Diagnosis Of COVID-19 Disease Research Articles

Bibliometric and Content Analysis of Articles on Artificial Intelligence in Healthcare

The use of artificial intelligence in the healthcare sector is becoming widespread for reasons such as analyzing digital patient data, including it in decision-making processes, improving the quality of healthcare services, and providing cost, time, and access advantages. This study aims to evaluate published articles on bibliometric indicators and the use of artificial intelligence in the healthcare sector and examine the content of the most cited articles. Articles about artificial intelligence in the health sector in the Web of Science database were included in the study using the criteria of “keyword, publication year, and publication language”. The research covers 2,680 articles published in English by 14,195 authors from 106 countries in 1084 journals between 2020-2024. 4,671 different keywords were used in the published articles. The country that published the most was “USA”, the journal was “Journal of Medical Internet Research”, the author was “Meng Ji”, and the most cited author was “Weihua Li”. The 55 most cited (≥50) articles focused on themes related to “diagnosis of COVID-19 disease”, “diagnosis of diseases”, “detection and classification of cancerous cells”, “identification of disease risk factors and disease prediction”, “prediction of treatment outcomes”, “prediction of disease course”, “personalized treatment recommendations”, “decision-making processes”, “ethical considerations, risks, and responsibilities”. With the COVID-19 pandemic, it is seen that the number of articles on artificial intelligence in the healthcare sector has increased exponentially. In the research, articles related to artificial intelligence in the health sector were examined, and a framework was created for researchers by revealing the most publishing countries, journals, authors, most cited authors, and keywords that were used the most.

Racial Disparities in Pulse Oximetry, in COVID-19 and ICU Settings.

This study aimed to assess the impact of race on pulse oximetry reliability, taking into account Spo2 ranges, COVID-19 diagnosis, and ICU admission. Retrospective cohort study covering admissions from January 2020 to April 2024. National COVID Cohort Collaborative (N3C) database, consisting of electronic health records from 80 U.S. institutions. Patients were selected from the N3C database based on the availability of data on self-identified race and both pulse oximetry estimated Spo2 and Sao2. Subgroups included patients in ICU and non-ICU settings, with or without a diagnosis of COVID-19 disease. None. The agreement between Spo2 and Sao2 was assessed across racial groups (American Indian or Alaska Native, Asian, Black, Hispanic or Latino, Pacific Islander, and White). Each patient's initial Sao2 measurement was matched with the closest Spo2 values recorded within the preceding 10-minute time frame. The risk of hidden hypoxemia (Spo2 ≥ 88% but Sao2 < 88%) was determined for various Spo2 ranges, races, and clinical scenarios. We used a generalized logistic mixed-effects model to evaluate the impact of relevant variables, such as COVID-19, ICU admission, age, sex, race, and Spo2, on the risk of hidden hypoxemia, while accounting for the random effects within each hospital. A total of 80,541 patients were included, consisting of 596 American Indian or Alaska Native, 2,729 Asian, 11,889 Black, 13,154 Hispanic or Latino, 221 Pacific Islander, and 51,952 White individuals. Discrepancies between Spo2 and Sao2 were observed across all racial groups, with the most pronounced bias in Black patients. Hidden hypoxemia rates were higher in Black patients across all Spo2 subgroups, for all clinical scenarios. The odds of hidden hypoxemia were higher for Black and Hispanic or Latino patients and for those with COVID-19 disease. Race significantly impacts pulse oximetry reliability. Not only Black and Hispanic or Latino patients were at higher risk for hidden hypoxemia, but also those admitted with a COVID-19 diagnosis. Future in-depth explorations into the underlying causes and potential solutions are needed.

