Publications by authors named "Saba Bashir"

13 Publications

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Surface-enhanced Raman spectral analysis for comparison of PCR products of hepatitis B and hepatitis C.

Photodiagnosis Photodyn Ther 2021 Jul 16;35:102440. Epub 2021 Jul 16.

Department of Chemistry, University of Central Punjab, Faisalabad Campus, Faisalabad, Pakistan.

Background: Surface-enhanced Raman spectroscopy is a reliable tool for identification and differentiation of two diseases showing similar symptoms, hepatitis B (HBV) and hepatitis C (HCV).

Objectives: To develop a polymerase chain reaction technique (PCR) based SERS technique for differentiation of two human pathological conditions sharing the same symptoms using multivariate data analysis techniques e.g. principle component analysis (PCA) and partial least square discriminate analysis (PLS-DA).

Methods: PCR products of HBV and HCV were differentiated by SERS using silver nanoparticles (AgNPs) as a SERS substrate. For this analysis, PCR products of both the diseases with predetermined viral loads were collected and analyzed under SERS instrument and unique SERS spectra of HBV and HCV was compared showing many differences at various points. Diseased classes of HBV and HCV and their negative control classes (viral load less than 1) were compared. PCR products of true healthy DNA and RNA were also compared, which were significantly separated. Moreover, SERS data was analyzed using multivariate data analysis techniques including principle component analysis (PCA) and partial least square discriminate analysis (PLS-DA) and differences were so prominent to observe.

Results: SERS spectral data of HBV and HCV showed clear differences and were significantly separated using PCA. Negative control samples of both disorders and their true healthy samples of DNA and RNA were separated according to 1 principle component. By analyzing data using partial least square discriminate analysis, differentiation of two disease classes was considered more valid with sensitivity, specificity and accuracy value of 96%, 94% and 98% respectively. Value of area under curve (AUROC) was 0.7527.

Conclusion: SERS can be employed for identification and comparison of two human pathological conditions sharing the same symptomology.
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http://dx.doi.org/10.1016/j.pdpdt.2021.102440DOI Listing
July 2021

Surface-enhanced Raman spectroscopy for comparison of serum samples of typhoid and tuberculosis patients of different stages.

Photodiagnosis Photodyn Ther 2021 Jul 1;35:102426. Epub 2021 Jul 1.

Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38040, Pakistan.

Background: Surface-enhanced Raman spectroscopy (SERS) is a reliable tool for the identification and differentiation of two different human pathological conditions sharing the same symptomology, typhoid and tuberculosis (TB).

Objectives: To explore the potential of surface-enhanced Raman spectroscopy for differentiation of two different diseases showing the same symptoms and analysis by principal component analysis (PCA) and partial least square discriminate analysis (PLS-DA).

Methods: Serum samples of clinically diagnosed typhoid and tuberculosis infected individuals were analyzed and differentiated by SERS using silver nanoparticles (Ag NPs) as a SERS substrate. For this purpose, the collected serum samples were analyzed under the SERS instrument and unique SERS spectra of typhoid and tuberculosis were compared showing notable spectral differences in protein, lipid and carbohydrates features. Different stages of the diseased class of typhoid (Early acute and late acute stage) and tuberculosis (Pulmonary and extra-pulmonary stage) were compared with each other and with healthy human serum samples, which were significantly separated. Moreover, SERS data was analyzed using multivariate data analysis techniques including principal component analysis (PCA) and partial least square discriminate analysis (PLS-DA) and differences were so prominent to observe.

Results: SERS Spectral data of typhoid and tuberculosis showed clear differences and were significantly separated using PCA. SERS spectral data of both stages of typhoid and tuberculosis were separated according to 1st principle component. Moreover, by analyzing data using partial least square discriminate analysis, differentiation of two disease classes were considered more valid with a 100% value of sensitivity, specificity and accuracy.

Conclusion: SERS can be employed for identification and comparison of two different human pathological conditions sharing same symptomology.
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http://dx.doi.org/10.1016/j.pdpdt.2021.102426DOI Listing
July 2021

Characterization and prediction of viral loads of Hepatitis B serum samples by using surface-enhance Raman spectroscopy (SERS).

Photodiagnosis Photodyn Ther 2021 Jun 9;35:102386. Epub 2021 Jun 9.

PCR Laboratory, PINUM Hospital, Faisalabad, Pakistan.

