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Introduction: The prevalence of antimicrobial resistance genes ARGs and their resistance genetic mechanisms among Covid- 19 patients are yet to be identified. The human microbiome is a significant reservoir of antimicrobial resistance genes. The overprescription of antimicrobials can select multi-resistant bacteria and modify the repertoire of ARGs in the gut. The World Health Organization has reported 148 million hospitalized cases worldwide. Objective(s): The purpose of the current study is to explore the genetic mechanisms of antimicrobial resistance among hospitalized COVID-19 patients, furthermore, to review their antibiotic resistance gene occurrence. Methodology 438 Microbiome of clinical hospitalized COVID-19 positive cases with 11 129 isolates were downloaded from the EMBL's European Bioinformatics Institute and the NCBI Pathogen Detection using the following keywords AMR, mechanism of resistance, and COVID-19 SARS CoV 2 bacterial Infection.We also have used the Comprehensive Antibiotic Resistance Database Card, and RESfinder are used for the metagenomics analysis based on programming languages JavaScript and R (v. 4) for data analysis. Result(s): We explored the AMR diversity among prevalent microbes(n = 410), including Klebsiella pneumoniae, Acinetobacter baumannii E. coli, Salmonella, Enterobacter and Pseudomonas aeruginosa.We found that Enzyme activation (72.7%) was the most prevalent mechanism due to the fosA gene 54.5%. Then the aadA2 gene (18%) and catA1 gene(9%). Moreover, the Increased efflux mechanisms were detected in Escherichia coli towards Quinolone using the oqxA gene (17.3%). FosA was also intermittently found in Salmonella (9.8%), and Pseudomonas aeruginosa (7.8%). Conclusion(s): We anticipate that FosA homologous is prevalent in Gram-negative bacterial infections among hospitalized COVID-19 patients, and it encodes for Fosfomycin resistance. The findings might shed light on controlling Fosfomycin resistance among hospitalized patients with COVID-19.Copyright © 2023 The American Society for Biochemistry and Molecular Biology, Inc.
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Background: The effect of psychiatric comorbidity on pregnancy outcome among SARS-CoV-2 positive women with asymptomatic and mildly symptomatic infections remains largely unknown Methods: Electronic medical records of all pregnant women who received care at Mayo Health System and tested positive for SARS-CoV-2 (RT-PCR) from March 2020 through October 2021 were reviewed Results: Among 789 patients, 34.2% (n=270) had psychiatric comorbidity. Of those, 62.2% (n=168) had depression prior to pregnancy and 5.2% (n=14) reported new onset depression during pregnancy. Anxiety diagnosis was present in 65.6% (n=177) prior to pregnancy and new onset anxiety in 4.4% (n=12). Thirteen percent of SARS-CoV-2 positive pregnant women (n=108) received a psychotropic medication during pregnancy. In addition, 6.7% (n=18) and 10.7% (n=29) of pregnant women with psychiatric comorbidity had a documented nicotine, cannabis and/ or illicit substance use during and prior to pregnancy, respectively. Asymptomatic and mildly symptomatic SARS-CoV-2 positive pregnant women with psychiatric comorbidity had significantly higher risk for cesarean delivery [35.6% vs.24.9%). Conclusion(s): In conclusion, the prevalence rates of depression, anxiety, and prescribed antidepressant medications during pregnancy among asymptomatic and mildly symptomatic SARS-CoV-2 infected women were substantially higher than average, which negatively impacted on pregnancy and neonatal outcomes. Keywords: SARS-CoV-2, Pregnancy, Depression, AnxietyCopyright © 2023
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Background. Patients with COVID-19 disease often receive antibiotics to treat suspected bacterial coinfections. Procalcitonin (PCT) is a biomarker used for suspected bacterial infections. The objective of this study is to evaluate the association between PCT and the use of antimicrobials in COVID-19 patients. Methods. This was a retrospective, cohort study of adult patients admitted with confirmed COVID-19 from March 30, 2020 to March 30, 2021. Data collected included demographics, baseline inflammatory markers including initial PCT and C-reactive protein (CRP) values, past medical history, initiation of empiric antibiotics, mechanical ventilation, in-hospital mortality, days of antibiotic therapy, and length of hospital stay (LOS). Univariate analyses were utilized to assess for any significant differences in demographics based on predefined initial PCT groupings (< 0.25 ng/ml (group 1), 0.25-0.49 ng/ml (group 2), and >= 0.5 ng/ml (group 3)). Multivariate analyses were performed to evaluate for any differences between initial PCT values and in-hospital mortality, LOS, and days of antibiotic therapy. Results. Out of 149 patients, 61.7% had an initial PCT value < 0.25 ng/ml, 17.45% had an initial value of 0.25-0.49 ng/ml, and 20.8% had an initial value >= 0.5 ng/ml. A total of 145 patients (97%) received empiric antibiotics. Univariate analysis among the three groups displayed a difference in the initial CRP value, which was higher in groups 2 and 3 versus group 1 (p < 0.001). Regression analysis controlling for initial CRP value found that patients in groups 2 and 3 had a higher duration of antibiotic therapy compared to group 1 (12 and 11 versus 8 days) (p < 0.001) and a longer LOS (17 and 15 vs 12 days) (p = 0.009). More patients (34.6%) were mechanically ventilated in group 2 compared to group 1 (14.1%) and group 3 (22.6%) with a trend toward significance (p = 0.059). Multivariate analysis found no significant association between PCT levels and mortality. The rate of in-hospital mortality in patients receiving invasive ventilation was higher in groups 2 and 3 (78% and 86%, respectively) compared to group 1 (54%, p < 0.001). Conclusion. When controlling for CRP, an initial PCT value > 0.25 ng/ml was associated with increased days of antibiotic therapy and longer duration of hospital stay in COVID-19 patients.
