Impact of asymptomatic COVID-19 patients in global surgical practice during the COVID-19 pandemic.

We conducted a systematic review and meta-analysis to investigate the possible difference in the SARS-CoV-2 viral load between asymptomatic and symptomatic COVID-19 patients. Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed in abstracting data and assessing validity. We searched MEDLINE, Scopus, Web of Science and Google Scholar for all investigations in the English language, reporting data on the threshold cycle (Ct) from real-time RT-PCR assays for the RNA-dependent RNA polymerase (RdRp), envelope (E) and nucleocapsid (N) SARS-CoV-2 genes in asymptomatic and symptomatic COVID-19 patients. Results: Overall, 703 COVID-19 patients (553 symptomatic and 150 asymptomatic) were analyzed. Five investigations reported the mean age of patients, evidencing that asymptomatic patients were younger than symptomatic patients (34.0 vs. 40.3 years, respectively). Pooled data regarding the levels of expression of the RdRp gene revealed no significant difference between symptomatic and asymptomatic subjects. Similarly, no differences were observed comparing the mean Ct values for the E and N genes. Based on real-time RT-PCR data, no differences exist in the viral load between symptomatic and asymptomatic COVID-19 subjects considering Ct values for RdRp, E and N genes’ expression. Asymptomatic subjects may represent a reservoir of the infection and significantly contribute to the maintenance of the pandemic.

COVID-19 broke out in Wuhan in 2019, and spread rapidly around the world. More than 80% of COVID-19 patients are asymptomatic and non-severe, which may be an important source of infection. Therefore, the infectivity, clinical characteristics and outcome of such patients should be studied. The epidemiological data of 108 COVID-19 patients, as well as the clinical characteristics and treatment conditions of 100 asymptomatic and non-severe patients in Bozhou City, Anhui Province were collected. A retrospective analysis of the infectivity of asymptomatic patients was carried out, and a comparative study on clinical characteristics between asymptomatic patients and non-severe patients was also conducted. Eighty-six non-severe patients mainly had a cough (81.4%) and fever (70.9%), and only a few had the symptoms of upper respiratory tract infection such as sore throat (9.3%) and running nose (4.7%). Lymphopenia (20.9%) and elevated C-reaction protein (57.0%) were more frequent in non-severe patients compared with asymptomatic patients which were 0% and 14.3%, respectively. Among 100 asymptomatic and non-severe patients, 84 patients showed abnormal findings on the first chest CT, but meanwhile, 19 patients showed negative results on the viral RNA test. In 14 asymptomatic patients, five had lung abnormalities, which were all pure ground glass opacity. Eleven patients were infected by two asymptomatic patients; in the whole course of the disease, the two patients remained asymptomatic, but chest CT showed multifocal GGO. The time for clearance of SARS-CoV-2 in 12 patients taking methylprednisolone was longer than in other patients (21.2 d vs. 16.4 d). During follow-up, lung lesions found by chest CT in 58.3% of patients completely disappeared. Asymptomatic patients with abnormal chest CT were highly infectious, so SARS-CoV-2 RNA test, combined with a chest CT scan, could discover asymptomatic patients with strong infectivity. The application of glucocorticoids might lengthen the clearance time for SARS-CoV-2 RNA. Both asymptomatic and non-severe COVID-19 patients showed a better prognosis.

