‏شیردهی در زمان مصرف داروی رمدسیویر در بیماران مبتلا به کووید19‏: شیردهی و داروی رمدسیویر

Coronavirus disease has quickly become a serious concern in the international community due to its high unbelievable incidence and mortality. Meanwhile, treatment management in special groups, including pregnant and lactating women, faced serious challenges. Due to the numerous benefits of breastfeeding and the irreversible damages of ceasing breastfeeding in affected mothers, limited clinical studies have been performed on the possibility of protecting breastfeeding in mothers under remdesivir. The only effective drug approved by the US Food and Drug Administration in emergency situations is Remdesivir. There is not much information about the secretion of this drug in breast milk. In this letter, we mentioned breastfeeding can be continued in mothers treated with remdesivir; however, close monitoring of the infant is recommendedپس از اینکه بیماری کرونا ویروس (COVID-19) در نهم ماه می 2021 بیش از 150 میلیون ابتلا و 3.2 میلیون مرگ را موجب شد، تبدیل به یک نگرانی جدی در مجامع بین المللی شد(1)  همچنان که پاندمی کووید- 19 ادامه می یابد زنان مبتلای بیشتری زایمان می کنند. واضح است که دوران بارداری و اوایل پس از زایمان مدیریت ویژه ای می طلبد و چالش هایی دارد که نیازمند ارزیابی دقیق خطرات و مزایا برای مادر، جنین و نوزاد است. &nbsp

Assessment of solar data estimation models for four cities in Iran

The estimated solar resources are important for designing renewable energy systems since measured data are not always available. The estimation models have been introduced in several studies. These models are mainly dependent on local meteorological data and need to be assessed for different locations and times. The current study compares the results of Angstrom's model and a neural network (NN) model developed for this study with measured data for four cities in Iran. The time resolution for the estimated global horizontal insolation is monthly. The results show that the developed NN model has promising performance and considering the calibration process for Angstrom's model it can be used as an alternative. The NN model uses climatic data to estimate the solar insolation which makes it more flexible in terms of being applicable for different regions. (C) 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinhei

TECHNICAL AND ECONOMIC ANALYSES FOR SIZING PV POWER PLANT WITH STORAGE SYSTEM FOR METU NCC

The use of renewable energy with storage systems is particularly important in small and unreliable grids, such as islands. This paper reports sizing of a photovoltaic (PV) power plant with storage system for Middle East Technical University Northern Cyprus Campus through technical and economic analyses. PV system was modeled considering fixed tilted, one axis and two-axis tracking systems using hourly data. Energy storage system was included in the model to overcome the temporal mismatch between the electricity demand of the campus and the electricity supplied by the PV system. The reduction in CO2 emissions by deploying these systems was studied. The results showed that although it would not be economically feasible to meet the entire demand of the campus, a PV system of 4.5 MW with 15 MWh of storage size would generate enough electricity to meet the demand for 83% of the time in a year, yielding the cost of 0.25 USD/kWh

Assessment of solar data estimation models for four cities in Iran

The estimated solar resources are important for designing renewable energy systems since measured data are not always available. The estimation models have been introduced in several studies. These models are mainly dependent on local meteorological data and need to be assessed for different locations and times. The current study compares the results of Angstrom's model and a neural network (NN) model developed for this study with measured data for four cities in Iran. The time resolution for the estimated global horizontal insolation is monthly. The results show that the developed NN model has promising performance and considering the calibration process for Angstrom's model it can be used as an alternative. The NN model uses climatic data to estimate the solar insolation which makes it more flexible in terms of being applicable for different regions. (C) 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinhei

Sizing of Photovoltaic-Wind-Battery Hybrid System for a Mediterranean Island Community Based on Estimated and Measured Meteorological Data

Deploying renewable energy systems (RES) to supply electricity faces many challenges related to cost and the variability of the renewable resources. One possible solution to these challenges is to hybridize RES with conventional power systems and include energy storage units. In this study, the feasibility analysis of a grid-connected photovoltaic (PV)wind-battery hybrid system is presented as a microgrid for a university campus-scale community on a Mediterranean island. Models for PV and wind turbine systems are presented to estimate energy production, and net present cost (NPC) and levelized cost of electricity (LCOE) are used as economic metrics. A parametric study is performed with hourly time-steps to determine the sizes of energy generation and storage units to minimize the NPC for a small community as the case study. Two alternate configurations with and without storage are proposed. In both cases, the resulting LCOE is 0.15 USD/kWh while the current electricity tariff for the analyzed location was 0.175 USD/kWh in 2015. This lower unit cost of electricity leads to a lower NPC considering a 25-year lifetime. Different estimated and measured solar irradiance and wind speed data sets are used to evaluate the performance of the designed microgrid. Sensitivity analysis on different available weather data sets shows that the uncertainty in wind resource estimations is much higher than the uncertainty in solar resource estimations. Moreover, the results show that solar and wind resources could be utilized synergistically for the studied location

