Construction and application of Chinese breast cancer knowledge graph based on multi-source heterogeneous data

Bo An; Bo An

doi:10.3934/mbe.2023292

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 4: 6776-6799. doi: 10.3934/mbe.2023292

Previous Article Next Article

Research article Special Issues

Construction and application of Chinese breast cancer knowledge graph based on multi-source heterogeneous data

Bo An ^{1,2
,
,}

1.
Institute of Ethnology and Anthropology, Chinese Academy of Social Sciences, Beijing 100732, China
2.
Beijing Academy of Artificial Intelligence, Beijing 100084, China

Academic Editor: Jinshan Tang

Received: 02 September 2022 Revised: 14 January 2023 Accepted: 17 January 2023 Published: 06 February 2023

The knowledge graph is a critical resource for medical intelligence. The general medical knowledge graph tries to include all diseases and contains much medical knowledge. However, it is challenging to review all the triples manually. Therefore the quality of the knowledge graph can not support intelligence medical applications. Breast cancer is one of the highest incidences of cancer at present. It is urgent to improve the efficiency of breast cancer diagnosis and treatment through artificial intelligence technology and improve the postoperative health status of breast cancer patients. This paper proposes a framework to construct a breast cancer knowledge graph from heterogeneous data resources in response to this demand. Specifically, this paper extracts knowledge triple from clinical guidelines, medical encyclopedias and electronic medical records. Furthermore, the triples from different data resources are fused to build a breast cancer knowledge graph (BCKG). Experimental results demonstrate that BCKG can support knowledge-based question answering, breast cancer postoperative follow-up and healthcare, and improve the quality and efficiency of breast cancer diagnosis, treatment and management.

Keywords:

Citation: Bo An. Construction and application of Chinese breast cancer knowledge graph based on multi-source heterogeneous data[J]. Mathematical Biosciences and Engineering, 2023, 20(4): 6776-6799. doi: 10.3934/mbe.2023292

Related Papers:

[1]	Zinia Pervin, Anika Tasnim, Hasib Ahamed, Md Al Hasibuzzaman . Epigenetic regulation of the COVID-19 pathogenesis: its impact on the host immune response and disease progression. AIMS Allergy and Immunology, 2023, 7(1): 60-81. doi: 10.3934/Allergy.2023005
[2]	DharmaSaranya Gurusamy, Vasuki Selvamurugesan, Swaminathan Kalyanasundaram, Shantaraman Kalyanaraman . Is COVID 19, a beginning of new entity called chronic viral systemic inflammatory syndrome?. AIMS Allergy and Immunology, 2021, 5(2): 127-134. doi: 10.3934/Allergy.2021010
[3]	Swathi Mukurala, Jahanavi Bandla, Swetha kappala . Angioedema-post COVID symptoms. AIMS Allergy and Immunology, 2023, 7(4): 273-280. doi: 10.3934/Allergy.2023018
[4]	Y. Desheva, T. Shvedova, A. Lerner, P. Kudar, O. Kopteva, D. Petrachkova, I. Koroleva, G. Leontieva, S. Ponkratov, A. Suvorov . Exploring immune response and lab markers across COVID-19 severity levels. AIMS Allergy and Immunology, 2024, 8(4): 216-231. doi: 10.3934/Allergy.2024013
[5]	Matsyura Oksana, Besh Lesya, Zubchenko Svitlana, Gutor Taras, Lukyanenko Natalia, Slivinska-Kurchak Khrystyna, Borysiuk Olena . Assessment of efficacy of secondary prophylactic complex of bronchial obstruction syndrome in young children with respiratory disorders in neonatal period: analysis of symptoms and serological markers. AIMS Allergy and Immunology, 2022, 6(2): 25-41. doi: 10.3934/Allergy.2022005
[6]	Kamyar Nasiri, Zohreh Mortezania, Sanaz Oftadehbalani, Mohammad Khosousi Sani, Amin Daemi, Seyyedeh Touran Hosseini, Yusuf Döğüş, Zafer Yönden . Parasitology and Covid-19: epidemiological, immunological, pathological, and therapeutic aspects. AIMS Allergy and Immunology, 2023, 7(1): 92-103. doi: 10.3934/Allergy.2023007
[7]	Ken S. Rosenthal, Jordan B. Baker . The immune system through the ages. AIMS Allergy and Immunology, 2022, 6(3): 170-187. doi: 10.3934/Allergy.2022013
[8]	Fuyong Jiao, Yan Pan, JAY N SHAH, Jiale Wang, Kaisheng Xie, Zhilong Mu, Qing Wei . Challenges and management of bleeding in Kawasaki disease with aspirin treatment. AIMS Allergy and Immunology, 2024, 8(4): 324-332. doi: 10.3934/Allergy.2024020
[9]	Andrey Mamontov, Alexander Polevshchikov, Yulia Desheva . Mast cells in severe respiratory virus infections: insights for treatment and vaccine administration. AIMS Allergy and Immunology, 2023, 7(1): 1-23. doi: 10.3934/Allergy.2023001
[10]	Daniel Danladi Gaiya, Jonathan Danladi Gaiya, Richard Auta, Aliyu Muhammad, Bege Jonathan, Stella Kuyet Udu, Ekpa Emmanuel, Amina Shehu Bature . The journey so far with SARS-CoV-2 variants: Pathogenesis, immunity and treatments. AIMS Allergy and Immunology, 2023, 7(4): 222-250. doi: 10.3934/Allergy.2023016

Abstract

1. Introduction

1.1. Background of the COVID-19 pandemic

The first case of COVID-19 was identified in Wuhan, December 2019, after several patients presented with pneumonias of unknown etiology. Subsequently, it rapidly spread across the globe with epicenters identified in both Europe and the United States of America (USA) [1]. On the 11th of March 2020, the World Health Organization declared it as a global pandemic. As of the 17th of November 2020, there are 54826773 million confirmed cases with a global death toll of 1323093 [2]. Initial reports revealed a more severe disease manifestation in adults, particularly in those with underlying co-morbidities, relative to the pediatrics cohort [3],[4]. Several theories were proposed to postulate this variance in presentation such as the protective function of the non-atrophied thymus, and the reduced angiotensin converting enzyme (ACE) 2 receptor density in their alveoli [5].

At the advent of April 2020, amidst the rising incidence and prevalence of COVID-19, there emerged new reports of a syndrome characterised by a severe hyperinflammatory reaction in the pediatrics cohort, predominately in Europe and the USA. These patients' clinical presentation mimicked Kawasaki disease and many required admission to intensive care units (ICU) due to shock and multi-organ failure [6]. These findings challenged the initial belief that COVID-19 causes merely a mild respiratory infection in the pediatric population.

