Login Register Cart 0
Generic placeholder image

Current Bioinformatics

Editor-in-Chief

ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Deep Learning Model for Pathogen Classification Using Feature Fusion and Data Augmentation

Author(s): Fareed Ahmad*, Amjad Farooq and Muhammad Usman Ghani Khan

Volume 16, Issue 3, 2021

Published on: 07 July, 2020

Page: [466 - 483] Pages: 18

DOI: 10.2174/1574893615999200707143535

Price: $65

Abstract

Background: Bacterial pathogens are deadly for animals and humans. The ease of their dissemination, coupled with their high capacity for ailments and death in infected individuals, makes them a threat to society.

Objective: Due to the high similarity among genera and species of pathogens, it is sometimes difficult for microbiologists to differentiate between them. Their automatic classification using deeplearning models can help in gaining reliable and accurate outcomes.

Methods: Deep-learning models, namely; AlexNet, GoogleNet, ResNet101, and InceptionV3 are used with numerous variations including training model from scratch, fine-tuning without pre-trained weights, fine-tuning along with freezing weights of initial layers, fine-tuning along with adjusting weights of all layers and augmenting the dataset by random translation and reflection. Moreover, as the dataset is small, fine-tuning and data augmentation strategies are applied to avoid overfitting and produce a generalized model. A merged feature vector is produced using two best-performing models and accuracy is calculated by xgboost algorithm on the feature vector by applying cross-validation.

Results: Fine-tuned models where augmentation is applied produces the best results. Out of these, two-best-performing deep models i.e. (ResNet101, and InceptionV3) selected for feature fusion, produced a similar validation accuracy of 95.83 with a loss of 0.0213 and 0.1066, and testing accuracy of 97.92 and 93.75, respectively. The proposed model used xgboost to attain a classification accuracy of 98.17% by using 35-folds cross-validation.

Conclusion: The automatic classification using these models can help experts in the correct identification of pathogens. Consequently, they can help in controlling epidemics and thereby minimizing the socio-economic impact on the community.

Keywords: Pathogen classification, augmentation, feature fusion, deep learning models, fine-tuning, transfer learning, zoonotic diseases, disease outbreaks.