BackgroundDespite abundant data on mental health during the COVID-19 pandemic, 3 important knowledge gaps continue to exist, i.e., 1) studies from low-/middle income countries (LMICs); 2) studies in the later period of the COVID-19 pandemic; and 3) studies on non-hospitalized asymptomatic and mild COVID-19 patients. To address the knowledge gaps, we assessed the prevalence of and the risk factors for mental health symptoms among non-hospitalized asymptomatic and mild COVID-19 patients in one LMIC (Indonesia) during the later period of the pandemic.MethodsThis cross-sectional study was conducted in September 2020 in East Java province, Indonesia. Study population consisted of non-hospitalized asymptomatic and mild COVID-19 patients who were diagnosed based on reverse transcriptase-polymerase chain reaction results from nasopharyngeal swab. Mental health symptoms were evaluated using the Depression Anxiety Stress Scale-21.ResultsFrom 778 non-hospitalized asymptomatic and mild COVID-19 patients, 608 patients were included in the analysis. Patients’ median age was 35 years old and 61.2% were male. Of these, 22 (3.6%) reported symptoms of depression, 87 (14.3%) reported symptoms of anxiety, and 48 (7.9%) reported symptoms of stress. Multivariate logistic regression analysis showed that females were more likely to report symptoms of stress (adjusted odds ratio (aOR) = 1.98, p-value = 0.028); healthcare workers were more likely to report symptoms of depression and anxiety (aOR = 5.57, p-value = 0.002 and aOR = 2.92, p-value = 0.014, respectively); and those with a recent history of self-quarantine were more likely to report symptoms of depression and stress (aOR 5.18, p = 0.004 and aOR = 1.86, p = 0.047, respectively).ConclusionThe reported prevalence of mental health symptoms, especially depression, was relatively low among non-hospitalized asymptomatic and mild COVID-19 patients during the later period of the COVID-19 pandemic in East Java province, Indonesia. In addition, several risk factors have been identified.

Despite abundant data on mental health during the COVID-19 pandemic, 3 important knowledge gaps continue to exist, i.e., 1) studies from low-/middle income countries (LMICs); 2) studies in the later period of the COVID-19 pandemic; and 3) studies on non-hospitalized asymptomatic and mild COVID-19 patients. To address the knowledge gaps, we assessed the prevalence of and the risk factors for mental health symptoms among non-hospitalized asymptomatic and mild COVID-19 patients in one LMIC (Indonesia) during the later period of the pandemic. This cross-sectional study was conducted in September 2020 in East Java province, Indonesia. Study population consisted of non-hospitalized asymptomatic and mild COVID-19 patients who were diagnosed based on reverse transcriptase-polymerase chain reaction results from nasopharyngeal swab. Mental health symptoms were evaluated using the Depression Anxiety Stress Scale-21. From 778 non-hospitalized asymptomatic and mild COVID-19 patients, 608 patients were included in the analysis. Patients' median age was 35 years old and 61.2% were male. Of these, 22 (3.6%) reported symptoms of depression, 87 (14.3%) reported symptoms of anxiety, and 48 (7.9%) reported symptoms of stress. Multivariate logistic regression analysis showed that females were more likely to report symptoms of stress (adjusted odds ratio (aOR) = 1.98, p-value = 0.028); healthcare workers were more likely to report symptoms of depression and anxiety (aOR = 5.57, p-value = 0.002 and aOR = 2.92, p-value = 0.014, respectively); and those with a recent history of self-quarantine were more likely to report symptoms of depression and stress (aOR 5.18, p = 0.004 and aOR = 1.86, p = 0.047, respectively). The reported prevalence of mental health symptoms, especially depression, was relatively low among non-hospitalized asymptomatic and mild COVID-19 patients during the later period of the COVID-19 pandemic in East Java province, Indonesia. In addition, several risk factors have been identified.

The pandemic caused by the SARS-CoV-2 has been a threat to humankind due to the rapid spread of infection and appearance of multiple new variants. In the present study, we report the dynamics and persistence of immunoglobulin G (IgG) and immunoglobulin M (IgM) antibodies in asymptomatic and symptomatic COVID-19 patients by chemiluminescent assay. A total of 463 serum samples from 218 SARS-CoV-2 PCR-positive patients were collected over a period of 124 days post-onset of disease (POD). Antibody levels were measured by chemiluminescence bioanalyzer. Neutralizing antibody titres were assessed by plaque reduction neutralization test (PRNT) for SARS-CoV-2. Both IgM and IgG started appearing from day five post-infection in symptomatic and asymptomatic patients. IgM antibody response peaked around day 35 POD and rapidly diminished thereafter, with the last IgM-positive sample observed at 90 days POD. IgG antibody response peaked around 45 days POD and persisted till 124 days. The chemiluminescence immunoassay (CLIA) results showed a moderate correlation (R=0.5846, P<0.001) compared with PRNT. Additional analysis indicated a neutralizing titre of 250 corresponded to 12.948 AU/ml of YHLO iFlash SARS-CoV-2 IgG units. Both symptomatic and asymptomatic COVID-19 patients seem to initiate production of antibody responses from day five of onset of disease. Although the CLIA gives high sensitivity and specificity and also its binding IgG antibody titres may correlate moderately with protective immunity, our results indicate that the values of binding antibody alone may not be a perfect guide to represent virus neutralization titre during donor selection for plasma therapy. However, IgM and IgG antibody detection may help in monitoring the status of disease progression and burden in the community.

