The structural basis for RNA slicing by human Argonaute2.

Argonaute (AGO) proteins associate with guide RNAs to form complexes that slice transcripts that pair to the guide. This slicing drives post-transcriptional gene-silencing pathways that are essential for many eukaryotes and the basis for new clinical therapies. Despite this importance, structural information on eukaryotic AGOs in a fully paired, slicing-competent conformation-hypothesized to be intrinsically unstable-has been lacking. Here we present the cryogenic-electron microscopy structure of a human AGO-guide complex bound to a fully paired target, revealing structural rearrangements that enable this conformation. Critically, the N domain of AGO rotates to allow the RNA full access to the central channel and forms contacts that license rapid slicing. Moreover, a conserved loop in the PIWI domain secures the RNA near the active site to enhance slicing rate and specificity. These results explain how AGO accommodates targets possessing the pairing specificity typically observed in biological and clinical slicing substrates.

Structural advances in transcription elongation.

Transcription is the first step of gene expression and involves RNA polymerases. After transcription initiation, RNA polymerase enters elongation followed by transcription termination at the end of the gene. Only recently, structures of transcription elongation complexes bound to key transcription elongation factors have been determined in bacterial and eukaryotic systems. These structures have revealed numerous insights including the basis for transcriptional pausing, RNA polymerase interaction with large complexes such as the ribosome and the spliceosome, and the transition into productive elongation. Here, we review these structures and describe areas for future research.

The COVID-19 pandemic: prediction study based on machine learning models.

COVID-19 was first discovered in Wuhan, China in December 2019. It is one of the worst pandemics in human history. Recent studies reported that COVID-19 is transmitted among humans by droplet infection or direct contact. COVID-19 pandemic has invaded more than 210 countries around the world and as of February 18th, 2021, just after a year has passed, a total of 110,533,973 confirmed cases of COVID-19 were reported and its death toll reached about 2,443,091. COVID-19 is a new member of the family of corona viruses, its nature, behaviour, transmission, spread, prevention, and treatment are to be investigated. Generally, a huge amount of data is accumulating regarding the COVID-19 pandemic, which makes hot research topics for machine learning researchers. However, the panicked world's population is asking when the COVID-19 will be over? This study considered machine learning approaches to predict the spread of the COVID-19 in many countries. The experimental results of the proposed model showed that the overall R2 is 0.99 from the perspective of confirmed cases. A machine learning model has been developed to predict the estimation of the spread of the COVID-19 infection in many countries and the expected period after which the virus can be stopped. Globally, our results forecasted that the COVID-19 infections will greatly decline during the first week of September 2021 when it will be going to an end shortly afterward.

Assessment of health related life quality in type 2 diabetic patients in zagazig university hospitals

Background: The fourth leading cause of death is type 2 Diabetes Mellitus (DM). Attention to the QOL of the patient is increasing today rather than the longevity of the patient. Therefore, to prevent aggravation of metabolic disorders, the quality of life of diabetic patients should be maintained. There is a growing awareness that patient QOL and satisfaction with treatment have been improved after good glycemic control. The aim of this study was to assess health related quality of life in patients with type 2 diabetes attending Zagazig University hospital plus associations between HRQOL scores and some variables playing role in scenario of DM were also studied.Methods:A cross sectional study included 100 type 2 diabetic patients, They were subdivided according to glycemic control into 2 groups and 50 non-diabetic participants as control. This study was conducted in Internal Medicine department and diabetes outpatient clinic in Zagazig University hospital in period from July 2016 to July 2018. The QOL was measured using SF-36 questionnaire. Results: Our results revealed that there was highly significant difference regarding physical health component among groups of study (p <0.001). There was also highly significant difference regarding mental health component among groups of study (p <0.001). There was highly significant negative correlation between QOL components and age & BMI & duration of DM & HBA1c and fasting blood sugar in diabetic patients (p <0.001). Conclusions: Type 2 diabetes mellitus in both the physical health and mental health domains is associated with a lower quality of life. Advanced age, obesity and poor glycemic control were factors related to lower quality of life in this study; therefore glycemic control is highly needed to improve the quality of life of diabetic patients