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Introduction The JAK2V617F mutation has emerged in recent years as a diagnostic as well as a treatment target in patients with polycythemia vera ( PV) and essential thrombocythemia ( ET). The disease phenotype is also influenced by other factors such as micro RNA (mi RNA) deregulation. The aim of this study was to investigate miR-125 expression level in these patients with those obtained from healthy control subjects and its correlation with JAK2 allele burden and laboratory findings. Methods In total, forty patients with a clinical diagnosis of PV and ET were examined at the time of diagnosis. Ten healthy subjects were checked as controls. We performed JAK2 V617F allele burdens measurement and expression analysis of miR-125b-5p, miR-125b-3p, miR-125a-5p, and miR-125a-3p in leukocytes isolated from peripheral blood by quantitative real-time polymerase chain reaction. Results MiR-125b-5p and miR-125a-5p were upregulated in both patients with PV ( P = 0.00 and P = 0.003, respectively) and ET ( P = 0.02 and P = 0.002, respectively). In PV group, a significant correlation was observed between miR-125a-5p and platelet counts ( P = 0.01, r = 0.531). The correlation between mi RNA and JAk2 allele burden was not significant. Conclusion In conclusion, our data indicate that other factors such as aberrant miR-125 expression may influence on the disease phenotype in patients with PV and ET. [ABSTRACT FROM AUTHOR]

This randomized clinical trial was conducted to evaluate the effects of silymarin supplementation on glycemic indices and serum lipid profile in type 2 diabetes mellitus (T2DM) patients. In this open-label randomized clinical trial study, 48 patients with T2DM were eligible to participate for 12 weeks and were divided into two groups randomly: 24 subjects in the intervention (received three 140 mg silymarin capsules daily and diet plan) and 24 in control (received a diet plan). Fasting blood samples and anthropometric data were collected, and glycemic indices and lipid profiles were determined at baseline and at the end of the study. Out of 60 patients included in the clinical trial, 48 people completed the study. In comparing silymarin and control groups before and after the study, a significant reduction was observed in weight and body mass index. However, after adjustment, no significant difference was seen between the two groups. Furthermore, daily consumption of three capsules of 140 mg silymarin for 12 weeks did not show any significant difference on the level of fasting blood sugar (p = 0.789), HbA1c (p = 0.719), and lipid profile. The findings of the present study show that silymarin did not lead to changes in the level of glycemic index and lipid profile in patients with T2DM., (© 2024 John Wiley & Sons Ltd.)

This study aims to correlate adaptive optics-transscleral flood illumination (AO-TFI) images of the retinal pigment epithelium (RPE) in central serous chorioretinopathy (CSCR) with standard clinical images and compare cell morphological features with those of healthy eyes. After stitching 125 AO-TFI images acquired in CSCR eyes (including 6 active CSCR, 15 resolved CSCR, and 3 from healthy contralateral), 24 montages were correlated with blue-autofluorescence, infrared and optical coherence tomography images. All 68 AO-TFI images acquired in pathological areas exhibited significant RPE contrast changes. Among the 52 healthy areas in clinical images, AO-TFI revealed a normal RPE mosaic in 62% of the images and an altered RPE pattern in 38% of the images. Morphological features of the RPE cells were quantified in 54 AO-TFI images depicting clinically normal areas (from 12 CSCR eyes). Comparison with data from 149 AO-TFI images acquired in 33 healthy eyes revealed significantly increased morphological heterogeneity. In CSCR, AO-TFI not only enabled high-resolution imaging of outer retinal alterations, but also revealed RPE abnormalities undetectable by all other imaging modalities. Further studies are required to estimate the prognosis value of these abnormalities. Imaging of the RPE using AO-TFI holds great promise for improving our understanding of the CSCR pathogenesis., (© 2024. The Author(s).)

Background: Cancer is among the serious health problems of the medical world, for treatment of which severe treatments are used. However, the prognosis of cancer patients is still poor. The application of NK cell-derived exosomes (NK-Exo) is a new method for cancer immunotherapy. These nanoparticles with a size range of 30-120 nm are a small model of mother cells. In this study, the anti-tumor activity of NK-Exo and LAK-Exo (activated NK cell-derived exosome) against acute myeloid leukemia (AML) is investigated in vitro. Materials and Methods: The MACS method was performed for the separation of NK cells from the buffy coats of healthy donors, and an EXOCIBE kit was used for the isolation of NK-Exo. After treating the KG-1 cell line with different doses of NK-Exo, MTT assay, and annexin V-PE were done to evaluate cell proliferation and apoptosis, respectively, and for confirmation of involved proteins, Real-Time PCR and western blotting were performed. Results: Anti-tumor activity of NK-Exo and LAK-Exo was dose- and time-dependent. Their highest activities were observed following 48 hours of incubation with 50 µg/ml exosome (p<0.0001). However, this cytotoxic activity was also seen over a short period of time with low concentrations of NK-Exo (p<0.05) and LAK-Exo (p<0.001).The cytotoxic effect of LAK-Exo on target cells was significantly higher than NK-EXO. The induction of apoptosis by different pathways was time-point dependent. Total apoptosis was 34.56% and 51.6% after 48 hours of tumor cell coculture with 50µg/ml NK-Exo and LAK-Exo, respectively. Significant expression of CASPASE3 , P38, and CYTOCHROME C genes was observed in the cells treated with 50 µg/ml NK-Exo and LAK-Exo. Conclusion : Our study confirmed the antileukemia activity of NK-Exo against AML tumor cells in vitro. Therefore, NK-Exo can be considered as a promising and effective treatment for leukemia therapy., Competing Interests: The authors have no conflicts of interest for publication of this study., (Copyright © 2024 Tehran University of Medical Sciences.)

