The role of Lung Ultrasound in the diagnosis of SARS-COV-2 disease in pregnant women.

AIM: To evaluate the role of lung ultrasound (LUS) in recognizing lung abnormalities in pregnant women affected by COVID-19 pneumonia. MATERIALS AND METHODS: An observational study analyzing LUS patterns in 60 consecutively enrolled pregnant women affected by COVID-19 infection was performed. LUS was performed by using a standardized protocol by Soldati et al. The scoring system of LUS findings ranged from 0 to 3 in increasing alteration severity. The highest score obtained from each landmark was reported and the sum of the 12 zones examined was calculated. RESULTS: Patients were divided into two groups: 26 (43.3%) patients with respiratory symptoms and 32 (53.3%) patients without respiratory symptoms; 2 patients were asymptomatic (3.3%). Among the patients with respiratory symptoms 3 (12.5%) had dyspnea that required a mild Oxygen therapy. A significant correlation was found between respiratory symptoms and LUS score (p < 0.001) and between gestational weeks and respiratory symptoms (p = 0.023). Regression analysis showed that age and respiratory symptoms were risk factors for highest LUS score (p < 0.005). DISCUSSION: LUS can affect the clinical decision course and can help in stratifying patients according to its findings. The lack of ionizing radiation and its repeatability makes it a reliable diagnostic tool in the management of pregnant women.

Development of prognostic models to estimate the probability of lung injury one year after COVID-19-related hospitalization-a prospective study.

Background: Long-term effects of severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) infection still under study. The objectives of this study were to identify persistent pulmonary lesions 1 year after coronavirus disease 2019 (COVID-19) hospitalization and assess whether it is possible to estimate the probability that a patient develops these complications in the future. Methods: A prospective study of ≥18 years old patients hospitalized for SARS-COV-2 infection who develop persistent respiratory symptoms, lung function abnormalities or have radiological findings 6-8 weeks after hospital discharge. Logistic regression models were used to identify prognostic factors associated with a higher risk of developing respiratory problems. Models performance was assessed in terms of calibration and discrimination. Results: A total of 233 patients [median age 66 years [interquartile range (IQR): 56, 74]; 138 (59.2%) male] were categorized into two groups based on whether they stayed in the critical care unit (79 cases) or not (154). At the end of follow-up, 179 patients (76.8%) developed persistent respiratory symptoms, and 22 patients (9.4%) showed radiological fibrotic lesions with pulmonary function abnormalities (post-COVID-19 fibrotic pulmonary lesions). Our prognostic models created to predict persistent respiratory symptoms [post-COVID-19 functional status at initial visit (the higher the score, the higher the risk), and history of bronchial asthma] and post-COVID-19 fibrotic pulmonary lesions [female; FVC% (the higher the FVC%, the lower the probability); and critical care unit stay] one year after infection showed good (AUC 0.857; 95% CI: 0.799-0.915) and excellent performance (AUC 0.901; 95% CI: 0.837-0.964), respectively. Conclusions: Constructed models show good performance in identifying patients at risk of developing lung injury one year after COVID-19-related hospitalization.

Prevalence and outcomes of COVID-19 in children with respiratory and gastrointestinal symptoms.

More than a year after its declaration as a pandemic, Coronavirus disease 2019 (COVID-19) remains a global health problem. The common symptoms of COVID-19 are like the presentation of common childhood illnesses. Hence, we determined the prevalence, and outcomes of COVID-19 in children with respiratory and gastrointestinal symptoms. This study was a retrospective review of data obtained from the screening of children with respiratory and or gastrointestinal symptoms for COVID-19. All children aged 1 month to 18 years with respiratory and or gastrointestinal symptoms were screened for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using real-time polymerase chain reaction (RT-PCR). The tests for COVID-19 along their socio-demographic and clinical parameters were extracted from the hospital records. We recruited 129 children with a median (IQR) age of 1.1(0.5 - 2.3) years. Most of the children 111(86.0%) were under-five. Out of 129 children, 13 (10.1%) were positive for COVID-19 and 10 of the 13 positive cases were under-five. The COVID-19 status had no relationship with age, gender, and socioeconomic status, p >0.05. Children with a history of contact with a confirmed case had a significant association with COVID-19 (OR 110.368, CI 5.519-2207.306). Amongst the clinical features, the presence of fever was associated with COVID-19 (OR 4.4 CI 1.239-14.750) Conclusion: This study shows a higher likelihood of COVID-19 in children with a history of contact and fever.

