Analyzing the Spread of Misinformation on Social Networks:A Process and Software Architecture for Detection and Analysis

The rapid dissemination of misinformation on social networks, particularly during public health crises like the COVID-19 pandemic, has become a significant concern. This study investigates the spread of misinformation on social network data using social network analysis (SNA) metrics, and more generally by using well known network science metrics. Moreover, we propose a process design that utilizes social network data from Twitter, to analyze the involvement of non-trusted accounts in spreading misinformation supported by a proof-of-concept prototype. The proposed prototype includes modules for data collection, data preprocessing, network creation, centrality calculation, community detection, and misinformation spreading analysis. We conducted an experimental study on a COVID-19-related Twitter dataset using the modules. The results demonstrate the effectiveness of our approach and process steps, and provides valuable insight into the application of network science metrics on social network data for analysing various influence-parameters in misinformation spreading.</p

Analyzing the Spread of Misinformation on Social Networks:A Process and Software Architecture for Detection and Analysis

The rapid dissemination of misinformation on social networks, particularly during public health crises like the COVID-19 pandemic, has become a significant concern. This study investigates the spread of misinformation on social network data using social network analysis (SNA) metrics, and more generally by using well known network science metrics. Moreover, we propose a process design that utilizes social network data from Twitter, to analyze the involvement of non-trusted accounts in spreading misinformation supported by a proof-of-concept prototype. The proposed prototype includes modules for data collection, data preprocessing, network creation, centrality calculation, community detection, and misinformation spreading analysis. We conducted an experimental study on a COVID-19-related Twitter dataset using the modules. The results demonstrate the effectiveness of our approach and process steps, and provides valuable insight into the application of network science metrics on social network data for analysing various influence-parameters in misinformation spreading.</p

Analyzing the Spread of Misinformation on Social Networks:A Process and Software Architecture for Detection and Analysis

The rapid dissemination of misinformation on social networks, particularly during public health crises like the COVID-19 pandemic, has become a significant concern. This study investigates the spread of misinformation on social network data using social network analysis (SNA) metrics, and more generally by using well known network science metrics. Moreover, we propose a process design that utilizes social network data from Twitter, to analyze the involvement of non-trusted accounts in spreading misinformation supported by a proof-of-concept prototype. The proposed prototype includes modules for data collection, data preprocessing, network creation, centrality calculation, community detection, and misinformation spreading analysis. We conducted an experimental study on a COVID-19-related Twitter dataset using the modules. The results demonstrate the effectiveness of our approach and process steps, and provides valuable insight into the application of network science metrics on social network data for analysing various influence-parameters in misinformation spreading.</p

Analyzing the Spread of Misinformation on Social Networks:A Process and Software Architecture for Detection and Analysis

The rapid dissemination of misinformation on social networks, particularly during public health crises like the COVID-19 pandemic, has become a significant concern. This study investigates the spread of misinformation on social network data using social network analysis (SNA) metrics, and more generally by using well known network science metrics. Moreover, we propose a process design that utilizes social network data from Twitter, to analyze the involvement of non-trusted accounts in spreading misinformation supported by a proof-of-concept prototype. The proposed prototype includes modules for data collection, data preprocessing, network creation, centrality calculation, community detection, and misinformation spreading analysis. We conducted an experimental study on a COVID-19-related Twitter dataset using the modules. The results demonstrate the effectiveness of our approach and process steps, and provides valuable insight into the application of network science metrics on social network data for analysing various influence-parameters in misinformation spreading.</p

Analyzing the Spread of Misinformation on Social Networks:A Process and Software Architecture for Detection and Analysis

