Ishan Barman

Facile Algae-Derived Route to Biogenic Silver Nanoparticles: Synthesis, Antibacterial, and Photocatalytic Properties.

Biogenic synthesis of metal nanoparticles is of considerable interest, as it affords clean, biocompatible, nontoxic, and cost-effective fabrication. Driven by their ability to withstand variable extremes of environmental conditions, several microorganisms, notably bacteria and fungi, have been investigated in the never-ending search for optimal nanomaterial production platforms. Here, we present a hitherto unexplored algal platform featuring Chlorella pyrenoidosa, which offers a high degree of consistency in morphology of synthesized silver nanoparticles. Using a suite of characterization methods, we reveal the intrinsic crystallinity of the algae-derived nanoparticles and the functional moieties associated with its surface stabilization. Significantly, we demonstrate the antibacterial and photocatalytic properties of these silver nanoparticles and discuss the potential mechanisms that drive these critical processes. The blend of photocatalytic and antibacterial properties coupled with their intrinsic biocompatibility and eco-friendliness make these nanoparticles particularly attractive for wastewater treatment.

Engineering tailored nanoparticles with microbes: quo vadis?

In the quest for less toxic and cleaner methods of nanomaterials production, recent developments in the biosynthesis of nanoparticles have underscored the important role of microorganisms. Their intrinsic ability to withstand variable extremes of temperature, pressure, and pH coupled with the minimal downstream processing requirements provide an attractive route for diverse applications. Yet, controlling the dispersity and facile tuning of the morphology of the nanoparticles of desired chemical compositions remains an ongoing challenge. In this Focus Review, we critically review the advances in nanoparticle synthesis using microbes, ranging from bacteria and fungi to viruses, and discuss new insights into the cellular mechanisms of such formation that may, in the near future, allow complete control over particle morphology and functionalization. In addition to serving as paradigms for cost-effective, biocompatible, and eco-friendly synthesis, microbes hold the promise for a unique template for synthesis of tailored nanoparticles targeted at therapeutic and diagnostic platform technologies.

Raman Spectroscopy Provides a Powerful Diagnostic Tool for Accurate Determination of Albumin Glycation

We present the first demonstration of glycated albumin detection and quantification using Raman spectroscopy without the addition of reagents. Glycated albumin is an important marker for monitoring the long-term glycemic history of diabetics, especially as its concentrations, in contrast to glycated hemoglobin levels, are unaffected by changes in erythrocyte life times. Clinically, glycated albumin concentrations show a strong correlation with the development of serious diabetes complications including nephropathy and retinopathy. In this article, we propose and evaluate the efficacy of Raman spectroscopy for determination of this important analyte. By utilizing the pre-concentration obtained through drop-coating deposition, we show that glycation of albumin leads to subtle, but consistent, changes in vibrational features, which with the help of multivariate classification techniques can be used to discriminate glycated albumin from the unglycated variant with 100% accuracy. Moreover, we demonstrate that the calibration model developed on the glycated albumin spectral dataset shows high predictive power, even at substantially lower concentrations than those typically encountered in clinical practice. In fact, the limit of detection for glycated albumin measurements is calculated to be approximately four times lower than its minimum physiological concentration. Importantly, in relation to the existing detection methods for glycated albumin, the proposed method is also completely reagent-free, requires barely any sample preparation and has the potential for simultaneous determination of glycated hemoglobin levels as well. Given these key advantages, we believe that the proposed approach can provide a uniquely powerful tool for quantification of glycation status of proteins in biopharmaceutical development as well as for glycemic marker determination in routine clinical diagnostics in the future.

Static and dynamic light scattering of healthy and malaria-parasite invaded red blood cells

We present the light scattering of individual Plasmodium falciparum-parasitized human red blood cells (Pf-RBCs), and demonstrate progressive alterations to the scattering signal arising from the development of malaria-inducing parasites. By selectively imaging the electric fields using quantitative phase microscopy and a Fourier transform light scattering technique, we calculate the light scattering maps of individual Pf-RBCs. We show that the onset and progression of pathological states of the Pf-RBCs can be clearly identified by the static scattering maps. Progressive changes to the biophysical properties of the Pf-RBC membrane are captured from dynamic light scattering.

Raman spectroscopy-based sensitive and specific detection of glycated hemoglobin.

