Exploring the effect of intravenous lipid emulsion in acute methamphetamine toxicity

Objective(s): The increasing use of methamphetamine (METH) in the last decades has made it the second most abused drug. Advancs in the area of intravenous lipid emulsion (ILE) have led to its potential application in the treatment of poisoning. The present study aims to investigate the potential role of ILE as an antidote for acute METH poisoning. Materials and Methods: Two groups of six male rats were treated by METH (45 mg/kg), intraperitoneally. Five to seven min later, they received an infusion of 18.6 ml/kg ILE 20% through the tail vein or normal saline (NS). Locomotor and behavioral activity was assessed at different time after METH administration. Body temperature and survival rates were also evaluated. Brain and internal organs were then removed for histological examination and TUNEL assay. Results: ILE therapy for METH poisoning in rats could prevent rats mortalities and returned the METH-induced hyperthermia to normal rates (

Improving Chilling Tolerance of Maize Seedlings under Cold Conditions by Spermine Application

Low temperature is an important abiotic stress which reduces crops growth and productivity and causes physiological damages to cellular structures. The aim of this study was to investigate the probability of spermine application to improve chilling tolerance of maize under stress conditions. The treatments were included seed priming with spermine (30, 60 and 90 mg/l solutions) and normal and stress condition. Seed emergence was improved by spermine priming on both conditions and mean emergence time (MET) was also decreased with priming. Shoot and root length was highly reduced under stress conditions, but the treated seeds were improved along with increased spermine concentration. Seedling dry weight was also affected by priming and reduced weight of stressful seedlings was alleviated by spermine priming. Decreased relative water content on seedlings under stress was elevated by the treatments and significantly increased. Electrolyte leakage was also recovered by applied treatments while it was adversely decreased on cold conditions. Antioxidative system was highly responded to spermine application. Superoxide dismutase (SOD) activity increased on both normal and stress conditions, but a little decrease was observed on seedlings treated with 90 ppm level and under chilling conditions. Catalase activity was also amplified by spermine treatments. Priming had a great effect on ascorbate peroxidase (APX) activity on both stressful and normal seedlings and increased it compare with non treated seedlings. It is also important to note that with increasing spermine concentration to 90 ppm, no considerable differences were observed. Thus, 60 ppm concentration could be proposed as the appropriate level of spermine in order to improve chilling tolerance of maize seedlings

A Randomized Crossover, Pilot Study Examining the Effect of Acupuncture in the Management of Competitive Anxiety in Athletes

Background : Excessive competitive anxiety induces adverse effects on athletic performance and planning efficient management methods is crucial. Objectives: We aimed to assess the effects of acupuncture on competitive anxiety. Methods : In this cross-over study, 20 male soccer players under 21 years (U-21) were randomized equally into acupuncture or control groups. The acupuncture group received acupuncture on fifteen anxiety-related points and the control group received acupuncture on fifteen points unrelated to anxiety for thirty minutes. We measured the participants’ resting heart rate and galvanic skin conduction and asked them to answer the Competitive State Anxiety Inventory-2 (CSAI-2) questionnaire at baseline and the end of the intervention. Results : We detected a significant treatment effect in the cognitive anxiety level (-1.05 ± 0.91; p = 0.02) but not in the somatic anxiety level (-0.46 ± 1.68; p = 0.43), Sport Self-Confidence (-1.06 ± 2.21; p = 0.11), heart rate (0.20 ± 2.2; p = 0.93), and skin conductance (-0.50 ± 0.77; p = 0.19). Conclusion : Based on these results, acupuncture might decrease cognitive anxiety but might not affect somatic anxiety

ARE Update Volume 6, Number 3

What Price Should be Paid to Keep U.S.Dependence on Foreign Oil in Check?; Privatization and Innovation in Agricultural Biotechnology; ARE Faculty Profile: Scott Rozelle; Prices,Volumes and Promotions in the Fresh Strawberry Marke

Breast cancer prediction using different machine learning methods applying multi factors

Objective: Breast cancer (BC) is a multifactorial disease and is one of the most common cancers globally. This study aimed to compare different machine learning (ML) techniques to develop a comprehensive breast cancer risk prediction model based on features of various factors. Methods: The population sample contained 810 records (115 cancer patients and 695 healthy individuals). 45 attributes out of 85 were selected based on the opinion of experts. These selected attributes are in genetic, biochemical, biomarker, gender, demographic and pathological factors. 13 Machine learning models were trained with proposed attributes and coefficient of attributes and internal relationships were calculated. Result: Compared to other methods random forest (RF) has higher performance (accuracy 99.26%, precision 99%, and area under the curve (AUC) 99%). The results of assessing the impact and correlation of variables using the RF method based on PCA indicated that pathology, biomarker, biochemistry, gene, and demographic factors with a coefficient of 0.35, 0.23, 0.15, 0.14, and 0.13 respectively, affected the risk of BC (r 2 = 0.54). Conclusion: Breast cancer has several risk factors. Medical experts use these risk factors for early diagnosis. Therefore, identifying related risk factors and their effect can increase the accuracy of diagnosis. Considering the broad features for predicting breast cancer leads to the development of a comprehensive prediction model. In this study, using RF technique a breast cancer prediction model with 99.3% accuracy was developed based on multifactorial features.</p

