Uraib Sharaha - Academia.edu (original) (raw)

Papers by Uraib Sharaha

Research paper thumbnail of Detection of Extended-Spectrum  -Lactamases in Members of the Family Enterobacteriaceae: Comparison of the Double-Disk and Three-Dimensional Tests

Antimicrobial Agents and Chemotherapy, 1992

Research paper thumbnail of Early Detection of Pre-Cancerous and Cancerous Cells Using Raman Spectroscopy-Based Machine Learning

Cells

Cancer is the most common and fatal disease around the globe, with an estimated 19 million newly ... more Cancer is the most common and fatal disease around the globe, with an estimated 19 million newly diagnosed patients and approximately 10 million deaths annually. Patients with cancer struggle daily due to difficult treatments, pain, and financial and social difficulties. Detecting the disease in its early stages is critical in increasing the likelihood of recovery and reducing the financial burden on the patient and society. Currently used methods for the diagnosis of cancer are time-consuming, producing discomfort and anxiety for patients and significant medical waste. The main goal of this study is to evaluate the potential of Raman spectroscopy-based machine learning for the identification and characterization of precancerous and cancerous cells. As a representative model, normal mouse primary fibroblast cells (NFC) as healthy cells; a mouse fibroblast cell line (NIH/3T3), as precancerous cells; and fully malignant mouse fibroblasts (MBM-T) as cancerous cells were used. Raman spe...

Research paper thumbnail of Instant detection of extended-spectrum β-lactamase-producing bacteria from the urine of patients using infrared spectroscopy combined with machine learning

The Analyst

Early detection of ESBL-producing bacteria is crucial for effective and accurate treatment and si... more Early detection of ESBL-producing bacteria is crucial for effective and accurate treatment and simultaneously limits the development and spread of MDR bacteria.

Research paper thumbnail of Rapid determination of Proteus mirabilis susceptibility to antibiotics using infrared spectroscopy in tandem with random forest

Research paper thumbnail of Fast identification and susceptibility determination of E. coli isolated directly from patients' urine using infrared-spectroscopy and machine learning

Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy

Research paper thumbnail of Culture-independent susceptibility determination of E. coli isolated directly from patients’ urine using FTIR and machine-learning

The Analyst

One of the most common human bacterial infections is the urinary tract infection (UTI).

Research paper thumbnail of Infra-red spectroscopy combined with machine learning algorithms enables early determination of Pseudomonas aeruginosa’s susceptibility to antibiotics

Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2022

Pseudomonas (P.) aeruginosa is a bacterium responsible for severe infections that have become a r... more Pseudomonas (P.) aeruginosa is a bacterium responsible for severe infections that have become a real concern in hospital environments. Nosocomial infections caused by P. aeruginosa are often hard to treat because of its intrinsic resistance and remarkable ability to acquire further resistance mechanisms to multiple groups of antimicrobial agents. Thus, rapid determination of the susceptibility of P. aeruginosa isolates to antibiotics is crucial for effective treatment. The current methods used for susceptibility determination are time-consuming; hence the importance of developing a new method. Fourier-transform infra-red (FTIR) spectroscopy is known as a rapid and sensitive diagnostic tool, with the ability to detect minor abnormal molecular changes including those associated with the development of antibiotic- resistant bacteria. The main goal of this study is to evaluate the potential of FTIR spectroscopy together with machine learning algorithms, to determine the susceptibility of P. aeruginosa to different antibiotics in a time span of ∼20 min after the first culture. For this goal, 590 isolates of P. aeruginosa, obtained from different infection sites of various patients, were measured by FTIR spectroscopy and analyzed by machine learning algorithms. We have successfully determined the susceptibility of P. aeruginosa to various antibiotics with an accuracy of 82-90%.