Background: Raman spectroscopy is a promising technique to analyze the body fluids for the purpose of non-invasive disease diagnosis.

Objectives: To develop a surface-enhanced Raman spectroscopy (SERS) based method for qualitative and quantitative analysis of hepatitis B viral (HBV) infection from blood serum samples.

Methods: Clinically diagnosed hepatitis B virus (HBV) infected serum samples of patients of different levels of viral loads have been subjected for SERS analysis in comparison with the healthy ones by using silver nanoparticles (Ag NPs) based SERS substrates. The SERS measurements were performed on blood serum samples of 11 healthy and 32 clinically diagnosed HBV patients of different viral load levels of different exponentials including (10, 10 called as low level), (10, 10 called as medium level) and (10, 10 called as high level). Furthermore, multivariate data analysis techniques, Principal Component Analysis (PCA) and Partial Least Square Regression (PLSR) were also performed on SERS spectral data.

Results: The SERS spectral features due to biochemical changes in HBV positive serum samples associated with the increasing viral loads were established which could be employed for HBV diagnostic purpose. PCA was found helpful for the differentiation between SERS spectral data of serum samples of different levels of HBV infection and healthy individuals. PLSR model developed with standard samples of known viral loads for predicting the viral loads of blind/unknown samples with 99% predicted accuracy.

Conclusion: SERS can be employed for qualitative and quantitative analysis of HBV infection from blood serum samples.
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http://dx.doi.org/10.1016/j.pdpdt.2021.102386DOI Listing
June 2021

Surface-enhanced Raman spectroscopy for identification of food processing bacteria.

Spectrochim Acta A Mol Biomol Spectrosc 2021 Nov 23;261:119989. Epub 2021 May 23.

Industrial Biotechnology Division, National Institute for Biotechnology and Genetic Engineering, Faisalabad, Pakistan.

Food processing bacteria play important role in providing flavors, ingredients and other beneficial characteristics to the food but at the same time some bacteria are responsible for food spoilage. Therefore, quick and reliable identification of these food processing bacteria is very necessary for the differentiation between different species which may help in the development of more useful food processing methodologies. In this study, analysis of different bacterial species (Lactobacillus fermentum, Fructobacillus fructosus, Pediococcus pentosaceus and Halalkalicoccus jeotgali) was performed with our in-house developed Ag NPs-based surface-enhanced Raman spectroscopy (SERS) method. The SERS spectral data was analyzed by multivariate data analysis techniques including principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA). Bacterial species were differentiated on the basis of SERS spectral features and potential of SERS was compared with the Raman spectroscopy (RS). SERS along with PCA and PLS-DA was found to be an efficient technique for identification and differentiation of food processing bacterial species. Differentiation with accuracy of 99.5% and sensitivity of 99.7% was depicted by PLS-DA model using leave one out cross validation.
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http://dx.doi.org/10.1016/j.saa.2021.119989DOI Listing
November 2021

Surface-enhanced Raman spectroscopy for analysis of PCR products of viral RNA of hepatitis C patients.

Spectrochim Acta A Mol Biomol Spectrosc 2021 Oct 5;259:119908. Epub 2021 May 5.

PCR Laboratory, PINUM Hospital, Faisalabad, Pakistan.

In the current study, for a qualitative and quantitative study of Polymerase Chain Reaction (PCR) products of viral RNA of Hepatitis C virus (HCV) infection, surface-enhanced Raman spectroscopy (SERS) methodology has been developed. SERS was used to identify the spectral features associated with the PCR products of viral RNA of Hepatitis C in various samples of HCV-infected patients with predetermined viral loads. The measurements for SERS were performed on 30 samples of PCR products, which included three PCR products of RNA of healthy individuals, six negative controls, and twenty-one HCV positive samples of varying viral loads (VLs) using Silver nanoparticles (Ag NPs) as a SERS substrates. Additionally, on SERS spectral data, the multivariate data analysis methods including Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR) were also carried out which help to illustrate the diagnostic capabilities of this method. The PLSR model is designed to predict HCV viral loads based on biochemical changes observed as SERS spectral features which can be associated directly with HCV RNA. Several SERS characteristic features are observed in the RNA of HCV which are not detected in the spectra of healthy RNA/controls. PCA is found helpful to differentiate the SERS spectral data sets of HCV RNA samples from healthy and negative controls. The PLSR model is found to be 99% accurate in predicting VLs of HCV RNA samples of unknown samples based on SERS spectral changes associated with the Hepatitis C development.
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http://dx.doi.org/10.1016/j.saa.2021.119908DOI Listing
October 2021

Surface-enhanced Raman spectroscopy analysis of serum samples of typhoid patients of different stages.