« Previous Next »
Graphical Abstract

[1]
Lederberg J, Hamburg MA, Smolinski MS, Eds. Microbial threats to health: emergence, detection, and response. National Academies Press 2003.
[2]
Salyer SJ, Silver R, Simone K, Barton Behravesh C. Prioritizing zoonoses for global health capacity building-themes from One Health zoonotic disease workshops in 7 countries, 2014-2016. Emerg Infect Dis 2017; 23(13): S57-64.
[http://dx.doi.org/10.3201/eid2313.170418] [PMID: 29155664]
[3]
Cantas L, Suer K. Review: the important bacterial zoonoses in “one health” concept. Front Public Health 2014; 2: 144.
[http://dx.doi.org/10.3389/fpubh.2014.00144] [PMID: 25353010]
[4]
Franconi R, Illiano E, Paolini F, Massa S, Venuti A, Demurtas OC. Rapid and low-cost tools derived from plants to face emerging/re-emerging infectious diseases and bioterrorism agents defence against bioterrorism. Dordrecht: Springer 2018; pp. 123-39.
[http://dx.doi.org/10.1007/978-94-024-1263-5_10]
[5]
Fitzmaurice C, Allen C, Barber RM, et al. Global, regional, and national cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life-years for 32 cancer groups, 1990 to 2015: a systematic analysis for the global burden of disease study. JAMA Oncol 2017; 3(4): 524-48.
[http://dx.doi.org/10.1001/jamaoncol.2016.5688] [PMID: 27918777]
[6]
McLinden T, Sargeant JM, Thomas MK, Papadopoulos A, Fazil A. Component costs of foodborne illness: a scoping review. BMC Public Health 2014; 14(1): 509.
[http://dx.doi.org/10.1186/1471-2458-14-509] [PMID: 24885154]
[7]
Gebreyes WA, Dupouy-Camet J, Newport MJ, et al. The global one health paradigm: challenges and opportunities for tackling infectious diseases at the human, animal, and environment interface in low-resource settings. PLoS Negl Trop Dis 2014; 8(11)e3257
[http://dx.doi.org/10.1371/journal.pntd.0003257] [PMID: 25393303]
[8]
Nabarro D, Wannous C. The potential contribution of Iivestock to food and nutrition security: the application of the One Health approach in livestock policy and practice 2014.
[9]
Tacconelli E, Carrara E, Savoldi A, et al. WHO pathogens priority list working group. discovery, research, and development of new antibiotics: the WHO priority list of antibiotic-resistant bacteria and tuberculosis. Lancet Infect Dis 2018; 18(3): 318-27.
[http://dx.doi.org/10.1016/S1473-3099(17)30753-3] [PMID: 29276051]
[10]
Ali M, Nelson AR, Lopez AL, Sack DA. Updated global burden of cholera in endemic countries. PLoS Negl Trop Dis 2015; 9(6)e0003832
[http://dx.doi.org/10.1371/journal.pntd.0003832] [PMID: 26043000]
[11]
Roser M, Ritchie H, Dadonaite B. Child & Infant Mortality. Our World in Data 2013.
[12]
Mostafavi E, Ghasemi A, Rohani M, et al. Molecular survey of tularemia and plague in small mammals from Iran. Front Cell Infect Microbiol 2018; 8: 215.
[http://dx.doi.org/10.3389/fcimb.2018.00215] [PMID: 30042927]
[13]
Crump JA. Progress in typhoid fever epidemiology. Clin Infect Dis 2019; 68(1): S4-9.
[http://dx.doi.org/10.1093/cid/ciy846]
[14]
Unemo M, Golparian D, Eyre DW. Antimicrobial resistance in Neisseria gonorrhoeae and treatment of gonorrhea Neisseria gonorrhoeae. Springer 2019; pp. 37-58.
[http://dx.doi.org/10.1007/978-1-4939-9496-0_3]
[15]
Kyu HH, Mumford JE, Stanaway JD, et al. Mortality from tetanus between 1990 and 2015: findings from the global burden of disease study 2015. BMC Public Health 2017; 17(1): 179.
[http://dx.doi.org/10.1186/s12889-017-4111-4] [PMID: 28178973]
[16]
Kaye KS, Petty LA, Shorr AF, Zilberberg MD. Current epidemiology, etiology, and burden of acute skin infections in the United States. Clin Infect Dis 2019; 68(3): S193-9.
[http://dx.doi.org/10.1093/cid/ciz002]
[17]
Ghasemi Basir HR, Ghobakhlou M, Akbari P, Dehghan A, Seif Rabiei MA. Correlation between the intensity of helicobacter pylori colonization and severity of gastritis. Gastroenterol Res Pract 2017; 20178320496
[http://dx.doi.org/10.1155/2017/8320496]
[18]
Shah M, Cabrera-Ghayouri S, Christie LA, Held KS, Viswanath V. Translational preclinical pharmacologic disease models for ophthalmic drug development. Pharm Res 2019; 36(4): 58.
[http://dx.doi.org/10.1007/s11095-019-2588-5] [PMID: 30805711]
[19]
Rapoport SK, Smith AJ, Bergman M, Scriven KA, Brook I, Mikula SK. Determining the utility of standard hospital microbiology testing: Comparing standard microbiology cultures with DNA sequence analysis in patients with chronic sinusitis. World J Otorhinolaryngol Head Neck Surg 2019; 5(2): 82-7.
[20]
Smart A, de Lacy Costello B, White P, et al. Sniffing out resistance - Rapid identification of urinary tract infection-causing bacteria and their antibiotic susceptibility using volatile metabolite profiles. J Pharm Biomed Anal 2019; 167: 59-65.
