Chemical Reaction between Boric Acid and Phosphine Indicates Boric Acid as an Antidote for Aluminium Phosphide Poisoning

Objectives: Aluminium phosphide (AlP) is a fumigant pesticide which protects stored grains from insects and rodents. When it comes into contact with moisture, AlP releases phosphine (PH3), a highly toxic gas. No efficient antidote has been found for AlP poisoning so far and most people who are poisoned do not survive. Boric acid is a Lewis acid with an empty p orbital which accepts electrons. This study aimed to investigate the neutralisation of PH3 gas with boric acid. Methods: This study was carried out at the Baharlou Hospital, Tehran University of Medical Sciences, Tehran, Iran, between December 2013 and February 2014. The volume of released gas, rate of gas evolution and changes in pH were measured during reactions of AlP tablets with water, acidified water, saturated boric acid solution, acidified saturated boric acid solution, activated charcoal and acidified activated charcoal. Infrared spectroscopy was used to study the resulting probable adduct between PH3 and boric acid. Results: Activated charcoal significantly reduced the volume of released gas (P <0.01). Although boric acid did not significantly reduce the volume of released gas, it significantly reduced the rate of gas evolution (P <0.01). A gaseous adduct was formed in the reaction between pure AlP and boric acid. Conclusion: These findings indicate that boric acid may be an efficient and non-toxic antidote for PH3 poisoning

Prevention of γ-Radiation-Induced DNA Damage in Human Lymphocytes Using a Serine-Magnesium Sulfate Mixture

 Objectives: Ionising radiation has deleterious effects on human cells. N-acetylcysteine (NAC) and cysteine, the active metabolite of NAC, are well-known radioprotective agents. Recently, a serine-magnesium sulfate combination was proposed as an antidote for organophosphate toxicity. This study aimed to investigate the use of a serine-magnesium sulfate mixture in the prevention of γ-radiation-induced DNA damage in human lymphocytes as compared to NAC and cysteine. Methods: This study was carried out at the Iran University of Medical Sciences, Tehran, Iran, between April and September 2016. Citrated blood samples of 7 mL each were taken from 22 healthy subjects. Each sample was divided into 1 mL aliquots, with the first aliquot acting as the control while the second was exposed to 2 Gy of γ-radiation at a dose rate of 102.7 cGy/minute. The remaining aliquots were separately incubated with 600 μM concentrations each of serine, magnesium sulfate, serine-magnesium sulfate, NAC and cysteine before being exposed to 2 Gy of γ-radiation. Lymphocytes were isolated using a separation medium and methyl-thiazole-tetrazolium and comet assays were used to evaluate cell viability and DNA damage, respectively. Results: The serine-magnesium sulfate mixture significantly increased lymphocyte viability and reduced DNA damage in comparison to serine, magnesium sulfate, NAC or cysteine alone (P <0.01 each). Conclusion: The findings of the present study support the use of a serine-magnesium sulfate mixture as a new, non-toxic, potent and efficient radioprotective agent

Imbalanced Domain Generalization for Robust Single Cell Classification in Hematological Cytomorphology

Accurate morphological classification of white blood cells (WBCs) is an important step in the diagnosis of leukemia, a disease in which nonfunctional blast cells accumulate in the bone marrow. Recently, deep convolutional neural networks (CNNs) have been successfully used to classify leukocytes by training them on single-cell images from a specific domain. Most CNN models assume that the distributions of the training and test data are similar, i.e., that the data are independently and identically distributed. Therefore, they are not robust to different staining protocols, magnifications, resolutions, scanners, or imaging protocols, as well as variations in clinical centers or patient cohorts. In addition, domain-specific data imbalances affect the generalization performance of classifiers. Here, we train a robust CNN for WBC classification by addressing cross-domain data imbalance and domain shifts. To this end, we use two loss functions and demonstrate the effectiveness on out-of-distribution (OOD) generalization. Our approach achieves the best F1 macro score compared to other existing methods, and is able to consider rare cell types. This is the first demonstration of imbalanced domain generalization in hematological cytomorphology and paves the way for robust single cell classification methods for the application in laboratories and clinics.Comment: Published as a ICLR 2023 workshop paper: What do we need for successful domain generalization

Urban bus fleet routing in transportation network equipped with park-and-ride: a case study of Babol, Iran

Recently, most cities have tried to connect park-and-ride facilities with public transit vehicles. The present study aims to design urban bus routes in the transportation network equipped with park-and-ride. Seven important factors which affect the design of urban bus network are identified through the literature review. These factors include demand coverage, route directness, passengers’ satisfaction, minimum length of bus route, budget, use of existing bus routes and number of lines. In this article, by use of the mentioned factors, a new model is developed to determine the urban bus routes. The new model figures the routes with park-and-ride as origin and Central Business District (CBD) as destination, in such a manner that the covered demand is maximized. Our novel method is more effective than other options currently available. In fact, it uses the most important factors in designing urban bus routes. Furthermore, an efficient Genetic Algorithm (GA) based approach is represented to solve large-scale problems. Numerical results show the effectiveness of this approach. At last, the developed model is applied to design the urban bus routes in the transportation network of Babol (Iran)