1.2. Overlap between Kawasaki disease and the hyperinflammatory syndrome

Kawasaki disease is a medium vessel vasculitis that presents mainly in Asian children less than 5 years of age. The etiology and pathophysiology of Kawasaki disease have not been fully identified to date; proposed hypothesis exist however they are not necessarily universally accepted. It is postulated to be a post-viral manifestation, where plasma cells produce IgA autoantibodies as part of an immune response to a viral infection. This is thought to be coupled with an imbalance between T-cell subtypes, particularly regulatory T-cells and interleukin (IL)-17 producing T-cells, and an initial CD14+/CD16+ neutrophilic infiltrate followed by dendritic cells, CD163+ monocytes, and CD3/8+ T-cells invasion of coronary vessels contributing to the production of cytokines, coronary artery vasculitis and the formation of coronary artery aneurysms [5],[7].

There are similarities in the cytokine storm of both Kawasaki disease and the hyperinflammatory condition that temporally emerged during the current pandemic, however differences in the T-cell subtypes and associated interleukins were documented [5]. Additionally, immune complexes formation and their interaction with the Fcγ receptors on immune cells are thought to play a major role in Kawasaki disease. They promote tissue inflammation and consequent complement depletion and organ failure. To date, no data on the role of immune complex formation in patients with COVID-19 exposure has been published [7].

1.3. Jargon clarification of ‘multisystem inflammatory syndrome in children’ (MIS-C)

This hyperinflammatory condition is yet to be granted a universally accepted definition and was initially termed ‘Kawasaki-like disease’. Currently, it is defined as ‘multisystem inflammatory syndrome in children’ (MIS-C), by the Centers for Disease Control and Prevention (CDC) in the USA. It is also known as ‘Pediatric Multisystem inflammatory syndrome’ by the Royal College of Pediatrics and Child Health (RCPCH) in Europe, and as ‘Multisystem inflammatory syndrome (MIS) in children and adolescents temporally related to COVID-19’ by the World Health Organization [8].

1.4. Diagnostic criteria for the pediatric multisystem inflammatory syndrome (PIMS) and MIS-C

The diagnostic criteria of these conditions are thought to be broad and overlaps with several inflammatory conditions including Kawasaki disease [9]. The main differences between these definitions include: the duration of fever, the means of assessment of exposure to SARS-CoV-2, and the different organs involved as shown in Figures 1 and 2.

Figure 1. Summary of definitions and diagnostic criteria for the pediatric multisystem inflammatory syndrome (PIMS).

DownLoad: Full-Size Img PowerPoint

Figure 2. Summary of definitions and diagnostic criteria for the multisystem inflammatory syndrome in children (MIS-C).

DownLoad: Full-Size Img PowerPoint

The overlap in both the clinical and laboratory features, of atypical Kawasaki and MIS-C resulted in diagnostic uncertainty. This was further complicated by a lack of a diagnostic test for Kawasaki disease and a vast majority of asymptomatic COVID-19 presentations in children implying that a positive SARS-CoV-2 antibody serology or a reverse transcriptase polymerase chain reaction (RT-PCR) does not confirm that the children's presentation is secondary to SARS-CoV-2. Consequently, incorrect diagnosis can potentially be made. However, given the available epidemiological data, SARS-CoV-2 is likely involved with MIS-C but a causality relationship is yet to be confirmed [10].

Therefore, it is prudent to identify and understand the differences in the pathophysiology, clinical presentation, and laboratory investigations between the two conditions to manage patients in the most optimal way possible. Amidst the pandemic, there is a large underlying fear of misdiagnosis, within the pediatrics cohort, of a very serious condition called ‘MIS-C’. Major variables influencing the propensity of misdiagnosis are a constant evolution in the definition of the disease and its criteria, the burden on the economic system, and the huge number of papers, that are only case reports, that are not compiled in an easy manner. Hence, we propose, this review to collate all the clinical variables spanning from the history of presentation, observation and physical examination, laboratory investigations, and efficacy of management. This review aims to present clarifications on the immunological understating of the disease and promote the clinical knowledge to identify this serious condition to treat it aptly to reduce morbidity, mortality, and mitigate the burden on the healthcare system.

2. Methods

A systematic literature review was conducted on 17th October 2020 by authors NSI and MSY in accordance with PRISMA (2015) guidelines as shown in Figure 3. This systematic review was independently verified by the authors AAA, CY, and PK. The review was restricted to publications between December 2019 to October 2020. The search terms utilized were ‘MIS-C’ OR ‘Kawasaki-like Disease’ OR ‘PIMS-TS’ AND ‘COVID-19’ were queried on Medline and Embase databases.

Figure 3. PRISMA flowchart outline the process of the systematic review.

DownLoad: Full-Size Img PowerPoint

Pediatric patients are defined as <18 years of age at the time of hospital presentation. Publications were excluded if not in English language, inaccessible via digital or local resource, or if the reviews lacked original patient data. Publications that combined pediatric and adult combined results without individualized data were also excluded. Each full-text article was assessed for parameters of the history of presenting complaint, the clinical observations and examination outcomes, investigations performed, and management options and strategies. Additionally, laboratory values were collated. Short term complications specific to the treatment were analyzed when reported. The collective outcomes were evaluated quantitatively when possible and qualitatively when not.

3. Results

3.1. Demographics

Between April 2020 and October 2020, 646 pediatric patients were diagnosed with MIS-C. The median age was 10 years (range: 0.5–17 years); 52.2% (n = 337/646) were male. Of the 646, 51.1% (n = 330/646) presented in the USA, 21.4% (n = 138/646) presented in the United Kingdom (UK), 8.7% (n = 56/646) presented in France and Switzerland combined, and 18.9 % (n = 122/646) presented in ‘other’ country as outlined in Table S1. The predominant ethnicity (33.5%, n = 128/382) was African followed by Hispanic/Latino, (28.5%, n = 109/382).

3.2. Clinical symptoms

Among the patient cohort, 99.5% of patients presented with fever (n = 626/629) with a documented median ‘max temperature’ of 39.4 °C (range: 38.2–41 °C). The median duration of fever before presentation to hospital was 5 days (range: 1–12 days). Predominant presenting complaints documented at the emergency department were gastrointestinal followed by respiratory symptoms. The prevalence of the different presenting complaints is described as below in Table 1.

Table 1. Summary of the analysis of the different presenting complaints (n = 646).