Objective: The primary objective of our study is to address the challenge of confidentially sharing medical images across different centers. This is often a critical necessity in both clinical and research environments, yet restrictions typically exist due to privacy concerns. Our aim is to design a privacy-preserving data-sharing mechanism that allows medical images to be stored as encoded and obfuscated representations in the public domain without revealing any useful or recoverable content from the images. In tandem, we aim to provide authorized users with compact private keys that could be used to reconstruct the corresponding images., Method: Our approach involves utilizing a neural auto-encoder. The convolutional filter outputs are passed through sparsifying transformations to produce multiple compact codes. Each code is responsible for reconstructing different attributes of the image. The key privacy-preserving element in this process is obfuscation through the use of specific pseudo-random noise. When applied to the codes, it becomes computationally infeasible for an attacker to guess the correct representation for all the codes, thereby preserving the privacy of the images., Results: The proposed framework was implemented and evaluated using chest X-ray images for different medical image analysis tasks, including classification, segmentation, and texture analysis. Additionally, we thoroughly assessed the robustness of our method against various attacks using both supervised and unsupervised algorithms., Conclusion: This study provides a novel, optimized, and privacy-assured data-sharing mechanism for medical images, enabling multi-party sharing in a secure manner. While we have demonstrated its effectiveness with chest X-ray images, the mechanism can be utilized in other medical images modalities as well., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Anti-P1 is immunoglobulin M type and occurs naturally; it is often detected as a weak- and cold-reactive antibody. However, in rare cases, it is reactive at 37°C or shows hemolytic transfusion reactions. The presence of high thermal amplitude of anti-P1 cannot be ignored and requires cross-match compatible red blood cells for transfusion. In the present study, we report three cases with ABO discrepancy due to anti-P1 that was identified as a cold-reacting antibody with high thermal amplitude., Competing Interests: There are no conflicts of interest., (Copyright: © 2022 Asian Journal of Transfusion Science.)

Background: The risk of transfusion-transmissible infections (TTIs) remains a concern in transfusion medicine. Since the rate of infection among first-time blood donors is higher than repeated donors, strategies to enhance blood safety can focus on new donors. The aim of the study was to investigate the effect of pre-donation viral screening of new donors on blood safety., Methods and Materials: The pre-donation screening of new donors was implemented in the Kurdistan blood center. In this program, new donors who met the blood donation criteria were informed about the program and only a blood sample was donated for HBs Ag, HCV Ab, and HIV Ab testing. New donors with negative results were invited to donate blood after 12 weeks. A unit of blood was collected from eligible returned donors. Laboratory tests were performed again using the same methods. Finally, the prevalence of confirmed positive TTI results among donated blood in Kurdistan blood center was compared before and after the establishment of program., Results: During the study, 4,434 new donors were screened for viral markers. A total of 41 new donors (0.92%, 95% CI, 0.007-0.13) had repeatedly reactive results and infection was confirmed in blood sample of 24 donors (0.54%, 95% CI, 0.003-0.008). Overall, 56% of new donors returned for blood donation. Prevalence of confirmed TTIs markers in collected blood units was 0.27% and 0 before and after implementing program, respectively., Conclusions: This study indicated that Pre-donation screening can reduce the risk of TTI transmission by identifying infected donors at the pre-donation phase., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Masson SAS.)