Chest CT Findings after Mild COVID-19 Do Not Explain Persisting Respiratory Symptoms: An Explanatory Study.

(1) Background: Lung tissue involvement is frequently observed in acute COVID-19. However, it is unclear whether CT findings at follow-up are associated with persisting respiratory symptoms after initial mild or moderate infection. (2) Methods: Chest CTs of patients with persisting respiratory symptoms referred to the post-COVID-19 outpatient clinic were reassessed for parenchymal changes, and their potential association was evaluated. (3) Results: A total of 53 patients (31 female) with a mean (SD) age of 46 (13) years were included, of whom 89% had mild COVID-19. Median (quartiles) time from infection to CT was 139 (86, 189) days. Respiratory symptoms were dyspnea (79%), cough (42%), and thoracic pain (64%). Furthermore, 30 of 53 CTs showed very discrete and two CTs showed medium parenchymal abnormalities. No severe findings were observed. Mosaic attenuation (40%), ground glass opacity (2%), and fibrotic-like changes (25%) were recorded. No evidence for an association between persisting respiratory symptoms and chest CT findings was found. (4) Conclusions: More than half of the patients with initially mild or moderate infection showed findings on chest CT at follow-up. Respiratory symptoms, however, were not related to any chest CT finding. We, therefore, do not suggest routine chest CT follow-up in this patient group if no other indications are given.

COVID-19 Data Clustering Using K-means and Fuzzy c-means Algorithm

Corona Virus Disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory symptoms. It has been declared a global pandemic since 2019 by the World Health Organization. Countries are in an authoritarian state of preventing and controlling this pandemic, and the USA is the central hub. The COVID-19 virus has also shown variance. As an outcome of the genetic recombination of genes that arise from coronavirus, their short life span results in mutations that promote new strains. However, the number of individuals who passed their lives is still counted. Additionally, it is crucial to analyze the spread of the virus before it is deferred in the lungs. In this research, the effort has been taken to predict the proliferation of the virus through various chest radiography images by data clustering. In this study, two clustering algorithms, i.e., the K-means algorithm and the Fuzzy c-means algorithm, have been used better to analyze the spread of the virus in the lungs. These algorithms are further being compared and evaluated for the precise result of both models. This study helps to recognize the most suitable clustering model for the COVID-19 prediction and spread of the virus in the lung. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

Trend in the admissions of patients with non-COVID-19 respiratory symptoms during COVID-19 pandemic and its impact on hospital finances in Surabaya, Indonesia.

Background: COVID-19 cases surge, it has a crucial impact on healthcare systems, with rapidly increasing demand for healthcare resources in hospitals and intensive care units (ICUs) in Indonesia and worldwide. It is necessary to quantify the extent to which the effects of the COVID-19 pandemic on the hospital admissions, and clinical and financial outcomes of patients with non-COVID-19 respiratory symptoms. Objective: To determine whether the COVID-19 pandemic changed the hospitalisation of child and adult patients with non-COVID-19 respiratory conditions and whether these changes affected the patient's disease condition, clinical outcomes, and hospital finances. Methods: A retrospective cohort study was conducted from May 1, 2018 (before the COVID-19 pandemic) until December 31, 2021. Total sampling was done to compare hospital admission of patients with non-COVID-19 respiratory symptoms before versus during the COVID-19 pandemic. The results were analyzed using SPSS 26.0 and SmartPLS.v.3.2.9. Results: There was a reduction in hospitalisations for respiratory disorders unrelated to COVID-19 during the pandemic by 55.3% in children and 47.8% in adult patients. During the pandemic, the average hospital revenue per patient of child and adult patients increased significantly, but the profit per patient decreased. Pathway analysis showed that in children, the COVID-19 Pandemic changed disease severity and complexity (ß = 0.132, P < 0.001), as well as clinical outcomes (ß = 0.029, P < 0.05). In adults, the COVID-19 pandemic improves disease severity and complexity (ß = -0.020, P < 0.001), as well as clinical outcomes (ß = -0.013, P < 0.001). COVID-19 pandemic increases care charges (in children with ß = 0.135, P < 0.001; and in the adult patients with ß = 0.110, P < 0.001), worsens hospital financial outcomes relating to child (ß = -0.093, P < 0.001) and adult patient (ß = -0.073, P < 0.001). In adult patients, seasonal variations moderate the impact of the COVID-19 pandemic on improving disease conditions (ß = -0.032, P=<0.001). The child structural model effectively predicted clinical outcomes (Q2 = 0.215) and financial outcomes (Q2 = 0.462). The adult structural model effectively predicted clinical outcomes (Q2 = 0.06) and financial outcomes (Q2 = 0.472). Conclusion: The conclusions are that the number of non-COVID respiratory patients decreased during the COVID-19 pandemic (47.8% in adult patients, 55.3% in child patients). Disease severity and complexity increased in child patients but decreased in adult patient. Costs of care and insurance payments increased. Since the insurance payments did not increase as much as the cost of care, hospital profit decreased.

