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Updates on laboratory investigations in coronavirus disease 2019 (COVID-19)
review-article
Publication date (Electronic):
07 September 2020
Journal:
Acta Bio Medica : Atenei Parmensis
Publisher:
Mattioli 1885
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Abstract
The coronavirus disease 2019 (COVID-19) pandemic is still spreading worldwide, affecting several million people. Unlike the previous two coronavirus outbreaks, COVID-19 has caused several thousand deaths for respiratory and multiple organ failure. As specifically concerns this latest infectious pathology, laboratory medicine can provide a substantial contribution to diagnosing an acute severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection through molecular testing, establishing the presence and extent of an immune response against the virus, mostly through serological testing. However, it can also help to predict the risk of unfavorable disease progression by measuring some conventional laboratory tests and, last but not least, can provide reliable therapeutic guidance. This article is hence aimed at offering recent updates on the important role and value of laboratory investigations in COVID-19, also providing information on some hot topics such as virus RNA detection in different biological samples, causes of recurrent positivity of reverse-transcription polymerase chain reaction (RT-PCR), potential strategies for enhancing the throughput of molecular testing (i.e., pre-test probability assessment, sample pooling, use of rapid tests), as well as pragmatic indications for enhancing the quality and value of serological testing and laboratory-based monitoring. (www.actabiomedica.it)
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Most cited references57
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WHO Declares COVID-19 a Pandemic
Domenico Cucinotta, Maurizio Vanelli (2020)
The World Health Organization (WHO) on March 11, 2020, has declared the novel coronavirus (COVID-19) outbreak a global pandemic (1). At a news briefing, WHO Director-General, Dr. Tedros Adhanom Ghebreyesus, noted that over the past 2 weeks, the number of cases outside China increased 13-fold and the number of countries with cases increased threefold. Further increases are expected. He said that the WHO is “deeply concerned both by the alarming levels of spread and severity and by the alarming levels of inaction,” and he called on countries to take action now to contain the virus. “We should double down,” he said. “We should be more aggressive.” Among the WHO’s current recommendations, people with mild respiratory symptoms should be encouraged to isolate themselves, and social distancing is emphasized and these recommendations apply even to countries with no reported cases (2). Separately, in JAMA, researchers report that SARS-CoV-2, the virus that causes COVID-19, was most often detected in respiratory samples from patients in China. However, live virus was also found in feces. They conclude: “Transmission of the virus by respiratory and extrarespiratory routes may help explain the rapid spread of disease.”(3). COVID-19 is a novel disease with an incompletely described clinical course, especially for children. In a recente report W. Liu et al described that the virus causing Covid-19 was detected early in the epidemic in 6 (1.6%) out of 366 children (≤16 years of age) hospitalized because of respiratory infections at Tongji Hospital, around Wuhan. All these six children had previously been completely healthy and their clinical characteristics at admission included high fever (>39°C) cough and vomiting (only in four). Four of the six patients had pneumonia, and only one required intensive care. All patients were treated with antiviral agents, antibiotic agents, and supportive therapies, and recovered after a median 7.5 days of hospitalization. (4). Risk factors for severe illness remain uncertain (although older age and comorbidity have emerged as likely important factors), the safety of supportive care strategies such as oxygen by high-flow nasal cannula and noninvasive ventilation are unclear, and the risk of mortality, even among critically ill patients, is uncertain. There are no proven effective specific treatment strategies, and the risk-benefit ratio for commonly used treatments such as corticosteroids is unclear (3,5). Septic shock and specific organ dysfunction such as acute kidney injury appear to occur in a significant proportion of patients with COVID-19–related critical illness and are associated with increasing mortality, with management recommendations following available evidence-based guidelines (3). Novel COVID-19 “can often present as a common cold-like illness,” wrote Roman Wöelfel et al. (6). They report data from a study concerning nine young- to middle-aged adults in Germany who developed COVID-19 after close contact with a known case. All had generally mild clinical courses; seven had upper respiratory tract disease, and two had limited involvement of the lower respiratory tract. Pharyngeal virus shedding was high during the first week of symptoms, peaking on day 4. Additionally, sputum viral shedding persisted after symptom resolution. The