Background and Objective: Generalizable and trustworthy deep learning models for PET/CT image segmentation necessitates large diverse multi-institutional datasets. However, legal, ethical, and patient privacy issues challenge sharing of datasets between different centers. To overcome these challenges, we developed a federated learning (FL) framework for multi-institutional PET/CT image segmentation., Methods: A dataset consisting of 328 FL (HN) cancer patients who underwent clinical PET/CT examinations gathered from six different centers was enrolled. A pure transformer network was implemented as fully core segmentation algorithms using dual channel PET/CT images. We evaluated different frameworks (single center-based, centralized baseline, as well as seven different FL algorithms) using 68 PET/CT images (20% of each center data). In particular, the implemented FL algorithms include clipping with the quantile estimator (ClQu), zeroing with the quantile estimator (ZeQu), federated averaging (FedAvg), lossy compression (LoCo), robust aggregation (RoAg), secure aggregation (SeAg), and Gaussian differentially private FedAvg with adaptive quantile clipping (GDP-AQuCl)., Results: The Dice coefficient was 0.80±0.11 for both centralized and SeAg FL algorithms. All FL approaches achieved centralized learning model performance with no statistically significant differences. Among the FL algorithms, SeAg and GDP-AQuCl performed better than the other techniques. However, there was no statistically significant difference. All algorithms, except the center-based approach, resulted in relative errors less than 5% for SUV max and SUV mean for all FL and centralized methods. Centralized and FL algorithms significantly outperformed the single center-based baseline., Conclusions: The developed FL-based (with centralized method performance) algorithms exhibited promising performance for HN tumor segmentation from PET/CT images., Competing Interests: Declaration of Competing Interest None, (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: The coronavirus disease 2019 (COVID-19) outbreak has led to an alteration in hygienic conditions. In this situation, improving standard operating procedures (SOPs) in blood donation centers is critical. The purpose of this study was the assessment of SOPs in the blood donation centers during the outbreak of COVID-19 by regular blood donors as external audits. Materials and Methods: Regular donors were selected as external inspectors in 31 provinces of Iran. The questionnaire containing 10 closed questions was provided to assess the hygienic SOPs of blood transfusion centers in the prevention of COVID-19 transmission. Comparison and evaluation of questionnaires were conducted by assigning an importance coefficient (IC) score to each question. Results: Assessment of SOPs in blood donation departments by regular donors in 31 provinces of Iran showed that 18 centers (58.1%) received IC scores >10(Strong performance), seven centers (22.6%) received the range of IC scores between7-10(acceptable performance), and six centers (19.4%) received IC scores <7(poor performance). The difference in IC scores between provinces was not statistically significant. Conclusion: This study confirms that the assessment of blood donation centers through regular blood donor inspection is a reliable method to identify the strengths and weaknesses of blood transfusion center services and ultimately leads to corrective intervention and improvement of hygienic SOPs to prevent COVID-19 transmission., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2023 Tehran University of Medical Sciences.)

Purpose: Attenuation correction and scatter compensation (AC/SC) are two main steps toward quantitative PET imaging, which remain challenging in PET-only and PET/MRI systems. These can be effectively tackled via deep learning (DL) methods. However, trustworthy, and generalizable DL models commonly require well-curated, heterogeneous, and large datasets from multiple clinical centers. At the same time, owing to legal/ethical issues and privacy concerns, forming a large collective, centralized dataset poses significant challenges. In this work, we aimed to develop a DL-based model in a multicenter setting without direct sharing of data using federated learning (FL) for AC/SC of PET images., Methods: Non-attenuation/scatter corrected and CT-based attenuation/scatter corrected (CT-ASC) 18 F-FDG PET images of 300 patients were enrolled in this study. The dataset consisted of 6 different centers, each with 50 patients, with scanner, image acquisition, and reconstruction protocols varying across the centers. CT-based ASC PET images served as the standard reference. All images were reviewed to include high-quality and artifact-free PET images. Both corrected and uncorrected PET images were converted to standardized uptake values (SUVs). We used a modified nested U-Net utilizing residual U-block in a U-shape architecture. We evaluated two FL models, namely sequential (FL-SQ) and parallel (FL-PL) and compared their performance with the baseline centralized (CZ) learning model wherein the data were pooled to one server, as well as center-based (CB) models where for each center the model was built and evaluated separately. Data from each center were divided to contribute to training (30 patients), validation (10 patients), and test sets (10 patients). Final evaluations and reports were performed on 60 patients (10 patients from each center)., Results: In terms of percent SUV absolute relative error (ARE%), both FL-SQ (CI:12.21-14.81%) and FL-PL (CI:11.82-13.84%) models demonstrated excellent agreement with the centralized framework (CI:10.32-12.00%), while FL-based algorithms improved model performance by over 11% compared to CB training strategy (CI: 22.34-26.10%). Furthermore, the Mann-Whitney test between different strategies revealed no significant differences between CZ and FL-based algorithms (p-value > 0.05) in center-categorized mode. At the same time, a significant difference was observed between the different training approaches on the overall dataset (p-value < 0.05). In addition, voxel-wise comparison, with respect to reference CT-ASC, exhibited similar performance for images predicted by CZ (R 2  = 0.94), FL-SQ (R 2  = 0.93), and FL-PL (R 2  = 0.92), while CB model achieved a far lower coefficient of determination (R 2  = 0.74). Despite the strong correlations between CZ and FL-based methods compared to reference CT-ASC, a slight underestimation of predicted voxel values was observed., Conclusion: Deep learning-based models provide promising results toward quantitative PET image reconstruction. Specifically, we developed two FL models and compared their performance with center-based and centralized models. The proposed FL-based models achieved higher performance compared to center-based models, comparable with centralized models. Our work provided strong empirical evidence that the FL framework can fully benefit from the generalizability and robustness of DL models used for AC/SC in PET, while obviating the need for the direct sharing of datasets between clinical imaging centers., (© 2022. The Author(s).)