[http://dx.doi.org/10.1016/j.jpba.2019.01.044] [PMID: 30743156]
[21]
Hament JM, Aerts PC, Fleer A, et al. Enhanced adherence of Streptococcus pneumoniae to human epithelial cells infected with respiratory syncytial virus. Pediatr Res 2004; 55(6): 972-8.
[http://dx.doi.org/10.1203/01.PDR.0000127431.11750.D9] [PMID: 15103015]
[22]
Morris FC, Dexter C, Kostoulias X, Uddin MI, Peleg AY. The mechanisms of disease caused by Acinetobacter baumannii. Front Microbiol 2019; 10: 1601.
[http://dx.doi.org/10.3389/fmicb.2019.01601] [PMID: 31379771]
[23]
Archambaud C, Derré-Bobillot A, Lapaque N, Rigottier-Gois L, Serror P. Intestinal translocation of enterococci requires a threshold level of enterococcal overgrowth in the lumen. Sci Rep 2019; 9(1): 8926.
[http://dx.doi.org/10.1038/s41598-019-45441-3] [PMID: 31222056]
[24]
Wang L, Ruan S. Modeling nosocomial infections of methicillin-resistant Staphylococcus aureus with environment contamination. Sci Rep 2017; 7(1): 580.
[http://dx.doi.org/10.1038/s41598-017-00261-1] [PMID: 28373644]
[25]
Méric G, Mageiros L, Pensar J, et al. Disease-associated genotypes of the commensal skin bacterium Staphylococcus epidermidis. Nat Commun 2018; 9(1): 5034.
[http://dx.doi.org/10.1038/s41467-018-07368-7] [PMID: 30487573]
[26]
Cebrián R, Arévalo S, Rubiño S, et al. Control of Propionibacterium acnes by natural antimicrobial substances: Role of the bacteriocin AS-48 and lysozyme. Sci Rep 2018; 8(1): 11766.
[http://dx.doi.org/10.1038/s41598-018-29580-7] [PMID: 30082920]
[27]
Zieliński B, Plichta A, Misztal K, Spurek P, Brzychczy-Włoch M, Ochońska D. Deep learning approach to bacterial colony classification. PLoS One 2017; 12(9)e0184554
[http://dx.doi.org/10.1371/journal.pone.0184554] [PMID: 28910352]
[28]
Abiyev RH, Maaitah MKS. Deep convolutional neural networks for chest diseases detection. J Healthc Eng 2018; 20184168538
[http://dx.doi.org/10.1155/2018/4168538]
[29]
Dawud AM, Yurtkan K, Oztoprak H. Application of deep learning in neuroradiology: brain haemorrhage classification using transfer learning. Comput Intell Neurosci 2019; 20194629859
[30]
Helwan A, Uzun Ozsahin D. Sliding window based machine learning system for the left ventricle localization in MR cardiac images. Appl Comput Intell Soft Comput 2017; 20173048181
[http://dx.doi.org/10.1155/2017/3048181]
[31]
Huang L, Wu T. Novel neural network application for bacterial colony classification. Theor Biol Med Model 2018; 15(1): 22.
[http://dx.doi.org/10.1186/s12976-018-0093-x] [PMID: 30373604]
[32]
Helwan A, El-Fakhri G, Sasani H, Uzun Ozsahin D. Deep networks in identifying CT brain hemorrhage. J Intell Fuzzy Syst 2018; 35(2): 2215-28.
[http://dx.doi.org/10.3233/JIFS-172261]
[33]
Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Imagenet: a largescale hierarchical image database 2009.
[34]
Sermanet P, Frome A, Real E. 2014.
[35]
Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 2012; •••: 1097-105.
[36]
Simonyan K, Zisserman A. 2014.
[37]
Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition USA. 1-9.
[38]
He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition. USA. 2016; pp. 770-8.
[39]
Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. Densely connected convolutional networks. Proceedings of the IEEE conference on computer vision and pattern recognition. USA. 2017; pp. 4700-8.
[40]
Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the inception architecture for computer vision. Proceedings of the IEEE conference on computer vision and pattern recognition. USA. 2016; pp. 2818-6.
[41]
Russakovsky O, Deng J, Su H, et al. Imagenet large scale visual recognition challenge. Int J Comput Vis 2015; 115(3): 211-52.
[http://dx.doi.org/10.1007/s11263-015-0816-y]
[42]
Han D, Liu Q, Fan W. A new image classification method using CNN transfer learning and web data augmentation. Expert Syst Appl 2018; 95: 43-56.
[http://dx.doi.org/10.1016/j.eswa.2017.11.028]
[43]
Aneja N, Aneja S. Transfer Learning using CNN for Handwritten Devanagari Character Recognition arXiv preprint arXiv 2019.
[44]
Flusser J, Suk T. Character recognition by affine moment invariants. International Conference on Computer Analysis of Images and Patterns. 572-7.
[45]
Trattner S, Greenspan H, Tepper G, Abboud S. Automatic identification of bacterial types using statistical imaging methods. IEEE Trans Med Imaging 2004; 23(7): 807-20.
[http://dx.doi.org/10.1109/TMI.2004.827481] [PMID: 15250633]
[46]
Blackburn N, Hagström A, Wikner J, Cuadros-Hansson R, Bjørnsen PK. Rapid determination of bacterial abundance, biovolume, morphology, and growth by neural network-based image analysis. Appl Environ Microbiol 1998; 64(9): 3246-55.