Clinical symptoms		Frequency % (n)*	References
Gastrointestinal system	Generalized abdominal pain	77.6 (562)	[4],[11]–[31]
	Vomiting	76.4 (505)	[4],[11]–[16],[21]–[23],[25],[28]–[32]
	Diarrhea	63.2 (321)	[4],[11],[12],[14]–[17],[21],[22],[25],[27],[28],[30],[31],[33]
Respiratory system	Dyspnea	80 (262)	[4],[12],[15],[16],[18],[22]–[24],[30],[31],[34]
	Coryza	60 (154)	[12],[16],[21],[30],[32]
	Cough	55 (177)	[12],[17],[18],[21],[26],[35]–[37]
	Sora throat	17 (205)	[4],[12],[18],[19],[21],[31],[37]
	Chest pain	13.6 (155)	[12],[16],[21],[23],[34]
Neurological system	Headache	30.4 (207)	[12],[14],[18],[21],[30]–[32],[36]
	Irritability	57.4 (21)	[14]
Others	Fatigue	61.5 (91)	[16],[18],[19],[21],[22],[24],[32]
	Myalgia	16.8 (143)	[12],[18],[19],[21]

*(n): Changes indicate the prevalence in those who reported the specific findings.

| Show Table

DownLoad: CSV

3.3. Observations & physical examination findings

On physical examination, 49.7% (n = 192/368) were hypotensive; 93.5% (n = 116/124) were tachycardic and 67.3% were tachypnoeic (n = 105/156). On further assessment, several patients exhibited features of Kawasaki disease, abdominal tenderness, meningeal signs, peripheral oedema, and bilateral crackles on auscultation. The prevalence of the different physical examination findings is described as below in Table 2.

Table 2. Summary of the analysis of the observations and physical examination findings (n = 646).

Observations & physical examination findings		Frequency % (n)*	References
Observations	Hypotension	49.7 (368)	[12],[13],[15],[16],[18],[19]–[21],[23],[24],[30],[31],[33],[35],[37]
	Tachycardia	93.5 (124)	[12],[19]–[24],[28],[34],[35],[37],[38]
	Tachypnea	67.3 (156)	[12],[13],[19],[21]–[24],[32],[34],[35],[37],[38]
Features of Kawasaki disease	Polymorphic rash	57.6 (613)	[11]–[18],[20],[21],[23],[24],[27],[29]–[31],[33],[35],[36]–[38]
	Non-exudative bilateral conjunctivitis	52.9 (501)	[11]–[13],[15],[21],[23],[24],[27],[29],[30],[34],[36]–[38]
	Lip/oral cavity cracking	37 (359)	[12],[14],[15],[21],[23],[29],[33],[36]–[38]
	Hand & feet anomalies	26 (462)	[11],[12],[14],[15],[23],[27],[29],[31],[33],[36]–[38]
	Pharyngeal erythema	24.1 (137)	[11],[13],[19],[24],[31]
	Unilateral cervical lymphadenopathy	13.1 (527)	[12],[14],[16],[29],[31],[33]
Others	Abdominal tenderness	51.9 (27)	[4],[11]–[18],[20],[22]–[31],[37]
	Meningeal signs	21.2 (94)	[14],[22],[28]
	Bilateral crackles on auscultation	15 (40)	[14],[35],[36]

*(n): Changes indicate the prevalence in those who reported the specific findings.

| Show Table

DownLoad: CSV

3.4. Investigations

3.4.1. Laboratory investigations and COVID-19 status

Laboratory values are useful for confirming the diagnosis as well as monitoring the disease progression in the patients. The analysis of laboratory parameters and the COVID-19 status of the patients are described as below in Tables 3 and 4 respectively.

Table 3. Summary of the COVID-19 status of patients (n = 646).

COVID-19 status	Frequency % (n)*	References
Positive SARS-CoV-2 nasopharyngeal PCR	42 (426)	[6],[11]–[22],[28]–[31],[33],[35],[37],[39]
Positive SARS-CoV-2 antibody	85.3 (300)	[6],[11]–[17],[21]–[23],[25],[26],[30],[31],[33],[34],[38],[39]
Elevated IgG only	70.7 (123)	[12],[16],[22],[23],[25],[33],[34]
Elevated IgA only	1.62 (2)	[12],[16],[33]
Elevated IgG and IgM	6.5 (123)	[12],[16],[33]
Elevated IgG and IgA	21.2 (123)	[12],[16],[33]
Exposure to COVID-19 within 4 weeks of the onset of symptoms	40.1 (182)	[13],[14],[16],[17],[33]

*(n): Changes indicate the prevelance in those who reported the specific findings.

| Show Table

DownLoad: CSV

Table 4. Summary of the quantitative and qualitative analysis of the various laboratory parameters (n = 646).

Laboratory parameters		Qualitative: frequency % (n)*	Quantitative: median (range) (n)*	References
Complete blood count	Leukocytosis (K/uL)	95% (116)	15.5 (3.6–23) (112)	[15],[16],[19],[22],[26],[31],[33],[36]–[38]
	Neutrophilia (K/uL)	95% (115)	13 (4.4–19) (108)	[15],[16],[22],[26],[27],[31],[33],[36]–[38]
	Lymphopenia (K/uL)	85% (235)	1.2 (0.09–16) (197)	[4],[11]–[13],[15],[19],[22]–[27],[31]–[33],[35],[37],[38]
	Thrombocytopenia (K/uL)	47% (71)	153 (42–516) (47)	[11],[13],[21],[27],[30],[33],[36]
Inflammatory markers	CRP (mg/mL)	99% (391)	185.5 (3.19–390) (377)	[4],[6],[11]–[13],[16]–[28],[30]–[34],[35],[37]–[39]
	ESR (mm/hr)	82% (142)	53 (13–77.5) (108)	[4],[11]–[13],[20],[22],[24],[27],[30],[32]–[34],[35],[37],[38]
	Procalcitonin (ng/L)	93% (225)	6.5 (0.11–99) (183)	[6],[12],[13],[16],[18],[19],[21],[26],[30],[32]
Markers of cardiac function	Troponin (ng/mL)	77% (254)	22.6 (0–2228) (195)	[4],[11]–[13],[15],[16],[19],[21]–[23],[25],[26],[28],[30]–[33],[35]
	Pro-BNP (pg/mL)	96% (208)	3180 (410–14255) (200)	[12],[13],[15],[18],[19],[25],[27],[31]
	BNP (pg/mL)	77% (88)	388 (19–12166) (63)	[4],[11],[13],[21],[22],[25],[26],[30]
Acute phase reactants	Ferritin (ng/mL)	90% (302)	596.8 (90–7791) (273)	[4],[6],[11]–[13],[15],[17]–[19],[21],[23],[25],[26],[31]–[34],[35]–[37]
	LDH (U/L)	24% (103)	320 (248–1291) (24)	[11],[12],[19],[26],[32],[35],[36]
	D dimers (ng/mL)	96% (352)	3578 (320–24500) (328)	[4],[6],[12],[15]–[23],[26],[26],[30]–[34],[35],[36],[39]
Cytokines	IL-6 (pg/mL)	96% (197)	156 (30.5–1449) (189)	[12],[13],[15],[16],[18]–[21],[26],[27],[29],[33],[34],[37],[39]
	IL-8 (pg/mL)	100% (46)	44.6 (9.4–54.4) (45)	[13],[20],[21]
	TNF alpha (pg/mL)	95% (19)	30.1 (10.7–68.7) (13)	[21],[21],[30]
Coagulation profile	Prothrombin time (s)	67% (42)	17 (15–21) (20)	[4],[11],[19],[30],[32],[35]
	Fibrinogen (mg/dL)	88% (214)	595 (22–1109) (183)	[4],[11]–[13],[19],[20],[22],[26]–[28],[30],[31],[33]
Renal function	Creatinine (mg/dL)	23% (73)	0.6 (0.3–2) (16)	[11],[13],[19],[22],[32]
	BUN (mg/dL)	31% (35)	12 (8–143) (12)	[11],[13],[19],[23],[26],[35]
	Hyponatremia (mEq/L)	73% (15)	132 (129–161) (15)	[27],[33],[37],[38]
Liver function tests	AST (U/L)	76% (76)	48 (27–239) (58)	[4],[11],[13],[20],[26],[32]–[33],[35]
	ALT (U/L)	35% (43)	51 (21–176) (15)	[4],[11],[13],[20],[26],[32]–[33],[35]
	Hypoalbuminemia (g/dL)	53% (124)	3 (1.9–4.3) (72)	[12],[20],[21],[22],[33],[35],[37],[38]