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Background: The administration of modified immune cells (MIC) prior to kidney transplantation led to specific immunosuppression against the allogeneic donor and a significant increase in regulatory B lymphocytes (Breg) (Morath et al., J Clin Invest 2020). We now wanted to investigate how this approach affects the clinical course of treated patients. Method(s): Clinical results of ten patients from a phase I clinical trial who had received MIC infusions before kidney transplantation were compared to results of 15 matched standard-risk recipients. Follow-up was until year five after surgery. Result(s): The 10 MIC patients had an excellent clinical course with stable kidney graft function and showed no donor-specific human leukocyte antigen antibodies (DSA) or acute rejections during follow-up. In contrast, 1 of 15 controls died and 5 of 15 controls developed DSA (log rank P = 0.046) (Figure 1 A, B). While the number of patients with a non-opportunistic infection did not differ significantly between groups (P = 0.36), opportunistic infections were reported more frequently in controls (log rank P = 0.033) (Figure 1 C). Compared to controls, MIC patients were found to have a trend towards a higher COVID-19 anti-S1 IgG index after vaccination with a median of 53 vs. 2 (P = 0.16). Importantly, the four MIC patients who had received the highest MIC cell dose 7 days before surgery and were on low immunosuppression during follow-up, continued to show absent anti-donor T lymphocyte reactivity in vitro and high CD19+CD24hiCD38hi transitional Breg as well as CD19+CD24hiCD27+ memory Breg. Conclusion(s): MIC infusions together with reduced conventional immunosuppression were associated with lower de novo DSA development and lower rates of opportunistic infections. In the future, MIC infusions could contribute to graft protection while reducing the side effects of immunosuppressive therapy. (Figure Presented).
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Deep Learning is a relatively new Artificial Intelligence technique that has shown to be extremely effective in a variety of fields. Image categorization and also the identification of artefacts in images are being employed in visual recognition. The goal of this study is to recognize COVID-19 artefacts like cough and also breath noises in signals from realworld situations. The suggested strategy considers two major steps. The first step is a signal-to-image translation that is aided by the Constant-Q Transform (CQT) and a Mel-scale spectrogram method. Next, nine deep transfer models (GoogleNet, ResNet18/34/50/100/101, SqueezeNet, MobileNetv2, and NasNetmobile) are used to extract and also categorise features. The digital audio signal will be represented by the recorded voice. The CQT will transform a time-domain audio input to a frequency-domain signal. To produce a spectrogram, the frequency will really be converted to a log scale as well as the colour dimension will be converted to decibels. To construct a Mel spectrogram, the spectrogram will indeed be translated onto a Mel scale. The dataset contains information from over 1,600 people from all over the world (1185 men as well as 415 women). The suggested DL model takes as input the CQT as well as Melscale spectrograms derived from the breathing and coughing tones of patients diagnosed using the coswara-combined dataset. With the better classification performance employing cough sound CQT and a Mel-spectrogram image, the current proposal outperformed the other nine CNN networks. For patients diagnosed, the accuracy, sensitivity, as well as specificity were 98.9%, 97.3%, and 98.1%, respectively. The Resnet18 is the most reliable network for symptomatic patients using cough and breath sounds. When applied to the Coswara dataset, we discovered that the suggested model's accuracy (98.7%) outperforms the state-of-the-art models (85.6%, 72.9%, 87.1%, and 91.4%) according to the SGDM optimizer. Finally, the method as a main screening tool to try and identify COVID-19 by of disease transmission.