The rapid dissemination of misinformation on social networks, particularly during public health crises like the COVID-19 pandemic, has become a significant concern. This study investigates the spread of misinformation on social network data using social network analysis (SNA) metrics, and more generally by using well known network science metrics. Moreover, we propose a process design that utilizes social network data from Twitter, to analyze the involvement of non-trusted accounts in spreading misinformation supported by a proof-of-concept prototype. The proposed prototype includes modules for data collection, data preprocessing, network creation, centrality calculation, community detection, and misinformation spreading analysis. We conducted an experimental study on a COVID-19-related Twitter dataset using the modules. The results demonstrate the effectiveness of our approach and process steps, and provides valuable insight into the application of network science metrics on social network data for analysing various influence-parameters in misinformation spreading.</p

A single hydrophobic cleft in the Escherichia coli processivity clamp is sufficient to support cell viability and DNA damage-induced mutagenesis in vivo

<p>Abstract</p> <p>Background</p> <p>The ubiquitous family of DnaN sliding processivity clamp proteins plays essential roles in DNA replication, DNA repair, and cell cycle progression, in part by managing the actions of the different proteins involved in these processes. Interactions of the homodimeric <it>Escherichia coli </it>β clamp with its known partners involves multiple surfaces, including a hydrophobic cleft located near the C-terminus of each clamp protomer.</p> <p>Results</p> <p>A mutant <it>E. coli </it>β clamp protein lacking a functional hydrophobic cleft (β^C) complemented the temperature sensitive growth phenotype of a strain bearing the <it>dnaN159 </it>allele, which encodes a thermolabile mutant clamp protein (β159). Complementation was conferred by a β^C/β159 heterodimer, and was observed only in the absence of the <it>dinB </it>gene, which encodes DNA polymerase IV (Pol IV). Furthermore, the complemented strain was proficient for <it>umuDC </it>(Pol V) -dependent ultraviolet light (UV) -induced mutagenesis.</p> <p>Conclusions</p> <p>Our results suggest that a single cleft in the homodimeric <it>E. coli </it>β sliding clamp protein is sufficient to support both cell viability, as well as Pol III, Pol IV, and Pol V function <it>in vivo</it>. These findings provide further support for a model in which different Pols switch places with each other on DNA using a single cleft in the clamp.</p

Prediction of coronary artery disease severity in lower extremity artery disease patients: A correlation study of TASC II classification, Syntax and Syntax II scores

Background: Lower extremity arterial disease (LEAD) is a well-established risk factor for concomitant coronary artery disease (CAD). There are no published data combining all three lower limb arterial segments (aortoiliac, femoropopliteal and below the knee vessels) in order to estimate CAD severity in LEAD patients. Herein has been derived a new scoring system for this purpose, which uses the wellknown TASC II classification, Syntax score and, for the first time in medical literature, a Syntax II score. Methods: The study population consisted of 178 patients who underwent lower limb and coronary diagnostic angiography for assessment of LEAD and CAD at the same session. Syntax and Syntax II scores were calculated. TASC II classifications of the lower limb arteries were done. A new scoring system, called “Total Peripheral Score” (TPS), for lower limbs was also calculated. Results: A positive correlation was found between TPS and Syntax score and a less prominent positive correlation between TPS and Syntax II score (p &lt; 0.001). A cut-off value of ‘6’ for the new score was found for estimating high risk subgorup of CAD (Syntax score &gt; 32; p &lt; 0.001). Critical femoropopliteal arterial segment stenosis was the most predictive lower limb arterial zone for presence of severe CAD (Syntax score &gt; 32; p = 0.011). Conclusions: Taking into account all lower limb arterial segments for predicting CAD during lower limb arterial angiography was recommended. A TPS of more than ‘6’ is the practical cut-off value for estimating severe CAD. Femoropopliteal arterial critical stenosis is the most predictive arterial zone for estimating severe CAD.