[http://dx.doi.org/10.1128/AEM.64.9.3246-3255.1998] [PMID: 9726867]
[47]
Shahbaz M, Parveen S, Ahmad F, Rabbani M. Detection of Francisella Tularensis Pathogen in Soil using Neural Networks. 20th International Conference on Computer, Electrical, Electronics and Communication Engineering (CEECE-18). May 7-9, 2018; Dubai. 2018; 58-64.
[48]
Qu K, Guo F, Liu X, Lin Y, Zou Q. Application of machine learning in microbiology. Front Microbiol 2019; 10: 827.
[49]
Cimpoi M, Maji S, Kokkinos I, Vedaldi A. Deep filter banks for texture recognition, description, and segmentation. Int J Comput Vis 2016; 118(1): 65-94.
[http://dx.doi.org/10.1007/s11263-015-0872-3] [PMID: 27471340]
[50]
Cabeen MT, Jacobs-Wagner C. Bacterial cell shape. Nat Rev Microbiol 2005; 3(8): 601-10.
[http://dx.doi.org/10.1038/nrmicro1205] [PMID: 16012516]
[51]
Bergmans L, Moisiadis P, Van Meerbeek B, Quirynen M, Lambrechts P. Microscopic observation of bacteria: review highlighting the use of environmental SEM. Int Endod J 2005; 38(11): 775-88.
[http://dx.doi.org/10.1111/j.1365-2591.2005.00999.x] [PMID: 16218968]
[52]
Hiremath P, Bannigidad P. Automated gram-staining characterization of digital bacterial cell images. Proc IEEE Int’l Conf on Signal and Image Processing ICSIP. 209-11.
[53]
Hiremath P, Bannigidad P. Digital microscopic image analysis of spiral bacterial cell groups.
[54]
Forero MG, Cristóbal G, Desco M. Automatic identification of Mycobacterium tuberculosis by Gaussian mixture models. J Microsc 2006; 223(Pt 2): 120-32.
[http://dx.doi.org/10.1111/j.1365-2818.2006.01610.x] [PMID: 16911072]
[55]
Ahmed WM, Bayraktar B, Bhunia A, Hirleman ED, Robinson JP, Rajwa B. Classification of bacterial contamination using image processing and distributed computing. IEEE J Biomed Health Inform 2013; 17(1): 232-9.
[http://dx.doi.org/10.1109/TITB.2012.2222654] [PMID: 23060342]
[56]
Holmberg M, Gustafsson F, Hornsten EG, et al. Bacteria classification based on feature extraction from sensor data. Biotechnol Tech 1998; 12(4): 319-24.
[http://dx.doi.org/10.1023/A:1008862617082]
[57]
Ates H, Gerek ON.
[58]
Liu J, Dazzo FB, Glagoleva O, Yu B, Jain AK. CMEIAS: a computer aided system for the image analysis of bacterial morphotypes in microbial communities. Microb Ecol 2001; 41(3): 173-94.
[http://dx.doi.org/10.1007/s002480000004] [PMID: 11391457]
[59]
Hiremath PS, Bannigidad P. Identification and classification of cocci bacterial cells in digital microscopic images. Int J Comput Biol Drug Des 2011; 4(3): 262-73.
[http://dx.doi.org/10.1504/IJCBDD.2011.041414] [PMID: 21778559]
[60]
Hiremath P, Bannigidad P, Yelgond SS. Identification of flagellated or fimbriated bacterial cells using digital image processing techniques. Int J Comput Appl 2012; 59: 12-6.
[61]
Koydemir HC, Feng S, Liang K, Nadkarni R, Benien P, Ozcan A. Comparison of supervised machine learning algorithms for waterborne pathogen detection using mobile phone fluorescence microscopy. Nanophotonics 2017; 6(4): 731-41.
[http://dx.doi.org/10.1515/nanoph-2017-0001]
[62]
He Y, Xu W, Zhi Y, Tyagi R, Hu Z, Cao G. Rapid bacteria identification using structured illumination microscopy and machine learning. J Innov Opt Health Sci 2018; 11(01)1850007
[http://dx.doi.org/10.1142/S1793545818500074]
[63]
Nasip OF, Zengin K.
[64]
Helwan A, Abiyev R. Shape and texture features for the identification of breast cancer. Proceedings of the World Congress on Engineering and Computer Science. vol. 2: 19-21.
[65]
Mitchell TM. 1997.
[66]
Lin C, Li L, Luo W, Wang KC, Guo J. Transfer learning based traffic sign recognition using inception-v3 model. Period Polytech Transport Eng 2019; 47(3): 242-50.
[http://dx.doi.org/10.3311/PPtr.11480]
[67]
Peleg AY, Seifert H, Paterson DL. Acinetobacter baumannii: emergence of a successful pathogen. Clin Microbiol Rev 2008; 21(3): 538-82.
[http://dx.doi.org/10.1128/CMR.00058-07] [PMID: 18625687]
[68]
Camp C, Tatum OL. A review of Acinetobacter baumannii as a highly successful pathogen in times of war. Lab Med 2010; 41(11): 649-57.
[http://dx.doi.org/10.1309/LM90IJNDDDWRI3RE]
[69]
Al-Anazi KA, Abdalhamid B, Alshibani Z, et al. Acinetobacter baumannii septicemia in a recipient of an allogeneic hematopoietic stem cell transplantation. Case Rep Transplant 2012; 2012646195
[70]
Al-Anazi KA, Al-Jasser AM. Infections caused by Acinetobacter baumannii in recipients of hematopoietic stem cell transplantation. Front Oncol 2014; 4: 186.
[http://dx.doi.org/10.3389/fonc.2014.00186] [PMID: 25072028]
[71]
Dexter C, Murray GL, Paulsen IT, Peleg AY. Community-acquired Acinetobacter baumannii: clinical characteristics, epidemiology and pathogenesis. Expert Rev Anti Infect Ther 2015; 13(5): 567-73.