A new multiplex polymerase chain reaction assay for the identification a panel of bacteria involved in bacteremia

Background: Throughout the world, bloodstream infections (BSI s ) are associated with high rates of morbidity and mortality. Rapid pathogens identification is central significance for the outcome of the patient than culture techniques for microbial identification. To develop an end point multiplex PCR to identify a group of bacteria including Enterococcus spp., Pseudomons aeruginosa, Staphylococcus spp., Acinetobacter baumannii, 16S rDNA, and Drosophila Melanogaster were used as internal control (IC). Materials and Methods: Design of primers was done using Mega4, Allel ID6, Oligo6 and Oligo analyzer softwares. Genetic targets for primer designing and identification of genus Enterococcus spp., Staphylococcus spp., and species of Acinetobacter baumannii, Pseudomons aeruginosa, included the rpoB, rpoB and gyrA, sss respectively. Then PCR and multiplex PCR were performed Results: The intended specificity was obtained for the bacteria, which used in this study and there wasn′t seen any unspecific amplification by the multiplex PCR. The test showed a sensitivity ranging from 1 to 100 target copies per reaction depending on the bacterial species. Conclusions: The presented multiplex PCR offers a rapid and accurate molecular diagnostic tool for simultaneous detection of some pathogenic microorganisms. The IC exists in the multiplex PCR accompanied by other primers in the system, can serve as a simple, cost- effective internal control for the multiplex PCR assay

Breast cancer prediction using different machine learning methods applying multi factors

ObjectiveBreast cancer (BC) is a multifactorial disease and is one of the most common cancers globally. This study aimed to compare different machine learning (ML) techniques to develop a comprehensive breast cancer risk prediction model based on features of various factors.MethodsThe population sample contained 810 records (115 cancer patients and 695 healthy individuals). 45 attributes out of 85 were selected based on the opinion of experts. These selected attributes are in genetic, biochemical, biomarker, gender, demographic and pathological factors. 13 Machine learning models were trained with proposed attributes and coefficient of attributes and internal relationships were calculated.ResultCompared to other methods random forest (RF) has higher performance (accuracy 99.26%, precision 99%, and area under the curve (AUC) 99%). The results of assessing the impact and correlation of variables using the RF method based on PCA indicated that pathology, biomarker, biochemistry, gene, and demographic factors with a coefficient of 0.35, 0.23, 0.15, 0.14, and 0.13 respectively, affected the risk of BC (r2 = 0.54).ConclusionBreast cancer has several risk factors. Medical experts use these risk factors for early diagnosis. Therefore, identifying related risk factors and their effect can increase the accuracy of diagnosis. Considering the broad features for predicting breast cancer leads to the development of a comprehensive prediction model. In this study, using RF technique a breast cancer prediction model with 99.3% accuracy was developed based on multifactorial features.</p

Antibiotic use during the first 6 months of COVID-19 pandemic in Iran : a large-scale multi-centre study

WHAT IS KNOWN AND OBJECTIVE: Although antibiotics are ineffective against viral infections, epidemiological studies have revealed that the COVID‐19 pandemic resulted in the overuse of antibiotics and disruption of antimicrobial stewardship programmes. We investigated the pattern of antibiotic use during the first 6 months of the COVID‐19 pandemic in Iran. METHODS: A multi‐centre retrospective study was designed to investigate the use of 16 broad‐spectrum antibiotics in 12 medical centres. The rate of antibiotic use was calculated and reported based on the Defined Daily Dose (DDD) per 100 hospital bed‐days. The bacterial co‐infection rate was also reported. RESULTS AND DISCUSSION: Totally, 43,791 hospitalized COVID‐19 patients were recruited in this study. It was found that 121.6 DDD of antibiotics were used per 100 hospital bed‐days, which estimated that each patient received approximately 1.21 DDDs of antibiotics every day. However, the bacterial co‐infections were detected only in 14.4% of the cases. A direct correlation was observed between the rate of antibiotic use and mortality (r[142] = 0.237, p = 0.004). The rate of antibiotic consumption was not significantly different between the ICU and non‐ICU settings (p = 0.15). WHAT IS NEW AND CONCLUSION: In this study, widespread antibiotic use was detected in the absence of the confirmed bacterial coinfection in COVID‐19 patients. This over‐consumption of broad‐spectrum antibiotics may be associated with increased mortality in hospitalized COVID‐19 patients, which can be an alarming finding