*(n): changes indicate the prevalence in those who reported the specific findings.

| Show Table

DownLoad: CSV

3.4.2. Radiological investigations

Of the 227 patients with reported data, 86.7% (n = 197/227) had a chest x-ray (CXR) performed. Majority of the CXR findings included ground glass opacification and pleural effusions. These findings were further confirmed and validated by computed tomography of the thorax (CT-T) which was performed in 45% of patients (n = 27/60).

To assess cardiac involvement, 93.8% (n = 439/468) of patients received echocardiography (ECHO); findings mainly included: left ventricular systolic dysfunction with a median ejection fraction of 40% (range: 30–55%), myocarditis, and coronary aneurysms. The qualitative analysis of the different CXR and ECHO findings is described as below in Table 5.

Table 5. Summary of the qualitative analysis of the various CXR and ECHO findings (n = 646).

Radiological investigations		Frequency % (n)*	References
CXR	Ground glass opacification	44.2 (104)	[11]–[14],[21],[28],[33],[39]
	Pleural effusion	15.4 (104)	[11]–[14],[21],[33]
	Mono/bilateral infiltrates	14.4 (104)	[11]–[14],[33]
	Cardiomegaly	12.5 (104)	[11]–[14],[24],[30],[33]
	Interstitial abnormality	7.7 (104)	[11]–[14],[24],[30],[33]
	Others	5.8 (104)	[4],[11]–[14],[21],[33]
ECHO	Left ventricular systolic dysfunction	22.6 (376)	[11]–[16],[19],[30],[31],[33],[39],[40]
	Myocarditis	22.3 (376)	[11]–[16],[30],[31],[33],[39],[40]
	Coronary artery aneurysm	20.2 (376)	[11]–[16],[30],[31],[33],[39],[40]
	Pericardial effusion	17.8 (376)	[11]–[16],[21],[30],[31],[33],[39],[40]
	Coronary artery dilation	9.3 (29)	[11]–[16],[30],[31],[33],[39],[40]
	Mitral regurgitation	4.5 (376)	[11]–[16],[21],[24],[30],[31],[33],[39],[40]
	Others	3.2 (376)	[11]–[16],[21]–[23],[25],[30],[31],[33],[35],[39],[40]

*(n): Changes indicate the prevalence in those who reported the specific findings.

| Show Table

DownLoad: CSV

3.5. Management

The management of the patients included pharmacological interventions and organ support in the Pediatric Intensive Care Unit (PICU). Generally, patients that required organ support were admitted to PICU amounting to 75% (n = 333/445) of the patients.

3.5.1. Medications

In terms of pharmacological management, majority of patients received intravenous immunoglobulins (IVIG). Furthermore, intravenous steroids were administered in 51% (n = 317/618). Further breakdown of the type of steroids used is described in Table 6.

Aspirin was used in 59% (n = 91/153) as either an anti-inflammatory or anti-platelet agent or both.

Given the overlap of the presentation of MIS-C with that of bacterial infections, 68% (n = 169/248) received empiric antibiotics; ceftriaxone was the most used antibiotic. An exhaustive list of the primary, secondary, and tertiary choices of antibiotics is available in the Tables S2–S5. Due to the prominence of hemodynamic instability, intravenous fluid boluses were administered in 48% (n = 21/44). Additionally, inotropes were administered in 51% (n = 287/571) due to refractory hypotension. Further analysis of the pharmacological management is available in Table 6.

Table 6. Summary of the pharmacological intervention in patients (n = 646).

Pharmacological therapy		Frequency % (n)*	References
-	Intravenous Immunoglobulins	76 (571)	[6],[11]–[16],[19],[21]–[24],[26]–[28],[30]–[33],[34],[37],[39]
-	Hydroxychloroquine	5 (40)	[22],[36]
Anti-retroviral	Remdesivir	90 (31)	[11],[13],[19],[21]
	Lopinavir	3 (31)	[36]
	Acyclovir	3 (31)	[32]
	Ritonavir	3 (31)	[36]
Intravenous steroids	Methylprednisolone	2 (317)	[19],[22],[26],[27],[35]
	Hydrocortisone	4 (317)	[30]
	Type not specified	9 (317)	[6],[11],[12],[14],[15],[29]–[31],[33],[39],[41]
Anticoagulants	Enoxaparin	86 (212)	[11],[16],[17],[19],[22],[29],[30],[35]
	Oral anticoagulant	14 (212)	[13],[19]
Antiplatelet	Aspirin	59 (153)	[6],[13],[14],[21],[24],[25],[27],[30],[33],[34],[37]–[39]
Biologics	Tocilizumab	81(62)	[13],[19],[21],[22],[29],[30],[36],[37]
	Infliximab	19 (62)	[6],[15],[20],[29],[31],[34]
	Anakinra	11 (380)	[11],[13],[16],[21],[29],[31]
Inotropes and vasopressors	Dopamine	5 (287)	[6],[11]–[14],[16],[21],[22],[29],[31],[33],[41]
	Epinephrine	3 (287)	[6],[11]–[14],[16],[21],[24],[29],[31],[33],[35],[41]
	Norepinephrine	7 (287)	[6],[11]–[14],[16],[21],[24],[29],[31],[33],[35],[41]
	Phenylephrine	0.3 (287)	[6],[11]–[14],[16],[24],[29],[31],[33],[41]
	Milrinone	2 (287)	[6],[11]–[14],[16],[19],[21],[24],[29],[31],[33],[41]
	Vasopressin	2 (287)	[6],[11]–[14],[21],[29],[31],[33],[41]
	Dobutamine	4 (287)	[13]
Diuretics	Furosemide	33 (73)	[13],[19],[24],[35]

*(n): Changes indicate the prevalence in those who reported the specific findings.

| Show Table

DownLoad: CSV

3.5.2. Respiratory support

Respiratory support played a crucial part in the management of patients. The different modes of respiratory support utilized are described in Table 7.