Viral metagenomics unveils MW (Malawi) polyomavirus infection in Brazilian pediatric patients with acute respiratory disease.

Viral metagenomics has been extensively applied for the identification of emerging or poorly characterized viruses. In this study, we applied metagenomics for the identification of viral infections among pediatric patients with acute respiratory disease, but who tested negative for SARS-CoV-2. Twelve pools composed of eight nasopharyngeal specimens were submitted to viral metagenomics. Surprisingly, in two of the pools, we identified reads belonging to the poorly characterized Malawi polyomavirus (MWPyV). Then, the samples composing the positive pools were individually tested using quantitative polymerase chain reaction for identification of the MWPyV index cases. MWPyV-positive samples were also submitted to respiratory virus panel testing due to the metagenomic identification of different clinically important viruses. Of note, MWPyV-positive samples tested also positive for respiratory syncytial virus types A and B. In this study, we retrieved two complete MWPyV genome sequences from the index samples that were submitted to phylogenetic inference to investigate their viral origin. Our study represents the first molecular and genomic characterization of MWPyV obtained from pediatric patients in South America. The detection of MWPyV in acutely infected infants suggests that this virus might participate (coparticipate) in cases of respiratory symptoms. Nevertheless, future studies based on testing of a larger number of clinical samples and MWPyV complete genomes appear to be necessary to elucidate if this emerging polyomavirus might be clinically important.

Factors Influencing the Length of Hospital Stay Among Pediatric COVID-19 Patients at Queen Rania Al Abdullah Hospital for Children: A Cross-Sectional Study.

Background COVID-19 caused by SARS-CoV-2 is a worldwide epidemic. Children are less commonly infected and have less severe symptoms than adults. However, they are at risk for COVID-19-associated severe sickness and hospitalization. The duration of stay is a major driver of effective health treatment during hospitalization; thus, it is only logical to attempt to comprehend the factors influencing the length of stay (LOS) for these patients, particularly in light of the ongoing pandemic caused by the new SARS-CoV-2 virus. As predictors of hospital LOS, several variables, including age, gender, disease severity, hospital mortality, insurance type, and hospital location, have been discovered. In our study, we focused on the severity of the patient's condition, the presence of comorbidities, and the necessary therapeutic regimen to predict the duration of stay. This study aimed to answer the following questions: If a patient has comorbidity and has COVID-19 requiring hospital treatment, will the patient's comorbidity elongate the duration of stay at the hospital for further management in the pediatric age group? What are the risk factors that play a significant role in the hospital stay duration in pediatrics? Methodology We gathered data from 100 hospitalized children aged up to 14 years who tested positive for COVID-19, which was not specific to variants of SARS-CoV-2, over 24 months (February 2020-February 2022) at Queen Rania Al Abdullah Hospital for Children, one of the Health Care Accreditation Council accredited facilities. Clinical symptoms, signs, oxygen demand, imaging study results, laboratory data, and usage of corticosteroid and antiviral medication were all taken from patients' medical records. There were no limitations in taking the sample of patients. All patients in the duration mentioned were included. Results Clinical data of 100 COVID-19-positive pediatric patients were analyzed; 52% of the patients had associated chronic illnesses, while 48% were medically free. The longest duration of LOS was 28 days, the shortest was one day, the median was eight days, and five days was the most frequent among patients owing to 21% of patients, using mean descriptive statistics. We compared LOS to having or not having comorbidities. The mean LOS of patients with the comorbid disease was 6.15 days, with a maximum of 28 days, while for patients without chronic illnesses, the mean was 4.81 days with a maximum of 14 days. The significance was 0.07. Our results also showed a significant correlation between using steroids and LOS, as it had an advantageous effect by decreasing it with a significance value of 0.04. Having abnormal findings on chest computed tomography (CT) scan was also associated with increased LOS with a significant value of 0.00. Conclusions According to our research, there was no direct association between comorbidity and hospital LOS, which is counterintuitive, as it was influenced by multiplayers of variables such as using steroids, which decreased the LOS, and abnormal findings on chest CT, which resulted in lengthening of the hospital stay. Our findings cannot be proven without further research and a larger patient sample.