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Real-time disease prediction has emerged as the main focus of study in the field of computerized medicine. Intelligent disease identification framework can assist medical practitioners in diagnosing disease in a way that is reliable, consistent, and timely, successfully lowering mortality rates, particularly during endemics and pandemics. To prevent this pandemic's rapid and widespread, it is vital to quickly identify, confine, and treat affected individuals. The need for auxiliary computer-aided diagnostic (CAD) systems has grown. Numerous recent studies have indicated that radiological pictures contained critical information regarding the COVID-19 virus. Utilizing advanced convolutional neural network (CNN) architectures in conjunction with radiological imaging makes it possible to provide rapid, accurate, and extremely useful susceptible classifications. This research work proposes a methodology for real-time detection of COVID-19 infections caused by the Corona Virus. The purpose of this study is to offer a two-way COVID-19 (2WCD) diagnosis prediction deep learning system that is built on Transfer Learning Methodologies (TLM) and features customized fine-tuning on top of fully connected layered pre-trained CNN architectures. 2WCD has applied modifications to pre-trained models for better performance. It is designed and implemented to improve the generalization ability of the classifier for binary and multi-class models. Along with the ability to differentiate COVID19 and No-Patient in the binary class model and COVID-19, No-Patient, and Pneumonia in the multi-class model, our framework is augmented with a critical add-on for visually demonstrating infection in any tested radiological image by highlighting the affected region in the patient's lung in a recognizable color pattern. The proposed system is shown to be extremely robust and reliable for real-time COVID-19 diagnostic prediction. It can also be used to forecast other lung-related disorders. As the system can assist medical practitioners in diagnosing the greatest number of patients in the shortest amount of time, radiologists can also be used or published online to assist any less-experienced individual in obtaining an accurate immediate screening for their radiological images.
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Background: Children’s everyday lives had undergone substantial alterations due to the COVID-19 epidemic. Children's insufficient physical exercise and excessive sedentary behavior are serious issues. Objective: To investigate the effects of COVID-19 limits on children's play and movement habits in Egypt. Methods: A cross-sectional sample of 400 Egyptian children aged 5-14years. The Arabic version of the online outbreak survey was used to evaluate children's movement and play behavior before and during the COVID-19 epidemic. Data analysis included descriptive statistics and test-retest reliability. Results: By comparing the collected data with WHO guidelines, it was found that 2.3% of screened children fulfilled the 24-hour combined movement behavior criteria;57% met sleep recommendations;36.9% met moderate-to-vigorous physical activity guidelines, and 12.3% met screen-time guidelines. During the epidemic, children in Egypt had decreased physical activity levels, more sluggish habits, and less sleep. The Arabic version of the survey showed excellent reliability. Conclusion: The COVID-19 epidemic restrictions had a bad impact on Egyptian children's play and movement patterns. Future initiatives to safeguard and advance child health should be considered. © 2022, Ain Shams University Faculty of Medicine. All rights reserved.
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This chapter reflects on how I harnessed the opportunities afforded from the pandemic to advance on my own journey, rather than succumb as a victim. A key to transforming the crisis into opportunity for personal and professional growth was recognizing the gift of a Mentor-Mentee relationship and prioritizing the development through nurturing of this connection. Looking back, the pandemic paused a paralyzing challenge in a country like Germany, but also the unexpected opportunity to develop a special relationship with my Mentor, complete my PhD, develop our own inner selves, and start our new business together to serve and promote entrepreneurship. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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INTRODUCTION: Although primarily a respiratory disorder, the coronavirus pandemic has paralyzed almost all aspects of healthcare delivery. Neurosurgeons provide critical care and their role in this pandemic is necessary and yet evolving. METHODS: Through professional registries and internet resources, we surveyed neurosurgeons from around the world. We looked at the national burden of illness, perception of preparedness, cancellation of clinics, surgeries and cessation of research and educational activities. Chisquare, Fisher's exact tests, and multivariate logistic regression model were used to analyze variables. RESULTS:Our survey was completed on April 3, 2020, and was wellreceived (661 completed, 60.7% response rate, 96 nations). Neurosurgeons in affluent nations still lacked adequate resources to combat this pandemic primarily due to lack of preparedness and insufficient medical equipment. The challenges to lower-income nations were similar but also included knowledge dissemination and inadequate resources. Hospital preparedness was central to effective healthcare delivery but also influenced the cancellation of surgeries and clinics. The impact on education, conferences and research was also significant. CONCLUSION: Neurosurgical disorders are significant casualties of the COVID-19 pandemic. Reduction of services, lack of preparedness, inadequate education, insufficient supplies and resources are important contributors. Improved preparedness requires careful planning, real-time surveillance and allocation of resources. This is more critical in poorer nations.