Table 7. Summary of the various modalities of respiratory support utilized (n = 646).

Respiratory support	Frequency % (n)*	References
Mechanical ventilation	30 (583)	[13],[14],[16],[21],[22],[34],[35],[36]
Noninvasive ventilation	57 (109)	[11],[13],[16],[17],[21],[22],[41]
Nasal cannula	43 (109)	[11]–[13],[16],[17],[41]
Extracorporeal membrane oxygenation (ECMO)	5 (305)	[6],[13],[16],[21],[29],[31],[34],[36]

*(n): Changes indicate the prevelance in those who reported the specific findings.

| Show Table

DownLoad: CSV

3.6. Complications & outcome

Complications developed over the course of admission are described in Table 8. Overall, 97% (n = 418/431) progressed well and were discharged; median length of stay was 8 days (range: 3–14 days). Of the 431 patients, 3% (13/431) deceased.

Table 8. Summary of the complications developed over the course of admission (n = 646).

Complications	Frequency % (n)*	References
Heart failure	23 (75)	[16],[26],[35],[36]
Hypoxia	15 (40)	[22],[28],[35],[36]
Renal failure	15 (225)	[11],[12],[22],[28],[31],[34]–[36]
Disseminated intravascular coagulation	3 (40)	[36]
Intracerebral hemorrhage	5 (4)	[32]

*(n): Changes indicate the prevelance in those who reported the specific findings.

| Show Table

DownLoad: CSV

Table 9 shows the outcomes of immunomodulation in the MIS-C patients treated with combinations of biologics (n = 24/40 tocilizumab, n = 2/40 anakinra, and n = 3/40 infliximab) and immunoglobulins (n = 31/40 IVIG, n = 2/40 convalescence plasma). Maximal data was available from the co-treatment group of biologics and immunoglobulins. Further analysis of this group did not yield any statistically significant outcomes due to low sample number (Table S6).

Table 9. The outcomes in the treatment groups with and without biologics and immunoglobulin therapy (n* = 40).

Treatments	LOS in days: mean ± SD	Outcomes	Complications	Conclusion & caveats	References
Biologics OR Immunoglobulin (n = 13)	9 ± 3	84.6% discharge, 15.4% deceased	ICH (n = 2), HF (n = 2), MOF (n = 2)	Longest LOS of the 3 treatment groups. More single organ failures. LOS data not available in 2 patients.	[4],[20]–[22],[24],[26],[28],[30],[32],[34]
Biologics AND Immunoglobulin (n = 22)	7 ± 3	95.5 % discharge, 4.5% deceased	RF (n = 1), MOF (n = 1)	Medium LOS of the 3 treatment groups. Best outcomes with the highest ratio of discharged/deceased. Low complication rates. LOS data not available in 4 patients.	[19],[21]–[23],[27],[30],[30],[36],[37]
NEITHER Biologics OR Immunoglobulin (n = 5)	6 ± 2	80% discharge, 20% deceased	MOF (n = 2)	Lowest LOS but outcomes are poor with the lowest ratio of discharged/deceased. Sample size is the smallest.	[22],[25],[30],[35],[38]

*(n): Changes indicate the prevalence in those who reported the specific findings.

| Show Table

DownLoad: CSV

4. Discussion

In this systematic review, we describe 646 patients with MIS-C. These patients fulfilled the CDC criteria of fever, multi-organ involvement with a severe clinical presentation, and no other possible diagnosis with evidence of SARS-CoV-2 infection on RT-PCR, serological testing, or exposure to COVID-19 within 4 weeks of onset of symptoms [42].

The median age of patients was 10 years (range: 0.5–17 years). Most of the patients were of African and Hispanic ethnicity. This contrasts with the demographic distribution associated with Kawasaki disease; age <5 years, Asian ethnicity, and Pacific Island decent [9]. Epidemiological differences indicate that they have distinct aetiologies and are associated with different pathophysiological and immunological processes. MIS-C syndrome has a heterogeneous clinical presentation as evident from the frequency distribution of the various presenting symptoms. The predominant presenting complaint was of gastrointestinal symptoms and the second most common presentation was with respiratory symptoms. It is thought that the pulmonary manifestation in children is less severe than that in adults due to a relatively lower ACE-2 receptor expression. In adults, ACE-2 receptors are highly expressed on type II alveolar cells and are a target of the SARS-CoV-2 virus. These receptors act as the portal of entry for the virus facilitating the release of its RNA into lung cells [5],[43]. Interestingly, ACE-2 receptors are also found in different parts of the body including the brush border membrane of the small intestine enterocytes. Given the predominant presentation with gastrointestinal symptoms in children, this raises the question as to whether children have a relatively higher level of ACE-2 receptor density on enterocytes. Despite that not being tested to date, a recent study in adults revealed differences in the expression of the receptors in the different age groups; perhaps a similar pattern exists in children [44]. A second plausible theory relates to differences in the expression of amino acid transporters between the adult and the pediatric population; this can have an impact on the proteolytic action of ACE-2 and consequently can affect the susceptibility of SARS-CoV-2 to infect enterocytes [44]. Furthermore, it is suggested that gastrointestinal symptoms are possibly due to intestinal ischemia secondary to the vasculitis of bowel vessels [14].

SARS-CoV-2 nasopharyngeal RT-PCR was positive in less than 50% of the patient cohort. On the contrary, majority tested positive for SARS-CoV-2 antibodies. Further analysis revealed that the predominating antibody was IgG and occasionally patients had dual antibody elevation (IgG and IgA or IgG and IgM) while no patient had an isolated IgM increase. Therefore, both the production of immunoglobulins by intraepithelial lymphocytes and the higher ACE-2 receptor density on gut enterocytes, relative to the alveolar cells, can possibly explain both the laboratory findings and clinical presentations. In fact, serological findings suggest that MIS-C is likely a post-viral manifestation rather than an early immune response to an acute infection. This further complicates the attempt to distinguish it from Kawasaki disease as they seem to share a similar pathophysiological timeline. Additionally, both conditions are associated with an increase in IgA antibodies; the initial immune response is likely activated in intestinal or respiratory mucosa and thus an elevated IgA antibody level is justifiable in both conditions [45].