Post-COVID interstitial lung disease in symptomatic patients after COVID-19 disease.

COVID-19 is often associated with long-lasting pulmonary symptoms. Data are scarce about interstitial lung disease (ILD) in patients following COVID-19 hospitalization with persistent symptoms. We retrospectively reviewed all cases sent to pulmonary post-COVID evaluation due to persistent symptoms between February 2021 and February 2022 (N = 318). All patients with suspected ILD (N = 44) were reviewed at the multidisciplinary discussion. Patient characteristics, symptoms, time since hospitalization, detailed lung function measurements and 6-min walk test (6MWT) were evaluated. The post-COVID ILD suspected group included more men (68.2 vs. 31.8%) with significantly older age compared to the control group (64.0 ± 12.3 vs. 51.3 ± 14.9 years). Most patient needed hospital care for COVID-19 pneumonia (68.6% of all patients and 84.1% of ILD suspected group) and average time since hospitalization was 2.4 ± 2.3 months. Persisting symptoms included fatigue (34%), dyspnoea (25.2%), cough (22.6%), and sleep disorders (insomnia 13.2%; sleepiness 8.2%). Post-COVID ILD presented more often with new symptoms of cough and sleepiness. Functional impairment, especially decreased walking distance and desaturation during 6-min walk test (6MWT) were detected in the ILD-suspected group. Respiratory function test in the post-COVID ILD group showed slight restrictive ventilatory pattern (FVC: 76.7 ± 18.1%, FEV1: 83.5 ± 19.1%, TLC: 85.6 ± 28.1%) and desaturation during 6MWT were detected in 41% of patients. LDCT changes were mainly ground glass opacities (GGO) and/or reticular abnormalities in most cases affecting < 10% of the lungs. Our data indicate that suspected post-COVID ILD is affecting 13.8% of symptomatic patients. High resolution chest CT changes were mainly low extent GGO/reticulation, while long-term lung structural changes need further evaluation.

Impact of prolonged use of facemask in COVID-19 pandemic on the health of the population of Jeddah, Saudi Arabia

Background: Face masks have become a crucial part of everyday life across the globe since COVID-19 was declared a pandemic. Objectives: To study the impact of prolonged use of facemasks on the health of the population of Jeddah, Saudi Arabia Method: This was a cross sectional study;the non-probability convenient sampling method was used to collect data on 248 subjects via online-Google form questionnaire. The questionnaire provided information on the personal, sociodemographic characteristics, and clinical aspects of the studied subjects. Statistical analysis: data were analyzed using SPSS version 23. The Chi square test of significance was used. The level of significance was 0.05. Results: Respiratory tract infection (RTI) is common in Saudi Arabia. About 25% of the subjects did not use facemasks. Almost all the subjects who used facemasks knew how to wear them and the reason for their use, and were worried about catching RTI. A minority of the studied subjects didn't know the association between chronic diseases and RTI. Males significantly felt more than females that wearing facemasks would make them embarrassed, and would affect critically their social communication. The majority of the studied subjects did not want to wear a mask when they had flu, and they thought that others should protect themselves. Facemasks interfere with smoking habits and practicing exercise. Wearing masks was associated with a feeling of difficulty of breathing when walking, causing headache, skin problems and sore throat. Also the majority could not handle wearing masks for 3 hours straight. About 60% of the subjects felt uncomfortable when they sit with people who do not wear masks. The females significantly felt that the surgical mask is better than the cloth mask, compared to males. The majority prefer to wear one mask only, and use it several times. Conclusion: Respiratory tract infection is common in Jeddah city. A great proportion of the subjects do not wear facemasks in public places, particularly when they have flu. Wearing of facemasks is associated with several clinical adverse effects. The knowledge about types and use of the facemasks is deficient in a great proportion of the population. These points will help the health care planners when they design health education programs to educate the public about use of facemasks and the ways to avoid physical side effects

Using search trends before and after the COVID-19 outbreak in China to analyze digestive symptoms: medical informatics study of the Baidu Index data.