Photodiagnosis Photodyn Ther 2021 Jun 6;34:102329. Epub 2021 May 6.

Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, 38040, Pakistan.

Background: Surface-enhanced Raman spectroscopy (SERS) of body fluids is considered a quick, simple and easy to use method for the diagnosis of disease.

Objectives: To evaluate rapid, reliable, and non-destructive SERS-based diagnostic tool with multivariate data analysis including principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) for classification of different stages of typhoid on the basis of characteristic SERS spectral features.

Methods: SERS has been used for analysis of serum samples of different stages of typhoid including early acute stage and late acute stage in comparison with healthy samples, in order to investigate capability of this technique for diagnosis of typhoid. SERS spectral features associated with the biochemical changes taking place during the development of the typhoid fever were analyzed and identified.

Results: The value of area under the receiver operating characteristics (AUROC) for early acute stage versus healthy is 0.87 and that for healthy versus late acute stage is 0.52. PLS-DA classifier model gives values of 100 % for accuracy, sensitivity and specificity, respectively for the SERS spectral data sets of healthy versus early acute stage. Moreover, this classifier model gives values of 91 %, 89 % and 97 % for accuracy, sensitivity and specificity, respectively for the SERS spectral data sets of healthy versus late acute stage.

Conclusions: Based on preliminary work it is concluded that SERS has potential to diagnose various stages of typhoid fever including early acute and late acute stage in comparison with healthy samples.
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http://dx.doi.org/10.1016/j.pdpdt.2021.102329DOI Listing
June 2021

Surface-enhanced Raman spectroscopy for the identification of tigecycline-resistant E. coli strains.

Spectrochim Acta A Mol Biomol Spectrosc 2021 Sep 24;258:119831. Epub 2021 Apr 24.

Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38040, Pakistan.

Tigecycline (TGC) is recognised as last resort of drugs against several antibiotic-resistant bacteria. Bacterial resistance to tigecycline due to presence of plasmid-mediated mobile TGC resistance genes (tet X3/X4) has broken another defense line. Therefore, rapid and reproducible detection of tigecycline-resistant E. coli (TREC) is required. The current study is designed for the identification and differentiation of TREC from tigecycline-sensitive E. coli (TSEC) by employing SERS by using Ag NPs as a SERS substrate. The SERS spectral fingerprints of E. coli strains associated directly or indirectly with the development of resistance against tigecycline have been distinguished by comparing SERS spectral data of TSEC strains with each TREC strain. Moreover, the statistical analysis including Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA) and Partial Least Squares-Discriminant Analysis (PLS-DA) were employed to check the diagnostic potential of SERS for the differentiation among TREC and TSEC strains. The qualitative identification and differentiation between resistant and sensitive strains and among individual strains have been efficiently done by performing both PCA and HCA. The successful discrimination among TREC and TSEC at the strain level is performed by PLS-DA with 98% area under ROC curve, 100% sensitivity, 98.7% specificity and 100% accuracy.
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http://dx.doi.org/10.1016/j.saa.2021.119831DOI Listing
September 2021

Rapid and sensitive discrimination among carbapenem resistant and susceptible E. coli strains using Surface Enhanced Raman Spectroscopy combined with chemometric tools.

Photodiagnosis Photodyn Ther 2021 Jun 3;34:102280. Epub 2021 Apr 3.

Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, 38040, Pakistan.

Background: Raman spectroscopy is a powerful technique for the robust, reliable and rapid detection and discrimination of bacteria.

Objectives: To develop a rapid and sensitive technique based on surface-enhanced Raman spectroscopy (SERS) with multivariate data analysis tools for discrimination among carbapenem resistant and susceptible E. coli strains.

Methods: SERS was employed to differentiate different strains of carbapenem resistant and susceptible E. coli by using silver nanoparticles (Ag NPs) as a SERS substrate. For this purpose, four strains of carbapenem resistant and three strains of carbapenem susceptible E. coli were analyzed by comparing their SERS spectral signatures. Furthermore, multivariate data analysis techniques including Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA) and Partial Least Squares-Discriminant Analysis (PLS-DA) were performed over the spectral range of 400-1800 cm (fingerprint region) for the identification and differentiation of different E. coli strains.