Further analysis of laboratory investigations revealed leukocytosis with neutrophilic predominance coupled with thrombocytopenia and a significant elevation in inflammatory markers. These findings were accompanied with an elevation of inflammatory cytokines, transaminases, LDH, ferritin, and D-dimers. All of which are consistent with a cytokine storm implying a possible defect in the innate immune system contributing to the manifestation of fever, neurological signs and symptoms, polymorphic rash, and significant coagulopathy. This can escalate to multiple organ failure and death [46]. The constellation of elevated cytokines mediated an increase in vascular permeability causing fluid leakage into the extravascular compartment contributing to the distributive shock seen in our patient cohort. Furthermore, the initial activation of macrophages and the resultant stimulation of T-helper cells resulted in a significant production of pro-inflammatory cytokines perpetuating the recruitment of further monocytes, macrophages, neutrophils, B-cells/plasma cells and the production of antibodies [47]. This contributed to the evolution of the delayed hyperinflammatory syndrome seen in the pediatric population.

It is prudent to note that the cytokine storm seen in patients with MIS-C has a different profile to that associated with either Kawasaki disease or adults with acute COVID-19 infection. This is attributed to the different T-cell subpopulations and their corresponding frequencies [5].

On closer assessment of CD 4+ T-cells, the patient cohort with MIS-C and mild SARS-CoV-2 infection had a similar pattern of distribution [5]. However, when compared to the cohort with Kawasaki disease, patients with COVID-19 infection (mild or MIS-C) had a higher prevalence of the effector and central memory CD4+ T-cells and less naïve and follicular helper T-cells [5]. Additionally, pediatric patients with MIS-C were found to have fewer total T-cell frequencies when compared to healthy children [5]. This validated COVID-19 as the culprit for the differences seen relative to healthy patients.

In our review, the most common findings on ECHO were left ventricular systolic dysfunction (heart failure) and myocarditis. The extent of myocardial damage was further corroborated by an elevated troponin and pro-BNP/BNP in a large proportion of our patient cohort. The coronary arteries were spared in most of our MIS-C patients demonstrating the possibility that coronary arteries are not commonly affected in the early stages of the syndrome. Our findings mirrored the findings of key European studies by Verdoni et al and Belhadjer et al. where no aneurysms were diagnosed on presentation or during admission [33],[46]. Follow up would be required to better understand the long-term impact of MIS-C on coronary arteries; due to the burden on the healthcare system during the pandemic no such studies are available. Future studies need to validate long term scope of disease. It is important to note that coronary artery aneurysms and coronary artery dilation are two distinct phenomena. The latter is merely a physiological response to increased oxygen demand implicated by a hyperinflammatory state [48]. Contrastingly, myocarditis appears to be a common early manifestation that develops secondary to the recognized COVID-19 associated systemic vasculitis [7]. The presentation with myocarditis in the context of a normal ejection fraction is subclinical and resolves as the inflammation subsides [48]. However, myocarditis, in children with reduced ejection fraction, presents severely with cardiovascular shock secondary to myocardial dysfunction and is coupled with a decrease in the peripheral vascular resistance secondary to the production of pro-inflammatory cytokines as discussed earlier [14].

In our analysis, a broad range of pharmacological therapies were used. Given the possible overlap with severe bacterial infection and septic shock, patients were empirically started on broad spectrum antibiotics, most commonly ceftriaxone. Generally, it is recommended to liaise with the microbiology team and the local antimicrobial guidelines to guide antibiotic choice. Additionally, adjunct treatment with anti-inflammatory and immunomodulatory agents, in addition to IV fluids and inotropic support, were initiated particularly in children that presented with profound shock. This included: IVIG, corticosteroids, aspirin, in addition to the IL-6 monoclonal antibody, tocilizumab, and anakinra, an IL-1 receptor antagonist. Occasionally, some children were treated with infliximab, a TNF-alpha blocker. Studies have demonstrated that TNF-alpha levels in children with MIS-C are lower than in adults with COVID-19. Also, levels are not significantly different from healthy children [5]. Therefore, infliximab might not be the most suitable agent to suppress the hyperinflammatory state. Our data analysis revealed a TNF-alpha elevation in 95%. However, data was documented in only 19 patients, hence limited conclusions can be extrapolated. Our analysis also showed that the mean length of stay with both biologics such as tocilizumab and immunoglobulins such as IVIG was 7 ± 3 days. This combination treatment had a better outcome of 95.5% (n = 21/22) discharge and lower complications than either treatment alone. More studies with higher sample size are required to validate this observation.

Overall, the pediatric patients in our review responded to both anti-inflammatory and immunomodulatory agents indicating that the manifestation of disease is primarily secondary to the hyperinflammatory state mediated by the immune detection of the virus rather than the virus itself. Furthermore, the massive cytokine production and neutrophilic infiltration of tissue, that forms extracellular traps to control further spread of SARS-CoV-2 infection, contributes to the histopathognomic systemic microangiopathy and thrombosis exhibited in MIS-C [7]. Therefore, as was the case in our review, thromboprophylaxis use is prudent. Most of our patients were discharged home with appropriate therapy and early detection of MIS-C.

5. Conclusions

Our systematic review collated all case reports and article reviews on patients admitted with MIS-C and highlighted both its heterogeneous presentation and evaluated the management options. As the pandemic evolves, it is prudent to continue to study and collate further patient data to identify the circulating immune cells profile and better understand the significance of the varying immunoglobulin levels and their association with prognosis in the post-infection phase. Additionally, further data on the role of IL-1 receptor antagonist as a possible adjunct or replacement to IVIG in patients with acute myocarditis and hemodynamic instability is needed.