Background: From the beginning of 2020, the world was plunged into a pandemic caused by the novel coronavirus disease-19 (COVID-19). People increasingly searched for information related to COVID-19 on internet websites. The Baidu Index is a data sharing platform. The main data provided is the search index (SI), which represents the frequency that keywords are used in searches. Methods: January 9, 2020 is an important date for the outbreak of COVID-19 in China. We compared the changes of SI before and after for 7 keywords, including "fever", "cough", "nausea", "vomiting", "abdominal pain", "diarrhea", "constipation". The slope and peak values of SI change curves are compared. Ten provinces in China were selected for a separate analysis, including Beijing, Gansu, Guangdong, Guangxi, Heilongjiang, Hubei, Sichuan, Shanghai, Xinjiang, Tibet. The change of SI was analyzed separately, and the correlation between SI and demographic and economic data was analyzed. Results: During period I, from January 9 to January 25, 2020, the average daily increase (ADI) of the SI for "diarrhea" was lower than that for "cough" (889.47 vs. 1,799.12, F=11.43, P=0.002). In period II, from January 25 to April 8, 2020, the average daily decrease (ADD) of the SI for "diarrhea" was significantly lower than that for "cough", with statistical significance (cough, 191.40 vs. 441.44, F=68.66, P<0.001). The mean SI after January 9, 2020 (pre-SI) was lower than that before January 9, 2020 (post-SI) (fever, 2,616.41±116.92 vs. 3,724.51±867.81, P<0.001; cough, 3,260.04±308.43 vs. 5,590.66±874.25, P<0.001; diarrhea, 4,128.80±200.82 vs. 4,423.55±1,058.01, P<0.001). The pre-SI mean was correlated with population (P=0.004, R=0.813) and gross domestic product (GDP) (P<0.001, R=0.966). The post-SI peak was correlated with population (P=0.007, R=0.789), GDP (P=0.005, R=0.804), and previously confirmed cases (PCC) (P=0.03, R=0.670). The growth rate of the SI was correlated with the post-SI peak (P=0.04, R=0.649), PCC (P=0.003, R=0.835). Conclusions: Diarrhea was of widespread concern in all provinces before and after the COVID-19 outbreak and may be associated with novel coronavirus infection. Internet big data can reflect the public's concern about diseases, which is of great significance for the study of the epidemiological characteristics of diseases.

An Experimental Measurement Through Folk Therapy to Strengthen the Immunity After Confirmed COVID-19

This study is to perform experimental measurements through folk therapy to strengthen the physical immunity after confirmed COVID-19. The survey was conducted from May 8 to July 10, 2021. The experimental group applied to the information system was classified as 46 people, and the control group not applied was classified as 46 people. In this study, a total of 92 people used the survey The process of time change of symptoms of COVID-19 was analyzed by a t-test. The effect of applying folk remedies was measured on 8, 16, 24, and 32 days before and after applying folk remedies for symptoms of COVID-19. The results of this study were as follows. Firstly, in terms of age, 54.3% of the experimental group was significantly higher than 34.8% of the control group at the age of 60 or older(X2=7.51, p<.05). Secondly, the residual cough decreased significantly after application compared to before folk therapy(t=5.21, p<.05). Thirdly, the residual cough continued to decrease significantly after 8 days than before the experiment. The results were confirmed that the folk remedies applied in this study were effective. The research derived from this results is expected to contribute to alleviating respiratory symptoms

MAGNETOFLUIDICS-ENABLED POINT-OF-CARE SARS-COV-2 DIAGNOSTICS

As the SARS-CoV-2 virus continues to mutate, global eradication of infections is unlikely, and COVID-19 is predicted to become a seasonal or endemic disease like influenza. Widespread detection of variant strains will be critical to inform policy decisions to mitigate further spread, and post-pandemic multiplexed screening of respiratory viruses will be necessary to properly manage patients presenting with similar respiratory symptoms. We have developed a portable, magnetofluidic platform for multiplexed PCR testing in <30 min. Cartridges were designed for multiplexed detection of SARS-CoV-2 with either distinctive variant mutations or with Influenza A and B and tested with clinical samples. © 2021 MicroTAS 2021 - 25th International Conference on Miniaturized Systems for Chemistry and Life Sciences. All rights reserved.