Hybrid Artificial Intelligence Approach to COVID-19 Diagnosis from CT Images: Deep Networks and Classification Analysis

Using lung images obtained by computed tomography (CT), this study aims to detect coronavirus (Covid-19) disease with deep learning (DL) techniques. The study included 751 lung CT images from 118 Covid-19 patients and 628 lung CT images from 100 healthy individuals. In total, 70% of the 1379 images were used for training and 30% for testing. In the study, two different methods were proposed on the same dataset. In the first method, the images were trained on AlexNet, VGG-16, VGG-19, GoogleNet and a proposed network. The performance metrics obtained from the five networks were compared and it was observed that the proposed network achieved the highest accuracy value with 95.61%. In the second method, the images were trained on VGG-16, VGG-19, DenseNet-121, ResNet-50 and MobileNet networks. Among the image features obtained from each of these networks, the best 1000 features were selected by Principal Component Analysis (PCA). The best 1000 features were classified with Random Forest (RF) and Support Vector Machines (SVM). According to the classification results, the best 1000 features selected from the features extracted by the VGG-16 and MobileNet networks were obtained with the highest accuracy rate of 93.94% using SVM. It is thought that this study can be a helpful tool in the diagnosis of Covid-19 disease while reducing time and labor costs with the use of artificial intelligence (AI).

COVID-19-associated pulmonary aspergillosis (CAPA) in hematological patients: Could antifungal prophylaxis be necessary? A nationwide study

BackgroundCOVID-19-associated pulmonary aspergillosis (CAPA) has emerged as a relatively common complication. Multiple studies described this relationship in critical patients, however its incidence and outcome in other risk groups such as immunosuppressed patients remains unknown. In this sense, we aimed to evaluate the rates and outcomes of CAPA in hematological patients and according to the different hematological malignances, comparing to invasive pulmonary aspergillosis (IPA) in non-COVID-19 ones. MethodsNationwide, population-based and retrospective observational cohort study including all adult patients with hematological malignancies admitted in Spain since March 1, 2020 to December 31, 2021. The main outcome variable was the diagnosis of IPA during hospitalization in hematological patients with or without COVID-19 at admission. The rate of CAPA compared to IPA in non-COVID-19 patients in each hematological malignancy was also performed, as well as survival curve analysis. FindingsCOVID-19 was diagnosed in 3.85 % (4367 out of 113,525) of the hematological adult inpatients. COVID-19 group developed more fungal infections (5.1 % vs. 3 %; p < 0.001). Candida spp. showed higher rate in non-COVID-19 (74.2 % vs. 66.8 %; p = 0.015), meanwhile Aspergillus spp. confirmed its predominance in COVID-19 hematological patients (35.4 % vs. 19.1 %; p < 0.001). IPA was diagnosed in 703 patients and 11.2 % (79 cases) were CAPA. The multivariate logistic regression analysis found that the diagnosis of COVID-19 disease at hospital admission increased more than two-fold IPA development [OR: 2.5, 95CI (1.9–3.1), p < 0.001]. B-cell malignancies – specifically B-cell non-Hodgkin lymphoma, multiple myeloma, chronic lymphocytic leukemia and acute lymphoblastic leukemia – showed between four- and six-fold higher CAPA development and 90-day mortality rates ranging between 50 % and 72 %. However, myeloid malignancies did not show higher CAPA rates compared to IPA in non-COVID-19 patients. ConclusionCOVID-19 constitutes an independent risk factor for developing aspergillosis in B-cell hematological malignancies and the use of antifungal prophylaxis during hospitalizations may be warranted.