Potential role of the geriatric nutritional risk index as a novel risk factor for the development of non-valvular atrial fibrillation in patients with heart failure

PURPOSE: The geriatric nutritional risk index (GNRI) is a simple and objective nutritional assessment tool for elderly patients. Lower GNRI values are associated with a worse prognosis in heart failure with reduced ejection fraction (HFrEF). Our aim is to investigate the relationship between malnutrition and follow-up cardiovascular (CV) events in HFrEF. METHODS: A retrospective study was performed on 362 patients with HFrEF. The baseline GNRI was calculated at the first visit. The patients were divided into three groups according to the GNRI: >98, no-risk group; 92 to <= 98, low risk group; 82 to <92, moderate-to-high-risk group. The study endpoint was a composite of follow-up CV events, including all-cause mortality, non-valvular atrial fibrillation (NVAF) , need for cardioverter defibrillator (ICD) therapy, HfrEF-related hospitalizations and need for percutaneous coronary interventions (PCIs). RESULTS: Follow-up data showed that the group with moderate-to-high risk had a significantly higher incidence of NVAF, PCIs and all-cause mortality compared to other groups (p0.05). Mean GNRI value was 83.3 in NVAF patients and 101.1 in patients without NVAF (p<0.001). Kaplan Meier survival analysis showed that patients from the group with moderate-to-high risk had a significantly worse survival rate (p < 0.001). In the multivariate Cox regression analysis, the group with moderate-to-high risk (HR =3.872) and ICD implantations (HR = 4.045) were associated with increased mortality. CONCLUSION: The GNRI value may have a potential role for predicting future events, especially NVAF in patients with HfrEF

Total bilirubin and fasting plasma glucose levels are associated with coronary collateral development in elderly patients

Background and objective: We aimed to investigate biochemical factors affecting coronary collateral circulation development in an elderly population aged 75 years and over. Material and methods: The study group consisted of patients with a prior coronary angiography for stable coronary artery disease (CAD). Patients with total occlusion of at least one vessel were included in the study. Enrolled patients were divided into two groups, good collateral (GC; n = 73) and bad collateral (BC; n = 55), in accordance with the Cohen-Rentop’s classification system. Results: In comparison to the GC group, bilirubin levels were significantly lower (p < 0.001), and fasting plasma glucose (FPG) levels were significantly higher in the BC group (p = 0.026). Low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C) levels were significantly lower in the BC group when compared to the GC group (p = 0.002 and p < 0.001, respectively). Backward elimination stepwise logistic regression analysis identified bilirubin and FPG as variables that strongly predicted the presence of a well-developed coronary collateral circulation and a poorly developed coronary collateral circulation, respectively. Conclusion: Bilirubin and FPG were seemed as the most important factors affecting coronary collateral circulation development in patients with stable CAD who were older than 75 years

Serum Nuclear Factor Erythroid-2 Related Factor-2 (NRF2) as an Indicator of Oxidative Stress is Related to Coronary in-Stent Restenosis

Objective: In the treatment of coronary artery disease, stent implantation has become the standard treatment, but development of in-stent restenosis (ISR) limits the benefit of this treatment modality. Methods: Based on the connection between oxidative stress and thiol/disulphate and NRF2, it was intended to measure NRF2 and thiol/disulphate levels. Results: Coronary angiography images of 76 stable angina pectoris patients were evaluated. Of the 51 patients with a history drug eluting stent implantation, we determined 25 patients with ISR (Group 1) and 26 patients without ISR (Group 2). Twenty-five patients with normal coronary arteries were included in the study as control group (Group 3). NRF2 level was found to be significantly higher in patients who did not develop ISR (p=0.01). Total thiol was significantly higher in group 3 (738.76 micromole/L) compared to group 1 (626.11 micromole/L) and group 2 (630.27 micromole/L) (p=0.014). Native thiol was also significantly higher in group 3 (570.53 micromole/L) compared to group 1 (483.91 micromole/L) and group 2 (501 micromole/L) (p=0.006). Conclusion: We think that total and native thiol levels might be useful as an indicator of oxidative stress in early diagnosis of coronary artery disease, and the NRF2 level can be used in predicting patients who might develop coronary ISR