The Psychological Impact of COVID-19 Pandemic on Persons with Multiple Sclerosis in Saudi Arabia.

OBJECTIVES: To evaluate, in a Saudi Arabian context, how the COVID-19 pandemic psychologically impacted persons with multiple sclerosis (PwMS). METHODS: A cross-sectional study was undertaken during the period from October 2021 to March 2022. 738 participants resident in the Kingdom of Saudi Arabia (KSA) completed a self-administered online questionnaire. The research focused on persons diagnosed with MS. RESULTS: Participant ages spanned from 18 to over 55. The mean was 36.1±12.9 years old. Four hundred eighty-nine (66.3%) of the 738 participants were female. Two hundred sixty-four (35.8%) were single. Four hundred twelve (55.8%) were married. Six hundred eighty-five (92.8%) had received a COVID-19 vaccine. Regarding MS duration, 117 (15.9%) had been diagnosed for less than 2 years, 171 (23.2%) for 2-5 years, while 251 (34%) had the condition for 10 or more years. Regarding psychological health, 11.2% of participants complained of minimal/no depression, 33.3% of mild depression, 28.3% of moderate depression, and 27.1% of moderately severe to severe depression symptoms. Concerning anxiety, 17.2% of participants reported minimal anxiety, 36.9% mild, 23.3% moderate, while 22.6% suffered from severe anxiety symptoms. CONCLUSION: A high prevalence of depression and anxiety was found, along with high prevalence of co-occurrence of these disorders among PwMS.

Automatic classification between COVID-19 pneumonia, lung cancer and normal lung tissues on chest CT Scans

Coronavirus sickness (COVID-19) may be a pandemic sickness, that has already caused thousands of casualties and infected many countless individuals worldwide. Whereas most of the individuals infected with the COVID-19 intimate with delicate to moderate respiratory disease, some developed deadly respiratory illness. Any technological tool sanctioning screening of the COVID-19 infection with high accuracy will be crucially useful to the attention professionals. The usage of chest CT scan pictures for classifying and diagnosing COVID-19 respiratory illness has shown an excellent range of exactness and accuracy quite the other tool that lessens the number of deaths within the severe cases. This paper presents a proposed model of convolutional neural network (CNN) with a large multi-national dataset that is able to classify covid-19 pneumonia;lung cancer and the normal lung tissues from chest computed tomography (CT) scans with a classification accuracy of 94.05%.

The role of Wastewater treatment plants as tools for SARS-CoV-2 early detection and removal

The world is facing the third coronavirus caused pandemic in less than twenty years. The SARS-CoV-2 virus not only affects the human respiratory system, but also the gastrointestinal tract. The virus has been found in human feces, in sewage and in wastewater treatment plants. It has the potential to become a panzootic disease, as it is now proven that several mammalian species become infected. Since it has been shown that the virus can be detected in sewage even before the onset of symptoms in the local population, Wastewater Based Epidemiology should be developed not only to localize infection clusters of the primary wave but also to detect a potential second, or subsequent, wave. To prevent a panzootic, virus removal techniques from wastewater need to be implemented to prevent the virus dissemination into the environment. In that context, this review presents recent improvements in all the fields of wastewater treatment from treatment ponds to the use of algae or nanomaterials with a particular emphasis on membrane-based techniques.