The role of human factor in incidence and severity of road crashes based on the CART and LR regression: a data mining approach

AbstractAccidents are one of the biggest public health problems in the world. As literature indicated, the traffic accidents were assessed to be most significant health problem in Iran. To date, no serious researches have analyzed high dimensional traffic data In Iran. This paper, therefore, aims to bridge the gap. In this study, the traffic data are analyzed by Data Mining techniques such as Logistic Regression, Classification and Regression Trees. In this paper the impact of such factors were investigated using these techniques. It is hoped that the current research findings will help governments in better road designs and traffic management

Effect of high-dose Ascorbic acid on vasopressor′s requirement in septic shock

Objective: Effects of ascorbic acid on hemodynamic parameters of septic shock were evaluated in nonsurgical critically ill patients in limited previous studies. In this study, the effect of high-dose ascorbic acid on vasopressor drug requirement was evaluated in surgical critically ill patients with septic shock. Methods: Patients with septic shock who required a vasopressor drug to maintain mean arterial pressure >65 mmHg were assigned to receive either 25 mg/kg intravenous ascorbic acid every 6 h or matching placebo for 72 h. Vasopressor dose and duration were considered as the primary outcomes. Duration of Intensive Care Unit (ICU) stay and 28-day mortality has been defined as secondary outcomes. Findings: During the study period, 28 patients (14 in each group) completed the trial. Mean dose of norepinephrine during the study period (7.44 ± 3.65 vs. 13.79 ± 6.48 mcg/min, P = 0.004) and duration of norepinephrine administration (49.64 ± 25.67 vs. 71.57 ± 1.60 h, P = 0.007) were significantly lower in the ascorbic acid than the placebo group. No statistically significant difference was detected between the groups regarding the length of ICU stay. However, 28-day mortality was significantly lower in the ascorbic acid than the placebo group (14.28% vs. 64.28%, respectively; P = 0.009). Conclusion: High-dose ascorbic acid may be considered as an effective and safe adjuvant therapy in surgical critically ill patients with septic shock. The most effective dose of ascorbic acid and the best time for its administration should be determined in future studies

Epidemiology of prothrombin G20210A polymorphism in the Southern Iran

Background: There are many genetic and non-hereditary risk factors that are known to causes venous thromboembolic (VTE) disorders, Cardiovascular diseases and types of cancer. One of these is the Prothrombin G20210A mutation. Prothrombin mutation (guanine to adenine; G→A) at nucleotide position 20210, which is present in the 3′ untranslated region of the prothrombin gene. Prothrombin G20210A mutation is present outside the coding region for prothrombin, and hence it does not affect the actual structure of the prothrombin molecule and it does not affect its function as a strong clotting factor when activated into thrombin. However, several studies have shown that, G20210A heterozygosity was associated with a threefold increased risk for VTE. Moreover, the association of PTH G20210A polymorphisms with cancer has been reported. The present study was designed to determine the frequency of PTH G20210A polymorphism in Southern Iran. Methods: In this cross-sectional study, 140 healthy women were from Southern Iran recruited among participants in Namazi Hospital, Shiraz, Iran, from March 2013 to February 2014. A total of 5 ml of peripheral blood was taken from individuals then Genomic DNA was extracted using blood DNA kit (Ron’s Blood and Cell DNA Mini Kit, BioRon, Germany). The amplification refractory mutation system- polymerase chain reaction (ARMS-PCR) method was used for the detection of PTH G20210A single nucleotide polymorphism in each subject. Results: The frequencies of the GG and GA genotypes were as 97.9%, 2.1% respectively. The frequency of G allele was and the frequency of A allele was 1.1%. Conclusion: Results of the present study might be important in understanding the distribution of PTH G20210A polymorphism in the Southern Iran. Minor allele frequency in this population is higher than in the Iranian and European population but similar to the prevalence in the Western Iran, Iranian Jews, American, Irish, Tunisian and Bahraini population

Artificial Intelligence in Cancer Care: From Diagnosis to Prevention and Beyond

<p>Artificial Intelligence (AI) has made significant strides in revolutionizing cancer care, encompassing various aspects from diagnosis to prevention and beyond. With its ability to analyze vast amounts of data, recognize patterns, and make accurate predictions, AI has emerged as a powerful tool in the fight against cancer. This article explores the applications of AI in cancer care, highlighting its role in diagnosis, treatment decision-making, prevention, and ongoing management. In the realm of cancer diagnosis, AI has demonstrated remarkable potential. By processing patient data, including medical imaging, pathology reports, and genetic profiles, AI algorithms can assist in early detection and accurate diagnosis. Image recognition algorithms can analyze radiological images, such as mammograms or CT scans, to detect subtle abnormalities and assist radiologists in identifying potential tumors. AI can also aid pathologists in analyzing tissue samples, leading to more precise and efficient cancer diagnoses. AI's impact extends beyond diagnosis into treatment decision-making. The integration of AI algorithms with clinical data allows for personalized treatment approaches. By analyzing patient characteristics, disease stage, genetic markers, and treatment outcomes, AI can provide valuable insights to oncologists, aiding in treatment planning and predicting response to specific therapies. This can lead to more targeted and effective treatment strategies, improving patient outcomes and reducing unnecessary treatments and side effects. Furthermore, AI plays a crucial role in cancer prevention. By analyzing genetic and environmental risk factors, AI algorithms can identify individuals at higher risk of developing certain cancers. This enables targeted screening programs and early interventions, allowing for timely detection and prevention of cancer. Additionally, AI can analyze population-level data to identify trends and patterns, contributing to the development of public health strategies for cancer prevention and control. AI's involvement in cancer care goes beyond diagnosis and treatment, encompassing ongoing management and survivorship. AI-powered systems can monitor treatment response, track disease progression, and detect recurrence at an early stage. By continuously analyzing patient data, including imaging, laboratory results, and clinical assessments, AI algorithms can provide real-time insights, facilitating timely interventions and adjustments to treatment plans. This proactive approach to disease management improves patient outcomes and enhances quality of life.</p&gt