The clinical course of COVID-19 in pregnant and non-pregnant women

Aims: Pregnancy is a physiological condition that predisposes women to respiratory complications of viral infections thus, bringing the risk of developing more severe disease. The aim of this research was to elucidate the clinical course of COVID-19 in pregnant and non-pregnant women of childbearing age. Mortality rate, laboratory parameters, the occurence of cytokine storm in both groups and the response to treatment have been investigated.&#x0D; Methods: A total of 88 women of childbearing age with a diagnosis of COVID-19 disease has been retrospectively analyzed. Age, comorbidity, length of stay in the intensive care unit and treatment regimen of patients have been obtained from hospital database. Ferritin, IL-6, CRP, procalcitonin, D-dimer, urea, creatinine, GFR, ALT, AST, LDH, lymphocyte count, neutrophil count, white blood cell count were evaluated. Clinical response such as reduction in oxygen requirement and vasopressor utilization before and after treatment were examined&#x0D; Results: The rate of RT - PCR positive results were statistical significantly higher in pregnant women (p=0.003). The median WBC, lymphocyte and leukocyte values of the pregnant patients were higher (p=0.038, p=0.006 and p=0.035, respectively). The median hemoglobin, LDH and ferritin values of pregnant women were lower than those of non-pregnant individuals (p=0.032, p

Bilgisayarlı Tomografi Görüntülerinden Derin Öğrenme ve Makine Öğrenmesi ile covid-19 Hastalığının Teşhisi

Abstract&#x0D; The new virus disease (COVID-19) first came to China towards the end of December 2019 and became a pandemic all over the world. The disease caused a large number of people to be infected and die. Rapid diagnosis of the disease is of great importance in controlling transmission. A computed Tomography device provides successful results in the diagnosis of COVID-19 disease. In this study, two-class (COVID-19 and normal) data sets were created from 7200 lung Computed Tomography images diagnosed between March 2020 and November 2020 in a private hospital with the help of specialist physicians. Verification and testing processes were carried out on Artificial Neural Network (ANN), Support Vector Machine (SVM), K-Nearest Neighbour (KNN) algorithms from Machine Learning algorithms, and ResNet-50, DenseNet-201, InceptionResNetV2, Inceptionv3, VGG-16, Xception architectures from Deep Learning models. As a result of the studies, the DenseNet-201 architecture obtained the highest result from deep learning models with %99,35 training and test %98,75 accuracy rates, respectively. ANN %97,6, KNN %97,4 and SVM %96,9 accuracy rates were obtained from machine learning.

Management of COVID-19 pandemic in hospital at home in two regional Spanish hospitals. what it meant, who we cared for, who died and how assistance has evolved over time

Home Hospitalization (HH) is an alternative hospitalization modality that can be very useful in times of health stress such as the COVID-19 pandemic. This paper includes the management of patients admitted with COVID-19 in HH in two county spanish hospitals for two years. A descriptive, observational and retrospective study of all patients admitted at HH with a diagnosis of COVID-19 disease was carried out. Subsequently, further analysis was carried out to characterize the patients who died in HH or 30 days after discharge and another to compare the management between the first phase of the study (2020) and the second one (2021 and part of 2022). A total of 167 patients were recruited. A 52.1% moved to watch that the recovery continued compared to 40.7% in which it was done to watch that they did not worsen. The patients who died in HAD were older (mean 87.5 years), more comorbid and more likely to have do-not resucitate orders (DNR) in case of cardiac arrest (85%). In the second phase of the study, older patients, more comorbid patients and with a greater degree of DNR orders were admitted than those admitted throughout 2020. HAD is a useful resource to increase the resilience of health systems in cases of stress such as the disease caused by COVID-19. The development and growth of existing units, as well as the creation of new ones where they do not exist, could be a basic tool for the medicine of the future.

Determination of the Effect of the Fowler and Prone Position on Oxygen Saturation in Patients Diagnosed with COVID-19