Results: The SERS spectral features associated with resistant development against carbapenem antibiotics were separated by comparing each spectrum of susceptible strains with each resistant strain. PCA and HCA were found effective for the qualitative differentiation of all the strains analysed. PLS-DA successfully discriminated the carbapenem resistant and susceptible E. coli pellets on the strain level with 99.8 % sensitivity, 100 % specificity, 100 % accuracy and 86 % area under receiver operating characteristic (AUROC) curve.

Conclusion: SERS can be employed for the rapid discrimination among carbapenem resistant and susceptible strains of E. coil.
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http://dx.doi.org/10.1016/j.pdpdt.2021.102280DOI Listing
June 2021

SERS-based viral load quantification of hepatitis B virus from PCR products.

Spectrochim Acta A Mol Biomol Spectrosc 2021 Jul 19;255:119722. Epub 2021 Mar 19.

PCR Laboratory, PINUM Hospital, Faisalabad, Pakistan.

Hepatitis B is a contagious liver disorder caused by hepatitis B virus and if not treated at an early stage, it becomes chronic and results in liver cirrhosis and hepatocellular carcinoma which can even lead to death. In present study, surface-enhanced Raman spectroscopy (SERS) is employed for the analysis of polymerase chain reaction (PCR) products of DNA extracted from hepatitis B virus (HBV) infected patients in comparison with healthy individuals. SERS spectral features are identified which are solely present in the HBV positive samples and consistently increase in intensities with increase in viral load which can be considered as a SERS spectral marker for HBV infection. For sake of understanding, these various levels of viral loads in this study are classified as low (1-1000 IU), medium (1000-10,000 IU), high (above 10,000 IU) and negative control (>1). In order to explore the efficiency of SERS for discrimination of SERS spectral datasets of different samples of varying viral loads and healthy individuals, principal component analysis (PCA) is applied. PCA is used for comparison of these classes including low, medium and high levels of viral loads with each other and with healthy class. Moreover, partial least square discriminant analysis and partial least square regression analysis are employed for the classification of different levels of viral loads in the HBV positive samples and prediction of viral loads in the unknown samples, respectively. PLS-DA is applied for validity of classification and its sensitivity and specificity was found to be 89% and 98% respectively. PLSR model was constructed for prediction of viral loads on the bases of SERS spectral markers of HBV infection with goodness value of 0.9031 and value of root means square error (RMSE) 0.2923. PLSR model also proved to be valid for prediction of blind sample.
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http://dx.doi.org/10.1016/j.saa.2021.119722DOI Listing
July 2021

Raman spectroscopy for the qualitative and quantitative analysis of solid dosage forms of Sitagliptin.

Spectrochim Acta A Mol Biomol Spectrosc 2021 Jan 2;245:118900. Epub 2020 Sep 2.

EA 6295 Nano-médicaments and Nano-sondes, Université de Tours, Tours, France.

To demonstrate the potential of Raman spectroscopy for the qualitative and quantitative analysis of solid dosage pharmacological formulations, different concentrations of Sitagliptin, an Active Pharmaceutical Ingredient (API) currently prescribed as an anti-diabetic drug, are characterised. Increase of the API concentrations induces changes in the Raman spectral features specifically associated with the drug and excipients. Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR), were used for the qualitative and quantitative analysis of the spectral responses. A PLSR model is constructed which enables the prediction of different concentrations of drug in the complex excipient matrices. During the development of the prediction model, the Root Mean Square Error of Cross Validation (RMSECV) was found to be 0.36 mg and the variability explained by the model, according to the (R) value, was found to be 0.99. Moreover, the concentration of the API in the unknown sample was determined. This concentration was predicted to be 64.28/180 mg (w/w), compared to the 65/180 mg (w/w). These findings demonstrate Raman spectroscopy coupled to PLSR analysis to be a reliable tool to verify Sitagliptin contents in the pharmaceutical samples based on calibration models prepared under laboratory conditions.
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http://dx.doi.org/10.1016/j.saa.2020.118900DOI Listing
January 2021

Quantitative analysis of solid dosage forms of cefixime using Raman spectroscopy.

