Scorpion fauna and epidemiological aspects of scorpionism in southeastern Iran

ABSTRACTObjectiveTo identify the scorpion fauna and classify the epidemiological aspects of scorpionism in an endemic region, Southeast Iran.MethodsScorpionism data were collected from health centers and hospitals in Sistan-Baluchestan Province during 2010-2011. Specimens were collected at night, using UV light, between May and October 2012.ResultsIn total, 246 scorpions were collected from two families (Buthidae and Scorpionidae). Five species including Odontobuthus odonturus, Hottentotta (Buthotus) jayakari, Compsobuthus matthiesseni, Scorpio maurus and Orthochirus scrobiculosus are reported for the first time from this area. Androctonus crassicauda was the dominant species. In total, 3 638 scorpion sting cases were recorded by health system, the majority of which were females. Stings mostly occurred in July and the age group of 15–24 years presented the highest frequency. Scorpionism decreased during 2011 compared with that in 2010 (68.2%).ConclusionsBased on the results, scorpionism is a serious health problem in this area and increasing knowledge of residents regarding the prevention methods of scorpion stings is recommended. Additional research on the scorpion fauna, their ecological and molecular variety in this part of the country is needed as well as the correlation between scorpions' species and the clinical signs and symptoms

Fibrosing mediastinitis: An unusual cause of superior vena cava symptoms

Fibrosing mediastinitis is a rare benign disorder caused by the proliferation of acellular collagen and fibrous tissue within the mediastinum. Although many cases are idiopathic, many (and perhaps most) cases are thought to be caused by an abnormal immunologic response to Mycobacterium tuberculosis and Histoplasma capsulatum infections. Collagen formation leads to compression of vital structures, resulting in cough, chest pain, and dyspnea. The following case is a former healthy middle-age man who presented with an 8-year history of cough, chest pain, facial swelling, and trouble breathing, and was subsequently found to have fibrosing mediastinitis. Fibrosing mediastinitis should be considered in the differential diagnosis of cough, chest pain, and dyspnea, primarily when findings such as increased venous pressure are present on physical exam, and hilar abnormalities are seen on chest radiograph

Fibrosing mediastinitis: An unusual cause of superior vena cava symptoms

Fibrosing mediastinitis is a rare benign disorder caused by the proliferation of acellular collagen and fibrous tissue within the mediastinum. Although many cases are idiopathic, many (and perhaps most) cases are thought to be caused by an abnormal immunologic response to Mycobacterium tuberculosis and Histoplasma capsulatum infections. Collagen formation leads to compression of vital structures, resulting in cough, chest pain, and dyspnea. The following case is a former healthy middle-age man who presented with an 8-year history of cough, chest pain, facial swelling, and trouble breathing, and was subsequently found to have fibrosing mediastinitis. Fibrosing mediastinitis should be considered in the differential diagnosis of cough, chest pain, and dyspnea, primarily when findings such as increased venous pressure are present on physical exam, and hilar abnormalities are seen on chest radiograph

Scenario-based stochastic optimal operation of wind/PV/FC/CHP/boiler/tidal/energy storage system considering DR programs and uncertainties

Abstract Background Micro-grid (MG) can be described as a group of controllable loads and distributed energy resources that can be connected and disconnected from the main grid and utilized in grid-connected or islanded modes considering certain electrical constraints. Methods The objective of this article are as follows: (1) predict the uncertainties through the hybrid method of WT-ANN-ICA and (2) determine the optimal generation strategy of a MG containing wind farms (WFs), photovoltaic (PV), fuel cell (FC), combined heat and power (CHP) units, tidal steam turbine (TST), and also boiler and energy storage devices (ESDs). The uncertainties include wind speed, tidal steam speed, photovoltaic power generation (PVPG), market price, power, and thermal load demand. For modeling uncertainties, an effort has been made to predict uncertainties through the hybrid method of wavelet transform (WT) in order to reduce fluctuations in the historical input data. An improved artificial neural network (ANN) based on the nonlinear structure is applied for better training and learning. Furthermore, the imperialist competitive algorithm (ICA) is applied to find the best weights and biases for minimizing the mean square error of predictions. Result The scenario-based stochastic optimization problem is proposed to determine the optimal points for the energy resources generation and to maximize the expected profit considering demand response (DR) programs and uncertainties. Conclusions In this study, three cases are assessed to confirm the performance of the proposed method. In the first case study programming, MG is isolated from grid. In the second case study, which is grid-connected mode, the WT-ANN-ICA and WT-ANN uncertainty prediction methods are compared. In the third case, which is grid-connected mode, the effect of DR programs on the expected profit of energy resources is assessed