Research paper thumbnail of Detection and Identification of Antibiotic Resistant Bacteria Using Infra-Red-Microscopy and Advanced Multivariate Analysis

World Academy of Science, Engineering and Technology, International Journal of Physical and Mathematical Sciences, 2017

Research paper thumbnail of 赤外分光法と多変量解析:種レベルでの混合フザリウム種とoxysporum分離株の分類【Powered by NICT】

Research paper thumbnail of Infrared spectroscopy and multivariate analysis: Classification of mixed fusarium species solani and oxysporum isolates at the species level

2016 IEEE International Conference on the Science of Electrical Engineering (ICSEE), 2016

Fungi are microorganisms that are divided into groups and subgroups according to their similarity... more Fungi are microorganisms that are divided into groups and subgroups according to their similarity, genera, species, and strains. Fusarium is considered a phytopathogen that attacks variety of crops throughout the world, causing diseases resulting in severe economic losses. Many of the Fusarium species cause similar symptoms, making it impossible to distinguish among them based on symptoms alone. Fungicides are commonly the most effective treatment of these pathogens, and their use is effective and could prevent or decrease its severity and spread when detected early. Currently, classical methods (microbiological, molecular) are time-consuming. In this study, we aimed to distinguish among three different groups: Fusarium oxysporum, Fusarium solani, and a mixture of both species. Thus, we used Fourier transform infrared microscopy combined with principal component analysis (PCA) and linear discriminant analysis (LDA) classifier. Using the first ten PCs, our classification results show...

Research paper thumbnail of Rapid detection of Klebsiella pneumoniae producing extended spectrum β lactamase enzymes by infrared microspectroscopy and machine learning algorithms

The Analyst, 2021

FTIR spectroscopy of Klebsiella pneumoniae in tandem with machine learning enables detection of E... more FTIR spectroscopy of Klebsiella pneumoniae in tandem with machine learning enables detection of ESBL producing isolates in 20 minutes after first culture, which helps physicians to treat bacterial infected patients appropriately.

Research paper thumbnail of Determination of Klebsiella pneumoniae Susceptibility to Antibiotics Using Infrared Microscopy

Analytical Chemistry, 2021

Klebsiella pneumoniae (K. pneumoniae) is one of the most aggressive multidrug-resistant bacteria ... more Klebsiella pneumoniae (K. pneumoniae) is one of the most aggressive multidrug-resistant bacteria associated with human infections, resulting in high mortality and morbidity. We obtained 1190 K. pneumoniae isolates from different patients with urinary tract infections. The isolates were measured to determine their susceptibility regarding nine specific antibiotics. This study's primary goal is to evaluate the potential of infrared spectroscopy in tandem with machine learning to assess the susceptibility of K. pneumoniae within approximately 20 min following the first culture. Our results confirm that it was possible to classify the isolates into sensitive and resistant with a success rate higher than 80% for the tested antibiotics. These results prove the promising potential of infrared spectroscopy as a powerful method for a K. pneumoniae susceptibility test.

Research paper thumbnail of Distinction between mixed genus bacteria using infrared spectroscopy and multivariate analysis

Vibrational Spectroscopy, 2019

Bacterial infections are significant causes of serious human health problems. Different bacterial... more Bacterial infections are significant causes of serious human health problems. Different bacterial pathogens might have similar symptoms, and it is important to detect the cause of the infection early in order to enable effective treatment. In many infections, a mixture of different bacteria might exist in the same tested sample. To treat such infections effectively, it is very important to identify the various types of infecting bacteria in the sample. In this pioneering study, we examined the potential of Fourier transform infrared (FTIR) microscopy for accurate identification and differentiation between different bacteria in experimentally mixed samples of bacteria in the genus level in time span of about 30 min. We have measured the FTIR spectra of bacteria in pure form as well as in various mixtures. Principal components analysis (PCA) was applied on the spectra of various classes, followed by linear discriminant analysis (LDA) as a linear classifier. Our results show that it is possible to differentiate between mixed categories of bacteria with high rates of success. The classification rate was higher when the mixed samples included Gram-positive and Gram-negative types. When the mixing range was comparable ([0.5,0.5] and [0.6,0.4]), a classification success rate > 95% was achieved using only the first 20 PCs.

