WHO. Dengue and severe dengue. Geneva: World Health Organization; 2020. Available from: https://www.who.int/news-room/fact-sheets/detail/dengue-and-severe-dengue. Accessed 10 Dec 2020.
Xin Tian C, Baharuddin KA, Shaik Farid AW, Andey R, Ridzuan MI, Siti-Azrin AH. Ultrasound findings of plasma leakage as imaging adjunct in clinical management of dengue fever without warning signs. Med J Malaysia. 2020;75(6):635–41 Epub 2020/11/22. PubMed PMID: 33219170.
CAS
PubMed
Google Scholar
Rafi A, Mousumi AN, Ahmed R, Chowdhury RH, Wadood A, Hossain G. Dengue epidemic in a non-endemic zone of Bangladesh: Clinical and laboratory profiles of patients. PLoS Negl Trop Dis. 2020;14(10):e0008567 Epub 2020/10/14. doi: 10.1371/journal.pntd.0008567. PubMed PMID: 33048921; PubMed Central PMCID: PMCPMC7553334.
Article
Google Scholar
Shepard DS, Undurraga EA, Halasa YA, Stanaway JD. The global economic burden of dengue: a systematic analysis. Lancet Infect Dis. 2016;16(8):935–41. https://doi.org/10.1016/S1473-3099(16)00146-8.
Article
PubMed
Google Scholar
Stanaway JD, Shepard DS, Undurraga EA, Halasa YA, Coffeng LE, Brady OJ, et al. The global burden of dengue: an analysis from the Global Burden of Disease Study 2013. Lancet Infect Dis. 2016;16(6):712–23. https://doi.org/10.1016/S1473-3099(16)00026-8.
Article
PubMed
PubMed Central
Google Scholar
WHO. Dengue guidelines for diagnosis, treatment, prevention and control : new edition. Geneva: World Health Organization; 2009.
Google Scholar
Bodinayake CK, Tillekeratne LG, Nagahawatte A, Devasiri V, Kodikara Arachchi W, Strouse JJ, et al. Evaluation of the WHO 2009 classification for diagnosis of acute dengue in a large cohort of adults and children in Sri Lanka during a dengue-1 epidemic. PLoS Negl Trop Dis. 2018;12(2):e0006258. https://doi.org/10.1371/journal.pntd.0006258 Epub 2018/02/10. PubMed PMID: 29425194; PubMed Central PMCID: PMCPMC5823472.
Article
PubMed
PubMed Central
Google Scholar
Macedo GA, Gonin MLC, Pone SM, Cruz OG, Nobre FF, Brasil P. Sensitivity and Specificity of the World Health Organization Dengue Classification Schemes for Severe Dengue Assessment in Children in Rio de Janeiro. PLoS One. 2014;9(4):e96314. https://doi.org/10.1371/journal.pone.0096314.
Article
CAS
PubMed
PubMed Central
Google Scholar
van de Weg CA, van Gorp EC, Supriatna M, Soemantri A, Osterhaus AD, Martina BE. Evaluation of the 2009 WHO dengue case classification in an Indonesian pediatric cohort. Am J Trop Med Hyg. 2012;86(1):166–70. https://doi.org/10.4269/ajtmh.2012.11-0491 Epub 2012/01/11. PubMed PMID: 22232468; PubMed Central PMCID: PMCPMC3247126.
Article
PubMed
PubMed Central
Google Scholar
Barniol J, Gaczkowski R, Barbato EV, da Cunha RV, Salgado D, Martínez E, et al. Usefulness and applicability of the revised dengue case classification by disease: multi-centre study in 18 countries. BMC Infect Dis. 2011;11:106. https://doi.org/10.1186/1471-2334-11-106 PubMed PMID: 21510901.
Article
PubMed
PubMed Central
Google Scholar
Jayarajah U, Dissanayake U, Abeysuriya V, De Silva PK, Jayawardena P, Kulatunga A, et al. Comparing the 2009 and 1997 World Health Organization dengue case classifications in a large cohort of South Asian patients. J Infect Dev Ctries. 2020;14(7):781–7. https://doi.org/10.3855/jidc.12468 Epub 2020/08/15. PubMed PMID: 32794470.