Success rate of clinical trials (CTs) is low, with the protocol design itself being considered a major risk factor. We aimed to investigate the use of deep learning methods to predict the risk of CTs based on their protocols. Considering protocol changes and their final status, a retrospective risk assignment method was proposed to label CTs according to low, medium, and high risk levels. Then, transformer and graph neural networks were designed and combined in an ensemble model to learn to infer the ternary risk categories. The ensemble model achieved robust performance (area under the receiving operator characteristic curve [AUROC] of 0.8453 [95% confidence interval: 0.8409-0.8495]), similar to the individual architectures but significantly outperforming a baseline based on bag-of-words features (0.7548 [0.7493-0.7603] AUROC). We demonstrate the potential of deep learning in predicting the risk of CTs from their protocols, paving the way for customized risk mitigation strategies during protocol design., Competing Interests: P.A. and N.B. are employees and shareholders of Risklick AG., (© 2023 The Authors.)

After a severe peripheral nerve injury, complete functional recovery is rare. Modulating the inflammatory response could be an effective way to enhance peripheral nerve regeneration. The present study aimed to determine the effect of azithromycin on functional recovery following sciatic nerve crush in Wistar rats. 40 male Wistar rats were used in four groups, including: the negative control, sham, and two groups of azithromycin (15 and 150 mg/kg/day) (n = 10).The rats' right sciatic nerve was crushed using a non-serrated clamp. In experimental groups, animals were treated with azithromycin (15 and 150 mg/kg/day) for 7 days. Then, sensory-motor functions were evaluated over eight weeks. Real-time PCR was used to measure the expression of NGF and BDNF genes. At the end of the 4th week, the sensory recovery accelerated in the azithromycin-treated rats so that the reaction times in the groups treated with 15 mg/kg and 150 mg/kg doses of azithromycin reached 5.14 s and 6.61 s, respectively, which were significantly lower than the 12 s in the negative control group (P < 0.05).Eventually, the mean SFI values in the negative control and both azithromycin-treated groups recovered to preoperative levels in the 8th week, with no significant difference between the sciatic lesion groups. Findings showed a seven-day course of azithromycin administered immediately after a sciatic nerve crush could accelerate regeneration and improve motor and sensory function recovery compared to negative controls. These significant effects were observed in both the azithromycin 15 mg/kg and the azithromycin 150 mg/kg treatment groups. Azithromycin treatment upregulated the expression of NGF and BDNF genes in crushed sciatic nerve. Our findings suggest that a seven-day treatment of azithromycin after a sciatic nerve injury could accelerate the regeneration process and improve functional recovery., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

The impact of peripheral nerve damage on a patient's quality of life is severe. The most frequent peripheral nerve crush damage is a sciatic nerve injury. Previous research has shown that glibenclamide (GB) has neuroprotective properties in a variety of oxidative stress-related disorders, including Alzheimer and Parkinson. The goal of this study was to see how GB affected nerve regeneration and improved function of the sciatic nerve in a rat model following a crush injury. We evaluated motor function, sensory recovery, gene expression, and histomorphometry following damage at different time points. Additionally, we assessed atrophy in the gastrocnemius muscle using histology and mass ratio analyses. Our results suggest that 2, 4, 6, and 8 weeks following glibenclamide therapy, promotes the recovery of motor and sensory function in the injured site. Following glibenclamid injection, the mRNA levels of neurotrophic factors (NGF and BDNF) are raised. According to histomorphometry assessment, glibenclamide injection also increased the number of myelinated fibers while decreasing their thickness. These results showed that glibenclamide therapy by decreasing the proinflammatory and oxidant factors may enhance the nerve regeneration. It is clear that more research is needed to confirm these findings., (© 2022 Société Française de Pharmacologie et de Thérapeutique. Published by John Wiley & Sons Ltd.)