Research paper thumbnail of Differentiation of mixed soil-borne fungi in the genus level using infrared spectroscopy and multivariate analysis

Journal of Photochemistry and Photobiology B: Biology, 2018

Early detection of soil-borne pathogens, which have a negative effect on almost all agricultural ... more Early detection of soil-borne pathogens, which have a negative effect on almost all agricultural crops, is crucial for effective targeting with the most suitable antifungal agents and thus preventing and/or reducing their severity. They are responsible for severe diseases in various plants, leading in many cases to substantial economic losses. In this study, infrared (IR) spectroscopic method, which is known as sensitive, accurate and rapid, was used to discriminate between different fungi in a mixture was evaluated. Mixed and pure samples of Colletotrichum, Verticillium, Rhizoctonia, and Fusarium genera were measured using IR microscopy. Our spectral results showed that the best differentiation between pure and mixed fungi was obtained in the 675-1800 cm −1 wavenumber region. Principal components analysis (PCA), followed by linear discriminant analysis (LDA) as a linear classifier, was performed on the spectra of the measured classes. Our results showed that it is possible to differentiate between mixed-calculated categories of phytopathogens with high success rates (~100%) when the mixing percentage range is narrow (40-60) in the genus level; when the mixing percentage range is wide (10-90), the success rate exceeded 85%. Also, in the measured mixed categories of phytopathogens it is possible to differentiate between the different categories with~100% success rate.

Research paper thumbnail of Using infrared spectroscopy and multivariate analysis to detect antibiotics' resistant Escherichia coli bacteria

Analytical chemistry, Jan 21, 2017

Bacterial pathogens are one of the primary causes of human morbidity worldwide. Historically, ant... more Bacterial pathogens are one of the primary causes of human morbidity worldwide. Historically, antibiotics have been highly effective against most bacterial pathogens; however, the increasing resistance of bacteria to a broad spectrum of commonly used antibiotics has become a global health-care problem. Early and rapid determination of bacterial susceptibility to antibiotics has become essential in many clinical settings, and, sometimes, can save lives. Currently classical procedures require at least 48 hours for determining bacterial susceptibility, which can constitute a life-threatening delay for effective treatment. Infrared (IR) microscopy is a rapid and inexpensive technique, which has been used successfully for the detection and identification of various biological samples; nonetheless, its true potential in routine clinical diagnosis has not yet been established. In this study, we evaluated the potential of this technique for rapid identification of bacterial susceptibility t...

Research paper thumbnail of Detection of antibiotic resistant Escherichia Coli bacteria using infrared microscopy and advanced multivariate analysis

The Analyst, Jan 12, 2017

Bacterial resistance to antibiotics is becoming a global health-care problem. Bacteria are involv... more Bacterial resistance to antibiotics is becoming a global health-care problem. Bacteria are involved in many diseases, and antibiotics have been the most effective treatment for them. It is essential to treat an infection with an antibiotic to which the infecting bacteria is sensitive; otherwise, the treatment is not effective and may lead to life-threatening progression of disease. Classical microbiology methods that are used for determination of bacterial susceptibility to antibiotics are time consuming, accounting for problematic delays in the administration of appropriate drugs. Infrared-absorption microscopy is a sensitive and rapid method, enabling the acquisition of biochemical information from cells at the molecular level. The combination of Fourier transform infrared (FTIR) microscopy with new statistical classification methods for spectral analysis has become a powerful technique, with the ability to detect structural molecular changes associated with resistivity of bacteri...