Deletion of region of difference 181 in Mycobacterium tuberculosis Beijing strains

AbstractObjectives/backgroundThe region of differences (RDs) polymorphisms is a potential molecular epidemiology method to distinguish origins of Mycobacterium tuberculosis. To date, 68 RDs have been identified in M. tuberculosis. This study was designed to determine the frequency of RD deletions in M. tuberculosis strains that were isolated from patients with pulmonary tuberculosis who were referred to the National Research Institute of Tuberculosis and Lung Disease for diagnosis and treatment. Therefore, highly polymorphic regions (RD1, RD150, and RD181) among M. tuberculosis strains isolates were investigated.MethodsA total of 250 M. tuberculosis isolates were identified by conventional and molecular methods. Subsequently, spoligotyping and RD typing (RD1, RD150 and RD181) were performed to genotype these strains.ResultsThe most frequent spoligotype belonged to Haarlem (n=85, 34.0%) followed by CAS (n=54, 21.6%), T1 (n=27, 10.8%), and Beijing (n=28, 11.2%) lineages. Deletion in RD181 was identified only among the Beijing lineage (Fig. 1).ConclusionAs we found a deletion in RD181 in the Beijing strains only, we propose to use this marker as an identification tool for genotyping of the Beijing strain

Methodical Design of Viral Vaccines Based on Avant-Garde Nanocarriers: A Multi-Domain Narrative Review

The current health crisis caused by coronavirus 2019 (COVID-19) and associated pathogens emphasize the urgent need for vaccine systems that can generate protective and long-lasting immune responses. Vaccination, employing peptides, nucleic acids, and other molecules, or using pathogen-based strategies, in fact, is one of the most potent approaches in the management of viral diseases. However, the vaccine candidate requires protection from degradation and precise delivery to the target cells. This can be achieved by employing different types of drug and vaccine delivery strategies, among which, nanotechnology-based systems seem to be more promising. This entry aims to provide insight into major aspects of vaccine design and formulation to address different diseases, including the recent outbreak of SARS-CoV-2. Special emphasis of this review is on the technical and practical aspects of vaccine construction and theranostic approaches to precisely target and localize the active compounds

Effect of sodium–glucose cotransporter 2 inhibitors on insulin resistance; a systematic review and meta-analysis

Aim Recent studies have indicated that Sodium-GLucose co-Transporter 2 Inhibitors (SGLT2Is) may increase insulin sensitivity (IS); however, these results are heterogeneous and need to be systematically assessed. Method We searched MEDLINE/PubMed, Embase, Web of Science, Scopus, Cochrane Library, Ovid, and ProQuest using a predefined search query. Randomized clinical trials on SGLT2Is with a passive control group or metformin controlled group were included. Risk of bias assessment was performed using the Cochrane risk of bias assessment tool. Meta-analysis was performed separately on studies with type 2 diabetes mellitus (T2DM) population and studies with non-T2DM population and also for passive- and active-controlled studies using standardized mean difference (SMD) as the measure of the effect size. Subgroup analysis was performed according to different types of SGLT2Is. Meta-regression analysis was performed according to the dose and duration of intervention. Results Twenty-two studies (6 on non-T2DM population) with a total of 1421 (243 non-T2DM) patients were included. Six studies (3 on T2DM and 3 on non-T2DM) were controlled by metformin, and others were passively controlled. SGLT2Is could significantly increase IS in T2DM patients (SMD = 0.72 [0.32-1.12]). SGLT2Is could reduce insulin resistance in non-T2DM population, but this was not significant. SGLT2Is were not inferior to metformin in reducing insulin resistance. Subgroup analysis indicated that dapagliflozin could significantly increase IS, but empagliflozin was not associated with significant improvement in IS. Meta-regression analysis indicated no effect for dose but duration of SGLT2I administration on IS. Conclusion SGLT2Is, particularly dapagliflozin, could increase IS. These results need to be consolidated by further studies