Background: Common treatments of liver disease failed to meet all the needs in this important medical field. It results in an urgent need for proper some new adjuvant therapies. Mesenchymal stem cells (MSCs) and their derivatives are promising tools in this regard. We aimed to compare the Silymarin, as traditional treatment with mesenchymal stem cell conditioned medium (MSC-CM), as a novel strategy, both with therapeutic potentialities in term of liver failure (LF) treatment., Methods and Results: Mice models with liver failure were induced with CCl 4 and were treated in the groups as follows: normal mice receiving DMEM-LG medium as control, LF-mice receiving DMEM-LG medium as sham, LF-mice receiving Silymarin as LF-SM, and LF-mice receiving MSC sphere CM as LF-MSC-CM. Biochemical, histopathological, molecular and protein level parameters were evaluated using blood and liver samples. Liver enzymes, MicroRNA-122 values as well as necrotic score were significantly lower in the LF-SM and LF-MSC-CM groups compared to sham. LF-SM showed significantly higher level of total antioxidant capacity and malondialdehyde than that of LF-MSC-CM groups. Sph-MSC-CM not only induced more down-regulated expression of fibrinogen-like protein 1 and receptor interacting protein kinases1 but also led to higher expression level of keratinocyte growth factor. LF-MSC-CM showed less mortality rate compared to other groups., Conclusions: Hepato-protective potentialities of Sph-MSC-CM are comparable to those of Silymarin. More inhibition of necroptosis/ necrosis and inflammation might result in rapid liver repair in case of MSC-CM administration., (© 2022. The Author(s), under exclusive licence to Springer Nature B.V.)

Background: A declining need for red blood cells coupled with strengthening demand for plasma-derived medicines has led to a strong focus on moving whole blood donors to plasmapheresis. The purpose of this study was to evaluate the four-year policies of the Iranian Blood Transfusion Organization (IBTO) in terms of plasmapheresis recruitment of first-time donors and its effect on plasmapheresis outcome.   Materials and Methods: Plasmapheresis data related to 16 centers from 2016 to 2019 was obtained from IBTO software. This information includes; (1) blood donation number, (2) plasmapheresis donation number, (3) number of plasmapheresis donors, (4) plasmapheresis donor demographic data, (5) plasmapheresis donor status, (6) frequency of plasma donation for each donor, (7) volume of plasma and (8) the prevalence of transfusion-transmissible infections (TTIs) in plasmapheresis donors. Results:  The result of this study demonstrated that plasmapheresis collection centers have recruited 85,515 (91%) first-time and 8,595(9%) regular and repeated donors from 2016 to 2019 years. Plasmapheresis to blood donation index was increased from 0.2% in 2016 to 4.9% in 2019. The mean donation number was 2 times per year. The trend of the yearly Whole Blood Donation (WBD) Index decreased from 26.69 to 24.11/1000 for the general population. The total volume of collected source plasma was 49,203 liters during this period. However, 46,000 liters of recovered plasma were decreased due to less WBD. Furthermore, the results indicated that the prevalence of HCV was significantly higher in first-time donors compared to repeated and regular donors (P = 0.000). Conclusion: It is concluded that during four years, the net volume of plasma did not increase and plasmapheresis led to reducing WBD in our country. Moreover, first-time plasmapheresis donors can be associated with challenges such as increasing screening costs and compromising the safety of plasma resources. Therefore IBTO decided to stop the project and focus on its main role to prepare safe and sufficient blood components through WB collection and also single donor platelet and concurrent plasma by plateletpheresis., (Copyright © 2022 Tehran University of Medical Sciences.)

A small dataset commonly affects generalization, robustness, and overall performance of deep neural networks (DNNs) in medical imaging research. Since gathering large clinical databases is always difficult, we proposed an analytical method for producing a large realistic/diverse dataset. Clinical brain PET/CT/MR images including full-dose (FD), low-dose (LD) corresponding to only 5 % of events acquired in the FD scan, non-attenuated correction (NAC) and CT-based measured attenuation correction (MAC) PET images, CT images and T1 and T2 MR sequences of 35 patients were included. All images were registered to the Montreal Neurological Institute (MNI) template. Laplacian blending was used to make a natural presentation using information in the frequency domain of images from two separate patients, as well as the blending mask. This classical technique from the computer vision and image processing communities is still widely used and unlike modern DNNs, does not require the availability of training data. A modified ResNet DNN was implemented to evaluate four image-to-image translation tasks, including LD to FD, LD+MR to FD, NAC to MAC, and MRI to CT, with and without using the synthesized images. Quantitative analysis using established metrics, including the peak signal-to-noise ratio (PSNR), structural similarity index metric (SSIM), and joint histogram analysis was performed for quantitative evaluation. The quantitative comparison between the registered small dataset containing 35 patients and the large dataset containing 350 synthesized plus 35 real dataset demonstrated improvement of the RMSE and SSIM by 29% and 8% for LD to FD, 40% and 7% for LD+MRI to FD, 16% and 8% for NAC to MAC, and 24% and 11% for MRI to CT mapping task, respectively. The qualitative/quantitative analysis demonstrated that the proposed model improved the performance of all four DNN models through producing images of higher quality and lower quantitative bias and variance compared to reference images., (© 2022. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