German researchers say the current case definition for COVID-19, which emphasizes lower respiratory tract disease, may need to be adjusted(6). But they considered only young and “normal” subjecta whereas the story is different in frail comorbid older patients, in whom COVID 19 may precipitate an insterstitial pneumonia, with severe respiratory failure and death (3). High level of attention should be paid to comorbidities in the treatment of COVID-19. In the literature, COVID-19 is characterised by the symptoms of viral pneumonia such as fever, fatigue, dry cough, and lymphopenia. Many of the older patients who become severely ill have evidence of underlying illness such as cardiovascular disease, liver disease, kidney disease, or malignant tumours. These patients often die of their original comorbidities. They die “with COVID”, but were extremely frail and we therefore need to accurately evaluate all original comorbidities. In addition to the risk of group transmission of an infectious disease, we should pay full attention to the treatment of the original comorbidities of the individual while treating pneumonia, especially in older patients with serious comorbid conditions and polipharmacy. Not only capable of causing pneumonia, COVID-19 may also cause damage to other organs such as the heart, the liver, and the kidneys, as well as to organ systems such as the blood and the immune system. Patients die of multiple organ failure, shock, acute respiratory distress syndrome, heart failure, arrhythmias, and renal failure (5,6). What we know about COVID 19? In December 2019, a cluster of severe pneumonia cases of unknown cause was reported in Wuhan, Hubei province, China. The initial cluster was epidemiologically linked to a seafood wholesale market in Wuhan, although many of the initial 41 cases were later reported to have no known exposure to the market (7). A novel strain of coronavirus belonging to the same family of viruses that cause severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), as well as the 4 human coronaviruses associated with the common cold, was subsequently isolated from lower respiratory tract samples of 4 cases on 7 January 2020. On 30 January 2020, the WHO declared that the SARS-CoV-2 outbreak constituted a Public Health Emergency of International Concern, and more than 80, 000 confirmed cases had been reported worldwide as of 28 February 2020 (8). On 31 January 2020, the U.S. Centers for Disease Control and Prevention announced that all citizens returning from Hubei province, China, would be subject to mandatory quarantine for up to 14 days. But from China COVID 19 arrived to many other countries. Rothe C et al reported a case of a 33-year-old otherwise healthy German businessman :she became ill with a sore throat, chills, and myalgias on January 24, 2020 (9). The following day, a fever of 39.1°C developed, along with a productive cough. By the evening of the next day, he started feeling better and went back to work on January 27. Before the onset of symptoms, he had attended meetings with a Chinese business partner at his company near Munich on January 20 and 21. The business partner, a Shanghai resident, had visited Germany between January 19 and 22. During her stay, she had been well with no signs or symptoms of infection but had become ill on her flight back to China, where she tested positive for 2019-nCoV on January 26. This case of 2019-nCoV infection was diagnosed in Germany and transmitted outside Asia. However, it is notable that the infection appears to have been transmitted during the incubation period of the index patient, in whom the illness was brief and nonspecific. The fact that asymptomatic persons are potential sources of 2019-nCoV infection may warrant a reassessment of transmission dynamics of the current outbreak (9). Our current understanding of the incubation period for COVID-19 is limited. An early analysis based on 88 confirmed cases in Chinese provinces outside Wuhan, using data on known travel to and from Wuhan to estimate the exposure interval, indicated a mean incubation period of 6.4 days (95% CI, 5.6 to 7.7 days), with a range of 2.1 to 11.1 days. Another analysis based on 158 confirmed cases outside Wuhan estimated a median incubation period of 5.0 days (CI, 4.4 to 5.6 days), with a range of 2 to 14 days. These estimates are generally consistent with estimates from 10 confirmed cases in China (mean incubation period, 5.2 days [CI, 4.1 to 7.0 days] and from clinical reports of a familial cluster of COVID-19 in which symptom onset occurred 3 to 6 days after assumed exposure in Wuhan (10-12). The incubation period can inform several important public health activities for infectious diseases, including active monitoring, surveillance, control, and modeling. Active monitoring requires potentially exposed persons to contact local health authorities to report their health status every day. Understanding the length of active monitoring needed to limit the risk for missing infections is necessary for health departments to effectively use resources. A recent paper provides additional evidence for a median incubation period for COVID-19 of approximately 5 days (13). Lauer et al suggest that 101 out of every 10 000 cases