Article
PubMed
Google Scholar
Kalayanarooj S. Dengue classification: current WHO vs. the newly suggested classification for better clinical application? J Med Assoc Thail. 2011;94(Suppl 3):S74–84 Epub 2011/11/03. PubMed PMID: 22043757.
Google Scholar
Leo Y-S, Gan VC, Ng E-L, Hao Y, Ng L-C, Pok K-Y, et al. Utility of warning signs in guiding admission and predicting severe disease in adult dengue. BMC Infect Dis. 2013;13(1):498. https://doi.org/10.1186/1471-2334-13-498.
Article
PubMed
PubMed Central
Google Scholar
Banerjee A, Shukla S, Pandey AD, Goswami S, Bandyopadhyay B, Ramachandran V, et al. RNA-Seq analysis of peripheral blood mononuclear cells reveals unique transcriptional signatures associated with disease progression in dengue patients. Transl Res. 2017;186:62–78 e9. Epub 2017/07/07. PubMed PMID: 28683259. https://doi.org/10.1016/j.trsl.2017.06.007.
Article
CAS
PubMed
Google Scholar
Simon-Loriere E, Duong V, Tawfik A, Ung S, Ly S, Casademont I, et al. Increased adaptive immune responses and proper feedback regulation protect against clinical dengue. Sci Transl Med. 2017;9(405). https://doi.org/10.1126/scitranslmed.aal5088 Epub 2017/09/01. PubMed PMID: 28855396.
Robinson M, Einav S. Towards Predicting Progression to Severe Dengue. Trends Microbiol. 2020;28(6):478–86. https://doi.org/10.1016/j.tim.2019.12.003 Epub 2020/01/27. PubMed PMID: 31982232.
Article
CAS
PubMed
Google Scholar
Nikolayeva I, Bost P, Casademont I, Duong V, Koeth F, Prot M, et al. A Blood RNA Signature Detecting Severe Disease in Young Dengue Patients at Hospital Arrival. J Infect Dis. 2018;217(11):1690–8. https://doi.org/10.1093/infdis/jiy086.
Article
CAS
PubMed
PubMed Central
Google Scholar
Sippy R, Farrell DF, Lichtenstein DA, Nightingale R, Harris MA, Toth J, et al. Severity Index for Suspected Arbovirus (SISA): Machine learning for accurate prediction of hospitalization in subjects suspected of arboviral infection. PLoS Negl Trop Dis. 2020;14(2):e0007969. https://doi.org/10.1371/journal.pntd.0007969.
Article
PubMed
PubMed Central
Google Scholar
Davi C, Pastor A, Oliveira T, Neto FBL, Braga-Neto U, Bigham AW, et al. Severe Dengue Prognosis Using Human Genome Data and Machine Learning. IEEE Trans Biomed Eng. 2019;66(10):2861–8. https://doi.org/10.1109/TBME.2019.2897285.
Article
PubMed
Google Scholar
Caicedo-Torres W, Paternina Á, Pinzón H. Machine Learning Models for Early Dengue Severity Prediction. Advances in Artificial Intelligence - IBERAMIA 2016. https://www.springerprofessional.de/en/machine-learning-models-for-early-dengue-severity-prediction/10871182.
Huang S-W, Tsai H-P, Hung S-J, Ko W-C, Wang J-R. Assessing the risk of dengue severity using demographic information and laboratory test results with machine learning. PLoS Negl Trop Dis. 2020;14(12):e0008960. https://doi.org/10.1371/journal.pntd.0008960.
Article
PubMed
PubMed Central
Google Scholar
Sweeney TE, Haynes WA, Vallania F, Ioannidis JP, Khatri P. Methods to increase reproducibility in differential gene expression via meta-analysis. Nucleic Acids Res. 2017;45(1):e1. https://doi.org/10.1093/nar/gkw797 Epub 2016/09/17. PubMed PMID: 27634930; PubMed Central PMCID: PMCPMC5224496.
