Classification of COVID-19 and Influenza Patients Using Deep Learning.

Coronavirus (COVID-19) is a deadly virus that initially starts with flu-like symptoms. COVID-19 emerged in China and quickly spread around the globe, resulting in the coronavirus epidemic of 2019-22. As this virus is very similar to influenza in its early stages, its accurate detection is challenging. Several techniques for detecting the virus in its early stages are being developed. Deep learning techniques are a handy tool for detecting various diseases. For the classification of COVID-19 and influenza, we proposed tailored deep learning models. A publicly available dataset of X-ray images was used to develop proposed models. According to test results, deep learning models can accurately diagnose normal, influenza, and COVID-19 cases. Our proposed long short-term memory (LSTM) technique outperformed the CNN model in the evaluation phase on chest X-ray images, achieving 98% accuracy.

Venovenous Extracorporeal Membrane Oxygenation Support in Patients with COVID-19 Respiratory Failure: A Multicenter Study.

Objective: The COVID -19 pandemic presents a high mortality rate amongst patients who develop severe acute respiratory distress syndrome (ARDS). The purpose of this study was to evaluate the outcomes of venovenous ECMO in COVID-19-related ARDS and identify the patients that benefit the most from this procedure. Methods: Adult COVID-19 patients with severe ARDS requiring VV-ECMO support at four academic insititutions between March and October 2020 were included. Data were collected through retrospective chart reviews. Bivariate and multivariable analysis were performed with the primary outcome of in-hospital mortality. Results: Fifty-one consecutive patients underwent VV-ECMO with a mean age of 50.4 years; 64.7% were male. Survival to hospital discharge was 62.8%. Median ICU and hospitalization duration were 27.4 (IQR:17-37) and 34.5 days (IQR:23-43), respectively. Survivors and non-survivors had a median ECMO cannulation time of 11 days (IQR 8-18) and 17 days (IQR: 12-25). The average post decannulation length of stay was 17.5 days (IQR: 12.4-25) for survivors and 0 days for non-survivors (IQR 0-6 days). Only one non-survivor was able to be decannulated. Clinical characteristics associated with mortality between non-surviors and survivors included increasing age (p=0.0048), hemorrhagic stroke (p=0.0014), and post operative dialysis (p=0.0013)were associated with mortality in a bivariate model and retained statistical significance in a multivariable model. Conclusion: This multicenter study confirms the effectiveness of VV-ECMO in selected critically ill patients with COVID-19-related severe ARDS. The survival of these patients is comparable to non-COVID-19-related ARDS.

Factors Affecting The Transition Planning Of Children With Disabilities: A Critical Review

The thought behind this research was to evaluate the factors associated with the effective transition planning for children with disabilities. The population of study was the special education teachers working in the Special education department of Punjab. Quantitative data was gathered from 300 teachers working in different areas of province of Punjab, Pakistan. The information was obtained through an online questionnaire because of restrictions and closure of school due to COVID-19. The survey questionnaire was structured on a five-point Likert scale, and the findings were assessed using SPSS after gathering information from teachers and other academic professionals. It has resulted that the role of students, teachers, parents, research & development, community attitude, school infrastructure and Cocurricular activities is very important in successful transition planning. Successful transition planning plays an important and key role for the successful transition of children with disabilities.

Current advances in the classification, production, properties and applications of microbial biosurfactants - A critical review.

This review discusses the classification, characteristics, and applications of biosurfactants. The biosynthesis pathways for different classes of biosurfactants are reviewed. An in-depth analysis of reported research is carried out emphasizing the synthetic pathways, culture media compositions, and influencing factors on production yield of biosurfactants. The environmental, pharmaceutical, industrial, and other applications of biosurfactants are discussed in detail. A special attention is given to the biosurfactants application in combating the pandemic COVID-19. It is found that biosurfactant production from waste materials can play a significant role in enhancing circular bioeconomy and environmental sustainability. This review also details the life cycle assessment methodologies for the production and applications of biosurfactants. Finally, the current status and limitations of biosurfactant research are discussed and the potential areas are highlighted for future research and development. This review will be helpful in selecting the best available technology for biosynthesis and application of particular biosurfactant under specific conditions.