will develop symptoms after 14 days of active monitoring or quarantinen (13). Whether this rate is acceptable depends on the expected risk for infection in the population being monitored and considered judgment about the cost of missing cases. Combining these judgments with the estimates presented here can help public health officials to set rational and evidence-based COVID-19 control policies. Note that the proportion of mild cases detected has increased as surveillance and monitoring systems have been strengthened. The incubation period for these severe cases may differ from that of less severe or subclinical infections and is not typically an applicable measure for those with asymptomatic infections In conclusion, in a very short period health care systems and society have been severely challenged by yet another emerging virus. Preventing transmission and slowing the rate of new infections are the primary goals; however, the concern of COVID-19 causing critical illness and death is at the core of public anxiety. The critical care community has enormous experience in treating severe acute respiratory infections every year, often from uncertain causes. The care of severely ill patients, in particular older persons with COVID-19 must be grounded in this evidence base and, in parallel, ensure that learning from each patient could be of great importance to care all population,
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Hematologic, biochemical and immune biomarker abnormalities associated with severe illness and mortality in coronavirus disease 2019 (COVID-19): a meta-analysis
As coronavirus disease 2019 (COVID-19) pandemic rages on, there is urgent need for identification of clinical and laboratory predictors for progression towards severe and fatal forms of this illness. In this study we aimed to evaluate the discriminative ability of hematologic, biochemical and immunologic biomarkers in patients with and without the severe or fatal forms of COVID-19. An electronic search in Medline (PubMed interface), Scopus, Web of Science and China National Knowledge Infrastructure (CNKI) was performed, to identify studies reporting on laboratory abnormalities in patients with COVID-19. Studies were divided into two separate cohorts for analysis: severity (severe vs. non-severe and mortality, i.e. non-survivors vs. survivors). Data was pooled into a meta-analysis to estimate weighted mean difference (WMD) with 95% confidence interval (95% CI) for each laboratory parameter. A total number of 21 studies was included, totaling 3377 patients and 33 laboratory parameters. While 18 studies (n = 2984) compared laboratory findings between patients with severe and non-severe COVID-19, the other three (n = 393) compared survivors and non-survivors of the disease and were thus analyzed separately. Patients with severe and fatal disease had significantly increased white blood cell (WBC) count, and decreased lymphocyte and platelet counts compared to non-severe disease and survivors. Biomarkers of inflammation, cardiac and muscle injury, liver and kidney function and coagulation measures were also significantly elevated in patients with both severe and fatal COVID-19. Interleukins 6 (IL-6) and 10 (IL-10) and serum ferritin were strong discriminators for severe disease. Several biomarkers which may potentially aid in risk stratification models for predicting severe and fatal COVID-19 were identified. In hospitalized patients with respiratory distress, we recommend clinicians closely monitor WBC count, lymphocyte count, platelet count, IL-6 and serum ferritin as markers for potential progression to critical illness.
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Antibody tests for identification of current and past infection with SARS‐CoV‐2
Jonathan Deeks, Jacqueline Dinnes, Yemisi Takwoingi … (2020)
Background The severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) virus and resulting COVID‐19 pandemic present important diagnostic challenges. Several diagnostic strategies are available to identify current infection, rule out infection, identify people in need of care escalation, or to test for past infection and immune response. Serology tests to detect the presence of antibodies to SARS‐CoV‐2 aim to identify previous SARS‐CoV‐2 infection, and may help to confirm the presence of current infection. Objectives To assess the diagnostic accuracy of antibody tests to determine if a person presenting in the community or in primary or secondary care has SARS‐CoV‐2 infection, or has previously had SARS‐CoV‐2 infection, and the accuracy of antibody tests for use in seroprevalence surveys. Search methods We undertook electronic searches in the Cochrane COVID‐19 Study Register and the COVID‐19 Living Evidence Database from the University of Bern, which is updated daily with published articles from PubMed and Embase and with preprints from medRxiv and bioRxiv. In addition, we checked repositories of COVID‐19 publications. We did not apply any language restrictions. We conducted searches for this review iteration up to 27 April 2020. Selection criteria We included test accuracy studies of any design that evaluated antibody tests (including enzyme‐linked immunosorbent assays, chemiluminescence