Objective: This study was conducted to investigate the effect of the Fowler position and prone position on oxygen saturation in patients receiving treatment in clinics with the diagnosis of COVID-19 disease.&#x0D; Method: A total of 40 patients, admitted to the pandemic ward who met the inclusion criteria, were included in the quasi-experimental type study without any sampling. The patients were first given the Fowler position and then the prone position. There was a time interval of 15 minutes wait between the two positions. For each position, peripheral oxygen saturation, heart rate, respiratory and blood pressure values were obtained at initial position placement, after the 30th minute and every hour for the first four hours.&#x0D; Results: The mean age of the participants was 57.57±12.64 years. Respiratory distress, cough, fever, weakness, sweating and headache were the main symptoms. A total of 22.5% of them had a diagnosis of hypertension and Diabetes Mellitus. The requirement for the positioning was found to be 95% in the first five days after admittance. After treatment, 85% of them were discharged home. The mean oxygen saturation values of the patients for every hour in the Prone position were 93.15±1.718 (p=0.035), 93.60±1.809 (p=0.019), 93.93±1.774 (p=0.006) and 94.15±1.718 (p=0.002), respectively in the first four hours. These findings were statistically significant compared to the Fowler position. Respiratory values&#x0D; in the prone position were 17.30±1.159 (p=0.005), 17.20±1.344 (p=0.010), 17.20±1.181 (p=0.005), and 17.05±1.280 (p=0.001), respectively in the first four hours, which were statistically lower than in the Fowler position. There was no significant difference in the mean heart rate and blood pressure in both positions (p&gt;0.05).&#x0D; Conclusion: The prone position was found to have a positive effect on oxygen saturation levels when Fowler and Prone positions were applied in patients receiving treatment with the diagnosis of COVID-19 in hospital wards. Therefore, it is recommended that patients admitted with the diagnosis of COVID-19 be placed in the prone position at regular intervals.

Hematologic abnormalities and pregnancy outcomes in pregnant women with COVID-19

Background: Since declaring coronavirus disease 19 as a pandemic by the World Health Organization, a great concern was directed toward pregnant women and their fetuses. Despite the substantial impact of COVID-19 disease on pregnancy, there is a scarcity of national researches discussing this important issue. Objectives: To study the relationship between peripheral blood abnormalities and COVID-19 in pregnant women. Patients and methods: A case control study was conducted in the labour wards of Baghdad Teaching Hospital in the Medical complex / Baghdad /Iraq during the period from 1st of February till 31st of July, 2021. Fifty pregnant women diagnosed with COVID-19 disease were compared to 50 healthy pregnant women as controls. The pregnant women enrolled in the study were tested by COVID-19-Reverse transcription RT-PCR test upon admission to hospital. The confirmation of COVID-19 diagnosis was done according to the Iraqi guidelines approved by the Iraqi Ministry of Health. Results: Dyspnea was a significant clinical presentation of pregnant women with COVID-19 disease. Those women had abnormal white blood cell count, lymphocytopenia, high neutrophil to lymphocyte ratio, high platelets to lymphocyte ratio and mild to moderate anemia which were significant when compared to controls. The maternal and neonatal morbidity and mortality rates were higher among pregnant women with COVID-19 disease. Abnormalities in peripheral blood system parameters like lymphocyte count, neutrophils count, platelets count and hemoglobin level were predictors of maternal morbidity and mortality. Conclusions: The clinical presentations and hematological abnormalities are useful in the diagnosis of COVID-19 disease in pregnant women and may be used as predictors of maternal and neonatal morbidity and mortality.

AMTLDC: a new adversarial multi-source transfer learning framework to diagnosis of COVID-19.

In recent years, deep learning techniques have been widely used to diagnose diseases. However, in some tasks, such as the diagnosis of COVID-19 disease, due to insufficient data, the model is not properly trained and as a result, the generalizability of the model decreases. For example, if the model is trained on a CT scan dataset and tested on another CT scan dataset, it predicts near-random results. To address this, data from several different sources can be combined using transfer learning, taking into account the intrinsic and natural differences in existing datasets obtained with different medical imaging tools and approaches. In this paper, to improve the transfer learning technique and better generalizability between multiple data sources, we propose a multi-source adversarial transfer learning model, namely AMTLDC. In AMTLDC, representations are learned that are similar among the sources. In other words, extracted representations are general and not dependent on the particular dataset domain. We apply the AMTLDC to predict Covid-19 from medical images using a convolutional neural network. We show that accuracy can be improved using the AMTLDC framework, and surpass the results of current successful transfer learning approaches. In particular, we show that the AMTLDC works well when using different dataset domains, or when there is insufficient data.

