Screening of high-risk patients and accelerating their therapeutic procedures can reduce the burden of acute coronary syndrome (ACS). This study aimed to evaluate the accuracy of HEART score in predicting the risk of one-month major adverse cardiac events (MACE) in these patients.

In this prospective cross-sectional study, the accuracy of HEART score in patients over 18 years old who presented to emergency department following acute chest pain, was evaluated during a 21-month period. Each patient was followed up regarding the incidence of MACE for one month via phone call and the hospital’s integrated health information system.

240 cases with the mean age of 60.50 ± 16.07 years were studied (56.3% male). MACE was observed in 77 (32.1%) cases. The most common MACE was percutaneous coronary artery revascularization (PCAR) (12.9%). The mean HEART score of studied cases was 4.74 ± 2.12. The mean score of cases with MACE was significantly higher than others (6.25 ± 1.97 versus 4.03 ± 1.79; p < 0.0001). Bathers and the incidence of MACE in high-risk patients was significantly higher. But the overall accuracy of score in predicting one-month MACE in ACS patients was in moderate range.

Violence against healthcare workers mostly occurs in emergency departments and is a serious global public health issue. This study aimed to evaluate the prevalence of violence directed towards emergency department healthcare personnel and to ascertain the factors that might be correlated with it.

In this cross-sectional study, an anonymous questionnaire was used to gather data from healthcare personnel working in the emergency departments under the direction of the Bangkok Metropolitan Administration between 1 August 2019 and 30 November 2019, regarding the experience of violence during the previous year.

A total of 258 (87.5%) responses were received from 295 personnel. The results showed that 88.4% (228 personnel) had experienced violence during the past year, of these, 37.6% involved physical abuse that caused minor injuries. Employees with shorter tenures, nurses, and those working in tertiary academic emergency departments in the central business district were found to have increased likelihood of y department increased the likelihood of being a victim of workplace violence.

Rhabdomyolysis-induced acute kidney injury (AKI) is one of the most common complications of catastrophic incidents, especially earthquakes. Early detection of AKI can reduce the burden of the disease. In this paper, data collected from the Bam earthquake was used to find a suitable model that can be used in prediction of AKI in the early stages of the disaster.

Models used in this paper utilized many inputs, which were extracted from the previously published dataset, but depending on the employed method, other inputs have also been considered. compound library chemical This work has been done in two parts. In the first part, the models were constructed from a smaller set of records, which included all of the required fields and in the second part; the main purpose was to find a way to replace the missing data, as data are mostly incomplete in catastrophic events. The data used belonged to the victims of the Bam earthquake, who were admitted to different hospitals. These data were collected on the first day of the incident via questionnaires that were provided by the Iranian Society of Nephrology, in collaboration with the International Society of Nephrology (ISN).

Overall, neural networks have more robust results and given that they can be trained on more data to gain better accuracy, and gain more generalization, they show promising results. Overall, the best specificity that was achieved on testing almost all of the records was 99.24% and the best sensitivity that was achieved in testing almost all of the records was 94.44%.

We introduced several machine learning-based methods for predicting rhabdomyolysis-induced AKI on the third day after a catastrophic incident. The introduced models show higher accuracy compared to previous works performed on the Bam earthquake dataset.

We introduced several machine learning-based methods for predicting rhabdomyolysis-induced AKI on the third day after a catastrophic incident. The introduced models show higher accuracy compared to previous works performed on the Bam earthquake dataset.

Many proteomics-based and bioinformatics-based efforts are made to detect the molecular mechanism of COVID-19 infection. Identification of the main protein targets and pathways of severe cases of COVID-19 infection is the aim of this study.

Published differentially expressed proteins were screened and the significant proteins were investigated via protein-protein interaction network using Cytoscape software V. 3.7.2 and STRING database. The studied proteins were assessed via action map analysis to determine the relationship between individual proteins using CluePedia. The related biological terms were investigated using ClueGO and the terms were clustered and discussed.

Among the 35 queried proteins, six of them (FGA, FGB, FGG, and FGl1 plus TLN1 and THBS1) were identified as critical proteins. A total of 38 biological terms, clustered in 4 groups, were introduced as the affected terms. “Platelet degranulation” and “hereditary factor I deficiency disease” were introduced as the main class of the terms disturbed by COVID-19 virus.

It can be concluded that platelet damage and disturbed haemostasis could be the main targets in severe cases of coronavirus infection. It is vital to follow patients’ condition by examining the introduced critical differentially expressed proteins (DEPs).

It can be concluded that platelet damage and disturbed haemostasis could be the main targets in severe cases of coronavirus infection. It is vital to follow patients’ condition by examining the introduced critical differentially expressed proteins (DEPs).

Molecular pathophysiology of COVID-19 is not completely known. Expression changes in patients’ plasma proteins have revealed new information about the disease. Introducing the key targeted plasma protein in fatal conditions of COVID-19 infection is the aim of this study.

Significant differentially expressed proteins (DEPs) in the plasma of cases with a fatal condition of COVID-19 were extracted from an original article. These proteins were included in a network via STRING database along with 100 first neighbor proteins to determine central nodes of the network for analyzing.

Queried and added proteins were included in a scale free network. Three hub nodes were identified as critical target proteins. The top queried hub proteins were chains of fibrinogen; Fibrinogen Alpha chain (FGA), Fibrinogen gamma chain (FGG), and Fibrinogen beta chain (FGB), which are related to the coagulation process.

It seems that fibrinogen dysregulation has a deep impact on the fatality of COVID-19 infection.

It seems that fibrinogen dysregulation has a deep impact on the fatality of COVID-19 infection.