Research paper thumbnail of Fast and reliable determination ofEscherichia colisusceptibility to antibiotics: Infrared microscopy in tandem with machine learning algorithms

Journal of Biophotonics, 2019

This article has been accepted for publication and undergone full peer review but has not been th... more This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as

Research paper thumbnail of Detection of Extended-Spectrum  -Lactamases in Members of the Family Enterobacteriaceae: Comparison of the Double-Disk and Three-Dimensional Tests

Antimicrobial Agents and Chemotherapy, 1992

Research paper thumbnail of Early Detection of Pre-Cancerous and Cancerous Cells Using Raman Spectroscopy-Based Machine Learning

Cells

Cancer is the most common and fatal disease around the globe, with an estimated 19 million newly ... more Cancer is the most common and fatal disease around the globe, with an estimated 19 million newly diagnosed patients and approximately 10 million deaths annually. Patients with cancer struggle daily due to difficult treatments, pain, and financial and social difficulties. Detecting the disease in its early stages is critical in increasing the likelihood of recovery and reducing the financial burden on the patient and society. Currently used methods for the diagnosis of cancer are time-consuming, producing discomfort and anxiety for patients and significant medical waste. The main goal of this study is to evaluate the potential of Raman spectroscopy-based machine learning for the identification and characterization of precancerous and cancerous cells. As a representative model, normal mouse primary fibroblast cells (NFC) as healthy cells; a mouse fibroblast cell line (NIH/3T3), as precancerous cells; and fully malignant mouse fibroblasts (MBM-T) as cancerous cells were used. Raman spe...

Research paper thumbnail of Instant detection of extended-spectrum β-lactamase-producing bacteria from the urine of patients using infrared spectroscopy combined with machine learning

The Analyst

Early detection of ESBL-producing bacteria is crucial for effective and accurate treatment and si... more Early detection of ESBL-producing bacteria is crucial for effective and accurate treatment and simultaneously limits the development and spread of MDR bacteria.

Research paper thumbnail of Rapid determination of Proteus mirabilis susceptibility to antibiotics using infrared spectroscopy in tandem with random forest

Research paper thumbnail of Fast identification and susceptibility determination of E. coli isolated directly from patients' urine using infrared-spectroscopy and machine learning

Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy

Research paper thumbnail of Culture-independent susceptibility determination of E. coli isolated directly from patients’ urine using FTIR and machine-learning

The Analyst

One of the most common human bacterial infections is the urinary tract infection (UTI).

Research paper thumbnail of Infra-red spectroscopy combined with machine learning algorithms enables early determination of Pseudomonas aeruginosa’s susceptibility to antibiotics

Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2022

Pseudomonas (P.) aeruginosa is a bacterium responsible for severe infections that have become a r... more Pseudomonas (P.) aeruginosa is a bacterium responsible for severe infections that have become a real concern in hospital environments. Nosocomial infections caused by P. aeruginosa are often hard to treat because of its intrinsic resistance and remarkable ability to acquire further resistance mechanisms to multiple groups of antimicrobial agents. Thus, rapid determination of the susceptibility of P. aeruginosa isolates to antibiotics is crucial for effective treatment. The current methods used for susceptibility determination are time-consuming; hence the importance of developing a new method. Fourier-transform infra-red (FTIR) spectroscopy is known as a rapid and sensitive diagnostic tool, with the ability to detect minor abnormal molecular changes including those associated with the development of antibiotic- resistant bacteria. The main goal of this study is to evaluate the potential of FTIR spectroscopy together with machine learning algorithms, to determine the susceptibility of P. aeruginosa to different antibiotics in a time span of ∼20 min after the first culture. For this goal, 590 isolates of P. aeruginosa, obtained from different infection sites of various patients, were measured by FTIR spectroscopy and analyzed by machine learning algorithms. We have successfully determined the susceptibility of P. aeruginosa to various antibiotics with an accuracy of 82-90%.