References

[1]	X. Zou, A survey on application of knowledge graph, J. Phys. Conf. Ser., 1487 (2020), 12016. https://doi.org/10.1088/1742-6596/1487/1/012016 doi: 10.1088/1742-6596/1487/1/012016
[2]	M. Kejriwal, Knowledge graphs and COVID-19: opportunities, challenges, and implementation, Harv. Data Sci. Rev., 11 (2020), 300.
[3]	Q. H. Nguyen, T. T. Do, Y. Wang, S. S. Heng, K. Chen, W. H. M. Ang, et al., Breast cancer prediction using feature selection and ensemble voting, in 2019 International Conference on System Science and Engineering (ICSSE), IEEE, (2019), 250–254.
[4]	K. Zhang, X. Ren, L. Zhuang, H. Zan, W. Zhang, Z. Sui, Construction of chinese medicine knowledge base, in Workshop on Chinese Lexical Semantics, Springer, (2020), 665–675. https://doi.org/10.1007/978-3-030-81197-6_56
[5]	P. H. Martins, Z. Marinho, A. Martins, Joint learning of named entity recognition and entity linking, preprint, arXiv: 1907.08243.
[6]	J. Noh, R. Kavuluru, Joint learning for biomedical ner and entity normalization: encoding schemes, counterfactual examples, and zero-shot evaluation, in Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, (2021), 1–10.
[7]	L. Liu, M. Wang, M. Zhang, L. Qing, X. He, Uamner: uncertainty-aware multimodal named entity recognition in social media posts, Appl. Intell., 52 (2022), 4109–4125. https://doi.org/10.1007/s10489-021-02546-5 doi: 10.1007/s10489-021-02546-5
[8]	S. S. Paliwal, D. Vishwanath, R. Rahul, M. Sharma, L. Vig, Tablenet: Deep learning model for end-to-end table detection and tabular data extraction from scanned document images, in 2019 International Conference on Document Analysis and Recognition (ICDAR), IEEE, (2019), 128–133.
[9]	W. Xiang, B. Wang, A survey of event extraction from text, IEEE Access, 7 (2019), 173111–173137. https://doi.org/10.1109/ACCESS.2019.2956831 doi: 10.1109/ACCESS.2019.2956831
[10]	Y. Lu, Q. Liu, D. Dai, X. Xiao, H. Lin, X. Han, et al., Unified structure generation for universal information extraction, preprint, arXiv: 2203.12277.
[11]	B. P. Nguyen, H. N. Pham, H. Tran, N. Nghiem, Q. H. Nguyen, T. T. Do, et al., Predicting the onset of type 2 diabetes using wide and deep learning with electronic health records, Comput. Methods Programs Biomed., 182 (2019), 105055. https://doi.org/10.1016/j.cmpb.2019.105055 doi: 10.1016/j.cmpb.2019.105055
[12]	X. Zhao, Y. Jia, A. Li, R. Jiang, Y. Song, Multi-source knowledge fusion: a survey, World Wide Web, 23 (2020), 2567–2592. https://doi.org/10.1007/s11280-020-00811-0 doi: 10.1007/s11280-020-00811-0
[13]	A. Hogan, E. Blomqvist, M. Cochez, C. D'Amato, G. D. Melo, C. Gutierrez, et al., Knowledge graphs, ACM Comput. Surv., 54 (2021), 1–37. https://doi.org/10.1145/3466817
[14]	M. Wang, X. He, L. Liu, L. Qing, H. Chen, Y. Liu, et al., Medical visual question answering based on question-type reasoning and semantic space constraint, Artif. Intell. Med., 131 (2022), 102346. https://doi.org/10.1016/j.artmed.2022.102346 doi: 10.1016/j.artmed.2022.102346
[15]	X. Zhu, Z. Li, X. Wang, X. Jiang, P. Sun, X. Wang, et al., Multi-modal knowledge graph construction and application: A survey, preprint, arXiv: 2202.05786.
[16]	L. Liu, M. Wang, X. He, L. Qing, H. Chen, Fact-based visual question answering via dual-process system, Knowl. Based Syst., 237 (2022), 107650. https://doi.org/10.1016/j.knosys.2021.107650 doi: 10.1016/j.knosys.2021.107650
[17]	A. Carlson, J. Betteridge, B. Kisiel, B. Settles, E. R. Hruschka, T. M. Mitchell, Toward an architecture for never-ending language learning, in Twenty-Fourth AAAI Conference on Artificial Intelligence, 24 (2010), 1306–1313.
[18]	D. Vrandečić, Wikidata: A new platform for collaborative data collection, in Proceedings of the 21st International Conference on World Wide Web, (2012), 1063–1064.
[19]	L. Liu, M. Wang, X. He, L. Qing, J. Zhang, Extracting relational facts based on hybrid syntax-guided transformer and pointer network, J. Intell. Fuzzy Syst., 40 (2021), 12167–12183. https://doi.org/10.3233/JIFS-210281 doi: 10.3233/JIFS-210281
[20]	H. Lv, H. Liang, F. Ma, Constructing knowledge graph for financial equities, Data Anal. Knowl. Discovery, 4 (2020), 27–37.
[21]	F. Sovrano, M. Palmirani, F. Vitali, Legal knowledge extraction for knowledge graph based question-answering, in Legal Knowledge and Information Systems, IOS Press, (2020), 143–153.
[22]	Y. Wei, H. Wang, J. Zhao, Y. Liu, Y. Zhang, B. Wu, Gelaigelai: a visual platform for analysis of classical chinese poetry based on knowledge graph, in 2020 IEEE International Conference on Knowledge Graph (ICKG), IEEE, (2020), 513–520.
[23]	F. Gong, M. Wang, H. Wang, S. Wang, M. Liu, Smr: medical knowledge graph embedding for safe medicine recommendation, Big Data Res., 23 (2021), 100174. https://doi.org/10.1016/j.bdr.2020.100174 doi: 10.1016/j.bdr.2020.100174
[24]	H. Chen, N. Hu, G. Qi, H. Wang, Z. Bi, J. Li, et al., Openkg chain: A blockchain infrastructure for open knowledge graphs, Data Intell., 3 (2021), 205–227.
[25]	A. Chatterjee, C. Nardi, C. Oberije, P. Lambin, Knowledge graphs for COVID-19: An exploratory review of the current landscape, J. Pers. Med., 11 (2021), 300. https://doi.org/10.3390/jpm11040300 doi: 10.3390/jpm11040300
[26]	S. Ji, S. Pan, E. Cambria, P. Marttinen, S. Y. Philip, A survey on knowledge graphs: Representation, acquisition, and applications, IEEE Trans. Neural Networks Learn. Syst., 33 (2021), 494–514. https://doi.org/10.1109/TNNLS.2021.3070843 doi: 10.1109/TNNLS.2021.3070843
[27]	B. Xie, S. Li, F. Lv, C. H. Liu, G. Wang, D. Wu, A collaborative alignment framework of transferable knowledge extraction for unsupervised domain adaptation, IEEE Trans. Knowl. Data Eng., 2022 (2022). https://doi.org/10.1109/TKDE.2022.3185233
[28]	J. Li, A. Sun, J. Han, C. Li, A survey on deep learning for named entity recognition, IEEE Trans. Knowl. Data Eng., 34 (2020), 50–70. https://doi.org/10.1007/s10618-019-00656-w doi: 10.1007/s10618-019-00656-w
[29]	A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, et al., Attention is all you need, in Advances in Neural Information Processing Systems 30 (NIPS 2017), (2017), 30.
[30]	S. Edunov, A. Baevski, M. Auli, Pre-trained language model representations for language generation, preprint, arXiv: 1903.09722.
[31]	L. X. Liang, L. Lin, E. Lin, W. S. Wen, G. Y. Huang, A joint learning model to extract entities and relations for chinese literature based on self-attention, Mathematics, 10 (2022), 2216. https://doi.org/10.3390/math10132216 doi: 10.3390/math10132216
[32]	M. Zhang, Y. Chen, J. Lin, A privacy-preserving optimization of neighborhood-based recommendation for medical-aided diagnosis and treatment, IEEE Internet Things J., 8 (2021), 10830–10842. https://doi.org/10.1109/JIOT.2021.3051060 doi: 10.1109/JIOT.2021.3051060
[33]	B. An, X. Han, C. Fu, L. Sun, Retrofitting soft rules for knowledge representation learning, Big Data Res., 24 (2021), 100156. https://doi.org/10.1016/j.bdr.2020.100156 doi: 10.1016/j.bdr.2020.100156
[34]	J. H. Gennari, M. A. Musen, R. W. Fergerson, W. E. Grosso, M. Crubézy, H. Eriksson, et al., The evolution of protégé: an environment for knowledge-based systems development, Int. J. Human Comput. Stud., 58 (2003), 89–123. https://doi.org/10.1016/S0031-9406(05)60588-3 doi: 10.1016/S0031-9406(05)60588-3
[35]	M. Peleg, Computer-interpretable clinical guidelines: a methodological review, J. Biomed. Inf., 46 (2013), 744–763. https://doi.org/10.1016/j.jbi.2013.06.009 doi: 10.1016/j.jbi.2013.06.009
[36]	Z. Dai, X. Wang, P. Ni, Y. Li, G. Li, X. Bai, Named entity recognition using bert bilstm crf for Chinese electronic health records, in 2019 12th International Congress on Image and Signal Processing, Biomedical Engineering and Informatics (cisp-bmei), IEEE, (2019), 1–5.
[37]	Z. Ni, L. Ma, H. Zeng, J. Chen, C. Cai, K. K. Ma, Esim: Edge similarity for screen content image quality assessment, IEEE Trans. Image Process., 26 (2017), 4818–4831. https://doi.org/10.1109/TIP.2017.2718185 doi: 10.1109/TIP.2017.2718185
[38]	L. Li, R. Ma, Q. Guo, X. Xue, X. Qiu, Bert-attack: Adversarial attack against bert using bert, preprint, arXiv: 2004.09984.
[39]	E. K. W. Leow, B. P. Nguyen, M. C. H. Chua, Robo-advisor using genetic algorithm and bert sentiments from tweets for hybrid portfolio optimisation, Expert Syst. Appl., 179 (2021), 115060. https://doi.org/10.1016/j.eswa.2021.115060 doi: 10.1016/j.eswa.2021.115060
[40]	T. Nguyen-Vo, Q. H. Trinh, L. Nguyen, T. T. Do, M. C. H. Chua, B. P. Nguyen, Predicting antimalarial activity in natural products using pretrained bidirectional encoder representations from transformers, J. Chem. Inf. Model., 62 (2021), 5050–5058. https://doi.org/10.1021/acs.jcim.1c00584 doi: 10.1021/acs.jcim.1c00584
[41]	Z. Niu, G. Zhong, H. Yu, A review on the attention mechanism of deep learning, Neurocomputing, 452 (2021), 48–62. https://doi.org/10.1007/s43830-021-0173-9 doi: 10.1007/s43830-021-0173-9
[42]	A. E. Patanwala, A practical guide to conducting and writing medical record review studies, Am. J. Health Syst. Pharm., 74 (2017), 1853–1864. https://doi.org/10.2146/ajhp170183 doi: 10.2146/ajhp170183
[43]	M. Lewis, Y. Liu, N. Goyal, M. Ghazvininejad, A. Mohamed, O. Levy, et al., Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension, preprint, arXiv: 1910.13461.
[44]	Z. Yuan, Z. Zhao, H. Sun, J. Li, F. Wang, S. Yu, Coder: Knowledge-infused cross-lingual medical term embedding for term normalization, J. Biomed. Inf., 126 (2022), 103983. https://doi.org/10.1016/j.jbi.2021.103983 doi: 10.1016/j.jbi.2021.103983
[45]	Y. Shen, N. Ding, H. T. Zheng, Y. Li, M. Yang, Modeling relation paths for knowledge graph completion, IEEE Trans. Knowl. Data Eng., 33 (2020), 3607–3617. https://doi.org/10.1109/TKDE.2020.2970044 doi: 10.1109/TKDE.2020.2970044