[http://dx.doi.org/10.1586/14787210.2015.1025055] [PMID: 25850806]
[72]
Zaki SR, Alves VA, Hale GL. 2017.
[73]
Khadka P, Koirala S. Primary cutaneous actinomycosis: a diagnosis consideration in people living with HIV/AIDS. AIDS Res Ther 2019; 16(1): 16.
[http://dx.doi.org/10.1186/s12981-019-0232-4] [PMID: 31362755]
[74]
Douglas CI, Naylor K, Phansopa C, Frey AM, Farmilo T, Stafford GP. 2014.
[75]
Tsujimura N, Takemoto H, Nakahara Y, et al. Intraabdominal actinomycosis resulting in a difficult to diagnose intraperitoneal mass: a case report. Int J Surg Case Rep 2018; 45: 101-3.
[http://dx.doi.org/10.1016/j.ijscr.2018.03.024] [PMID: 29604528]
[76]
Könönen E, Wade WG. Actinomyces and related organisms in human infections. Clin Microbiol Rev 2015; 28(2): 419-42.
[http://dx.doi.org/10.1128/CMR.00100-14] [PMID: 25788515]
[77]
Boff RC, Salum FG, Figueiredo MA, Cherubini K. Important aspects regarding the role of microorganisms in bisphosphonate-related osteonecrosis of the jaws. Arch Oral Biol 2014; 59(8): 790-9.
[http://dx.doi.org/10.1016/j.archoralbio.2014.05.002] [PMID: 24859766]
[78]
Puig A, Queralt N, Jofre J, Araujo R. Diversity of bacteroides fragilis strains in their capacity to recover phages from human and animal wastes and from fecally polluted wastewater. Appl Environ Microbiol 1999; 65(4): 1772-6.
[http://dx.doi.org/10.1128/AEM.65.4.1772-1776.1999] [PMID: 10103280]
[79]
Uzal FA, Plattner BL, Hostetter JM. Alimentary system. Jubb, Kennedy. Palmer Pathology of Domestic Animals 2015; 2: 1-257.
[80]
Goulas T, Gomis-Ruth F. Fragilysin Rawlings ND. Salvesen GS 2013; pp. 887-91.
[81]
Ballesté E, Blanch AR. Bifidobacterial diversity and the development of new microbial source tracking indicators. Appl Environ Microbiol 2011; 77(10): 3518-25.
[http://dx.doi.org/10.1128/AEM.02198-10] [PMID: 21460117]
[82]
Lamendella R, Santo Domingo JW, Kelty C, Oerther DB. Bifidobacteria in feces and environmental waters. Appl Environ Microbiol 2008; 74(3): 575-84.
[http://dx.doi.org/10.1128/AEM.01221-07] [PMID: 17993557]
[83]
Butta H, Sardana R, Vaishya R, Singh KN, Mendiratta L. Bifidobacterium: an emerging clinically significant metronidazole-resistant anaerobe of mixed pyogenic infections. Cureus 2017; 9(4)e1134
[http://dx.doi.org/10.7759/cureus.1134] [PMID: 28480152]
[84]
Babič MN, Gunde-Cimerman N, Vargha M, et al. Fungal contaminants in drinking water regulation? A tale of ecology, exposure, purification and clinical relevance. Int J Environ Res Public Health 2017; 14(6): 636.
[http://dx.doi.org/10.3390/ijerph14060636]
[85]
Forbes D, Ee L, Camer-Pesci P, Ward PB. Faecal candida and diarrhoea. Arch Dis Child 2001; 84(4): 328-31.
[http://dx.doi.org/10.1136/adc.84.4.328] [PMID: 11259233]
[86]
Jain A, Malhotra S, Das A, Madan P, Kaur N. Candida diarrhoea in a patient of nephrotic syndrome. J Case Rep 2015; 4(2): 474-7.
[87]
Thill D, Could A. Rare, deadly “Superbug” fungus Be gaining a foothold? Biomed Safety Stand 2018; 48(7): 49-51.
[http://dx.doi.org/10.1097/01.BMSAS.0000532018.25843.cf]
[88]
Erdogan A, Rao SS. Small intestinal fungal overgrowth. Curr Gastroenterol Rep 2015; 17(4): 16.
[http://dx.doi.org/10.1007/s11894-015-0436-2] [PMID: 25786900]
[89]
Marini RP, Wachtman LM, Tardif SD, Mansfield K, Fox JG, Eds. The common marmoset in captivity and biomedical research. Academic Press 2018; p. 570.
[90]
Azimirad M, Gholami F, Yadegar A, et al. Prevalence and characterization of Clostridium perfringens toxinotypes among patients with antibiotic-associated diarrhea in Iran. Sci Rep 2019; 9(1): 7792.
[http://dx.doi.org/10.1038/s41598-019-44281-5] [PMID: 31127185]
[91]
Ramos CP, Santana JA, Morcatti Coura F, et al. Identification and characterization of Escherichia coli, Salmonella spp., Clostridium perfringens, and C. difficile isolates from reptiles in Brazil. BioMed Res Int 2019; 20199530732
[92]
Huycke MM, Sahm DF, Gilmore MS. Multiple-drug resistant enterococci: the nature of the problem and an agenda for the future. Emerg Infect Dis 1998; 4(2): 239-49.
[http://dx.doi.org/10.3201/eid0402.980211] [PMID: 9621194]
[93]
Barnes AMT, Dale JL, Chen Y, et al. Enterococcus faecalis readily colonizes the entire gastrointestinal tract and forms biofilms in a germ-free mouse model. Virulence 2017; 8(3): 282-96.
[http://dx.doi.org/10.1080/21505594.2016.1208890] [PMID: 27562711]
[94]
Partoazar A, Talaei N, Bahador A, et al. Antibiofilm activity of natural zeolite supported NanoZnO: inhibition of Esp gene expression of Enterococcus faecalis. Nanomedicine (Lond) 2019; 14(6): 675-87.
[95]