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Background: stroke is one of most common causes of motor impairment, and most of stroke patients remain their life with residual upper limb function which needs long-term of rehabilitation at hospitals or rehab centres. Spread of COVID19 around the world prevent hospital admission for long period,so many patients continue their treatment at home Objective: to detect the role of home-based interactive therapy in improving upper limb function post stroke. Data sources: Electronic databases of PubMed, Cochrane library and Google scholar were searched up to May 2021, in addition to manual search of reference lists of relevant studies and reviews. Methods: Randomized controlled trials were included if they involved any form of home-based interactive therapy. Two authors independently screened articles, extracted data, and assessed the methodological quality using the PEDro scale, with any conflict resolved by the third author. Modified Sackett's Scale was used to determine the level of evidence for each outcome. Because of the clinical heterogeneity between the included studies;only 2 studies were appropriate for meta-analysis. Results: Out of 583 records screened, 3studies with 286 participants met the inclusion criteria. The duration of treatment ranged from 30 to 60 min, 5-6 times a week and for 5–8weeks. The quality of studies was good for the 3 studies, with a mean PEDro score of 6.3out of 10. All included studies showed positive effects in the measured outcomes. The results showed level1a evidence for the effectiveness of home-based interactive therapy in improving upper extremity function post stroke. Meta-analysis of 2 included trials used the action research arm score test showed no significant difference (P=0.28) in the overall effect of (SMD= -0.14;95% CI) between intervention and control groups Conclusion: Findings of this review found a strong evidence that home-based interactive therapy for stroke patients can improve their upper limb function but its effect is not superior to conventional therapy. This evidence is based on a limited number of RCTs and more high-quality RCTs are needed to support this evidence.
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Objectives: Review the present use of 18F-FDG-PET in lung inflammation/interstitial lung diseases. Review thepotential value of 18F-FDG-PET in the global measurement and monitoring of lung inflammation/interstitial lungdiseases. Assess the added prospective value of 18F-FDG-PET imaging in inflammatory diseases involving thelung. Methods: In light of the ongoing SARS-COV-2 pandemic, more attention has been drawn to lung disorders. Medicalimaging can play a major role in disease assessment here, given that early diagnosis of various lung disorders iscrucial. Regarding such diseases, increased glucose uptake in lung parenchyma is a hallmark of severalpathophysiological processes, including inflammation and development of benign/malignant tumors. Major riskfactors for such increased glucose uptake include smoking, environmental irritants, viral infections etc. Numerousstudies have directly shown that global 18F-fluorodeoxyglucose-positron emission tomography (18F-FDG-PET) isone of the most sensitive modalities to measure glucose uptake, albeit in other areas of the body like the heart and liver. 18F-FDG-PET is especially effective in early stage measurement, given its inherent property as a method toassess increased metabolic activity in inflammation occurring long before structural changes. As of yet though, littleoriginal research exists in the current literature specifically regarding the utility of 18F-FDG-PET for the assessmentof inflammation in the human lung. We believe that this is an area of high interest, given the reasons listed aboveand the increased emphasis on earlier diagnosis of common lung disorders (thus necessitating the prioritization ofmolecular as compared to structural imaging). Results: Numerous research by our own lab and fellow colleagues has found that 18F-FDG-PET can successfullyquantify tumor growth for various forms of lung cancer, in both human and animal models. Despite this, asmentioned above, no such definitive conclusion can be made about human lung inflammation. The current state ofthe field centers mainly around studying in vivo application of 18F-FDG-PET to animal models, which has beensuccessfully done in both sheep and rat lungs. We believed that this application could extend to the human body,and thus hypothesized that quantifying human lung inflammation through 18F-FDG-PET would show similarpromise. And indeed, our retrospective research (currently in submission) has found that inflammation of the lung,as globally assessed by 18F-FDG-PET, is significantly higher in smokers compared to nonsmokers, while also beingpositively correlated with age. Global assessment of the lung here was key, in order to represent the true extent ofinflammation across the entire lung rather than cherry-picking isolated focal nodules. Conclusions: 18F-FDG-PET can likely play a valuable role in management of lung disorders, especially in novelareas such as lung inflammation. However, the lack of original, prospective data on human models necessitatesfurther research. Specifically, well-powered studies should be conducted to explore in vivo 18F-FDG-PET uptake inlung parenchyma and to compare 18F-FDG-PET sensitivity/specificity with other imaging modalities in the lung.