A Death Case Report in an Adult Woman With COVID-19 after Septoplasty

The mortality rate of Coronavirus Decease 2019 (COVID-19) is very high, but specific situations can increase the rate including severe hypoxemia, multiple organ injury, and thromboembolic events in various organs. Another factor is the stress caused after surgery that require general anesthesia. This study aims to report a case of death in an adult woman with COVID-19 infection who had underwent septoplasty and admitted to hospital after worsening of her general condition and treated when diagnosed with COVID- 19. One day after admission, she was intubated due to progressive respiratory failure and deceased following bradycardia and cardiac arrest. It seems that the elective surgery should be avoided in patients infected with COVID-19 and should be postponed until complete recovery. Moreover, the possibility of this infection should be considered in all candidates for surgery with subtle respiratory symptoms

Evaluation of mobile phone-based tele-monitoring of cystic fibrosis patients during the COVID-19 pandemic: A 3-year experience in Iran

Background: Telemedicine has been used for cystic fibrosis (CF) in a wide range of signs and symptoms even before the COVID 19 pandemic, however, little is known about the health consequences and use of specific health care for cystic CF. This study aimed to evaluate the evolution of clinical trends and data related to mobile based monitoring activities in CF patients at home for 3 years. Methods: This is a semi experimental single group study. Forty five CF patients under 7 years' old who were referred to the Masih Daneshvari Hospital between 2018 and 2021 were selected. A mobile phone_based customized Short Message Service (SMS) application used to monitor patients. Remotely monitored variables included the amount and color of sputum, cough, wheezing, and shortness of breath at rest. SPSS using Chi square and Friedman tests. Results: The condition of patients based on the number and type of cough increased sputum, decreased appetite, fatty stool, fever and dyspnea, headache, noninvasive ventilation, and drug comfortably remained almost unchanged in the study of the 1st, 2nd, and 3rd years, and the studied parameters did not show a significant difference (P > 0.05). Of course, the number of outpatient visits decreased significantly (P value: 0.02). The respiratory rate and arterial oxygen saturation variables were almost the same in three consecutive annual measurements (P values: 0.544 and 0.639, respectively). Conclusion: Telemedicine is a method that is useful in the follow up of chronic diseases such as CF and improves the quality of life and reduces the deterioration of lung function; therefore, there is less need for invasive treatments in the long run, and a fundamental change in referral motivation brings to the hospital

Differential privacy preserved federated learning for prognostic modeling in COVID‐19 patients using large multi‐institutional chest CT dataset

Background Notwithstanding the encouraging results of previous studies reporting on the efficiency of deep learning (DL) in COVID‐19 prognostication, clinical adoption of the developed methodology still needs to be improved. To overcome this limitation, we set out to predict the prognosis of a large multi‐institutional cohort of patients with COVID‐19 using a DL‐based model. Purpose This study aimed to evaluate the performance of deep privacy‐preserving federated learning (DPFL) in predicting COVID‐19 outcomes using chest CT images. Methods After applying inclusion and exclusion criteria, 3055 patients from 19 centers, including 1599 alive and 1456 deceased, were enrolled in this study. Data from all centers were split (randomly with stratification respective to each center and class) into a training/validation set (70%/10%) and a hold‐out test set (20%). For the DL model, feature extraction was performed on 2D slices, and averaging was performed at the final layer to construct a 3D model for each scan. The DensNet model was used for feature extraction. The model was developed using centralized and FL approaches. For FL, we employed DPFL approaches. Membership inference attack was also evaluated in the FL strategy. For model evaluation, different metrics were reported in the hold‐out test sets. In addition, models trained in two scenarios, centralized and FL, were compared using the DeLong test for statistical differences. Results The centralized model achieved an accuracy of 0.76, while the DPFL model had an accuracy of 0.75. Both the centralized and DPFL models achieved a specificity of 0.77. The centralized model achieved a sensitivity of 0.74, while the DPFL model had a sensitivity of 0.73. A mean AUC of 0.82 and 0.81 with 95% confidence intervals of (95% CI: 0.79–0.85) and (95% CI: 0.77–0.84) were achieved by the centralized model and the DPFL model, respectively. The DeLong test did not prove statistically significant differences between the two models ( p ‐value = 0.98). The AUC values for the inference attacks fluctuate between 0.49 and 0.51, with an average of 0.50 ± 0.003 and 95% CI for the mean AUC of 0.500 to 0.501. Conclusion The performance of the proposed model was comparable to centralized models while operating on large and heterogeneous multi‐institutional datasets. In addition, the model was resistant to inference attacks, ensuring the privacy of shared data during the training process.</p