Simultaneous interpreting (SI) is a cognitively complex activity due to the concurrent nature of receiving and producing messages. Previous research confirms that SI is profoundly influenced by cognitive, attitudinal, and psychological mechanisms. Following this line of enquiry, the present investigation proposes a unique model by integrating cognitive and psychological factors related to the professional performance in SI. Specifically, this study examined a model to test the predictive and mediational effects of emotional intelligence, creativity, self-efficacy, and field dependence/independence (FD/FI) on simultaneous interpreting. A total of 248 university students majoring in Translation Studies completed measures of General Self-Efficacy Scale (GSES), Emotional-Quotient Inventory (EQ-I), Torrance Test of Creative Thinking (TTCT), General Embedded Figures Test (GMFT), and two SI tasks, namely the oral cloze test (OCT) and the listening and memory recall exercise (LMRE). The path analysis supported the direct effect of creativity and its indirect effects mediated by FD/FI on SI. Emotional intelligence made only a significant indirect effect on SI through FD/FI. Self-efficacy, on the other hand, made only a significant direct effect on SI. Emotional intelligence and creativity also contributed significantly to the prediction of FD/FI. The analyses also revealed a significant correlation between emotional intelligence and self-efficacy and also between creativity and emotional intelligence. Finally, FD/FI directly predicted simultaneous interpreting. Other hypothesized associations were not found to be statistically significant. The findings suggest that psychological attributes can have a great impact on students' performance in simultaneous interpreting training exercises. Implications of the study and the research avenues are discussed., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Purpose: Reducing the injected activity and/or the scanning time is a desirable goal to minimize radiation exposure and maximize patients' comfort. To achieve this goal, we developed a deep neural network (DNN) model for synthesizing full-dose (FD) time-of-flight (TOF) bin sinograms from their corresponding fast/low-dose (LD) TOF bin sinograms., Methods: Clinical brain PET/CT raw data of 140 normal and abnormal patients were employed to create LD and FD TOF bin sinograms. The LD TOF sinograms were created through 5% undersampling of FD list-mode PET data. The TOF sinograms were split into seven time bins (0, ±1, ±2, ±3). Residual network (ResNet) algorithms were trained separately to generate FD bins from LD bins. An extra ResNet model was trained to synthesize FD images from LD images to compare the performance of DNN in sinogram space (SS) vs implementation in image space (IS). Comprehensive quantitative and statistical analysis was performed to assess the performance of the proposed model using established quantitative metrics, including the peak signal-to-noise ratio (PSNR), structural similarity index metric (SSIM) region-wise standardized uptake value (SUV) bias and statistical analysis for 83 brain regions., Results: SSIM and PSNR values of 0.97 ± 0.01, 0.98 ± 0.01 and 33.70 ± 0.32, 39.36 ± 0.21 were obtained for IS and SS, respectively, compared to 0.86 ± 0.02and 31.12 ± 0.22 for reference LD images. The absolute average SUV bias was 0.96 ± 0.95% and 1.40 ± 0.72% for SS and IS implementations, respectively. The joint histogram analysis revealed the lowest mean square error (MSE) and highest correlation (R 2  = 0.99, MSE = 0.019) was achieved by SS compared to IS (R 2  = 0.97, MSE= 0.028). The Bland & Altman analysis showed that the lowest SUV bias (-0.4%) and minimum variance (95% CI: -2.6%, +1.9%) were achieved by SS images. The voxel-wise t-test analysis revealed the presence of voxels with statistically significantly lower values in LD, IS, and SS images compared to FD images respectively., Conclusion: The results demonstrated that images reconstructed from the predicted TOF FD sinograms using the SS approach led to higher image quality and lower bias compared to images predicted from LD images., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

The 2019 coronavirus (COVID-19) pandemic revealed the urgent need for the acceleration of vaccine development worldwide. Rapid vaccine development poses numerous risks for each category of vaccine technology. By using the Risklick artificial intelligence (AI), we estimated the risks associated with all types of COVID-19 vaccine during the early phase of vaccine development. We then performed a postmortem analysis of the probability and the impact matrix calculations by comparing the 2020 prognosis to the contemporary situation. We used the Risklick AI to evaluate the risks and their incidence associated with vaccine development in the early stage of the COVID-19 pandemic. Our analysis revealed the diversity of risks among vaccine technologies currently used by pharmaceutical companies providing vaccines. This analysis highlighted the current and future potential pitfalls connected to vaccine production during the COVID-19 pandemic. Hence, the Risklick AI appears as an essential tool in vaccine development for the treatment of COVID-19 in order to formally anticipate the risks, and increases the overall performance from the production to the distribution of the vaccines. The Risklick AI could, therefore, be extended to other fields of research and development and represent a novel opportunity in the calculation of production-associated risks., Competing Interests: QH, NB, LM, and PA are work for Risklick. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Haas, Borisov, Alvarez, Ferdowsi, von Mayenn, Teodoro and Amini.)