Article
CAS
PubMed
Google Scholar
Haynes WA, Vallania F, Liu C, Bongen E, Tomczak A, Andres-Terrè M, et al. Empowering Multi-Cohort Gene Expression Analysis to Increase Reproducibility. Pac Symp Biocomput. 2017;22:144–53. https://doi.org/10.1142/9789813207813_0015 Epub 2016/11/30. PubMed PMID: 27896970; PubMed Central PMCID: PMCPMC5167529.
Article
PubMed
Google Scholar
Robinson M, Sweeney TE, Barouch-Bentov R, Sahoo MK, Kalesinskas L, Vallania F, et al. A 20-Gene Set Predictive of Progression to Severe Dengue. Cell Rep. 2019;26(5):1104–11 e4. https://doi.org/10.1016/j.celrep.2019.01.033 Epub 2019/01/31. PubMed PMID: 30699342; PubMed Central PMCID: PMCPMC6352713.
Article
CAS
PubMed
PubMed Central
Google Scholar
Simmons CP, Popper S, Dolocek C, Chau TN, Griffiths M, Dung NT, et al. Patterns of host genome-wide gene transcript abundance in the peripheral blood of patients with acute dengue hemorrhagic fever. J Infect Dis. 2007;195(8):1097–107. https://doi.org/10.1086/512162 Epub 2007/03/16. . PubMed PMID: 17357045; PubMed Central PMCID: PMCPMC4042601.
Article
CAS
PubMed
PubMed Central
Google Scholar
Nascimento EJ, Braga-Neto U, Calzavara-Silva CE, Gomes AL, Abath FG, Brito CA, et al. Gene expression profiling during early acute febrile stage of dengue infection can predict the disease outcome. PLoS One. 2009;4(11):e7892. https://doi.org/10.1371/journal.pone.0007892 Epub 2009/11/26. PubMed PMID: 19936257; PubMed Central PMCID: PMCPMC2775946.
Article
CAS
PubMed
PubMed Central
Google Scholar
Long HT, Hibberd ML, Hien TT, Dung NM, Van Ngoc T, Farrar J, et al. Patterns of gene transcript abundance in the blood of children with severe or uncomplicated dengue highlight differences in disease evolution and host response to dengue virus infection. J Infect Dis. 2009;199(4):537–46. https://doi.org/10.1086/596507 Epub 2009/01/14. PubMed PMID: 19138155; PubMed Central PMCID: PMCPMC4333209.
Article
CAS
PubMed
Google Scholar
Hoang LT, Lynn DJ, Henn M, Birren BW, Lennon NJ, Le PT, et al. The early whole-blood transcriptional signature of dengue virus and features associated with progression to dengue shock syndrome in Vietnamese children and young adults. J Virol. 2010;84(24):12982–94. https://doi.org/10.1128/JVI.01224-10 Epub 2010/10/15. PubMed PMID: 20943967; PubMed Central PMCID: PMCPMC3004338.
Article
CAS
PubMed
PubMed Central
Google Scholar
Devignot S, Sapet C, Duong V, Bergon A, Rihet P, Ong S, et al. Genome-wide expression profiling deciphers host responses altered during dengue shock syndrome and reveals the role of innate immunity in severe dengue. PLoS One. 2010;5(7):e11671. https://doi.org/10.1371/journal.pone.0011671 Epub 2010/07/24. PubMed PMID: 20652028; PubMed Central PMCID: PMCPMC2907396.
Article
CAS
PubMed
PubMed Central
Google Scholar
Popper SJ, Gordon A, Liu M, Balmaseda A, Harris E, Relman DA. Temporal dynamics of the transcriptional response to dengue virus infection in Nicaraguan children. PLoS Negl Trop Dis. 2012;6(12):e1966. https://doi.org/10.1371/journal.pntd.0001966 Epub 2013/01/04. PubMed PMID: 23285306; PubMed Central PMCID: PMCPMC3527342.
Article
PubMed
PubMed Central
Google Scholar
Sun P, Garcia J, Comach G, Vahey MT, Wang Z, Forshey BM, et al. Sequential waves of gene expression in patients with clinically defined dengue illnesses reveal subtle disease phases and predict disease severity. PLoS Negl Trop Dis. 2013;7(7):e2298. https://doi.org/10.1371/journal.pntd.0002298 Epub 2013/07/23. PubMed PMID: 23875036; PubMed Central PMCID: PMCPMC3708824.