Surgical and Clinical Reactivation for Elective Procedures during the COVID-19 Era: A Global Perspective

Objectives:To analyze symptomatic and asymptomatic COVID-19 patients in Saudi Arabia in terms of initial presentation, risk factors, laboratory findings, clinical outcomes and healthcare utilization.Methods:All laboratory-confirmed reverse transcription–polymerase chain reaction positive COVID-19 patients who had been tested at three governmental hospitals in Saudi Arabia (two in Riyadh and one in Makkah) between March 8 and May 18, 2020 were included. Demographics, COVID-19 variables, clinical characteristics and healthcare utilization variables were extracted and combined, and a descriptive analysis was conducted. Symptomatic and asymptomatic (on presentation) patients' data were compared.Results:Eighty percent of the patients were males (81.4% of symptomatic and 73.2% of asymptomatic patients, P = 0.02). Moreover, 47.6% and 38.4% of symptomatic and asymptomatic patients were aged 40–64 years, respectively. Fever, cough and breathing difficulties were frequent presenting symptoms. Overall, diabetes (16.4%), hypertension (11.7%), chronic respiratory disease (7.1%) were the most frequent comorbidities, with no differences between the two groups. Symptomatic patients had higher C-reactive protein levels (3.55 vs. 0.30 mg/L; P < 0.0001) and lower total lymphocytes (1.41 vs. 1.70; P = 0.02). ICU admission and mortality were 12.1% and 4.1% in symptomatic, compared to 6.0% and 2.9% in asymptomatic patients, respectively.Conclusion:In the studied COVID-19 cohort, symptomatic patients tended to be older, had higher C-reactive protein and more lymphopenia with worse outcome than asymptomatic patients. This granular analysis of COVID-19 cohorts enables identification of at-risk cohorts in future waves, optimizing development of patient pathways and public health interventions.

Introduction: The rapid spread of the COVID-19 worldwide has led the World Health Organization (WHO) to declare COVID-19 outbreak as a pandemic on March 11, 2020. This study aims to determine the factors associated with asymptomatic COVID-19 patients in Petaling District, Selangor, Malaysia. Methods: Data on COVID-19 patients were extracted from the database of confirmed cases in Petaling District Health Office, Selangor, Malaysia from 3rd February 2020 to 30th April 2020. An asymptomatic laboratory-confirmed case is a person infected with COVID-19 who does not develop any symptoms. The study included socio-demographic variables, the detailed information on clinical manifestations and co-morbidity of the patients. Descriptive and multivariable logistic regression analyses were conducted to determine the factors associated with asymptomatic patients. Results: The overall COVID-19 patients in Petaling District were 434. Approximately 70% (N = 292) of the patients were symptomatic while 32.7% (N = 142) were asymptomatic. Multivariable logistic regression analyses revealed that factors significantly associated with asymptomatic patients were age below 40 years old (aOR: 1.79, 95% CI 1.11, 2.86), non-Malaysians (aOR: 3.22, 95% CI 1.44, 7.19) and local cases (aOR: 2.51, 95% CI 1.42, 4.42). Gender, ethnicity, co-morbidity and township were not significantly associated with asymptomatic patients. Conclusion: Approximately one-third of COVID-19 patients were asymptomatic and the risk factors identified were younger age, non-Malaysians and local cases. Rigorous epidemiological investigation and laboratory examinations are helpful in identifying COVID-19 cases among these group of people who are asymptomatic. Keywords: COVID-19 - asymptomatic - pandemic - Malaysia