Introduction: Peripheral blood leukocytes are a suitable cell model for science research. However, blood samples from healthy volunteers are limited in volume and difficult to obtain due to the complexity of volunteer recruitment., Objective: Therefore, it is urgent to find an alternative source of peripheral blood leukocytes., Method: One of the possibilities is the use of leukocyte reduction filters (LRFs) in blood banks that is used for preparation of leukoreduced blood products. More than 90% of the leukocytes are trapped in the leukofilters allowing the desired blood product to pass through., Results: It has been reported that the biological function of leukocytes collected from the filters are no different from those isolated from buffy coats, leukapheresis products and whole blood (WB) cells. Moreover, LRFs are waste products that are discarded after leukoreduction., Conclusion: Thus, leukofilters represent an economic source of human cell populations that can be used for a variety of investigative purposes, with no cost. In the present study, we reviewed the different usage of LRFs in the research, clinical and commercial applications., (Copyright © 2020 Associação Brasileira de Hematologia, Hemoterapia e Terapia Celular. Published by Elsevier España, S.L.U. All rights reserved.)

Background: The COVID-19 global health crisis has led to an exponential surge in published scientific literature. In an attempt to tackle the pandemic, extremely large COVID-19-related corpora are being created, sometimes with inaccurate information, which is no longer at scale of human analyses., Objective: In the context of searching for scientific evidence in the deluge of COVID-19-related literature, we present an information retrieval methodology for effective identification of relevant sources to answer biomedical queries posed using natural language., Methods: Our multistage retrieval methodology combines probabilistic weighting models and reranking algorithms based on deep neural architectures to boost the ranking of relevant documents. Similarity of COVID-19 queries is compared to documents, and a series of postprocessing methods is applied to the initial ranking list to improve the match between the query and the biomedical information source and boost the position of relevant documents., Results: The methodology was evaluated in the context of the TREC-COVID challenge, achieving competitive results with the top-ranking teams participating in the competition. Particularly, the combination of bag-of-words and deep neural language models significantly outperformed an Okapi Best Match 25-based baseline, retrieving on average, 83% of relevant documents in the top 20., Conclusions: These results indicate that multistage retrieval supported by deep learning could enhance identification of literature for COVID-19-related questions posed using natural language., (©Douglas Teodoro, Sohrab Ferdowsi, Nikolay Borissov, Elham Kashani, David Vicente Alvarez, Jenny Copara, Racha Gouareb, Nona Naderi, Poorya Amini. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 17.09.2021.)

Acute myeloid leukemia (AML) is one of the major hematological malignancies. Advances in molecular research have greatly improved our understanding of the process of leukemia formation in AML. Osteopontin (OPN) is a novel molecule that mediates critical processes for cancer progression. The aim of this study was to investigate the relative expression of OPN gene isoforms in AML patients on days 0, 14, and 28 after chemotherapy. The bone marrow samples were collected from 40 newly diagnosed AML patients (24 male and 16 female with a mean age of 30 years) at the initial time of diagnosis, 14 and 28 days after treatment. The peripheral blood samples of 10 healthy individuals were also collected as the control group. The expression of OPN isoforms was investigated by Real-Time Quantitative PCR. The expression of VEGFc/STAT3/CXCR4 was also investigated by Real-Time PCR. Findings indicated that OPNb and OPNc isoforms had significantly overexpression in AML patients on 14 and 28 days after treatment compared to normal samples (P < 0.05). The level of OPNb and OPNc isoforms was increased significantly in M0, M1, and M2 subgroups with overexpression of VEGFc/STAT3/CXCR4, 28 days after starting chemotherapy (P < 0.05). Our results suggested that OPNb and OPNc isoforms play a major role in cancer relapse. Therefore, they can be used as a valuable prognostic and diagnostic biomarker for relapse of the AML disease. However, these findings need confirmation with further studies., (© 2021. Springer Science+Business Media, LLC, part of Springer Nature.)

Competing Interests: The authors declare no conflicts of interest.

Introduction: The SARS-CoV-2 pandemic has led to one of the most critical and boundless waves of publications in the history of modern science. The necessity to find and pursue relevant information and quantify its quality is broadly acknowledged. Modern information retrieval techniques combined with artificial intelligence (AI) appear as one of the key strategies for COVID-19 living evidence management. Nevertheless, most AI projects that retrieve COVID-19 literature still require manual tasks., Methods: In this context, we pre-sent a novel, automated search platform, called Risklick AI, which aims to automatically gather COVID-19 scientific evidence and enables scientists, policy makers, and healthcare professionals to find the most relevant information tailored to their question of interest in real time., Results: Here, we compare the capacity of Risklick AI to find COVID-19-related clinical trials and scientific publications in comparison with clinicaltrials.gov and PubMed in the field of pharmacology and clinical intervention., Discussion: The results demonstrate that Risklick AI is able to find COVID-19 references more effectively, both in terms of precision and recall, compared to the baseline platforms. Hence, Risklick AI could become a useful alternative assistant to scientists fighting the COVID-19 pandemic., (© 2021 S. Karger AG, Basel.)