Article
PubMed
PubMed Central
Google Scholar
Kwissa M, Nakaya HI, Onlamoon N, Wrammert J, Villinger F, Perng GC, et al. Dengue virus infection induces expansion of a CD14(+)CD16(+) monocyte population that stimulates plasmablast differentiation. Cell Host Microbe. 2014;16(1):115–27. https://doi.org/10.1016/j.chom.2014.06.001 Epub 2014/07/02. PubMed PMID: 24981333; PubMed Central PMCID: PMCPMC4116428.
Article
CAS
PubMed
PubMed Central
Google Scholar
Warsinske H, Vashisht R, Khatri P. Host-response-based gene signatures for tuberculosis diagnosis: A systematic comparison of 16 signatures. PLoS Med. 2019;16(4):e1002786. https://doi.org/10.1371/journal.pmed.1002786 Epub 2019/04/24. . PubMed PMID: 31013272; PubMed Central PMCID: PMCPMC6478271 following competing interests: PK is a co-founder of and a scientific advisor to Inflammatix, Inc. Inflammatix played no role in this manuscript. PK is an inventor on the Sweeney3 signature pending patent owned by Stanford University, which has been licensed for commercialization.
Article
CAS
PubMed
PubMed Central
Google Scholar
Andres-Terre M, McGuire HM, Pouliot Y, Bongen E, Sweeney TE, Tato CM, et al. Integrated, Multi-cohort Analysis Identifies Conserved Transcriptional Signatures across Multiple Respiratory Viruses. Immunity. 2015;43(6):1199–211. https://doi.org/10.1016/j.immuni.2015.11.003 Epub 2015/12/20. PubMed PMID: 26682989; PubMed Central PMCID: PMCPMC4684904.
Article
CAS
PubMed
PubMed Central
Google Scholar
Sweeney TE, Shidham A, Wong HR, Khatri P. A comprehensive time-course-based multicohort analysis of sepsis and sterile inflammation reveals a robust diagnostic gene set. Sci Transl Med. 2015;7(287):287ra71. https://doi.org/10.1126/scitranslmed.aaa5993 Epub 2015/05/15. PubMed PMID: 25972003; PubMed Central PMCID: PMCPMC4734362.
Article
CAS
PubMed
PubMed Central
Google Scholar
Cleveland WS, Devlin SJ. Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting. J Am Stat Assoc. 1988;83(403):596–610. https://doi.org/10.1080/01621459.1988.10478639.
Article
Google Scholar
Stekhoven DJ, Bühlmann P. MissForest - nonparametric missing value imputation for mixed-type data2011 May 01, 2011:[arXiv:1105.0828 p.]. Available from: https://ui.adsabs.harvard.edu/abs/2011arXiv1105.0828S.
Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2006;8(1):118–27. https://doi.org/10.1093/biostatistics/kxj037.
Article
PubMed
Google Scholar
Chen T, Guestrin C. XGBoost: A Scalable Tree Boosting System. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco, California, USA: Association for Computing Machinery; 2016. p. 785–94.
Chapter
Google Scholar
Kuhn M. Building Predictive Models in R Using the caret Package. 2008;28(5):26. Epub 2008-09-23. https://doi.org/10.18637/jss.v028.i05.
Mayhew MB, Buturovic L, Luethy R, Midic U, Moore AR, Roque JA, et al. A generalizable 29-mRNA neural-network classifier for acute bacterial and viral infections. Nat Commun. 2020;11(1):1177. https://doi.org/10.1038/s41467-020-14975-w Epub 2020/03/07. PubMed PMID: 32132525; PubMed Central PMCID: PMCPMC7055276.
Article
CAS
PubMed
PubMed Central
Google Scholar
Larner AJ. Number Needed to Diagnose, Predict, or Misdiagnose: Useful Metrics for Non-Canonical Signs of Cognitive Status? Dementia Geriatric Cognitive Disorders Extra. 2018;8(3):321–7. https://doi.org/10.1159/000492783.