COVID-19 HASTALIK TEŞHİSİNİN ADABOOST VE NAİVE BAYES ALGORİTMALARIYLA TAHMİN EDİLMESİ

Coronavirus disease (COVID-19) has infected millions of people all over the world and caused the death of many people. Identifying people with this disease as soon as possible is an important factor to prevent the disease from spreading. For disease detection, PCR (Polymerase Chain Reaction) tests performed. The results of tests always cannot give 100% accurate. In addition, obtaining information about test results sometimes may take a few days. Regarding the persons who applied to health institutions with suspicion of that illness, the diagnosis of COVID-19 disease takes place with the emergence of different disease symptoms. In this study, diagnostic estimates made for patients in the COVID-19 Surveillance dataset implementing Adaboost and Naive Bayes machine learning (ML) algorithm. It is possible to make predictions about new data by gaining experience from pre-existing data by means of using ML algorithms. In dataset determined within international disease codes for COVID-19 disease diagnosis estimates. Symptoms of patients used as attribute data in the dataset and used in binary format to be suitable for machine learning algorithms. According to the results obtained in this study, the classification forecast made with 85% accuracy with the Naive Bayes algorithm and 100% with the Adaboost algorithm.

Biphasic majority voting-based comparative COVID-19 diagnosis using chest X-ray images

The COVID-19 pandemic has been affecting the world since December 2019, and nowadays, the number of infected is increasing rapidly. Chest X-ray images are clinical adjuncts that can be used in the diagnosis of COVID-19 disease. Because of the rapid spread of COVID-19 disease worldwide and the limited number of expert radiologists, the proposed method uses the automatic diagnosis method rather than a manual diagnosis method. In the paper, COVID-19 Positive/Negative (2275 Positive, 4626 Negative) and Normal/Pneumonia (2313 Normal, 2313 Pneumonia) are diagnosed using chest X-ray images. Herein, 80 % and 20 % of the images are used in the training and validation set, respectively. In the proposed method, six different classifiers are trained using chest X-ray images, and the five most successful classifiers are used in both phases. In Phase-1 and Phase-2, image features are extracted using the Bag of Features method for Cosine K-Nearest Neighbor (KNN), Linear Discriminant, Logistic Regression, Bagged Trees Ensemble, Medium Gaussian Support Vector Machine (SVM), excluding SqueezeNet Deep Learning (K = 2000 and K = 1500 for Phase-1 and Phase-2, respectively). In both phases, the five most successful classifiers are determined, and images classify with the help of the Majority Voting (Mathematical Evaluation) method. The application of the proposed method is designed for users to diagnose COVID-19 Positive, Normal, and Pneumonia. The results show that accuracy values obtained by Majority Voting (Mathematical Evaluation) method for Phase-1 and Phase-2 are equal to 99.86 % and 99.28 %, respectively. Thus, it indicates that the accuracy of the whole system is 99.63 %. When we analyze the classification performance metrics for Phase-1 and Phase-2, Specificity (%), Precision (%), Recall (%), F1 Score (%), Area Under Curve (AUC), and Matthews Correlation Coefficient (MCC) are equal to 99.98–99.83–99.07–99.51–0.9974–0.9855 and 99.73–99.69–98.63–99.23–0.9928–0.9518, respectively. Moreover, if the classification performance metrics of the whole system are examined, it is seen that Specificity (%), Precision (%), Recall (%), F1 Score (%), AUC, and MCC are 99.88, 99.78, 98.90, 99.40, 0.9956, and 0.9720, respectively. When the studies in the literature are examined, the results show that the proposed model is better than its counterparts. Because the best performance metrics for the dataset used were obtained in this study. In addition, since the biphasic majority voting technique is used in the study, it is seen that the proposed model is more reliable. On the other hand, although there are tens of thousands of studies on this subject, the usability of these models is debatable since most of them do not have graphical user interface applications. Already, in artificial intelligence technologies, besides the performance of the developed models, their usability is also important. Because the developed models can generally be used by people who are less knowledgeable about artificial intelligence.