Optimal sensor location and origin–destination matrix observation with and without sensors on uncongested networks

The Origin–Destination (O–D) matrix, is an important information in transportation planning and traffic control. Rapid changes in land use, particularly in developing countries, have been and are on an increase, which makes the estimation and observation of this matrix more significant. The objective of this paper is to observe O–D matrix under two scenarios. In the first scenario, it is assumed that the traffic network is equipped with path-ID sensors. In this situation, the goal is to determine the optimal number and location of these sensors in the network, where by applying collected information through these sensors, the O–D matrix is observed. Because path-ID sensors are not available in many cities, in the second scenario the interview alternative is proposed in order to observe O–D matrix. The interview method has encountered some restrictions. Several mathematical programming models have been developed to overcome these restrictions. To illustrate these proposed methodologies, they are applied in the Nguyen–Dupuis transportation network and the results are analysed. By applying the model on the intercity road network in the Province of Isfahan (Iran), a large network, the efficiency of these proposed models is demonstrated. Finally, some conclusions and final recommendations are included. First published online 10 October 201

A Continual Learning Approach for Cross-Domain White Blood Cell Classification

Accurate classification of white blood cells in peripheral blood is essential for diagnosing hematological diseases. Due to constantly evolving clinical settings, data sources, and disease classifications, it is necessary to update machine learning classification models regularly for practical real-world use. Such models significantly benefit from sequentially learning from incoming data streams without forgetting previously acquired knowledge. However, models can suffer from catastrophic forgetting, causing a drop in performance on previous tasks when fine-tuned on new data. Here, we propose a rehearsal-based continual learning approach for class incremental and domain incremental scenarios in white blood cell classification. To choose representative samples from previous tasks, we employ exemplar set selection based on the model's predictions. This involves selecting the most confident samples and the most challenging samples identified through uncertainty estimation of the model. We thoroughly evaluated our proposed approach on three white blood cell classification datasets that differ in color, resolution, and class composition, including scenarios where new domains or new classes are introduced to the model with every task. We also test a long class incremental experiment with both new domains and new classes. Our results demonstrate that our approach outperforms established baselines in continual learning, including existing iCaRL and EWC methods for classifying white blood cells in cross-domain environments.Comment: Accepted for publication at workshop on Domain Adaptation and Representation Transfer (DART) in International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023

A hybrid egalitarian bargaining game-DEA and sustainable network design approach for evaluating, selecting and scheduling urban road construction projects

Selecting and scheduling urban road construction projects (URCPs) is inherently an Urban Network Design Problem (UNDP) with a complex decision making process. Recently some studies have focused on sustainable UNDP, using different mathematical methods. In this paper, first a new network data envelopment analysis (NDEA) model has been developed. Then, considering sustainability dimensions, by integrating data envelopment analysis (DEA), game theory and sustainable UNDP, a bi-level model has been proposed for selecting and scheduling URCPs. A meta-heuristic algorithm is proposed to solve the presented bi-level model. Different test instances are solved to show the acceptable performance of proposed algorithm in both solution quality and execution time. Afterwards, the proposed model is applied to study the problem of urban road construction projects selection in a real-world case study of urban transportation network of Isfahan city in Iran. The results show that by applying obtained solution the environmental and social performance of the network has been improved and the performance of the network is almost efficient in all evaluation periods

Benefits of Zataria multiflora Boiss in Experimental Model of Mouse Inflammatory Bowel Disease

Inflammatory bowel disease (IBD) is a chronic condition of the intestine with unknown etiology involving multiple immune, genetic and environmental factors. We were interested to examine the effect of total extract from Zataria multiflora Boiss, a folk medicinal plant on prevention and treatment of experimental IBD. Z. multiflora was administered (400, 600, 900 p.p.m.) through drinking water to IBD mice induced by intrarectal administration of acetic acid. Prednisolone was used as the standard drug for comparison. Biochemical, macroscopic and microscopic examinations of colon were performed. Biochemical evaluation of inflamed colon was done using assay of myeloperoxidase (MPO) activity and thiobarbituric acid reactive substances (TBARS) concentration as indicators of free radical activity and cell lipid peroxidation. The activity of MPO and lipid peroxidation products (TBARS) increased in acetic acid-treated groups while recovered by pretreatment of animals with Z. multiflora (400–900 p.p.m.) and prednisolone. Z. multiflora (600 and 900 p.p.m.) and prednisolone-treated groups showed significantly lower score values of macroscopic and microscopic characters when compared with the acetic acid-treated group. The beneficial effect of Z. multiflora (900 p.p.m.) was comparable with that of prednisolone. The antioxidant, antimicrobial and anti-inflammatory potentials of Z. multiflora might be the mechanisms by which this herbal extract protects animals against experimentally induced IBD. Proper clinical investigation should be carried out to confirm the activity in human