Article
CAS
PubMed
PubMed Central
Google Scholar
Cook RJ, Sackett DL. The number needed to treat: a clinically useful measure of treatment effect. BMJ. 1995;310(6977):452–4. https://doi.org/10.1136/bmj.310.6977.452.
Article
CAS
PubMed
PubMed Central
Google Scholar
Linn S, Grunau PD. New patient-oriented summary measure of net total gain in certainty for dichotomous diagnostic tests. Epidemiol Perspect Innov. 2006;3(1):11. https://doi.org/10.1186/1742-5573-3-11.
Article
PubMed
PubMed Central
Google Scholar
Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez J-C, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics. 2011;12(1):77. https://doi.org/10.1186/1471-2105-12-77.
Article
PubMed
PubMed Central
Google Scholar
DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44(3):837–45 Epub 1988/09/01. PubMed PMID: 3203132.
Article
CAS
Google Scholar
Marill KA, Chang Y, Wong KF, Friedman AB. Estimating negative likelihood ratio confidence when test sensitivity is 100%: A bootstrapping approach. Stat Methods Med Res. 2015;26(4):1936–48. https://doi.org/10.1177/0962280215592907.
Article
PubMed
Google Scholar
Wang SM, Sekaran SD. Early Diagnosis of Dengue Infection Using a Commercial Dengue Duo Rapid Test Kit for the Detection of NS1, IGM, and IGG. Am J Trop Med Hygiene. 2010;83(3):690–5. https://doi.org/10.4269/ajtmh.2010.10-0117 PubMed PMID: PMC2929071.
Article
Google Scholar
Alexander N, Balmaseda A, Coelho ICB, Dimaano E, Hien TT, Hung NT, et al. Multicentre prospective study on dengue classification in four South-east Asian and three Latin American countries. Tropical Med Int Health. 2011;16(8):936–48. https://doi.org/10.1111/j.1365-3156.2011.02793.x.
Article
Google Scholar
WHO. Dengue: Guidelines for Diagnosis, Treatment, Prevention and Control. Geneva: World Health Organization; 2009. https://apps.who.int/iris/handle/10665/44188. Accessed 10 Dec 2020.
WHO. Dengue haemorrhagic fever : diagnosis, treatment, prevention and control. 2nd ed ed. Geneva: World Health Organization; 1997.
Google Scholar
Tomashek KM, Wills B, See Lum LC, Thomas L, Durbin A, Leo Y-S, et al. Development of standard clinical endpoints for use in dengue interventional trials. PLoS Negl Trop Dis. 2018;12(10):e0006497. https://doi.org/10.1371/journal.pntd.0006497.
Article
PubMed
PubMed Central
Google Scholar
Obuchowski NA, McClish DK. Sample size determination for diagnostic accuracy studies involving binormal ROC curve indices. Stat Med. 1997;16(13):1529–42 Epub 1997/07/15. https://onlinelibrary.wiley.com/doi/10.1002/(SICI)1097-0258(19970715)16:13%3C1529::AID-SIM565%3E3.0.CO;2-H. PubMed PMID: 9249923.
Platt JC. Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods. Advances in Large Margin Classifiers. 1999. http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.41.1639.
Zheng H, Rao AM, Dermadi D, Toh J, Murphy Jones L, Donato M, et al. Multi-cohort analysis of host immune response identifies conserved protective and detrimental modules associated with severity across viruses. Immunity. 2021. https://doi.org/10.1016/j.immuni.2021.03.002.
de Steenhuijsen Piters WA, Heinonen S, Hasrat R, Bunsow E, Smith B, Suarez-Arrabal MC, et al. Nasopharyngeal Microbiota, Host Transcriptome, and Disease Severity in Children with Respiratory Syncytial Virus Infection. Am J Respir Crit Care Med. 2016;194(9):1104–15. https://doi.org/10.1164/rccm.201602-0220OC Epub 2016/11/01. PubMed PMID: 27135599; PubMed Central PMCID: PMCPMC5114450.