An Overview of Potential Natural Photosensitizers in Cancer Photodynamic Therapy

Cancer is one of the main causes of death worldwide. There are several different types of cancer recognized thus far, which can be treated by different approaches including surgery, radiotherapy, chemotherapy or a combination thereof. However, these approaches have certain drawbacks and limitations. Photodynamic therapy (PDT) is regarded as an alternative noninvasive approach for cancer treatment based on the generation of toxic oxygen (known as reactive oxygen species (ROS)) at the treatment site. PDT requires photoactivation by a photosensitizer (PS) at a specific wavelength (λ) of light in the vicinity of molecular oxygen (singlet oxygen). The cell death mechanisms adopted in PDT upon PS photoactivation are necrosis, apoptosis and stimulation of the immune system. Over the past few decades, the use of natural compounds as a photoactive agent for the selective eradication of neoplastic lesions has attracted researchers’ attention. Many reviews have focused on the PS cell death mode of action and photonanomedicine approaches for PDT, while limited attention has been paid to the photoactivation of phytocompounds. Photoactivation is ever-present in nature and also found in natural plant compounds. The availability of various laser light setups can play a vital role in the discovery of photoactive phytocompounds that can be used as a natural PS. Exploring phytocompounds for their photoactive properties could reveal novel natural compounds that can be used as a PS in future pharmaceutical research. In this review, we highlight the current research regarding several photoactive phytocompound classes (furanocoumarins, alkaloids, poly-acetylenes and thiophenes, curcumins, flavonoids, anthraquinones, and natural extracts) and their photoactive potential to encourage researchers to focus on studies of natural agents and their use as a potent PS to enhance the efficiency of PDT

Differentiation of COVID‐19 pneumonia from other lung diseases using CT radiomic features and machine learning : A large multicentric cohort study

To derive and validate an effective machine learning and radiomics‐based model to differentiate COVID‐19 pneumonia from other lung diseases using a large multi‐centric dataset. In this retrospective study, we collected 19 private and five public datasets of chest CT images, accumulating to 26 307 images (15 148 COVID‐19; 9657 other lung diseases including non‐COVID‐19 pneumonia, lung cancer, pulmonary embolism; 1502 normal cases). We tested 96 machine learning‐based models by cross‐combining four feature selectors (FSs) and eight dimensionality reduction techniques with eight classifiers. We trained and evaluated our models using three different strategies: #1, the whole dataset (15 148 COVID‐19 and 11 159 other); #2, a new dataset after excluding healthy individuals and COVID‐19 patients who did not have RT‐PCR results (12 419 COVID‐19 and 8278 other); and #3 only non‐COVID‐19 pneumonia patients and a random sample of COVID‐19 patients (3000 COVID‐19 and 2582 others) to provide balanced classes. The best models were chosen by one‐standard‐deviation rule in 10‐fold cross‐validation and evaluated on the hold out test sets for reporting. In strategy#1, Relief FS combined with random forest (RF) classifier resulted in the highest performance (accuracy = 0.96, AUC = 0.99, sensitivity = 0.98, specificity = 0.94, PPV = 0.96, and NPV = 0.96). In strategy#2, Recursive Feature Elimination (RFE) FS and RF classifier combination resulted in the highest performance (accuracy = 0.97, AUC = 0.99, sensitivity = 0.98, specificity = 0.95, PPV = 0.96, NPV = 0.98). Finally, in strategy #3, the ANOVA FS and RF classifier combination resulted in the highest performance (accuracy = 0.94, AUC =0.98, sensitivity = 0.96, specificity = 0.93, PPV = 0.93, NPV = 0.96). Lung radiomic features combined with machine learning algorithms can enable the effective diagnosis of COVID‐19 pneumonia in CT images without the use of additional tests