagyna [SPF = 24.47] followed by methanolic extract of Feijoa sellowiana [SPF = 1.30]. Good correlation was found between SPF and phenolic contents [Correlation Coefficient = 0.55 and p = 0.01] but no correlations were found between SPF and flavonoid contents or antioxidant activity. These extracts can be used alone or as additives in other sun screen formulations to enhance their SPF