Article
PubMed
PubMed Central
Google Scholar
Michlmayr D, Pak TR, Rahman AH, Amir ED, Kim EY, Kim-Schulze S, et al. Comprehensive innate immune profiling of chikungunya virus infection in pediatric cases. Mol Syst Biol. 2018;14(8):e7862. https://doi.org/10.15252/msb.20177862 Epub 2018/08/29. PubMed PMID: 30150281; PubMed Central PMCID: PMCPMC6110311.
Article
CAS
PubMed
PubMed Central
Google Scholar
Giamarellos-Bourboulis EJ, Netea MG, Rovina N, Akinosoglou K, Antoniadou A, Antonakos N, et al. Complex Immune Dysregulation in COVID-19 Patients with Severe Respiratory Failure. Cell Host Microbe. 2020;27(6):992–1000.e3. https://doi.org/10.1016/j.chom.2020.04.009 Epub 2020/04/23. PubMed PMID: 32320677; PubMed Central PMCID: PMCPMC7172841.
Article
CAS
PubMed
PubMed Central
Google Scholar
Tang BM, Shojaei M, Teoh S, Meyers A, Ho J, Ball TB, et al. Neutrophils-related host factors associated with severe disease and fatality in patients with influenza infection. Nat Commun. 2019;10(1):3422. https://doi.org/10.1038/s41467-019-11249-y Epub 2019/08/02. PubMed PMID: 31366921; PubMed Central PMCID: PMCPMC6668409.
Article
CAS
PubMed
PubMed Central
Google Scholar
Zerbib Y, Jenkins EK, Shojaei M, Meyers AFA, Ho J, Ball TB, et al. Pathway mapping of leukocyte transcriptome in influenza patients reveals distinct pathogenic mechanisms associated with progression to severe infection. BMC Med Genet. 2020;13(1):28. https://doi.org/10.1186/s12920-020-0672-7 Epub 2020/02/19. PubMed PMID: 32066441; PubMed Central PMCID: PMCPMC7027223.
Article
CAS
Google Scholar
Sweeney TE, Braviak L, Tato CM, Khatri P. Genome-wide expression for diagnosis of pulmonary tuberculosis: a multicohort analysis. Lancet Respir Med. 2016;4(3):213–24. https://doi.org/10.1016/S2213-2600(16)00048-5 Epub 2016/02/26. PubMed PMID: 26907218; PubMed Central PMCID: PMCPMC4838193.
Article
CAS
PubMed
PubMed Central
Google Scholar
Temprasertrudee S, Thanachartwet V, Desakorn V, Keatkla J, Chantratita W, Kiertiburanakul S. A Multicenter Study of Clinical Presentations and Predictive Factors for Severe Manifestation of Dengue in Adults. Jpn J Infect Dis. 2018;71(3):239–43. https://doi.org/10.7883/yoken.JJID.2017.457.
Article
PubMed
Google Scholar
Wang C-C, Lee I-K, Su M-C, Lin H-I, Huang Y-C, Liu S-F, et al. Differences in clinical and laboratory characteristics and disease severity between children and adults with dengue virus infection in Taiwan, 2002. Trans R Soc Trop Med Hyg. 2009;103(9):871–7. https://doi.org/10.1016/j.trstmh.2009.04.024.
Article
CAS
PubMed
Google Scholar
Hanafusa S, Chanyasanha C, Sujirarat D, Khuankhunsathid I, Yaguchi A, Suzuki T. Clinical features and differences between child and adult dengue infections in Rayong Province, southeast Thailand. Southeast Asian J Trop Med Public Health. 2008;39(2):252–9 Epub 2008/06/21. PubMed PMID: 18564710.
PubMed
Google Scholar
Kittigul L, Pitakarnjanakul P, Sujirarat D, Siripanichgon K. The differences of clinical manifestations and laboratory findings in children and adults with dengue virus infection. J Clin Virol. 2007;39(2):76–81. https://doi.org/10.1016/j.jcv.2007.04.006.