Risk factors for symptoms of infection and the acquisition of pathogens among pilgrims at the Grand Magal of Touba, 2017-2021.

BACKGROUND: Respiratory and gastrointestinal symptoms are frequent in pilgrims at the Grand Magal of Touba (GMT). METHODS: Pilgrims were prospectively investigated in 2017-2021 for demographics, chronic conditions, preventive measures, respiratory and gastrointestinal symptoms, and pathogen carriage using PCR assays. RESULTS: 535 pilgrims were included. 54.8% and 13.3% reported respiratory and gastrointestinal symptoms, respectively. 18.4% acquired respiratory viruses, notably rhinovirus (10.1%) and coronaviruses (5.6%) and 39.9% bacteria, notably Haemophilus influenzae (18.9%) and Streptococcus pneumoniae (14.1%). The acquisition of gastrointestinal pathogens was lower, with enteroaggregative Escherichia coli (18.9%) and enteropathogenic Escherichia coli (10.5%) being the most frequent. A decrease was observed in the acquisition rates of pathogens in 2020-2021 GMT. Female pilgrims were more at risk of respiratory and gastrointestinal symptoms. Respiratory symptoms were associated with virus acquisition (aRR: 2.20, 95%CI [1.38-3.50]) and S. pneumoniae acquisition (aRR: 2.76, 95%CI = [1.64-4.62]). Using hand soap was associated with a decrease in the acquisition of rhinovirus (aRR: 0.42, 95%CI [0.22-0.80]) and coronavirus (aRR: 0.42, 95%CI [0.22-0.81]). Using face masks was associated with a decrease in reporting of respiratory symptoms (aRR: 0.54, 95% [0.35-0.86]). CONCLUSION: Hand washing with soap and wearing face masks should be recommended to GMT pilgrims.

Time taken by individuals with respiratory symptoms to present to primary care: a descriptive study of assessments at Australian General Practitioner-led respiratory clinics.

Effective control of coronavirus disease 2019 (COVID-19) has been challenging, in part due to significant asymptomatic and pre-symptomatic transmission of disease. Reducing the time between symptom onset and COVID-19 testing and isolation allows enhanced outbreak control. The purpose of this study is to describe the time taken by participants to present to general practitioner-led (GP) respiratory clinics for assessment following the development of symptoms, and to explore associations between demographic and geographic characteristics and the time to presentation. A total of 314,148 participants, who were assessed in GP respiratory clinics between 1 February and 31 August 2021, were included in the analysis. The median age of participants at presentation was 33 years (interquartile range, IQR: 15-49). The median time from development of symptoms to presentation for assessment at GP respiratory clinics was 2 days (IQR: 1-3). Participants were more likely to present within one day of symptom onset if they were aged between 15 and 64 years (43.4%), lived in urban areas (40.9%) or were non-Indigenous (40.2%). Participants in New South Wales and Victoria had twice the odds (OR 2.01; 95% confidence interval (CI): 1.95, 2.08) of presenting at a GP respiratory clinic within one day of symptom onset in August 2021, when there was a COVID-19 outbreak in those states, than they did in March 2021, when there was no COVID-19 outbreak in Australia. The number of days from symptom onset to presentation at a GP respiratory clinic was strongly associated with the presence of a COVID-19 outbreak. Participant age, location of the clinic, and Indigenous status of participants were also associated with the time to presentation. This study highlights the importance of recognising COVID-19 as a potential cause of symptoms, as well as the importance of providing easily accessible, and culturally appropriate, testing facilities for the population.

Non-invasive Vagus Nerve Stimulation for COVID-19: Results From a Randomized Controlled Trial (SAVIOR I).

