Multicomponent synthesis of propargylamines in the presence of magnetic nanocatalyst

In current study Fe3O4 nanoparticles were used as a catalyst in the synthesis of propargylamines via three component reaction between aldehyde, alkyne, and an amine. The effect of different reaction parameters on conversion of aldehyde was investigated and the samples were characterized by SEM, EDS, GC, and NMR spectroscopy. It was observed that in the presence of Fe3O4 nanoparticles and by applying microwave irradiation reaction time decreased significantly

Neuronal Spike Train Analysis in Likelihood Space

Conventional methods for spike train analysis are predominantly based on the rate function. Additionally, many experiments have utilized a temporal coding mechanism. Several techniques have been used for analyzing these two sources of information separately, but using both sources in a single framework remains a challenging problem. Here, an innovative technique is proposed for spike train analysis that considers both rate and temporal information.Point process modeling approach is used to estimate the stimulus conditional distribution, based on observation of repeated trials. The extended Kalman filter is applied for estimation of the parameters in a parametric model. The marked point process strategy is used in order to extend this model from a single neuron to an entire neuronal population. Each spike train is transformed into a binary vector and then projected from the observation space onto the likelihood space. This projection generates a newly structured space that integrates temporal and rate information, thus improving performance of distribution-based classifiers. In this space, the stimulus-specific information is used as a distance metric between two stimuli. To illustrate the advantages of the proposed technique, spiking activity of inferior temporal cortex neurons in the macaque monkey are analyzed in both the observation and likelihood spaces. Based on goodness-of-fit, performance of the estimation method is demonstrated and the results are subsequently compared with the firing rate-based framework.From both rate and temporal information integration and improvement in the neural discrimination of stimuli, it may be concluded that the likelihood space generates a more accurate representation of stimulus space. Further, an understanding of the neuronal mechanism devoted to visual object categorization may be addressed in this framework as well

The Use of Cumulative Disciplinary Score in an Integrated Curriculum to Prevent Deliberate Omission of Course Content

Background & Objective: Student assessment is one of the most challenging issues of an integrated curriculum. While calculating an overall score is in line with the goals of integrated curriculum, it poses the risk that some students will deliberately leave out the content of some disciplines, based on the fact that they have lower credits in each block exam. In the present study, we describe the experience of Tehran University of Medical Sciences, Iran, where an integrated medical curriculum has been launched since September 2011 as part of curriculum reform initiative. Methods: In the first academic year, students passed 4 blocks: Molecule and Cell; Tissue; Development and Function; Cardiovascular Function; Blood circulation; and Blood-Respiratory Function. Each block included anatomy, histology, physiology, and embryology. The overall block score was reported at the end of each block. In addition, a cumulative disciplinary score was calculated at the end of the academic year through the summation of the weighted subscores of each discipline in each block. Results: At the end of the year, the number of students who had failed in histology, anatomy, embryology, and physiology were 15, 17, 44, and 3, respectively. They were required to take a disciplinary examination before the beginning of the next academic year. Conclusion: A comparison of the number of students who failed disciplines with low credits (e.g. histology) with those who failed disciplines with high credits (e.g. physiology) suggests that the former had systematically been ignored by some students. The calculation of a cumulative disciplinary score may reduce the deliberate omission of course content in integrated blocks. Key Words: Integration, Student assessment, Cumulative disciplinary scor

Content Analysis of Teleconsultation Enquiries in Positive Health Club, Imam Khomeini Hospital, Tehran, Iran

Objective: With the availability of specialists and the overflowing information in public and social networks, individuals have easy access to information about HIV and AIDS. However, medical counselling and healthcare settings still have an essential role. The aim of the present study was to analyze phone enquiries directed to the Positive Club of Imam Khomeini Hospital in Tehran; this analysis was based on demographic features of participants.Design/Methodology/Approach: In this cross-sectional study, 5255 questions were extracted and coded from the Positive Club's counselling questionnaires; the coding procedure was based on Huber and Gilapsy's decimal classification. Data were processed by descriptive statistics and SPSS software.Findings: A majority of callers were men (59.43%), most callers were aged between 26 and 30 years, and HIV transmission and high-risk sexual behaviours were among frequently asked questions (47%).Originality/Value: Taking into account that most callers were concern about transmission via sexual contact (anal, vaginal, and oral) rather than injection and its related equipment, it seems reasonable to assume that HIV transmission flow may shift from shared injection equipment to high-risk sexual behaviors

Machine learning screening of COVID-19 patients based on X-ray images for unbalanced classes

Background: COVID-19 is a pandemic that had already infected more than forty-six million people and caused more than a million deaths by 1st of November 2020. The virus pandemic appears to have had a catastrophic effect on the global population's safety. Therefore, efficient detection of infected patients is a key phase in the battle against COVID-19. One of the main screening methods is radiological testing. The goal of this study is using chest X-ray images to detect COVID-19 pneumonia patients while optimizing detection efficiency. Methods: As shown in Figure 1, we combined three methods to detect COVID-19 namely: convolutional neural network, transfer learning, and the focal loss 1 function which are used for unbalanced classes, to build three binary classifiers which are COVID-19 versus normal, COVID-19 versus pneumonia, and COVID-19 versus normal pneumonia (normal and pneumonia). The database used 2 includes a mixture of 400 COVID-19, 1,340 viral pneumonia, 2,560 bacterial pneumonia, and 1,340 normal chest X-ray images for training, validation, and testing of four pre-trained deep convolutional neural networks. Then, the pre-trained model that gives the best results was chosen to improve its performances by two enhancement techniques which are image augmentation, allowing us to reach approximately 2,500 images per class, and the adjustment of focal loss hyperparameters. Results: A comparative study was conducted of our proposed classifiers with well-known classifiers and obtained much better results in terms of accuracy, specificity, sensitivity and precision, as illustrated in Table 1. Conclusion: The high performance of this computer-aided diagnostic technique may greatly increase the screening speed and reliability of COVID-19 diagnostic cases. Particularly, at the crowded emergency services, it will be particularly helpful in this pandemic when the risk of infection and the necessity for prevention initiatives run contrary to the available resources.qscienc