Problem—Since the outbreak of the COVID-19 pandemic, mass testing has become essential to reduce the spread of the virus. Several recent studies suggest that a significant number of COVID-19 patients display no physical symptoms whatsoever. Therefore, it is unlikely that these patients will undergo COVID-19 testing, which increases their chances of unintentionally spreading the virus. Currently, the primary diagnostic tool to detect COVID-19 is a reverse-transcription polymerase chain reaction (RT-PCR) test from the respiratory specimens of the suspected patient, which is invasive and a resource-dependent technique. It is evident from recent researches that asymptomatic COVID-19 patients cough and breathe in a different way than healthy people. Aim—This paper aims to use a novel machine learning approach to detect COVID-19 (symptomatic and asymptomatic) patients from the convenience of their homes so that they do not overburden the healthcare system and also do not spread the virus unknowingly by continuously monitoring themselves. Method—A Cambridge University research group shared such a dataset of cough and breath sound samples from 582 healthy and 141 COVID-19 patients. Among the COVID-19 patients, 87 were asymptomatic while 54 were symptomatic (had a dry or wet cough). In addition to the available dataset, the proposed work deployed a real-time deep learning-based backend server with a web application to crowdsource cough and breath datasets and also screen for COVID-19 infection from the comfort of the user’s home. The collected dataset includes data from 245 healthy individuals and 78 asymptomatic and 18 symptomatic COVID-19 patients. Users can simply use the application from any web browser without installation and enter their symptoms, record audio clips of their cough and breath sounds, and upload the data anonymously. Two different pipelines for screening were developed based on the symptoms reported by the users: asymptomatic and symptomatic. An innovative and novel stacking CNN model was developed using three base learners from of eight state-of-the-art deep learning CNN algorithms. The stacking CNN model is based on a logistic regression classifier meta-learner that uses the spectrograms generated from the breath and cough sounds of symptomatic and asymptomatic patients as input using the combined (Cambridge and collected) dataset. Results—The stacking model outperformed the other eight CNN networks with the best classification performance for binary classification using cough sound spectrogram images. The accuracy, sensitivity, and specificity for symptomatic and asymptomatic patients were 96.5%, 96.42%, and 95.47% and 98.85%, 97.01%, and 99.6%, respectively. For breath sound spectrogram images, the metrics for binary classification of symptomatic and asymptomatic patients were 91.03%, 88.9%, and 91.5% and 80.01%, 72.04%, and 82.67%, respectively. Conclusion—The web-application QUCoughScope records coughing and breathing sounds, converts them to a spectrogram, and applies the best-performing machine learning model to classify the COVID-19 patients and healthy subjects. The result is then reported back to the test user in the application interface. Therefore, this novel system can be used by patients in their premises as a pre-screening method to aid COVID-19 diagnosis by prioritizing the patients for RT-PCR testing and thereby reducing the risk of spreading of the disease.

Objective: The present study aims to evaluate coronavirus shedding in the tears of asymptomatic pediatric COVID-19 positive patients. Methods: A prospective interventional study that included a total of 145 pediatric asymptomatic COVID-19 patients hospitalized from 17th May 2020 to 16th July 2020 in Sohag Tropical Hospital. On admission, all of them were COVID-19 positive detected through nasopharyngeal swab. They were in intimate contact with positive symptomatic COVID-19 patients before testing and admission. Reverse Transcriptase Polymerase chain reaction (RT-PCR) was done for tears samples at an interval of 5 days after admission and twice before discharge. Results: Of the 145 asymptomatic pediatric COVID-19 positive patients, no one showed ocular or systemic manifestations. They were silent carriers. Ten were positive for tears sample on admission. They became negative for nasopharyngeal and tear samples before discharge. Conclusion: Pediatric positive COVID-19 patients can shed coronavirus through their tears. Even among asymptomatic patients, transmission through tears is possible.