In recent years, glycated hemoglobin (HbA1c) has been increasingly accepted as a functional metric of mean blood glucose in the treatment of diabetic patients. Importantly, HbA1c provides an alternate measure of total glycemic exposure due to the representation of blood glucose throughout the day, including post-prandially. In this article, we propose and demonstrate the potential of Raman spectroscopy as a novel analytical method for quantitative detection of HbA1c, without using external dyes or reagents. Using the drop coating deposition Raman (DCDR) technique, we observe that the nonenzymatic glycosylation (glycation) of the hemoglobin molecule results in subtle but discernible and highly reproducible changes in the acquired spectra, which enable the accurate determination of glycated and nonglycated hemoglobin using standard chemometric methods. The acquired Raman spectra display excellent reproducibility of spectral characteristics at different locations in the drop and show a linear dependence of the spectral intensity on the analyte concentration. Furthermore, in hemolysate models, the developed multivariate calibration models for HbA1c show a high degree of prediction accuracy and precision--with a limit of detection that is a factor of ~15 smaller than the lowest physiological concentrations encountered in clinical practice. The excellent accuracy and reproducibility achieved in this proof-of-concept study opens substantive avenues for characterization and quantification of the glycosylation status of (therapeutic) proteins, which are widely used for biopharmaceutical development. We also envision that the proposed approach can provide a powerful tool for high-throughput HbA1c sensing in multicomponent mixtures and potentially in hemolysate and whole blood lysate samples.

Incorporation of Support Vector Machines in the LIBS Toolbox for Sensitive and Robust Classification Amidst Unexpected Sample and System Variability

Despite the intrinsic elemental analysis capability and lack of sample preparation requirements, laser-induced breakdown spectroscopy (LIBS) has not been extensively used for real-world applications, e.g., quality assurance and process monitoring. Specifically, variability in sample, system, and experimental parameters in LIBS studies present a substantive hurdle for robust classification, even when standard multivariate chemometric techniques are used for analysis. Considering pharmaceutical sample investigation as an example, we propose the use of support vector machines (SVM) as a nonlinear classification method over conventional linear techniques such as soft independent modeling of class analogy (SIMCA) and partial least-squares discriminant analysis (PLS-DA) for discrimination based on LIBS measurements. Using over-the-counter pharmaceutical samples, we demonstrate that the application of SVM enables statistically significant improvements in prospective classification accuracy (sensitivity), because of its ability to address variability in LIBS sample ablation and plasma self-absorption behavior. Furthermore, our results reveal that SVM provides nearly 10% improvement in correct allocation rate and a concomitant reduction in misclassification rates of 75% (cf. PLS-DA) and 80% (cf. SIMCA)-when measurements from samples not included in the training set are incorporated in the test data-highlighting its robustness. While further studies on a wider matrix of sample types performed using different LIBS systems is needed to fully characterize the capability of SVM to provide superior predictions, we anticipate that the improved sensitivity and robustness observed here will facilitate application of the proposed LIBS-SVM toolbox for screening drugs and detecting counterfeit samples, as well as in related areas of forensic and biological sample analysis.

Laser-induced breakdown spectroscopy-based investigation and classification of pharmaceutical tablets using multivariate chemometric analysis

We report the effectiveness of laser-induced breakdown spectroscopy (LIBS) in probing the content of pharmaceutical tablets and also investigate its feasibility for routine classification. This method is particularly beneficial in applications where its exquisite chemical specificity and suitability for remote and on site characterization significantly improves the speed and accuracy of quality control and assurance process. Our experiments reveal that in addition to the presence of carbon, hydrogen, nitrogen and oxygen, which can be primarily attributed to the active pharmaceutical ingredients, specific inorganic atoms were also present in all the tablets. Initial attempts at classification by a ratiometric approach using oxygen (∼777 nm) to nitrogen (742.36 nm, 744.23 nm and 746.83 nm) compositional values yielded an optimal value at 746.83 nm with the least relative standard deviation but nevertheless failed to provide an acceptable classification. To overcome this bottleneck in the detection process, two chemometric algorithms, i.e. principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA), were implemented to exploit the multivariate nature of the LIBS data demonstrating that LIBS has the potential to differentiate and discriminate among pharmaceutical tablets. We report excellent prospective classification accuracy using supervised classification via the SIMCA algorithm, demonstrating its potential for future applications in process analytical technology, especially for fast on-line process control monitoring applications in the pharmaceutical industry.