immunoassays, and lateral flow assays) in people suspected of current or previous SARS‐CoV‐2 infection, or where tests were used to screen for infection. We also included studies of people either known to have, or not to have SARS‐CoV‐2 infection. We included all reference standards to define the presence or absence of SARS‐CoV‐2 (including reverse transcription polymerase chain reaction tests (RT‐PCR) and clinical diagnostic criteria). Data collection and analysis We assessed possible bias and applicability of the studies using the QUADAS‐2 tool. We extracted 2x2 contingency table data and present sensitivity and specificity for each antibody (or combination of antibodies) using paired forest plots. We pooled data using random‐effects logistic regression where appropriate, stratifying by time since post‐symptom onset. We tabulated available data by test manufacturer. We have presented uncertainty in estimates of sensitivity and specificity using 95% confidence intervals (CIs). Main results We included 57 publications reporting on a total of 54 study cohorts with 15,976 samples, of which 8526 were from cases of SARS‐CoV‐2 infection. Studies were conducted in Asia (n = 38), Europe (n = 15), and the USA and China (n = 1). We identified data from 25 commercial tests and numerous in‐house assays, a small fraction of the 279 antibody assays listed by the Foundation for Innovative Diagnostics. More than half (n = 28) of the studies included were only available as preprints. We had concerns about risk of bias and applicability. Common issues were use of multi‐group designs (n = 29), inclusion of only COVID‐19 cases (n = 19), lack of blinding of the index test (n = 49) and reference standard (n = 29), differential verification (n = 22), and the lack of clarity about participant numbers, characteristics and study exclusions (n = 47). Most studies (n = 44) only included people hospitalised due to suspected or confirmed COVID‐19 infection. There were no studies exclusively in asymptomatic participants. Two‐thirds of the studies (n = 33) defined COVID‐19 cases based on RT‐PCR results alone, ignoring the potential for false‐negative RT‐PCR results. We observed evidence of selective publication of study findings through omission of the identity of tests (n = 5). We observed substantial heterogeneity in sensitivities of IgA, IgM and IgG antibodies, or combinations thereof, for results aggregated across different time periods post‐symptom onset (range 0% to 100% for all target antibodies). We thus based the main results of the review on the 38 studies that stratified results by time since symptom onset. The numbers of individuals contributing data within each study each week are small and are usually not based on tracking the same groups of patients over time. Pooled results for IgG, IgM, IgA, total antibodies and IgG/IgM all showed low sensitivity during the first week since onset of symptoms (all less than 30.1%), rising in the second week and reaching their highest values in the third week. The combination of IgG/IgM had a sensitivity of 30.1% (95% CI 21.4 to 40.7) for 1 to 7 days, 72.2% (95% CI 63.5 to 79.5) for 8 to 14 days, 91.4% (95% CI 87.0 to 94.4) for 15 to 21 days. Estimates of accuracy beyond three weeks are based on smaller sample sizes and fewer studies. For 21 to 35 days, pooled sensitivities for IgG/IgM were 96.0% (95% CI 90.6 to 98.3). There are insufficient studies to estimate sensitivity of tests beyond 35 days post‐symptom onset. Summary specificities (provided in 35 studies) exceeded 98% for all target antibodies with confidence intervals no more than 2 percentage points wide. False‐positive results were more common where COVID‐19 had been suspected and ruled out, but numbers were small and the difference was within the range expected by chance. Assuming a prevalence of 50%, a value considered possible in healthcare workers who have suffered respiratory symptoms, we would anticipate that 43 (28 to 65) would be missed and 7 (3 to 14) would be falsely positive in 1000 people undergoing IgG/IgM testing at days 15 to 21 post‐symptom onset. At a prevalence of 20%, a likely value in surveys in high‐risk settings, 17 (11 to 26) would be missed per 1000 people tested and 10 (5 to 22) would be falsely positive. At a lower prevalence of 5%, a likely value in national surveys, 4 (3 to 7) would be missed per 1000 tested, and 12 (6 to 27) would be falsely positive. Analyses showed small differences in sensitivity between assay type, but methodological concerns and sparse data prevent comparisons between test brands. Authors' conclusions The sensitivity of antibody tests is too low in the first week since symptom onset to have a primary role for the diagnosis of COVID‐19, but they may still have a role complementing other testing in individuals presenting later, when RT‐PCR tests are negative, or are not done. Antibody tests are likely to have a useful role for detecting previous SARS‐CoV‐2 infection if used 15 or more days after the onset of symptoms. However, the duration of antibody rises is currently unknown, and we found very little data beyond 35 days post‐symptom onset. We are therefore uncertain about the utility of these tests for seroprevalence surveys for