This article has been cited by:

1.	Ahmed Yaqinuddin, Abdul Hakim Almakadma, Junaid Kashir, Kawasaki like disease in SARS-CoV-2 infected children – a key role for neutrophil and macrophage extracellular traps, 2021, 8, 2372-0301, 174, 10.3934/molsci.2021013
2.	Kaixi Ji, Zitong Yang, Mengqian Zhou, Muhammad Tayyab Sohail, Cognitive Attitudes of International Mainstream Media to China during the Contaminated Water and Human Health Under Big Data, 2022, 2022, 1687-9813, 1, 10.1155/2022/9033781
3.	Stasa Krasic, Vladislav Vukomanovic, Sanja Ninic, Srdjan Pasic, Gordana Samardzija, Nemanja Mitrovic, Maja Cehic, Dejan Nesic, Milica Bajcetic, Mechanisms of redox balance and inflammatory response after the use of methylprednisolone in children with multisystem inflammatory syndrome associated with COVID-19, 2023, 14, 1664-3224, 10.3389/fimmu.2023.1249582
4.	Víctor Manuel Gutiérrez-Gómez, Beatriz Archundia-Jiménez, Rodrigo Miguel González-Sánchez, Jerónimo Amado López-Arriaga, Beatriz X. Pasco-Velázquez, Alejandra Gómez-Flores, 2023, Chapter 6, 978-0-85466-305-7, 10.5772/intechopen.1003874
5.	Staša Krasić, Milica Bajčetić, Vladislav Vukomanović, Multisystem inflammatory syndrome associated with COVID-19 in children: Etiopathogenesis, clinical presentation and therapy, 2024, 75, 0369-1527, 48, 10.5937/mp75-41612

Reader Comments

Your name:*

Email:*
© 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)