Arias CA, Murray BE. The rise of the Enterococcus: beyond vancomycin resistance. Nat Rev Microbiol 2012; 10(4): 266-78.
[http://dx.doi.org/10.1038/nrmicro2761] [PMID: 22421879]
[96]
Srinivasan L, Evans JR. Health care-associated infections Avery’s Diseases of the Newborn. Elsevier 2018; pp. 566-80.
[97]
Guzman Prieto AM, van Schaik W, Rogers MR, et al. Global emergence and dissemination of enterococci as nosocomial pathogens: attack of the clones? Front Microbiol 2016; 7: 788.
[http://dx.doi.org/10.3389/fmicb.2016.00788] [PMID: 27303380]
[98]
Yang SC, Lin CH, Aljuffali IA, Fang JY. Current pathogenic Escherichia coli foodborne outbreak cases and therapy development. Arch Microbiol 2017; 199(6): 811-25.
[http://dx.doi.org/10.1007/s00203-017-1393-y] [PMID: 28597303]
[99]
Ercumen A, Pickering AJ, Kwong LH, et al. Animal feces contribute to domestic fecal contamination: evidence from E. coli measured in water, hands, food, flies, and soil in Bangladesh. Environ Sci Technol 2017; 51(15): 8725-34.
[http://dx.doi.org/10.1021/acs.est.7b01710] [PMID: 28686435]
[100]
Johannesen K, Dessau R, Heltberg O, Bodtger U. Bad news itself or just the messenger? The high mortality of Fusobacterium spp. infections is related to disseminated malignancy and other comorbidities. Eur Clin Respir J 2016; 3(1): 30287.
[http://dx.doi.org/10.3402/ecrj.v3.30287] [PMID: 27171316]
[101]
Underwood WJ, Blauwiekel R, Delano ML, Gillesby R, Mischler SA, Schoell A. Biology and diseases of ruminants (sheep, goats, and cattle) Laboratory animal medicine. Elsevier 2015; pp. 623-94.
[http://dx.doi.org/10.1016/B978-0-12-409527-4.00015-8]
[102]
Constable PD, Hinchcliff KW, Done SH, Grünberg W. Diseases of the alimentary tract: Nonruminant Veterinary Medicine. 11th ed. Philadelphia, Pennsylvania: WB Saunders 2017; pp. 175-435.
[103]
Tarrah A, da Silva Duarte V, de Castilhos J, et al. Probiotic potential and bio_lm inhibitory activity of Lactobacillus casei group strains isolated from infant feces. J Funct Foods 2019; 54: 489-97.
[http://dx.doi.org/10.1016/j.jff.2019.02.004]
[104]
Kim HJ, Lee HJ, Lim B, et al. Lactobacillus terrae sp. nov., a novel species isolated from soil samples in the Republic of Korea. Int J Syst Evol Microbiol 2018; 68(9): 2906-11.
[http://dx.doi.org/10.1099/ijsem.0.002918] [PMID: 30010525]
[105]
Guerra AF, Junior L, Fernandes WJ, et al. Lactobacillus paracasei probiotic properties and survivability under stress-induced by processing and storage of ice cream bar or ice-lolly. Cienc Rural 2018; 48(9)
[http://dx.doi.org/10.1590/0103-8478cr20170601]
[106]
Zhang D, Zhang S, Guidesi E, et al. 2017.
[107]
Tommasi C, Equitani F, Masala M, et al. Diagnostic difficulties of Lactobacillus casei bacteraemia in immunocompetent patients: a case report. J Med Case Reports 2008; 2(1): 315.
[http://dx.doi.org/10.1186/1752-1947-2-315] [PMID: 18826603]
[108]
Westerik N, Kort R, Sybesma W, Reid G. Lactobacillus rhamnosus probiotic food as a tool for empowerment across the value chain in Africa. Front Microbiol 2018; 9: 1501.
[http://dx.doi.org/10.3389/fmicb.2018.01501] [PMID: 30042747]
[109]
Treven P, Mrak V, Bogovič Matijašić B, Horvat S, Rogelj I. Administration of probiotics lactobacillus rhamnosus GG and Lactobacillus gasseri K7 during pregnancy and lactation changes mouse mesenteric lymph nodes and mammary gland microbiota. J Dairy Sci 2015; 98(4): 2114-28.
[http://dx.doi.org/10.3168/jds.2014-8519] [PMID: 25622869]
[110]
Ambesh P, Stroud S, Franzova E, et al. Recurrent Lactobacillus bacteremia in a patient with leukemia. J Investig Med High Impact Case Rep 2017; 5(4)2324709617744233
[http://dx.doi.org/10.1177/2324709617744233] [PMID: 29204452]
[111]
Nathaniel BR, Ghai M, Druce M, Maharaj I, Olaniran AO. Development of a loop-mediated isothermal amplification assay targeting lmo0753 gene for detection of Listeria monocytogenes in wastewater. Lett Appl Microbiol 2019; 69(4): 264-70.
[http://dx.doi.org/10.1111/lam.13200] [PMID: 31323126]
[112]
Becattini S, Littmann ER, Carter RA, et al. Commensal microbes provide first line defense against Listeria monocytogenes infection. J Exp Med 2017; 214(7): 1973-89.
[http://dx.doi.org/10.1084/jem.20170495] [PMID: 28588016]
[113]
Okoliegbe IL, Solomon L, Dick AA. Exploiting microbial communities associated with marine fish: An indispensable approach to sustainable aquaculture. Nat Sci 2017; 15(4): 84-91.
[114]
Prussin AJ II, Marr LC. Sources of airborne microorganisms in the built environment. Microbiome 2015; 3(1): 78.
[http://dx.doi.org/10.1186/s40168-015-0144-z] [PMID: 26694197]
[115]
Suwangool P. 2017.
[116]
Liu G, Tang CM, Exley RM. Non-pathogenic Neisseria: members of an abundant, multi-habitat, diverse genus. Microbiology 2015; 161(7): 1297-312.