In this paper the classification of motor imagery brain signals is addressed. The innovative idea is to use both temporal and spatial knowledge of the input data to increase the performance. Definitely, the electrode locations on the scalp is as important as the acquired temporal signals from every individual electrode. In order to incorporate this knowledge, a deep neural network is employed in this work. Both motor-imagery EEG and bi-modal EEG-fNIRS datasets were used for this purpose. The results are compared for different scenarios and using different methods. The achieved results are promising and imply that combining both temporal and spatial information of the brain signals could be really effective and increases the performance.

In Iran, breast cancer (BC) is the most prevalent cancer among women. The standard treatment for this cancer is partial or total removal of breast tissue, followed by chemotherapy and radiation. Tissue engineering (TE) has made new treatments for tissue loss in these patients by creating functional substitutes in the laboratory. In addition, cancer biology combined with TE provides a new strategy for evaluation of anti-BC therapy. Several innovations in TE have led to the design of scaffold or matrix based culture systems that more closely mimic the native extracellular matrix (ECM). Currently, engineered three-dimensional (3D) cultures are being developed for modelling of the tumour microenvironment. These 3D cultures fulfil the need for in vitro approaches that allow an accurate study of the molecular mechanisms and a better analysis of the drugs effect. In the present study, we review recent developments in utilising of TE in BC. Moreover, this review describes achievements of Iranian researchers in the field of breast TE.

The challenge with γ-Al 2 O 3 is its pH-dependent removal efficiency and relatively low adsorption capacity in recovery of wastewaters contaminated by cationic dyes. Therefore; the objective of present investigation was to fabricate a pH-independent nano-sized alumina powder by dealumination of kaolin for uptake of dye. The dealumination of meta-kaolin was carried out by nitric acid solution and the amorphous aluminum hydroxide was precipitated with ammonia in the presence of combined surfactants containing cetyltrimethylammonium bromide (CTAB), polyethylene glycol (PEG) and polyoxyethylene octyl phenyl ether (TX100). The nano-sized gamma alumina particles were fabricated after calcination at 800 °C and then were analyzed based on spectrophotometrical method. The rod-like nano-particles with average width of 5 nm and length of 20 nm were synthesized by precipitation in the presence of admixed surfactant, CTAB-TX100, which improves the removal efficiency due to efficient modification in the interaction of particles. TX100 also showed a significant affinity for increasing the specific surface area because of ability to prevent the particle aggregation which provides a potential for synthesis of alumina adsorbent without foaming, normally observed in the presence of CTAB. Both of mentioned powders indicated relatively pH-independent removal efficiency with higher adsorption capacity. Moreover, adsorption isotherm parameters were determined to verify the improvement of removal efficiency. The presented fabrication technique eventually overcame the pH-dependent recovery problem which is feasible for the development of adsorbents because the starting material is abundant, very low energy is needed, no hazardous waste is generated and applicable in the acidic environments., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Aims: The α-defensins or human neutrophil peptides (HNP 1-3) that exist in azurophilic granules are found to have anticancer activity. The pattern of disulfide bonds in α-defensins is crucial for the functional properties. Therefore, synthesis using the chemical and recombinant approaches is a challenging. A safe source for the production of α-defensins can be the use of leukoreduction filters in blood banks that contain large quantities of neutrophils and are discarded after use. The aim of this study was to purify α-defensins from neutrophils trapped in leukofilters and to investigate its anticancer activity., Materials and Methods: Immunoprecipitation was performed to purify α-defensins and the presence of protein was confirmed by Western Blot. The Jurkat T-cell line was incubated with different concentrations (5, 10 and 15 μg/ml) of purified HNP1-3 for 16 h. Cell viability was measured using a WST-1 assay and apoptosis was analyzed for Annexin V/PI markers. Caspase-3/7 activity was determined using fluorescence assay. The effects of purified α-defensins were compared to commercial HNP 1-3., Key Findings: Purified HNP 1-3 decreased the viability at 10 and 15 μg/ml and commercial HNP 1-3 at 15 μg/ml concentrations. Following to the purified HNP1-3 treatment, the percentage of Annexin V positive population and caspase-3 activity were significantly increased compared to control (p = 0.000 and p = 0.001, respectively) and commercial HNP1-3 (p = 0.034 and p = 0.018, respectively)., Significance: Results indicated the anticancer activity of HNP1-3 which can be used as future chemotherapeutic drugs. Furthermore, leukofilters can be considered as economic source for purifying these peptides., (Copyright © 2019. Published by Elsevier Inc.)