Diagnosis of COVID-19 Disease in Chest CT-Scan Images Based on Combination of Low-Level Texture Analysis and MobileNetV2 Features.

Since two years ago, the COVID-19 virus has spread strongly in the world and has killed more than 6 million people directly and has affected the lives of more than 500 million people. Early diagnosis of the virus can help to break the chain of transmission and reduce the death rate. In most cases, the virus spreads in the infected person's chest. Therefore, the analysis of a chest CT scan is one of the most efficient methods for diagnosing a patient. Until now, various methods have been presented to diagnose COVID-19 disease in chest CT-scan images. Most recent studies have proposed deep learning-based methods. But handcrafted features provide acceptable results in some studies too. In this paper, an innovative approach is proposed based on the combination of low-level and deep features. First of all, local neighborhood difference patterns are performed to extract handcrafted texture features. Next, deep features are extracted using MobileNetV2. Finally, a two-level decision-making algorithm is performed to improve the detection rate especially when the proposed decisions based on the two different feature set are not the same. The proposed approach is evaluated on a collected dataset of chest CT scan images from June 1, 2021, to December 20, 2021, of 238 cases in two groups of patient and healthy in different COVID-19 variants. The results show that the combination of texture and deep features can provide better performance than using each feature set separately. Results demonstrate that the proposed approach provides higher accuracy in comparison with some state-of-the-art methods in this scope.

OWAE-Net: COVID-19 detection from ECG images using deep learning and optimized weighted average ensemble technique

• In this study, pre-trained deep learning models are used for covid-19 detection in ECG images. • The optimized weighted average ensemble method is used to improve the performance for multi-class classification. • The obtained results shows that the deep models can be used in early detection of covid-19 in ECG images. Covid-19 is an infectious disease that has cost millions of lives all over the world. A faster and safer diagnosis of covid-19 is highly desirable in order to stop its spread. An electrocardiogram (ECG) signal-based diagnosis has shown its potential in the diagnosis of cardiac, stroke, and covid-19. In this study, an ensemble of three deep learning models are used for covid-19 detection in ECG images for multi-class classification. The results obtained with the weighted average ensemble technique have been improved by using the grid search technique. For multi-class classification, an optimized weighted average ensemble (OWAE) model classifies the ECG images with an accuracy of 95.29%, an F1-score of 95.4%, a precision of 95.5%, and a recall of 95.3%. In case of binary classification, VGG-19, EfficientNet-B4, and DenseNet-121 performed comparatively well with 100% accuracy. These results show that deep learning can be used in the diagnosis of covid-19 disease using ECG images.

The expression patterns of MALAT-1, NEAT-1, THRIL, and miR-155-5p in the acute to the post-acute phase of COVID-19 disease

IntroductionOne of the hallmarks of COVID-19 is overwhelming inflammation, which plays a very important role in the pathogenesis of COVID-19. Thus, identification of inflammatory factors that interact with the SARS-CoV-2 can be very important to control and diagnose the severity of COVID-19. The aim of this study was to investigate the expression patterns of inflammation-related non-coding RNAs (ncRNAs) including MALAT-1, NEAT-1, THRIL, and miR-155-5p from the acute phase to the recovery phase of COVID-19. MethodsTotal RNA was extracted from Peripheral Blood Mononuclear Cell (PBMC) samples of 20 patients with acute COVID-19 infection and 20 healthy individuals and the expression levels of MALAT-1, NEAT-1, THRIL, and miR-155-5p were evaluated by real-time PCR assay. Besides, in order to monitor the expression pattern of selected ncRNAs from the acute phase to the recovery phase of COVID-19 disease, the levels of ncRNAs were re-measured 6‒7 weeks after the acute phase. ResultThe mean expression levels of MALAT-1, THRIL, and miR-155-5p were significantly increased in the acute phase of COVID-19 compared with a healthy control group. In addition, the expression levels of MALAT-1 and THRIL in the post-acute phase of COVID-19 were significantly lower than in the acute phase of COVID-19. According to the ROC curve analysis, these ncRNAs could be considered useful biomarkers for COVID-19 diagnosis and for discriminating between acute and post-acute phase of COVID-19. DiscussionInflammation-related ncRNAs (MALAT-1, THRIL, and miR-150-5p) can act as hopeful biomarkers for the monitoring and diagnosis of COVID-19 disease.