[http://dx.doi.org/10.1099/mic.0.000086] [PMID: 25814039]
[117]
Osman AGA. Molecular Detection of Helicobacter pylori GLmM Gene among Gastritis and Duodenitis Patients in Albogaa Specialized Hospital-Omdurman. Sudan University of Science & Technology 2019.
[118]
How KY, Song KP, Chan KG. Porphyromonas gingivalis: an overview of periodontopathic pathogen below the gum line. Front Microbiol 2016; 7: 53.
[http://dx.doi.org/10.3389/fmicb.2016.00053] [PMID: 26903954]
[119]
Kugaji MS, Kumbar VM, Peram MR, Patil S, Bhat KG, Diwan PV. Effect of Resveratrol on biofilm formation and virulence factor gene expression of Porphyromonas gingivalis in periodontal disease. APMIS 2019; 127(4): 187-95.
[http://dx.doi.org/10.1111/apm.12930] [PMID: 30861212]
[120]
Dominy SS, Lynch C, Ermini F, et al. Porphyromonas gingivalis in Alzheimer’s disease brains: Evidence for disease causation and treatment with small-molecule inhibitors. Sci Adv 2019; 5(1)eaau3333
[http://dx.doi.org/10.1126/sciadv.aau3333] [PMID: 30746447]
[121]
Drzewiecka D. Significance and roles of Proteus spp. bacteria in natural environments. Microb Ecol 2016; 72(4): 741-58.
[http://dx.doi.org/10.1007/s00248-015-0720-6] [PMID: 26748500]
[122]
Hamilton AL, Kamm MA, Ng SC, Morrison M. Proteus spp. as putative gastrointestinal pathogens. Clin Microbiol Rev 2018; 31(3): e00085-17.
[http://dx.doi.org/10.1128/CMR.00085-17] [PMID: 29899011]
[123]
Alouf JE, Popoff M. Bacterial protein toxins. Bac Tox 2006; p. 1.
[124]
Wu M, Li X. Klebsiella pneumoniae and Pseudomonas aeruginosa Molecular Medical Microbiology. Elsevier 2015; pp. 1547-64.
[125]
Almutawif Y, Hartmann B, Lloyd M, Lai CT, Rea A, Geddes D. Staphylococcus aureus enterotoxin production in raw and pasteurized milk: The effect of selected different storage durations and temperatures. Breastfeed Med 2019; 14(4): 256-61.
[http://dx.doi.org/10.1089/bfm.2018.0227] [PMID: 30844297]
[126]
Andersen JL, He GX, Kakarla P, et al. Multidrug efflux pumps from Enterobacteriaceae, Vibrio cholerae and Staphylococcus aureus bacterial food pathogens. Int J Environ Res Public Health 2015; 12(2): 1487-547.
[http://dx.doi.org/10.3390/ijerph120201487] [PMID: 25635914]
[127]
Firyal S, Awan AR, Baigh S, et al.
[128]
Tong SY, Davis JS, Eichenberger E, Holland TL, Fowler VG Jr. Staphylococcus aureus infections: epidemiology, pathophysiology, clinical manifestations, and management. Clin Microbiol Rev 2015; 28(3): 603-61.
[http://dx.doi.org/10.1128/CMR.00134-14] [PMID: 26016486]
[129]
Suzuki Y, Kubota H, Ono HK, et al. Food poisoning outbreak in Tokyo, Japan caused by Staphylococcus argenteus. Int J Food Microbiol 2017; 262: 31-7.
[http://dx.doi.org/10.1016/j.ijfoodmicro.2017.09.005] [PMID: 28961520]
[130]
Ciupescu LM, Auvray F, Nicorescu IM, et al. Characterization of Staphylococcus aureus strains and evidence for the involvement of non-classical enterotoxin genes in food poisoning outbreaks. FEMS Microbiol Lett 2018; 365(13)fny139
[http://dx.doi.org/10.1093/femsle/fny139] [PMID: 29878105]
[131]
Wieser M, Busse HJ. Rapid identification of Staphylococcus epidermidis. Int J Syst Evol Microbiol 2000; 50(Pt 3): 1087-93.
[http://dx.doi.org/10.1099/00207713-50-3-1087] [PMID: 10843049]
[132]
Sharma A, Vadehra D, Montesano P, Singvi A. Staphylococcus Epidermidis and Hemodialysis: a deadly duo causing native valve endocarditis InC53 critical care case reports: you give me (more) fever-infection and sepsis. Am Thor Soc 2018; pp. A5308-8.
[133]
Farajzadeh Sheikh A, Asareh Zadegan Dezfuli A, Navidifar T, Fard SS, Dehdashtian M. Association between biofilm formation, structure and antibiotic resistance in Staphylococcus epidermidis isolated from neonatal septicemia in southwest Iran. Infect Drug Resist 2019; 12: 1771-82.
[http://dx.doi.org/10.2147/IDR.S204432] [PMID: 31303772]
[134]
Sousa VS, da-Silva AP, Sorenson L, et al. Staphylococcus saprophyticus recovered from humans, food, and recreational waters in Rio de Janeiro, Brazil. Int J Microbiol 2017; 20174287547
[135]
de Paiva-Santos W, de Sousa VS, Giambiagi-deMarval M. Occurrence of virulence-associated genes among Staphylococcus saprophyticus isolated from different sources. Microb Pathog 2018; 119: 9-11.
[http://dx.doi.org/10.1016/j.micpath.2018.03.054] [PMID: 29604423]
[136]
Rosen GH, Randis TM, Desai PV, et al. Streptococcus and the vaginal microbiota. J Infect Dis 2017; 216(6): 744-51.
[http://dx.doi.org/10.1093/infdis/jix395] [PMID: 28934437]
[137]
Laith AA, Ambak MA, Hassan M, et al. Molecular identification and histopathological study of natural Streptococcus agalactiae infection in hybrid tilapia (Oreochromis niloticus). Vet World 2017; 10(1): 101-11.