Research paper thumbnail of Detection and Identification of Antibiotic Resistant Bacteria Using Infra-Red-Microscopy and Advanced Multivariate Analysis

World Academy of Science, Engineering and Technology, International Journal of Physical and Mathematical Sciences, 2017

Research paper thumbnail of 赤外分光法と多変量解析:種レベルでの混合フザリウム種とoxysporum分離株の分類【Powered by NICT】

Research paper thumbnail of Infrared spectroscopy and multivariate analysis: Classification of mixed fusarium species solani and oxysporum isolates at the species level

2016 IEEE International Conference on the Science of Electrical Engineering (ICSEE), 2016

Fungi are microorganisms that are divided into groups and subgroups according to their similarity... more Fungi are microorganisms that are divided into groups and subgroups according to their similarity, genera, species, and strains. Fusarium is considered a phytopathogen that attacks variety of crops throughout the world, causing diseases resulting in severe economic losses. Many of the Fusarium species cause similar symptoms, making it impossible to distinguish among them based on symptoms alone. Fungicides are commonly the most effective treatment of these pathogens, and their use is effective and could prevent or decrease its severity and spread when detected early. Currently, classical methods (microbiological, molecular) are time-consuming. In this study, we aimed to distinguish among three different groups: Fusarium oxysporum, Fusarium solani, and a mixture of both species. Thus, we used Fourier transform infrared microscopy combined with principal component analysis (PCA) and linear discriminant analysis (LDA) classifier. Using the first ten PCs, our classification results show...

Research paper thumbnail of Rapid detection of Klebsiella pneumoniae producing extended spectrum β lactamase enzymes by infrared microspectroscopy and machine learning algorithms

The Analyst, 2021

FTIR spectroscopy of Klebsiella pneumoniae in tandem with machine learning enables detection of E... more FTIR spectroscopy of Klebsiella pneumoniae in tandem with machine learning enables detection of ESBL producing isolates in 20 minutes after first culture, which helps physicians to treat bacterial infected patients appropriately.

Research paper thumbnail of Determination of Klebsiella pneumoniae Susceptibility to Antibiotics Using Infrared Microscopy

Analytical Chemistry, 2021

Klebsiella pneumoniae (K. pneumoniae) is one of the most aggressive multidrug-resistant bacteria ... more Klebsiella pneumoniae (K. pneumoniae) is one of the most aggressive multidrug-resistant bacteria associated with human infections, resulting in high mortality and morbidity. We obtained 1190 K. pneumoniae isolates from different patients with urinary tract infections. The isolates were measured to determine their susceptibility regarding nine specific antibiotics. This study's primary goal is to evaluate the potential of infrared spectroscopy in tandem with machine learning to assess the susceptibility of K. pneumoniae within approximately 20 min following the first culture. Our results confirm that it was possible to classify the isolates into sensitive and resistant with a success rate higher than 80% for the tested antibiotics. These results prove the promising potential of infrared spectroscopy as a powerful method for a K. pneumoniae susceptibility test.

Research paper thumbnail of Distinction between mixed genus bacteria using infrared spectroscopy and multivariate analysis

Vibrational Spectroscopy, 2019

Bacterial infections are significant causes of serious human health problems. Different bacterial... more Bacterial infections are significant causes of serious human health problems. Different bacterial pathogens might have similar symptoms, and it is important to detect the cause of the infection early in order to enable effective treatment. In many infections, a mixture of different bacteria might exist in the same tested sample. To treat such infections effectively, it is very important to identify the various types of infecting bacteria in the sample. In this pioneering study, we examined the potential of Fourier transform infrared (FTIR) microscopy for accurate identification and differentiation between different bacteria in experimentally mixed samples of bacteria in the genus level in time span of about 30 min. We have measured the FTIR spectra of bacteria in pure form as well as in various mixtures. Principal components analysis (PCA) was applied on the spectra of various classes, followed by linear discriminant analysis (LDA) as a linear classifier. Our results show that it is possible to differentiate between mixed categories of bacteria with high rates of success. The classification rate was higher when the mixed samples included Gram-positive and Gram-negative types. When the mixing range was comparable ([0.5,0.5] and [0.6,0.4]), a classification success rate > 95% was achieved using only the first 20 PCs.