Spectrochim Acta A Mol Biomol Spectrosc 2020 Sep 6;238:118446. Epub 2020 May 6.

Department of Chemistry, University of Agriculture, Faisalabad, Pakistan.

Quantification of antibiotics is of significant importance because of their use in the prevention and treatment of different diseases. Cefixime (CEF) is a cephalosporin antibiotic that is used against bacterial infections. In the present study, Raman spectroscopy has been applied for the identification and quantification of Raman spectral features of cefixime with different concentrations of Active Pharmaceutical Ingredient (API) and excipients in solid dosage forms. The changes in Raman spectral features of API and excipients in the solid dosage forms of cefixime were studied and Raman peaks were assigned based on the literature. Multivariate data analysis techniques including the Principal Component Analysis (PCA) and Partial Least Squares Regression analysis (PLSR) have been performed for the qualitative and quantitative analysis of solid dosage forms of cefixime. PCA was found helpful in differentiating all the Raman spectral data associated with the different solid dosage forms of cefixime. The coefficient of determination (R), mean absolute error (MAE), and mean relative error (MRE) for the calibration data-set were 0.99, 0.72, and 0.01 respectively and for the validation data-set were 0.99, 3.15, and 0.02 respectively, that shows the performance of the model. The root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were found to be 0.56 mg and 3.13 mg respectively.
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http://dx.doi.org/10.1016/j.saa.2020.118446DOI Listing
September 2020

IntelliHealth: A medical decision support application using a novel weighted multi-layer classifier ensemble framework.

J Biomed Inform 2016 Feb 15;59:185-200. Epub 2015 Dec 15.

Computer Engineering Department, College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan. Electronic address:

Accuracy plays a vital role in the medical field as it concerns with the life of an individual. Extensive research has been conducted on disease classification and prediction using machine learning techniques. However, there is no agreement on which classifier produces the best results. A specific classifier may be better than others for a specific dataset, but another classifier could perform better for some other dataset. Ensemble of classifiers has been proved to be an effective way to improve classification accuracy. In this research we present an ensemble framework with multi-layer classification using enhanced bagging and optimized weighting. The proposed model called "HM-BagMoov" overcomes the limitations of conventional performance bottlenecks by utilizing an ensemble of seven heterogeneous classifiers. The framework is evaluated on five different heart disease datasets, four breast cancer datasets, two diabetes datasets, two liver disease datasets and one hepatitis dataset obtained from public repositories. The analysis of the results show that ensemble framework achieved the highest accuracy, sensitivity and F-Measure when compared with individual classifiers for all the diseases. In addition to this, the ensemble framework also achieved the highest accuracy when compared with the state of the art techniques. An application named "IntelliHealth" is also developed based on proposed model that may be used by hospitals/doctors for diagnostic advice.
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http://dx.doi.org/10.1016/j.jbi.2015.12.001DOI Listing
February 2016

BagMOOV: A novel ensemble for heart disease prediction bootstrap aggregation with multi-objective optimized voting.

Australas Phys Eng Sci Med 2015 Jun 10;38(2):305-23. Epub 2015 Mar 10.

Computer Engineering Department, College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan,

Conventional clinical decision support systems are based on individual classifiers or simple combination of these classifiers which tend to show moderate performance. This research paper presents a novel classifier ensemble framework based on enhanced bagging approach with multi-objective weighted voting scheme for prediction and analysis of heart disease. The proposed model overcomes the limitations of conventional performance by utilizing an ensemble of five heterogeneous classifiers: Naïve Bayes, linear regression, quadratic discriminant analysis, instance based learner and support vector machines. Five different datasets are used for experimentation, evaluation and validation. The datasets are obtained from publicly available data repositories. Effectiveness of the proposed ensemble is investigated by comparison of results with several classifiers. Prediction results of the proposed ensemble model are assessed by ten fold cross validation and ANOVA statistics. The experimental evaluation shows that the proposed framework deals with all type of attributes and achieved high diagnosis accuracy of 84.16 %, 93.29 % sensitivity, 96.70 % specificity, and 82.15 % f-measure. The f-ratio higher than f-critical and p value less than 0.05 for 95 % confidence interval indicate that the results are extremely statistically significant for most of the datasets.
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http://dx.doi.org/10.1007/s13246-015-0337-6DOI Listing
June 2015
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