Efficacy of levamisole with standard care treatment vs. standard care in clinical presentations of non-hospitalized patients with COVID-19: a randomized clinical trial

Objective: The aim of this study was to evaluate the influence of adding a 10-day course of levamisole (LVM) to the standard care compared with standard care alone, on the clinical status of COVID-19 patients with mild to moderate disease. Methods:  In this randomized open-label trial, we enrolled non-hospitalized patients with mild to moderate COVID-19 at nine health centers in Tehran province, Iran, in 2021. Patients were randomly assigned to receive a 10-day course of LVM with standard care (n=185) or standard care alone (n=180) in a 1:1 ratio. On days 1 to 10, LVM was administered orally at a dosage of 50 mg. The participants were called and followed on days 1, 3, 5, 7, 9, and 14. The measured parameters were general health condition, hospitalization rate, signs and symptoms, and adverse events. The generalized estimating equations model was used for analysis. Results: Among 507 randomized patients, 473 patients started the experiment and received LVM plus standard care or received the standard care alone; 385 patients included in the analysis; 346 (98%) patients completed the trial. The median age of the patients was 40 years [IQR: 32-50.75]; and ‎201 (55.1%)‎ patiens were male. The mean age, sex ratio, and frequency of the underlying diseases of the patients in the two study groups had no ‎statistically significant differences (P>0.05). Compared to the control group, LVM improved the general health condition of the patients (B=-0.635; 95% CI: -0.041,-0.329; P<0.001). Patients receiving LVM compared with standard care group had significantly lower odds of developing fever (OR=0.260; 95% CI: 0.11‎‎3‎,0.59‎‎9‎; P=0.002), chills (OR=0.223; 95% CI:‎‎ 0.07‎‎6,‎0.64‎‎8‎; P= 0.006), fatigue (OR=0.576; 95% CI:‎ 0.34‎‎6,‎0.96‎‎0‎‎; P=0.034), and myalgia (OR=0.54‎‎4‎; 95% CI:‎ 0.31‎‎7‎,0.93‎‎2‎‎; P=0.027). No significant difference was observed in the rate of hospitalization. Although the intervention group had greater adverse effects than the control group, the difference was not statistically significant. Conclusion: Findings of this study suggest that LVM has clinical benefits in improving patients’ health condition with mild to moderate COVID-19

Recent trends in three-dimensional bioinks based on alginate for biomedical applications

Three-dimensional (3D) bioprinting is an appealing and revolutionary manufacturing approach for the accurate placement of biologics, such as living cells and extracellular matrix (ECM) components, in the form of a 3D hierarchical structure to fabricate synthetic multicellular tissues. Many synthetic and natural polymers are applied as cell printing bioinks. One of them, alginate (Alg), is an inexpensive biomaterial that is among the most examined hydrogel materials intended for vascular, cartilage, and bone tissue printing. It has also been studied pertaining to the liver, kidney, and skin, due to its excellent cell response and flexible gelation preparation through divalent ions including calcium. Nevertheless, Alg hydrogels possess certain negative aspects, including weak mechanical characteristics, poor printability, poor structural stability, and poor cell attachment, which may restrict its usage along with the 3D printing approach to prepare artificial tissue. In this review paper, we prepare the accessible materials to be able to encourage and boost new Alg-based bioink formulations with superior characteristics for upcoming purposes in drug delivery systems. Moreover, the major outcomes are discussed, and the outstanding concerns regarding this area and the scope for upcoming examination are outlined

Tomato Leaf Diseases Detection Using Deep Learning Technique

Plants are a major source of food for the world population. Plant diseases contribute to production loss, which can be tackled with continuous monitoring. Manual plant disease monitoring is both laborious and error-prone. Early detection of plant diseases using computer vision and artificial intelligence (AI) can help to reduce the adverse effects of diseases and also helps to overcome the shortcomings of continuous human monitoring. In this study, we have extensively studied the performance of the different state-of-the-art convolutional neural networks (CNNs) classification network architectures i.e. ResNet18, MobileNet, DenseNet201, and InceptionV3 on 18,162 plain tomato leaf images to classify tomato diseases. The comparative performance of the models for the binary classification (healthy and unhealthy leaves), six-class classification (healthy and various groups of diseased leaves), and ten-class classification (healthy and various types of unhealthy leaves) are also reported. InceptionV3 showed superior performance for the binary classification using plain leaf images with an accuracy of 99.2%. DenseNet201 also outperform for six-class classification with an accuracy of 97.99%. Finally, DenseNet201 achieved an accuracy of 98.05% for ten-class classification. It can be concluded that deep architectures performed better at classifying the diseases for the three experiments. The performance of each of the experimental studies reported in this work outperforms the existing literature