Article
PubMed
Google Scholar
Youden WJ. Index for rating diagnostic tests. Cancer. 1950;3(1):32–5. Epub 1950/01/01. https://acsjournals.onlinelibrary.wiley.com/doi/10.1002/1097-0142(1950)3:1%3C32::AID-CNCR2820030106%3E3.0.CO;2-3. PubMed PMID: 15405679.
P’ng L, Hammond SN, Leung JM, Kim CC, Batra S, Rocha C, et al. Gene Expression Patterns of Dengue Virus-Infected Children from Nicaragua Reveal a Distinct Signature of Increased Metabolism. PLoS Negl Trop Dis. 2010;4(6):e710. https://doi.org/10.1371/journal.pntd.0000710.
Article
CAS
Google Scholar
Thomas NJ, Carcillo JA, Doughty LA, Sasser H, Heine RP. Plasma concentrations of defensins and lactoferrin in children with severe sepsis. Pediatr Infect Dis J. 2002;21(1). https://journals.lww.com/pidj/Fulltext/2002/01000/Plasma_concentrations_of_defensins_and_lactoferrin.8.aspx.
Berkestedt I, Herwald H, Ljunggren L, Nelson A, Bodelsson M. Elevated Plasma Levels of Antimicrobial Polypeptides in Patients with Severe Sepsis. J Innate Immun. 2010;2(5):478–82. https://doi.org/10.1159/000317036.
Article
CAS
PubMed
Google Scholar
Agarwal R, Elbishbishi EA, Chaturvedi UC, Nagar R, Mustafa AS. Profile of transforming growth factor-beta 1 in patients with dengue haemorrhagic fever. Int J Exp Pathol. 1999;80(3):143–9. https://doi.org/10.1046/j.1365-2613.1999.00107.x PubMed PMID: 10469270.
Article
CAS
PubMed
PubMed Central
Google Scholar
de Oliveira LF, de Andrade AAS, Pagliari C, de Carvalho LV, Silveira TS, Cardoso JF, et al. Differential expression analysis and profiling of hepatic miRNA and isomiRNA in dengue hemorrhagic fever. Sci Rep. 2021;11(1):5554. https://doi.org/10.1038/s41598-020-72892-w.
Article
CAS
PubMed
PubMed Central
Google Scholar
Sangkaew S, Ming D, Boonyasiri A, Honeyford K, Kalayanarooj S, Yacoub S, et al. Risk predictors of progression to severe disease during the febrile phase of dengue: a systematic review and meta-analysis. Lancet Infect Dis. 2021. https://doi.org/10.1016/S1473-3099(20)30601-0.
Lee AJ, Park Y, Doing G, Hogan DA, Greene CS. Correcting for experiment-specific variability in expression compendia can remove underlying signals. GigaScience. 2020;9(11). https://doi.org/10.1093/gigascience/giaa117.
Lazar C, Meganck S, Taminau J, Steenhoff D, Coletta A, Molter C, et al. Batch effect removal methods for microarray gene expression data integration: a survey. Brief Bioinform. 2013;14(4):469–90. https://doi.org/10.1093/bib/bbs037.
Article
CAS
PubMed
Google Scholar
Mirza B, Wang W, Wang J, Choi H, Chung NC, Ping P. Machine Learning and Integrative Analysis of Biomedical Big Data. Genes (Basel). 2019;10(2):87. https://doi.org/10.3390/genes10020087 PubMed PMID: 30696086.
Article
CAS
Google Scholar
Onyilagha C, Mistry H, Marszal P, Pinette M, Kobasa D, Tailor N, et al. Evaluation of mobile real-time polymerase chain reaction tests for the detection of severe acute respiratory syndrome coronavirus 2. Sci Rep. 2021;11(1):9387. https://doi.org/10.1038/s41598-021-88625-6.
Article
CAS
PubMed
PubMed Central
Google Scholar
Ganguli A, Mostafa A, Berger J, Aydin MY, Sun F. Ramirez SASd, et al. Rapid isothermal amplification and portable detection system for SARS-CoV-2. Proc Natl Acad Sci. 2020;117(37):22727–35. https://doi.org/10.1073/pnas.2014739117.
Article
CAS
PubMed
PubMed Central
Google Scholar