Internal dose assessment for environmental monitoring in nuclear power plant accidents

A method for exploiting human's internal contamination data for radioactive release estimation in nuclear power plant accidents is proposed. Nevertheless, such data is often very rough and uncertain; it is accessible even in toughest situations when most of the active and passive monitors are damaged by the accident. These data can be used in combination with other collectable data for estimating the event scale in severe nuclear power plan accidents. The rationale behind the method is that nuclear power plant accidents are often associated with internal contamination of radiation workers involved in the early stages of emergency response activities mainly due to the release of 131I in atmosphere. The proposed inverse analytical approach uses the 131I intake of contaminated workers, their working conditions, chronology of events, and applied personal safety measures during the first hours or days of the emergency response activities to estimate the magnitude of 131I concentration in the air

Detection of Nocardia, Streptomyces and Rhodococcus from bronchoalveolar lavage specimens of patients with HIV by Multiplex PCR Assay

Background: Nocardia, Streptomyces and Rhodococcus are life threatening opportunistic pathogens under immunodeficiency conditions, particularly among patients infected with HIV. Rapid and accurate detection of these infections can improve immune health quality, patient management and appropriate treatment. The aim of this study was to design a novel multiplex-PCR assay for rapid diagnosis of these three organisms directly from bronchoalveolar lavage (BAL) specimens of patients infected with HIV.Methods: The genus specific primers were designed for directdetection of Nocardia, Streptomyces and Rhodococcus in a single tube multiplex PCR. This PCR specifically amplified the target genes from pure cultures. It subsequently was applied on BAL specimens of 29 HIV positive patients that had previously been culture negative for actinomycete bacteria, of which Nocardia, Streptomyces and Rhodococcus are members.Results: Of 29 respiratory clinical specimens, there were positive for Nocardia spp. and one was positive for Streptomyces spp using the multiplex PCR assay. The sequencing of the PCR products identified the species as Nocardia cyriacigeorgica (n=2), Nocardia farcinica and Streptomyces albus.Conclusion: This novel multiplex PCR assay yielded reliable results for accurate identification of Nocardia, Streptomyces and Rhodococcus from BAL while the results of bacterial culture were negative.

Growth Nutritive Value of Saffron Residues Harvested at Different Stages by in situ and in vitro (Gas Production) Methods

The chemical composition, ruminal degradability parameters, ruminal and post ruminal digestibility of saffron residues were determined using in situ and in vitro (gas production) methods. The harvested residues at late vegetative phase were compared with the residues harvested at the early dormant phase. The results showed that NDF and ADF concentration of harvested residues at early dormant phase were higher where as it content of CP was significantly (

Using machine learning to determine acceptable levels of groundwater consumption in Iran

Groundwater footprint index (GFI) is an essential indicator to assess the sustainability of groundwater aquifers. Prediction of future GFI can significantly help managers and decision-makers of groundwater supply to better plan for future resilient consumption of surface and groundwater. In this context, artificial intelligence and machine learning models can aid to predict GFI in view of lacking or uncertain data. We used this technique to predict GFI for 178 Iranian aquifers. To our knowledge, this is the first time that GFI was predicted using machine learning models. Four models, i.e., adaptive neuro-fuzzy inference system, least-squares support vector regression, random forest, and gene expression programming, were used to predict GFI. Systematic combinations of eight variables, including precipitation, recharge, return water, infiltration from the river to the aquifer, groundwater exploitation, aquifer area, evaporation, and river drainage from the aquifer were used in the form of nine input scenarios for GFI prediction. The results showed that inclusion of all input variables gave the best results for predicting the GFI. Predicted GFIs were generally between 0.5 and 8 with an average of 1.9. A value above 1 indicates that groundwater consumption is not resilient that can adversely affect available groundwater resources in the future. Over-use of groundwater can lead to land subsidence. Especially, aquifers located in Qom, Qazvin, Varamin, and Hamedan provinces of Iran may be affected due to large over-use. Among the four models, least-squares support vector regression resulted in the highest prediction performance. Due to the poor performance of adaptive neuro-fuzzy inference system, the novel Harris hawks optimization algorithm was used to improve the performance of adaptive neuro-fuzzy inference system. The Harris hawks optimization - adaptive neuro-fuzzy inference system hybrid model improved the GFI prediction performance. Machine learning methods improve prediction of GFI for aquifers and thus, can be used to better manage groundwater in areas with less reliable data

Novel therapeutic strategy for obesity through the gut microbiota-brain axis: A review article

Background: The interaction between commensal bacteria and the host is essential for health and the gut microbiota-brain axis plays a vital role in this regard. Obesity as a medical problem not only affect the health of the individuals, but also the economic and social aspects of communities. The presence of any dysbiosis in the composition of the gut microbiota disrupts in the gut microbiota-brain axis, which in turn leads to an increase in appetite and then obesity. Because common treatments for obesity have several drawbacks, the use of microbiota-based therapy in addition to treatment and prevention of obesity can have other numerous benefits for the individual. In this review, we intend to investigate the relationship between obesity and the gut microbiota-brain axis as well as novel treatment strategies based on this axis with an emphasis on gut microbiota

Evaluation of Psychopathology and Quality of Life in Patients with Anogenital Wart Compared to Control Group

Anogenital warts (AGW) are one of the most common venereal diseases. Psychosocial complications and quality of life (QoL) of AGW patients have been considered only in recent years. Herein, the QoL and psychopathology in patients with AGW are evaluated. In total, 37 AGW patients and 37 healthy controls were recruited in the present cross-sectional study. All participants were provided with the symptom checklist 90-R (SCL-90-R) and short-form (SF-36) questionnaires. All analyses were performed using the SPSS software, version 16.0.1 for Windows. QoL was not significantly different between the study groups (P=0.12). The data showed that mental health, general health, and social functioning were significantly decreased in AGW patients (P<0.05). In addition, AGW patients were significantly more depressed and anxious than the control group (P=0.01 and P=0.04, respectively). AGW has adverse effects on psychological and QoL elements of the infected individuals. Psychological factors should be carefully considered when treating a patient with the HPV virus; hence, referral to a psychiatrist seems mandatory in these cases