Noteworthy Cardiac Literature From 2021: Coronary Guideline Change Without New Data, Heart Transplant Donation After Cardiac Death, Covid Effects on Global Cardiac Surgery, and Attempt to Improve Dissection Remodeling.

Cardiac surgery continues to evolve. The last year has been notable for many reasons. The guidelines for coronary revascularization introduced significant discord. The pandemic continues to affect the care on a global scale. Advances in organ procurement and dissection care move forward with better understanding and better technology.

Transfer Learning and Semisupervised Adversarial Detection and Classification of COVID-19 in CT Images

The ongoing coronavirus 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in a severe ramification on the global healthcare system, principally because of its easy transmission and the extended period of the virus survival on contaminated surfaces. With the advances in computer-aided diagnosis and artificial intelligence, this paper presents the application of deep learning and adversarial network for the automatic identification of COVID-19 pneumonia in computed tomography (CT) scans of the lungs. The complexity and time limitation of the reverse transcription-polymerase chain reaction (RT-PCR) swab test makes it disadvantageous to depend solely on as COVID-19’s central diagnostic mechanism. Since CT imaging systems are of low cost and widely available, we demonstrate that the drawback of the RT-PCR can be alleviated with a faster, automated, and reduced contact diagnostic process via the use of a neural network model for the classification of infected and noninfected CT scans. In our proposed model, we explore the benefit of transfer learning as a means of resolving the problem of inadequate dataset and the importance of semisupervised generative adversarial network for the extraction of well-mapped features and generation of image data. Our experimental evaluation indicates that the proposed semisupervised model achieves reliable classification, taking advantage of the reflective loss distance between the real data sample space and the generated data.

Association between procalcitonin levels and duration of mechanical ventilation in COVID-19 patients.

BACKGROUND: Patients diagnosed with COVID-19 frequently require mechanical ventilation. Knowledge of laboratory tests associated with the prolonged need for mechanical ventilation may guide resource allocation. We hypothesized that an elevated plasma procalcitonin level (>0.1 ng/ml) would be associated with the duration of invasive mechanical ventilation. METHODS: Patients diagnosed with COVID-19, who were admitted to any of our health system's hospitals between March 9th-April 20th, 2020 and required invasive mechanical ventilation, were eligible for this observational cohort study. Demographics, comorbidities, components of the Sequential Organ Failure Assessment score, and procalcitonin levels on admission were obtained from the electronic health record. The primary outcome was the duration of mechanical ventilation; secondary outcomes included 28-day mortality and time to intubation. Outcomes were assessed within the first 28 days of admission. Baseline demographics and comorbidities were summarized by descriptive statistics. Univariate comparisons were made using Pearson's chi-square test for binary outcomes and Mann-Whitney U test for continuous outcomes. A multiple linear regression was fitted to assess the association between procalcitonin levels and the duration of mechanical ventilation. RESULTS: Patients with an initial procalcitonin level >0.1 ng/ml required a significantly longer duration of mechanical ventilation than patients with a level of ≤0.1 ng/ml (p = 0.021) in the univariate analysis. There was no significant difference in 28-day mortality or time to intubation between the two groups. After adjusted analysis using multivariable linear regression, the duration of mechanical ventilation was, on average, 5.6 (p = 0.016) days longer in patients with an initial procalcitonin level >0.1 ng/ml. CONCLUSION: In this cohort of 93 mechanically ventilated COVID-19 patients, we found an association between an initial plasma procalcitonin level >0.1 ng/ml and the duration of mechanical ventilation. These findings may help to identify patients at risk for prolonged mechanical ventilation upon admission.