[http://dx.doi.org/10.14202/vetworld.2017.101-111] [PMID: 28246454]
[138]
Mózsik G, Figler M. Nutrition in Health and Disease-Our Challenges Now and Forthcoming Time 2019.
[139]
Mukesi M, Iweriebor BC, Obi LC, Nwodo UU, Moyo SR, Okoh AI. Prevalence and capsular type distribution of Streptococcus agalactiae isolated from pregnant women in Namibia and South Africa. BMC Infect Dis 2019; 19(1): 179.
[http://dx.doi.org/10.1186/s12879-019-3809-6] [PMID: 30786878]
[140]
Long SS, Prober CG, Fischer M. Principles and practice of pediatric infectious diseases E-Book. Elsevier Health Sciences 2017.
[141]
Mashima I, Theodorea CF, Thaweboon B, Thaweboon S, Nakazawa F. Identification of Veillonella species in the tongue bio_lm by using a novel one-step polymerase chain reaction method. PLoS One 2016; 11(6)e0157516
[http://dx.doi.org/10.1371/journal.pone.0157516] [PMID: 27326455]
[142]
Kanasi E, Dewhirst FE, Chalmers NI, et al. Clonal analysis of the microbiota of severe early childhood caries. Caries Res 2010; 44(5): 485-97.
[http://dx.doi.org/10.1159/000320158] [PMID: 20861633]
[143]
Mohamed BA, Afify HM. Automated classification of bacterial images extracted from digital microscope via bag of words model.
[144]
Yuewu L, Yan P, Li Q, Xiangquan X. A review of epidemic models related to meteorological factors. Curr Bioinform 2018; 13(4): 360-6.
[http://dx.doi.org/10.2174/1574893612666170619083048]
[145]
Saleem TJ, Chishti MA. Exploring the applications of machine learning in healthcare. Int J Sensors Wirel Commun Control 2020; 10(4): 458-72.
[http://dx.doi.org/10.2174/2210327910666191220103417]
[146]
Abenavoli L, Cinaglia P, Luzza F, Gentile I, Boccuto L. Epidemiology of coronavirus disease outbreak: the italian trends. Rev Recent Clin Trials 2020; 15(2): 87-92.
[http://dx.doi.org/10.2174/1574887115999200407143449]
[147]
Liu NN, Tan JC, Li J, Li S, Cai Y, Wang H. COVID-19 Pandemic: Experiences in China and implications for its prevention and treatment worldwide. Curr Cancer Drug Targets 2020; 20: 1.
[http://dx.doi.org/10.2174/1568009620666200414151419]
[148]
Jefferies M, Rashid H, Hill-Cawthorne GA, Kayser V. A Brief History of Ebolavirus Disease: Paving the Way Forward by Learning from the Previous Outbreaks. Infect Disord Drug Targets 2018; 18: 1.
[http://dx.doi.org/10.2174/1871526518666181001125106]
[149]
Sang H, Wang C, He D, Liu Q. Multi-information flow CNN and attribute-aided reranking for person reidentification. Comput Intell Neurosci 2019; 20197028107
[http://dx.doi.org/10.1155/2019/7028107]
[150]
de Menezes RS, Magalhaes RM, Maia H. Object Recognition Using Convolutional Neural Networks. 2019.
[151]
Lundervold AS, Lundervold A. An overview of deep learning in medical imaging focusing on MRI. Z Med Phys 2019; 29(2): 102-27.
[http://dx.doi.org/10.1016/j.zemedi.2018.11.002] [PMID: 30553609]
[152]
Wahid MF, Ahmed T, Habib MA.
[153]
Buetti-Dinh A, Galli V, Bellenberg S, et al. Deep neural networks outperform human expert’s capacity in characterizing bioleaching bacterial biofilm composition. Biotechnol Rep (Amst) 2019; 22e00321
[http://dx.doi.org/10.1016/j.btre.2019.e00321] [PMID: 30949441]
[154]
Wang Y, Guan Q, Lao I, et al. Using deep convolutional neural networks for multi-classification of thyroid tumor by histopathology: a large-scale pilot study. Ann Transl Med 2019; 7(18): 468.
[http://dx.doi.org/10.21037/atm.2019.08.54] [PMID: 31700904]
[155]
Huynh BQ, Li H, Giger ML. Digital mammographic tumor classification using transfer learning from deep convolutional neural networks. J Med Imaging (Bellingham) 2016; 3(3)034501
[http://dx.doi.org/10.1117/1.JMI.3.3.034501] [PMID: 27610399]
[156]
Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017; 542(7639): 115-8.
[http://dx.doi.org/10.1038/nature21056] [PMID: 28117445]
[157]
Yosinski J, Clune J, Bengio Y, Lipson H. How transferable are features in deep neural networks? 2014.
[158]
Shin HC, Roth HR, Gao M, et al. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 2016; 35(5): 1285-98.
[http://dx.doi.org/10.1109/TMI.2016.2528162] [PMID: 26886976]
[159]
Vesal S, Ravikumar N, Davari A, Ellmann S, Maier A. Classification of breast cancer histology images using transfer learning
[160]
Cireşan DC, Giusti A, Gambardella LM, Schmidhuber J. Mitosis detection in breast cancer histology images with deep neural networks. 2013.
[161]
Litjens G, Sánchez CI, Timofeeva N, et al. Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci Rep 2016; 6: 26286.
[http://dx.doi.org/10.1038/srep26286] [PMID: 27212078]

Rights & Permissions Print Cite
Article Metrics
26
1
© 2024 Bentham Science Publishers | Privacy Policy