A Deep Learning Approach for the Detection of COVID-19 from Chest X-Ray images using Convolutional Neural Networks

The COVID-19 (coronavirus) is an ongoing pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The virus was first identified in mid-December 2019 in the Hubei province of Wuhan, China and by now has spread throughout the planet with more than 75.5 million confirmed cases and more than 1.67 million deaths. With limited number of COVID-19 test kits available in medical facilities, it is important to develop and implement an automatic detection system as an alternative diagnosis option for COVID-19 detection that can used on a commercial scale. Chest X-ray is the first imaging technique that plays an important role in the diagnosis of COVID-19 disease. Computer vision and deep learning techniques can help in determining COVID-19 virus with Chest X-ray Images. Due to the high availability of large-scale annotated image datasets, great success has been achieved using convolutional neural network for image analysis and classification. In this research, we have proposed a deep convolutional neural network trained on five open access datasets with binary output: Normal and Covid. The performance of the model is compared with four pre-trained convolutional neural network- based models (COVID-Net, ResNet18, ResNet and MobileNet-V2) and it has been seen that the proposed model provides better accuracy on the validation set as compared to the other four pre-trained models. This research work provides promising results which can be further improvise and implement on a commercial scale.

A review

In late 2019, December coronaviruses were identified in china causing respiratory tract disease. SARS-CoV-2 was the name given to the triggering virus, and COVID-19, or novel coronaviruse-19, was given to the disease. Since the genome sequencing of SARS-CoV-2 is identical to bat CoV RaTG13, this strain is most likely from bats. To lower the rate of transmission people should avoid public gathering, wear masks, self-hygiene is must. Cold, cough and fever are the early symptoms of the infection. If a person is sick should start self-quarantine in the house itself or consult to the doctor for further diagnosis of COVID-19 disease. RT-PCR and CT scan are the best methods to detect the virus in the body. If the person is infected sever it might take longer time for the recovery and called long-COVID-19. Vaccinations have been started in the countries to reduce the effect of virus and decrease the transmission of virus.

Clinical Factors and Characteristics that Affect Prognosis of COVID-19 Patients

Background: COVID-19 disease has been linked to severe and deadly respiratory disease in various age groups. In team of COVID-19 disease prognosis and mortality, age is a key factor. The chance of mortality climbs from 0.66 percent to 7.8 percent in those over the age of 80. Males have a greater COVID-19 prevalence rate than females. Furthermore, even in the absence of additional comorbidities, sex was an essential biological determinant in prognosis and death risk among COVID-19 positive people. Another factor that influences the prognosis of COVID-19 illness is comorbidity. It's critical to comprehend the clinical characteristics and variables that influence the prognosis of COVID-19 illness patients. Objective: To describe the clinical factors and characteristics that affect COVID-19 patients’ prognosis. Methods: Retrospective research performed at Queen Alia Military Hospital by examining clinical characteristics of hospitalized individuals with a verified diagnosis of COVID-19 disease, as confirmed by polymerase-chain-reaction (PCR) assay. The research comprised laboratory outcomes, demographic features, chest imaging, and clinical data. Participants were categorized into the progression category and the improvement/stabilization group. Results: A total of 51 participants with a mean age of 33.6 years were involved in the research. Forty-six participants (90.2%) were categorized as an improvement/stabilization group, and five patients were classified as a progression group (9.8%). Fever, diarrhea, breath shortness, and cough were the most common signs and symptoms. Significant differences were detected between groups concerning age, comorbid diseases (diabetes and hypertension), disease severity, length of stay in the hospital, WBCs, creatinine, total bilirubin, ALP, LDH, CRP, albumin, oxygen saturation, and pulmonary consolidation (P &lt; 0.05). Moreover, multivariate logistic regression determined age (OR, 1.79; CI: .950 3.28; P = 0.035), diabetes (OR, 1.62; CI: 1.02 - 4.19; P 0.012), hypertension (OR, 1.13; CI: 1.04 - 3.62; P =0.023), increased creatinine (OR, 3.57; CI: 2.30 - 6.71; P = 0.01), and increased total bilirubin (OR, 2.82; CI: 1.72 - 3.28; P = 0.031) as risk factors for disease progression. Conclusion: Chronic disease and abnormal creatinine and bilirubin level including other factors contribute to poor prognosis of COVID-19 patients. This study shows that the clinical characteristics of the patient should be prioritized in disease management.