Background: Severe coronavirus disease 2019 (COVID-19) is characterized, in part, by an excessive inflammatory response. Evidence from animal and human studies suggests that vagus nerve stimulation can lead to reduced levels of various biomarkers of inflammation. We conducted a prospective randomized controlled study (SAVIOR-I) to assess the feasibility, efficacy, and safety of non-invasive vagus nerve stimulation (nVNS) for the treatment of respiratory symptoms and inflammatory markers among patients who were hospitalized for COVID-19 (ClinicalTrials.gov identifier: NCT04368156). Methods: Participants were randomly assigned in a 1:1 allocation to receive either the standard of care (SoC) alone or nVNS therapy plus the SoC. The nVNS group received 2 consecutive 2-min doses of nVNS 3 times daily as prophylaxis. Efficacy and safety were evaluated via the incidence of specific clinical events, inflammatory biomarker levels, and the occurrence of adverse events. Results: Of the 110 participants who were enrolled and randomly assigned, 97 (nVNS, n = 47; SoC, n = 50) had sufficient available data and comprised the evaluable population. C-reactive protein (CRP) levels decreased from baseline to a significantly greater degree in the nVNS group than in the SoC group at day 5 and overall (i.e., all postbaseline data points collected through day 5, combined). Procalcitonin level also showed significantly greater decreases from baseline to day 5 in the nVNS group than in the SoC group. D-dimer levels were decreased from baseline for the nVNS group and increased from baseline for the SoC group at day 5 and overall, although the difference between the treatment groups did not reach statistical significance. No significant treatment differences were seen for clinical respiratory outcomes or any of the other biochemical markers evaluated. No serious nVNS-related adverse events occurred during the study. Conclusions: nVNS therapy led to significant reductions in levels of inflammatory markers, specifically CRP and procalcitonin. Because nVNS has multiple mechanisms of action that may be relevant to COVID-19, additional research into its potential use earlier in the course of COVID-19 and its potential to mitigate some of the symptoms associated with post-acute sequelae of COVID-19 is warranted.

Effects of prenatal exposure to maternal COVID-19 and perinatal care on neonatal outcome: results from the INTERCOVID Multinational Cohort Study.

BACKGROUND: The effect of COVID-19 in pregnancy on maternal outcomes and its association with preeclampsia and gestational diabetes mellitus have been reported; however, a detailed understanding of the effects of maternal positivity, delivery mode, and perinatal practices on fetal and neonatal outcomes is urgently needed. OBJECTIVE: To evaluate the impact of COVID-19 on fetal and neonatal outcomes and the role of mode of delivery, breastfeeding, and early neonatal care practices on the risk of mother-to-child transmission. STUDY DESIGN: In this cohort study that took place from March 2020 to March 2021, involving 43 institutions in 18 countries, 2 unmatched, consecutive, unexposed women were concomitantly enrolled immediately after each infected woman was identified, at any stage of pregnancy or delivery, and at the same level of care to minimize bias. Women and neonates were followed up until hospital discharge. COVID-19 in pregnancy was determined by laboratory confirmation and/or radiological pulmonary findings or ≥2 predefined COVID-19 symptoms. The outcome measures were indices of neonatal and perinatal morbidity and mortality, neonatal positivity and its correlation with mode of delivery, breastfeeding, and hospital neonatal care practices. RESULTS: A total of 586 neonates born to women with COVID-19 diagnosis and 1535 neonates born to women without COVID-19 diagnosis were enrolled. Women with COVID-19 diagnosis had a higher rate of cesarean delivery (52.8% vs 38.5% for those without COVID-19 diagnosis, P<.01) and pregnancy-related complications, such as hypertensive disorders of pregnancy and fetal distress (all with P<.001), than women without COVID-19 diagnosis. Maternal diagnosis of COVID-19 carried an increased rate of preterm birth (P≤.001) and lower neonatal weight (P≤.001), length, and head circumference at birth. In mothers with COVID-19 diagnosis, the length of in utero exposure was significantly correlated to the risk of the neonate testing positive (odds ratio, 4.5; 95% confidence interval, 2.2-9.4 for length of in utero exposure >14 days). Among neonates born to mothers with COVID-19 diagnosis, birth via cesarean delivery was a risk factor for testing positive for COVID-19 (odds ratio, 2.4; 95% confidence interval, 1.2-4.7), even when severity of maternal conditions was considered and after multivariable logistic analysis. In the subgroup of neonates born to women with COVID-19 diagnosis, the outcomes worsened when the neonate also tested positive, with higher rates of neonatal intensive care unit admission, fever, gastrointestinal and respiratory symptoms, and death, even after adjusting for prematurity. Breastfeeding by mothers with COVID-19 diagnosis and hospital neonatal care practices, including immediate skin-to-skin contact and rooming-in, were not associated with an increased risk of newborn positivity. CONCLUSION: In this multinational cohort study, COVID-19 in pregnancy was associated with increased maternal and neonatal complications. Cesarean delivery was significantly associated with newborn COVID-19 diagnosis. Vaginal delivery should be considered the safest mode of delivery if obstetrical and health conditions allow it. Mother-to-child skin-to-skin contact, rooming-in, and direct breastfeeding were not risk factors for newborn COVID-19 diagnosis, thus well-established best practices can be continued among women with COVID-19 diagnosis.