Leveraging the Attributes of Mucor hiemalis-Derived Silver Nanoparticles for a Synergistic Broad-Spectrum Antimicrobial Platform

Driven by the need to engineer robust surface coatings for medical devices to prevent infection and sepsis, incorporation of nanoparticles has surfaced as a promising avenue to enhance non-fouling efficacy. Microbial synthesis of such nanoscale metallic structures is of substantive interest as this can offer an eco-friendly, cost-effective, and sustainable route for further development. Here we present a Mucor hiemalis-derived fungal route for synthesis of silver nanoparticles, which display significant antimicrobial properties when tested against six pathological bacterial strains (Klebsiella pneumoniae, Pseudomonas brassicacearum, Aeromonas hydrophila, Escherichia coli, Bacillus cereus, and Staphylococcus aureus) and three pathological fungal strains (Candida albicans, Fusarium oxysporum, and Aspergillus flavus). These antimicrobial attributes were comparable to those of established antibiotics (streptomycin, tetracycline, kanamycin, and rifampicin) and fungicides (amphotericin B, fluconazole, and ketoconazole), respectively. Importantly, these nanoparticles show significant synergistic characteristics when combined with the antibiotics and fungicides to offer substantially greater resistance to microbial growth. The blend of antibacterial and antifungal properties, coupled with their intrinsic “green” and facile synthesis, makes these biogenic nanoparticles particularly attractive for future applications in nanomedicine ranging from topical ointments and bandages for wound healing to coated stents.

Accurate Spectroscopic Calibration for Noninvasive Glucose Monitoring by Modeling the Physiological Glucose Dynamics

The physiological lag between blood and interstitial fluid (ISF) glucose is a major challenge for noninvasive glucose concentration measurements. This is a particular problem for spectroscopic techniques, which predominantly probe ISF glucose, creating inconsistencies in calibration, where blood glucose measurements are used as a reference. To overcome this problem, we present a dynamic concentration correction (DCC) scheme, based on the mass transfer of glucose between blood and ISF, to ensure consistency with the spectral measurements. The proposed formalism allows the transformation of glucose in the concentration domain, ensuring consistency with the acquired spectra in the calibration model. Taking Raman spectroscopy as a specific example, we demonstrate that the predicted glucose concentrations using the DCC-based calibration model closely match the measured glucose concentrations, while those generated with the conventional calibration methods show significantly larger deviations from the measured values. In addition, we provide an analytical formula for a previously unidentified source of limiting uncertainty arising in spectroscopic glucose monitoring from a lack of knowledge of glucose kinetics in prediction samples. A study with human volunteers undergoing glucose tolerance tests indicates that this lag uncertainty, which is comparable in magnitude to the uncertainty arising from noise and nonorthogonality in the spectral data set, can be reduced substantially by employing the DCC scheme in spectroscopic calibration.

Development of robust calibration models using support vector machines for spectroscopic monitoring of blood glucose.

Sample-to-sample variability has proven to be a major challenge in achieving calibration transfer in quantitative biological Raman spectroscopy. Multiple morphological and optical parameters, such as tissue absorption and scattering, physiological glucose dynamics and skin heterogeneity, vary significantly in a human population introducing nonanalyte specific features into the calibration model. In this paper, we show that fluctuations of such parameters in human subjects introduce curved (nonlinear) effects in the relationship between the concentrations of the analyte of interest and the mixture Raman spectra. To account for these curved effects, we propose the use of support vector machines (SVM) as a nonlinear regression method over conventional linear regression techniques such as partial least-squares (PLS). Using transcutaneous blood glucose detection as an example, we demonstrate that application of SVM enables a significant improvement (at least 30%) in cross-validation accuracy over PLS when measurements from multiple human volunteers are employed in the calibration set. Furthermore, using physical tissue models with randomized analyte concentrations and varying turbidities, we show that the fluctuations in turbidity alone causes curved effects which can only be adequately modeled using nonlinear regression techniques. The enhanced levels of accuracy obtained with the SVM based calibration models opens up avenues for prospective prediction in humans and thus for clinical translation of the technology.