SARS-CoV-2 Direct Real Time PCR without RNA Extraction: Appropriate Method or Not?

Real time PCR (RT-PCR) detection method is the widely used for COVID-19 virus detection. This includes sample collection in viral transport medium (VTM), viral RNA extraction followed by detection of virus using fluorescence dye-based system using RT-PCR machine. Several studies have demonstrated a new method which replaces the extraction step by a simple method involving DTT and Proteinase-K and heat treatment. ICMR and few other governing bodies have approved such protocols but are they appropriate in clinical context? In present study, we tried to evaluate one such protocol by using ICMR and WHO approved COVID-19 detection protocol (of CoviPath™ COVID-19 RT-PCR Kit) by replacing RNA extraction step. We used 228 clinical COVID-19 samples for studying method which includes 176 positive (CT values from 14 to 23; 24 to 31 and 32 to 37 were considered as high, moderate and low positive respectively) and 52 negative nasopharyngeal and oropharyngeal swabs samples. We got 100% concordant results with negative samples and 92% concordant and 8% non-concordant results for positive samples. Non-concordant results are with low positive samples. Low level of positivity in the samples could indicate the initial/end stage of COVID-19 disease. If they are at the initial stage, they can be the potential carrier and spread the disease. Authors believe that direct methods can be used for screening bit not for diagnosis of COVID-19 disease.

Medical device related pressure injuries in COVID-19 patients followed up in an intensive care unit

Aim: The aim of this study is to determine the prevalence of medical device-related pressure injuries in COVID-19 patients.&#x0D; Material and Method: This study was conducted with a cross-sectional and retrospective design. The data of 436 patients who were followed up and treated in the Anesthesia and Reanimation Intensive Care Unit with the diagnosis of COVID-19 disease between 11.03.2020- 31.02.2021 in a Training and Research Hospital in İstanbul were included in the study. The sample of the study consisted of 32 patients out of 436 patients who met the sampling criteria. The data obtained by retrospective reviewing of the patient records were analyzed through the "Patient Information Form" and "Pressure Injury Stage" forms.&#x0D; Results: Medical device-related pressure injury developed in 32 (7.3%) of 436 patients examined in the study on the specified dates. 90.6% of these patients were male, and the average age was 67.5. 43.7% had comorbid diseases. According to the Braden Risk Assessment Scale, 25% of these patients had medium and 71.8% high risk. Medical devices that cause pressure injury were continuous positive airway pressure mask (n=13), intubation tube (n=7), nasogastric tube (n=5), nasal cannula (n=3), gel pads (n=3), and oxygen mask (n=1).&#x0D; Conclusion: In this study, the potential factors in the study that may have led to the incidence of medical device-related pressure injury specific to COVID-19 disease include the rapid increase in the need for respiratory support, ischemia caused by this infection, and the use of prone position.