public health management purposes. Concerns about high risk of bias and applicability make it likely that the accuracy of tests when used in clinical care will be lower than reported in the included studies. Sensitivity has mainly been evaluated in hospitalised patients, so it is unclear whether the tests are able to detect lower antibody levels likely seen with milder and asymptomatic COVID‐19 disease. The design, execution and reporting of studies of the accuracy of COVID‐19 tests requires considerable improvement. Studies must report data on sensitivity disaggregated by time since onset of symptoms. COVID‐19‐positive cases who are RT‐PCR‐negative should be included as well as those confirmed RT‐PCR, in accordance with the World Health Organization (WHO) and China National Health Commission of the People's Republic of China (CDC) case definitions. We were only able to obtain data from a small proportion of available tests, and action is needed to ensure that all results of test evaluations are available in the public domain to prevent selective reporting. This is a fast‐moving field and we plan ongoing updates of this living systematic review. What is the diagnostic accuracy of antibody tests for the detection of infection with the COVID‐19 virus? Background COVID‐19 is an infectious disease caused by the SARS‐CoV‐2 virus that spreads easily between people in a similar way to the common cold or ‘flu. Most people with COVID‐19 have a mild to moderate respiratory illness, and some may have no symptoms (asymptomatic infection). Others experience severe symptoms and need specialist treatment and intensive care. The immune system of people who have COVID‐19 responds to infection by developing proteins that can attack the virus (antibodies) in their blood. Tests to detect antibodies in peoples' blood might show whether they currently have COVID‐19 or have had it previously. Why are accurate tests important? Accurate testing allows identification of people who might need treatment, or who need to isolate themselves to prevent the spread of infection. Failure to detect people with COVID‐19 when it is present (a false negative result) may delay treatment and risk further spread of infection to others. Incorrect identification of COVID‐19 when it is not present (a false positive result) may lead to unnecessary further testing, treatment, and isolation of the person and close contacts. Correct identification of people who have previously had COVID‐19 is important in measuring disease spread, assessing the success of public health interventions (like isolation), and potentially in identifying individuals with immunity (should antibodies in the future be shown to indicate immunity). To identify false negative and false positive results, antibody test results are compared in people known to have COVID‐19 and known not to have COVID‐19. Study participants are classified as to whether they are known or not known to have COVID‐19 based on criteria known as the ‘reference standard’. Many studies use samples taken from the nose and throat to identify people with COVID‐19. The samples undergo a test called reverse transcriptase polymerase chain reaction (RT‐PCR). This testing process can sometimes miss infection (false negative result), but additional tests can identify COVID‐19 infection in people with a negative RT‐PCR result. These include measuring clinical symptoms, like coughing or high temperature, or ‘imaging’ tests like chest X‐rays. People known not to have COVID‐19 are sometimes identified from stored blood samples taken before COVID‐19 existed, or from patients with respiratory symptoms found to be caused by other diseases. What did the review study? The studies looked at three types of antibody, IgA, IgG and IgM. Most tests measure both IgG and IgM, but some measure a single antibody or combinations of the three antibodies. Levels of antibodies rise and fall at different times after infection. IgG is the last to rise but lasts longest. Levels of antibodies are usually highest a few weeks after infection. Some antibody tests need specialist laboratory equipment. Others use disposable devices, similar to pregnancy tests. These tests can be used in laboratories or wherever the patient is (point‐of‐care), in hospital or at home. We wanted to find out whether antibody tests: ‐ are accurate enough to diagnose infection in people with or without symptoms of COVID‐19, and ‐ can be used to find out if someone has already had COVID‐19. What did we do? We looked for studies that measured the accuracy of antibody tests compared with reference standard criteria to detect current or past COVID‐19 infection. Studies could assess any antibody test compared with any reference standard. People could be tested in hospital or the community. Studies could test people known to have – or not to have – or be suspected of having COVID‐19. Study characteristics We found 54 relevant studies. Studies took place in Asia (38), Europe (15), and in both USA and China (1). Forty‐six studies included people who were in hospital with suspected or confirmed COVID‐19 infection only. Twenty‐nine studies compared test results in people with COVID‐19 with test results in healthy people or people with other diseases. Not