Diaqua­(2,2′-bipyridine-5,5′-dicarboxyl­ato-κ2 N,N′)(ethyl­enediamine-κ2 N,N′)copper(II) 2.5-hydrate

In the mol­ecule of the title compound, [Cu(C12H6N2O4)(C2H8N2)(H2O)2]·2.5H2O, the CuII atom is six-coordinated in a distorted octa­hedral configuration by two N atoms from a 2,2′-bipyridine-5,5′-dicarboxyl­ate anion, two N atoms from ethyl­enediamine and two O atoms from two water mol­ecules. There are also two and a half water mol­ecules in the asymmetric unit. The planar five-membered ring is nearly coplanar with the adjacent pyridine rings, while the other five-membered ring adopts a twisted conformation, probably due to hydrogen bonding. In the crystal structure, intra- and inter­molecular N—H⋯O and O—H⋯O hydrogen bonds link the mol­ecules

Designing and construction a DNA vaccine encoding the fusion fragment of cfp10 and Ag85A immunodominant genes of Mycobacterium tuberculosis

Background: Pathogenic mycobacteria are one of major causes of human morbidity and mortality. Mycobacterium tuberculosis (M. tuberculosis) is an etiological agent of human tuberculosis. Designing new vaccines including DNA vaccines may be considered as new approaches for preventing of TB.Materials and Methods: M. tuberculosis H37Rv was grown on Lowenstein Jensen medium for 4 weeks at 37ºC and then DNA was extracted. The cfp10 gene was amplified by PCR. After digesting the PCR product and the plasmid, cfp10 fragment was ligated into the vector using T4 DNA ligase. Then, Ag85A was subcloned into pcDNA/cfp10. Escherichia coli strain JM109 bacteria were transformed by the desired construct. Clone confirmations were performed by colony PCR, restriction enzyme digestion and DNA sequencing. Recombinant vector was transfected into HeLa cells and total RNA was extracted, then cDNA was synthesized using oligo-dT. Finally PCR was performed by cfp10 primers.Results: The cfp10 was amplified by PCR method and the PCR products were visualized by agarose gel electrophoresis. The cfp10 fragments showed 303 bp in length. The cfp10 cloned into pcDNA. Then, Ag85Awas ligated into pcDNA/cfp10 after digestion correctly. Colony-PCR and restriction enzyme digestion and sequencing confirmed the cloning the fusion Ag85A/cfp10 fragment. Finally, after cDNA synthesis, expression of vector was confirmed in eukaryotic system.Conclusion: Cloning of Ag85A/cfp10 genes of M. tuberculosis were performed correctly. It can use as a DNA vaccine for investigation the immune responses in animal models in future studies

Persistent isolated right atrial standstill associated with left atrial tachycardia

Introduction: Atrial standstill is a rare condition, characterized by absence of atrial electrical and mechanical activity evident in surface electrocardiography echocardiography, or fluoroscopy, which is associated with unresponsiveness of atria to maximal output electrical stimulation. This condition can be present with thromboembolic complication, low cardiac output, and sometimes palpitation. Case Presentation: Here we presented a woman with right atrial stand still and left atrial tachycardia. It was confirmed by electrocardiogram, echocardiography, and intracardiac electrogram in basal state and during maximal output electrical stimulation. We treated her by implanting pacemaker to control bradycardia, oral calcium channel blocker to control palpitation episodes, and anticoagulation. Conclusions: Atrial standstill can be present partially that can be localized in one atrium and is associated with tachycardia in the other atrium

UnbiasedDTI: Mitigating Real-World Bias of Drug-Target Interaction Prediction by Using Deep Ensemble-Balanced Learning

Drug-target interaction (DTI) prediction through in vitro methods is expensive and time-consuming. On the other hand, computational methods can save time and money while enhancing drug discovery efficiency. Most of the computational methods frame DTI prediction as a binary classification task. One important challenge is that the number of negative interactions in all DTI-related datasets is far greater than the number of positive interactions, leading to the class imbalance problem. As a result, a classifier is trained biased towards the majority class (negative class), whereas the minority class (interacting pairs) is of interest. This class imbalance problem is not widely taken into account in DTI prediction studies, and the few previous studies considering balancing in DTI do not focus on the imbalance issue itself. Additionally, they do not benefit from deep learning models and experimental validation. In this study, we propose a computational framework along with experimental validations to predict drug-target interaction using an ensemble of deep learning models to address the class imbalance problem in the DTI domain. The objective of this paper is to mitigate the bias in the prediction of DTI by focusing on the impact of balancing and maintaining other involved parameters at a constant value. Our analysis shows that the proposed model outperforms unbalanced models with the same architecture trained on the BindingDB both computationally and experimentally. These findings demonstrate the significance of balancing, which reduces the bias towards the negative class and leads to better performance. It is important to note that leaning on computational results without experimentally validating them and by relying solely on AUROC and AUPRC metrics is not credible, particularly when the testing set remains unbalanced

Spatial Distribution Variation and Probabilistic Risk Assessment of Exposure to Fluoride in Ground Water Supplies: A Case Study in an Endemic Fluorosis Region of Northwest Iran

Prevalence of fluorosis is a worldwide public health issue, especially in the West Azerbaijan province of Iran. The aim of this study was to investigate fluoride concentration in drinking water resources within Maku city, in both the warm and cold seasons, to perform a health risk assessment. Fluoride were measured using UV-visible spectrophotometry. The spatial distribution was calculated by the software ArcGIS and Hazard Quotients (HQs) were calculated according to the US EPA method. The fluoride concentrations ranged between 0.29 to 6.68 and 0.1 to 11.4 mg/L in the cold and warm seasons, respectively. Based on this report, 30.64 and 48.15% of the samples revealed a fluoride level higher than the permissible level in the cold and warm seasons, respectively. Moreover, results showed that the HQ value in the warm season for different age groups was higher than the HQ value in the cold season. In both seasons, the non-carcinogenic risks of fluoride for the four exposed populations varied according to the order: children > teenagers > adults > infants. The HQ values for three age groups (children, teenager and adults) for both seasons were higher than 1 with a high risk of fluorosis. The results of this study, support the requests that government authorities better manage water supplies to improve health quality

Potential Adverse Effects of COVID-19 Vaccines on Iranian Healthcare Workers: Comparison of Four Available Vaccines in Tehran: A Retrospective Cross-sectional Study

Objectives: This study aimed to compare four COVID-19 vaccines for their potential extensive side effects and the relationship between the side effects and age, body mass index (BMI), and history of COVID-19 infection. Methods: This cross-sectional study was conducted from June to August 2021 among 1474 healthcare workers of seven selected hospitals in Tehran, Iran. All the subjects were vaccinated (91.7% received two doses and 8.3% received one dose) with one of four vaccines, Sputnik, Covaxin, AstraZeneca, and Sinopharm, at least 10 days before the study. The incidence of 47 side effects was measured after vaccination. Results: Over half of the participants (59.4%; n = 876) were 20–29 years of age, with the mean and average BMI being 26.1±9.0 and 23.5±3.4, respectively; 36.0% (n = 530) were previously diagnosed with COVID-19. There was no significant relationship between age and the incidence of side effects for AstraZeneca, Sputnik, and Covaxin; however, the occurrence of side effects of Sinopharm was significantly higher (p < 0.001) among younger healthcare workers. There was no significant relationship between BMI and the incidence of side effects for all four vaccines. However, in the group with a history of COVID-19 disease, health care workers vaccinated with the Sinopharm vaccine showed significantly (p < 0.001) more complications. The occurrence rate of at least one adverse effect and referral to medical centers for AstraZeneca, Sputnik, Covaxin, and Sinopharm vaccines were 24.9–93.9%, 18.2–86.0%, 14.8–77.0%, and 3.5–37.2%, respectively. The highest and lowest rates were found for AstraZeneca and Sinopharm showing a significant (p < 0.001) difference. The most commonly observed side effects for the AstraZeneca vaccine included fever (64.4%), fatigue (62.5%), and muscle pain (59.9%); for Sputnik muscle pain (59.8%), fever (49.5%), and fatigue (49.5%); for Covaxin fever (49.2%), topical reaction (41.0), and fatigue (34.4%); and for Sinopharm fever (18.7%), topical reaction (17.9%), and fatigue (16.6%). Inactivated virus vaccines (Sinopharm and Covaxin) showed a lower (39.7%) occurrence rate of side effects compared to viral vector vaccines (AstraZeneca and Sputnik; 90.6%). The most likely time for the vaccines to exert side effects was the first 24 hours after vaccination. Conclusions: We found no significant relationship between age, BMI, history of COVID-19 disease, and the incidence of side effects in healthcare workers vaccinated with any of the four vaccines. All four vaccines are safe and have controlled side effects