Research paper thumbnail of Differentiation of mixed soil-borne fungi in the genus level using infrared spectroscopy and multivariate analysis

Journal of Photochemistry and Photobiology B: Biology, 2018

Early detection of soil-borne pathogens, which have a negative effect on almost all agricultural ... more Early detection of soil-borne pathogens, which have a negative effect on almost all agricultural crops, is crucial for effective targeting with the most suitable antifungal agents and thus preventing and/or reducing their severity. They are responsible for severe diseases in various plants, leading in many cases to substantial economic losses. In this study, infrared (IR) spectroscopic method, which is known as sensitive, accurate and rapid, was used to discriminate between different fungi in a mixture was evaluated. Mixed and pure samples of Colletotrichum, Verticillium, Rhizoctonia, and Fusarium genera were measured using IR microscopy. Our spectral results showed that the best differentiation between pure and mixed fungi was obtained in the 675-1800 cm −1 wavenumber region. Principal components analysis (PCA), followed by linear discriminant analysis (LDA) as a linear classifier, was performed on the spectra of the measured classes. Our results showed that it is possible to differentiate between mixed-calculated categories of phytopathogens with high success rates (~100%) when the mixing percentage range is narrow (40-60) in the genus level; when the mixing percentage range is wide (10-90), the success rate exceeded 85%. Also, in the measured mixed categories of phytopathogens it is possible to differentiate between the different categories with~100% success rate.

Research paper thumbnail of Using infrared spectroscopy and multivariate analysis to detect antibiotics' resistant Escherichia coli bacteria

Analytical chemistry, Jan 21, 2017

Bacterial pathogens are one of the primary causes of human morbidity worldwide. Historically, ant... more Bacterial pathogens are one of the primary causes of human morbidity worldwide. Historically, antibiotics have been highly effective against most bacterial pathogens; however, the increasing resistance of bacteria to a broad spectrum of commonly used antibiotics has become a global health-care problem. Early and rapid determination of bacterial susceptibility to antibiotics has become essential in many clinical settings, and, sometimes, can save lives. Currently classical procedures require at least 48 hours for determining bacterial susceptibility, which can constitute a life-threatening delay for effective treatment. Infrared (IR) microscopy is a rapid and inexpensive technique, which has been used successfully for the detection and identification of various biological samples; nonetheless, its true potential in routine clinical diagnosis has not yet been established. In this study, we evaluated the potential of this technique for rapid identification of bacterial susceptibility t...

Research paper thumbnail of Detection of antibiotic resistant Escherichia Coli bacteria using infrared microscopy and advanced multivariate analysis

The Analyst, Jan 12, 2017

Bacterial resistance to antibiotics is becoming a global health-care problem. Bacteria are involv... more Bacterial resistance to antibiotics is becoming a global health-care problem. Bacteria are involved in many diseases, and antibiotics have been the most effective treatment for them. It is essential to treat an infection with an antibiotic to which the infecting bacteria is sensitive; otherwise, the treatment is not effective and may lead to life-threatening progression of disease. Classical microbiology methods that are used for determination of bacterial susceptibility to antibiotics are time consuming, accounting for problematic delays in the administration of appropriate drugs. Infrared-absorption microscopy is a sensitive and rapid method, enabling the acquisition of biochemical information from cells at the molecular level. The combination of Fourier transform infrared (FTIR) microscopy with new statistical classification methods for spectral analysis has become a powerful technique, with the ability to detect structural molecular changes associated with resistivity of bacteri...

Research paper thumbnail of Fast and reliable determination ofEscherichia colisusceptibility to antibiotics: Infrared microscopy in tandem with machine learning algorithms

Journal of Biophotonics, 2019

This article has been accepted for publication and undergone full peer review but has not been th... more This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as