all studies provided details about participants’ age and gender. Often, we could not tell whether studies were evaluating current or past infection, as few reported whether participants were recovering. We did not find any studies that tested only asymptomatic people. Main results Our findings come mainly from 38 studies that provided results based on the time since people first noticed symptoms. Antibody tests one week after first symptoms only detected 30% of people who had COVID‐19. Accuracy increased in week 2 with 70% detected, and was highest in week 3 (more than 90% detected). Little evidence was available after week 3. Tests gave false positive results in 2% of those without COVID‐19. Results from IgG/IgM tests three weeks after symptoms started suggested that if 1000 people had antibody tests, and 50 (5%) of them really had COVID‐19 (as we might expect in a national screening survey): ‐ 58 people would test positive for COVID‐19. Of these, 12 people (21%) would not have COVID‐19 (false positive result). ‐ 942 people would test negative for COVID‐19. Of these, 4 people (0.4%) would actually have COVID‐19 (false negative result). If we tested 1000 healthcare workers (in a high‐risk setting) who had had symptoms, and 500 (50%) of them really had COVID‐19: ‐ 464 people would test positive for COVID‐19. Of these, 7 people (2%) would not have COVID‐19 (false positive result). ‐ 537 people would test negative for COVID‐19. Of these, 43 (8%) would actually have COVID‐19 (false negative result). We did not find convincing differences in accuracy for different types of antibody test. How reliable were the results of the studies of this review? Our confidence in the evidence is limited for several reasons. In general, studies were small, did not use the most reliable methods and did not report their results fully. Often, they did not include patients with COVID‐19 who may have had a false negative result on PCR, and took their data for people without COVID‐19 from records of tests done before COVID‐19 arose. This may have affected test accuracy, but it is impossible to identify by how much. Who do the results of this review apply to? Most participants were in hospital with COVID‐19, so were likely to have more severe disease than people with mild symptoms who were not hospitalised. This means that we don't know how accurate antibody tests are for people with milder disease or no symptoms. More than half of the studies assessed tests they had developed themselves, most of which are not available to buy. Many studies were published quickly online as ‘preprints’. Preprints do not undergo the normal rigorous checks of published studies, so we are not certain how reliable they are. As most studies took place in Asia, we don't know whether test results would be similar elsewhere in the world. What are the implications of this review? The review shows that antibody tests could have a useful role in detecting if someone has had COVID‐19, but the timing of when the tests are used is important. Antibody tests may help to confirm COVID‐19 infection in people who have had symptoms for more than two weeks and do not have a RT‐PCR test, or have negative RT‐PCR test results. The tests are better at detecting COVID‐19 in people two or more weeks after their symptoms started, but we do not know how well they work more than five weeks after symptoms started. We do not know how well the tests work for people who have milder disease or no symptoms, because the studies in the review were mainly done in people who were in hospital. In time, we will learn whether having previously had COVID‐19 provides individuals with immunity to future infection. Further research is needed into the use of antibody tests in people recovering from COVID‐19 infection, and in people who have experienced mild symptoms or who never experienced symptoms. How up‐to‐date is this review? This review includes evidence published up to 27 April 2020. Because a lot of new research is being published in this field, we will update this review frequently.
Author and article information
Journal
Journal ID (nlm-ta): Acta Biomed
Journal ID (iso-abbrev): Acta Biomed
Title:
Acta Bio Medica : Atenei Parmensis
Publisher:
Mattioli 1885
(Italy
)
ISSN
(Print):
0392-4203
ISSN
(Electronic):
2531-6745
Publication date
(Print):
2020
Publication date
(Electronic):
07
September
2020
Volume: 91
Issue: 3
Page: e2020030
Affiliations
[2
] Cardiac Intensive Care Unit, The Heart Institute, Cincinnati Children’s Hospital
Medical Center, Cincinnati, OH, USA
Author notes
Correspondence: Prof. Giuseppe Lippi Section of Clinical Biochemistry, University
Hospital of Verona Piazzale L.A. Scuro, 10 - 37134 Verona - Italy Tel. 0039-045-8122970
Fax 0039-045-8124308 E-mail:
giuseppe.lippi@
123456univr.it
Article
Publisher ID: ACTA-91-30DOI: 10.23750/abm.v91i3.10187
PMC ID: 7716967
PubMed ID: 32921725
SO-VID: f9209c8a-4dde-4a3d-8302-beeac6e0412c
Copyright © Copyright: © 2020 ACTA BIO MEDICA SOCIETY OF MEDICINE AND NATURAL SCIENCES OF PARMA
License:
This work is licensed under a Creative Commons Attribution 4.0 International License
History
Date
received
: 09
July
2020
Date
accepted
: 09
July
2020
Categories
Subject:
Reviews / Focus on
Data availability: