Determination of cadmium, nickel, lead and vanadium concentrations in white Indian prawn sold in Shiraz town

زمینه و هدف: با گسترش آلاینده ها در محیط زیست و وابستگی انسان به محیط برای تأمین مواد غذایی و سایر نیازها، بررسی در مورد انواع آلودگی به خصوص آب ها و سایر آبزیان حائز اهمیت می باشد. هدف از این مطالعه بررسی و تعیین میزان غلظت فلزات سنگین کادمیوم (Cd)، نیکل (Ni)، سرب (Pb) و وانادیوم (V) در عضلات و پوست میگوی سفید هندی (Fenneropenaeus indicus) خوراکی در شهرستان شیراز بوده است. روش بررسی: در این مطالعه مقطعی در پاییز سال 1390 با مراجعه به بازار عمده عرضه آبزیان تعداد 120 نمونه میگو به صورت تصادفی از سطح شهر شیراز تهیه شد. آماده سازی و آنالیز نمونه ها مطابق با دستورالعمل های توصیه شده صورت پذیرفته و میزان فلزات سنگین با دستگاه نشر اتمی (ICP) مدل Varian V10-ES تعیین و با مقادیر توصیه شده استاندارهای جهانی WHO و FAO مورد مقایسه قرار گرفت. یافته ها: میانگین غلظت عناصر کادمیوم، نیکل، سرب و وانادیوم در نمونه های مورد مطالعه در بافت عضله به ترتیب برابر با 45/0±08/1، 25/1±62/8، 1/2±63/1 و 93/0±61/0 میلی گرم در کیلوگرم و در پوست 38/0±28/1، 53/1±61/7، 6/4±15/7 و 45/0±4/1 میلی گرم در کیلوگرم وزن بدن اندازه گیری گردید. میزان فلزات سنگین کادمیوم، سرب و وانادیوم در پوست میگو و میزان نیکل در عضله میگو در مقایسه با یکدیگر بیشتر بود (05/0

A Business Model to Detect Disease Outbreaks

Introduction: Every year several disease outbreaks, such as influenza-like illnesses (ILI) and other contagious illnesses, impose various costs to public and non-government agencies. Most of these expenses are due to not being ready to handle such disease outbreaks. An appropriate preparation will reduce the expenses. A system that is able to recognize these outbreaks can earn income in two ways: first, selling the predictions to government agencies to equip and make preparations in order to reduce the imposed costs and second, selling predictions to pharmaceutical companies to guide them in producing the required drugs when a disease spreads. This production can specify probable markets to these companies. Methods: Both earning methods would be considered in this modeling and costs and incomes will be discussed according to basic business models (especially in the health field). To execute this model, the internet is used as a recipient of information from the doctors and the service providers for prediction. To ensure collaboration of doctors in the data collection process, the amount of money that is paid is proportional to the rate of sending the patients’ information. On the other hand, customers can access outbreak prediction information about a specific illness after payment or subscription of system for monthly periods. All the money transfered in this system would be via online credit systems. Results: This business model has three main values: recognizing disease outbreaks at the right time, identifying factors and estimating the spreading rate of the disease and, the categorization of customers in this model is based on the value provided including pharmaceutical companies and importers of drugs, the government, insurance companies, universities and research centers. By considering various markets, this model has the ROI of 0.5 which means the investment in it reverses in 6 months. Conclusion: According to the results, the business model developed in this study, has fair value and is feasible and suitable for the web. This model develops medical information network and proper marketing, earns good profits and the most critical resource of it is the algorithm that detects the disease outbreak which must be properly constructed and used

Relationship between serum anti-heat shock protein 27 antibody levels and obesity

Background Heat shock protein 27 (HSP27) is an intracellular molecular chaperone that is expressed at high levels following the exposure of cells to environmental stressors such as heat, toxins, and free radicals. High levels of HSP antigens and antibody titers have been reported in several conditions including cardiovascular disease and cancers. We measured serum anti-HSP27 antibody levels in 993 subjects and assessed the associations between serum anti-HSP27 antibody levels and demographic characteristics including coronary risk factors. Methods A total of 993 subjects were recruited as part of the Mashhad Stroke and Heart Atherosclerotic Disorders (MASHAD) cohort study. Demographic, clinical, and biochemical parameters and serum anti-HSP27 antibody titers were determined in all the subjects. Results Serum anti-HSP27 antibody levels increased with increasing age in men. No significant differences in levels were detected between men and women. Serum anti-HSP27 antibody levels were significantly higher in obese subjects than in nonobese subjects (P = 0.046); however, no significant influence of smoking status was observed. Moreover, serum anti-HSP27 antibody titers were positively associated with age, body mass index, waist/hip ratio, the presence of diabetes mellitus, nonsmoking habit, serum triglycerides, cholesterol, and high-sensitivity c-reactive protein. Conclusion We have found that serum anti-HSP27 antibody titers are related to several cardiovascular risk factors, necessitating further studies on the value of this emerging marker for risk stratification

Robust Fault-Tolerant Control Design for Fuzzy Networked Control Systems with Data Drift and Sensor Failure

This paper deals with the problem of robust H∞ fault-tolerant controller design for fuzzy networked control systems using static state feedback. The stability of networked control systems is affected by delay, sensor failure and data drift. Therefore, a Lyapunov-Krasovskii functional are exploited to establish asymptotic stability conditions for the underlying system considering these imperfections. It is assumed that sensor failure and data drift, which occur during data transmission over the network, are modeled by a stochastic variable with Bernoulli distribution. The design conditions are presented in terms of linear matrix inequalities, and the efficiency of the proposed approach is shown through a numerical example

Correlation between coronary artery calcification and COVID-19

Background: Coronary heart disease (CHD) is an underlying cardiac condition contributing to increased COVID-19 mortality and morbidity which can be assessed by several diagnosis methods including coronary artery calcification (CAC). The goal of this study was to find out if there were potential links between CAC, clinical findings, severity of COVID-19, and in-hospital outcomes. Methods: This retrospective study evaluated 551 suspected patients admitted to teaching hospitals of the Babol University of Medical Sciences, Babol, Iran, from March to October 2021. Data included previous diseases, comorbidities, clinical examinations, routine laboratory tests, demographic characteristics, duration of hospitalization, and number of days under ventilation were recorded in a checklist. Results: Findings of current study provide evidence of a significant relationship between coronary artery calcification (CAC) and in-hospital mortality. Additionally, we observed significant correlations between CAC and several clinical parameters including age, duration of hospitalization, pulse rate, maximum blood pressure, erythrocyte sedimentation rate (ESR), blood urea nitrogen (BUN), neutrophil count, white blood cell (WBC) count, and oxygen saturation. However, we did not observe a significant association between CAC and the severity index of COVID-19. In addition, logistic regression tests did not find a significant value of CAC to predict in-hospital mortality. Conclusion: Our findings showed a significant relationship between CAC and in-hospital mortality