Sentiment Analysis of Microblogging Dataset on Coronavirus Pandemic

Sentiment analysis can largely influence the people to get the update of the current situation. Coronavirus (COVID-19) is a contagious illness caused by the SARS-CoV-2 virus that causes severe respiratory symptoms. The lives of millions have continued to be affected by this pandemic, several countries have resorted to a full lockdown. During this lockdown, people have taken social networks to express their emotions to find a way to calm themselves down. People are spreading their sentiments through microblogging websites as one of the most preventive steps of this disease is the socialization to gain people's awareness to stay home and keep their distance when they are outside home. Twitter is a popular online social media platform for exchanging ideas. People can post their different sentiments, which can be used to aware people. But, some people want to spread fake news to frighten the people. So, it is necessary to identify the positive, negative, and neutral thoughts so that the positive opinions can be delivered to the mass people for spreading awareness to the people. Moreover, a huge volume of data is floating on Twitter. So, it is also important to identify the context of the dataset. In this paper, we have analyzed the twitter dataset for evaluating the sentiment using several machine learning algorithms, where the random forest algorithm achieved the highest accuracy of 93%. Later, we have found out the context learning of the dataset based on the sentiments. © 2021 IEEE.

SARS-COV-2: Symptoms Severity Assessment using Data Mining

COVID-19 spreads from person to person in communities (local transmission). Symptoms experienced vary from one person to another. Some may be asymptomatic, while others can experience mild to severe respiratory illness. Adults with existing medical problems are said to have more severe respiratory symptoms. As SARS-Cov-2 cases continually increase in the Philippines, hospitals and quarantine facilities can no longer accommodate new patients. People exposed to the virus are considered Person Under Investigation (PUI). And those experiencing COVID-like symptoms are Person Under Monitoring (PUM). Patients assessed as PUI/PUM are no longer admitted to the hospitals but observe their condition at home. In this study, the researchers developed a System that will closely monitor the symptoms of the probable SARS-COV-2 patients. It aims to assist healthcare professionals, including Barangay health workers, in tracking the patient's condition and informing them once the System evaluates the severity of the patient's symptoms. A clinical symptom dataset was used in this analysis to identify the COVID-like symptoms using the Decision Tree algorithm. The result shows that the System could determine if the patient's symptoms are severe or not. © 2021 IEEE.

A Scalable and Domain Adaptive Respiratory Symptoms Detection Framework using Earables

The COVID-19 pandemic has brought a devastating impact on human health across the globe, and people are still observing face-masking as a preventive measure to contain the spread of COVID-19. Coughing is one of the major transmission mediums of COVID-19, and early cough detection could play a significant r ole i n p reventing t he s pread o f t his life-threatening virus. Many approaches have been proposed for developing systems to detect coughing and other respiratory symptoms in literature, but earable devices are not well-studied and investigated for respiratory symptom detection. In this work, we posited an acoustic research prototype (earable device) - eSense that has acoustic and IMU sensors embedded into user-convenient earbuds to address the following issues: (i) feasibility of the earables in detecting respiratory symptoms, and (ii) scalability of trained machine learning models in the presence of unseen data samples. We performed experimentation with both shallow and deep learning models on the eSense collected data samples. We observed that the deep learning model outperforms the shallow learning models achieving 97% accuracy. Furthermore, we investigated the scalability of the deep learning model on unseen datasets and noticed that the performance of the deep learning model deteriorates when trained on a particular dataset and tested on an unseen dataset. To mitigate such challenges, we postulated an adversarial domain adaptation technique that helps improve the performance of our respiratory symptoms detection framework by a substantial margin. © 2021 IEEE.