The COVID-19 pandemic has cost a large number of lives worldwide. Most of the COVID-19 patients recover within two weeks of illness, but many survivors are experiencing different post COVID-19 clinical complications. In this cross-sectional retrospective study, we investigated the immediate and post COVID-19 complications with secondary effects in symptomatic and asymptomatic COVID-19 patients of Bangladesh. A total of 632 patients diagnosed with COVID-19 from December, 2020 to February, 2021 were included in this study. The data were collected by telephone interview with patients consent and reviewing their call records using questionnaire and checklist. Results demonstrate that among the 632 patients, 77.53 % of cases were symptomatic, where fever was the most common symptom (82.24%). The other symptoms were headache (58.16%), sore throat (53.65%), cough (45.51%), weakness (41.22%), breathlessness (40%), loss of smell (37.55%), tastelessness (31.84%), diarrhea (19.39%), and vomiting (14.69%). Comorbidities like asthma, hypertension, diabetes mellitus (DM), cardiovascular disease, and other chronic diseases were pronounced in symptomatic patients. Post COVID-19 complications varied significantly (P&lt;0.05) between the symptomatic and asymptomatic observations. Asthma, hypertension, and diabetes were newly reported in symptomatic patients with the rate of 3.06 %, 2.45 %, and 2.24 %, respectively, while the proportions were 1.41%, 1.41%, and 0.70% for the asymptomatic group. Tiredness, weight loss, hair loss, and insomnia were the most observed post COVID-19 complications found higher in symptomatic patients than in asymptomatic groups. A newly developed visual anomaly was also identified in the symptomatic group (1.42%), which was absent in asymptomatic COVID-19 recovered patients. These findings concluded that post-COVID-19 complications were high in symptomatic and comorbid patients compared with asymptomatic individuals. We hope that this study will contribute in post COVID-19 management and help the concerned authority toward decision making in the treatment of post-COVID-19 complications. Asian J. Med. Biol. Res. 2021, 7 (2), 191-201

Transcatheter Aortic Valve Implantation During the COVID-19 Pandemic

Viral load in symptomatic and asymptomatic patients infected with SARS-CoV-2. What have we learned?

IntroductionSince the beginning of the COVID-19 pandemic, a wide clinical spectrum, from asymptomatic infection to mild or severe disease and death, have been reported in COVID-19 patients. Studies have suggested several possible factors, which may affect the clinical outcome of COVID-19. A pro-inflammatory state and impaired antiviral response have been suggested as major contributing factors in severe COVID-19. Considering that mitochondria have an important role in regulating the immune responses to pathogens, pro-inflammatory signaling, and cell death, it has received much attention in SARS-CoV-2 infection. Recent studies have demonstrated that high levels of cell-free mitochondrial DNA (cf-mtDNA) are associated with an increased risk of COVID-19 intensive care unit (ICU) admission and mortality. However, there have been few studies on cf-mtDNA in SARS-CoV-2 infection, mainly focusing on critically ill COVID-19 cases. In the present study, we investigated cf-mtDNA copy number in COVID-19 patients and compared between asymptomatic and symptomatic cases, and assessed the clinical values. We also determined the cf-nuclear DNA (cf-nDNA) copy number and mitochondrial transcription factor A (TFAM) mRNA level in the studied groups.Materials and methodsPlasma and buffy coat samples were collected from 37 COVID-19 patients and 33 controls. Briefly, after total DNA extraction, plasma cf-mtDNA, and cf-nDNA copy numbers were measured by absolute qPCR using a standard curve method. Furthermore, after total RNA extraction from buffy coat and cDNA synthesis, TFAM mRNA levels were evaluated by qPCR.ResultsThe results showed that cf-mtDNA levels in asymptomatic COVID-19 patients were statistically significantly higher than in symptomatic cases (p value = 0.01). However, cf-nDNA levels were higher in symptomatic patients than in asymptomatic cases (p value = 0.00). There was no significant difference between TFAM levels in the buffy coat of these two groups (p value > 0.05). Also, cf-mtDNA levels showed good diagnostic potential in COVID-19 subgroups.Conclusioncf-mtDNA is probably important in the outcome of SARS-CoV-2 infection due to its role in inflammation and immune response. It can also be a promising candidate biomarker for the diagnosis of COVID-19 subgroups. Further investigation will help understanding the COVID-19 pathophysiology and effective diagnostic and therapeutic strategies.

Restarting Essential Surgery in the Era of COVID-19: A Cautious Data Driven Approach Based on the Literature and Local Data.