Influence of phosphate on bacterial release from activated carbon point-of-use filters and on biofilm characteristics.

Point-of-use (POU) filters certified to remove lead are often composed of activated carbon and have been shown to release high concentrations of bacteria, including opportunistic pathogens. In this study, we examine the impacts of the common corrosion inhibitor phosphate on biofilm characteristics and the relationship between biofilm structure and bacterial release from POU filters. This knowledge is essential for understanding how best to use the filters and where these filters fit in a system where other lead contamination prevention measures may be in place. We measured the bacterial release from activated carbon POU filters fed with groundwater - a common source of drinking water - with and without phosphate. We used optical coherence tomography (OCT) to quantitatively characterize biofilm growing on activated carbon filter material in which the biofilms were fed groundwater with and without phosphate. Phosphate filters released significantly less (57-87 %) bacteria than groundwater filters, and phosphate biofilms (median thickness: 82-331 µm) grew to be significantly thicker than groundwater biofilms (median thickness: 122-221 µm). The phosphate biofilm roughness ranged from 97 to 142 % of the groundwater biofilm roughness and was significantly greater in most weeks. Phosphate biofilms also had fewer pores per biofilm volume and shorter channels connecting those pores.

A multimodal nonlinear optical microscopy study of the responses of Pseudomonas aeruginosa to blue light and antibiotic treatment.

Pseudomonas aeruginosa (P. aeruginosa) is a multidrug-resistant human pathogen involved in numerous infections. Understanding the response of P. aeruginosa to various treatments is critical to developing new ways for the antimicrobial susceptibly test and more effective treatment methods. Conventional antimicrobial susceptibility tests lack molecular information at the single bacterium level. In this study, we used label-free multimodal nonlinear optical microscopy to identify an autofluorescence signal from pyoverdine, a siderophore of the bacteria, for quantification of P. aeruginosa responses to antibiotics and blue light treatment. We also discovered that the bleaching of the pyoverdine autofluorescence signals is correlated with the inactivation of P. aeruginosa and is perhaps one of the mechanisms involved in the blue light inactivation of P. aeruginosa.

Texture-based speciation of otitis media-related bacterial biofilms from optical coherence tomography images using supervised classification.

Otitis media (OM) is primarily a bacterial middle-ear infection prevalent among children worldwide. In recurrent and/or chronic OM cases, antibiotic-resistant bacterial biofilms can develop in the middle ear. A biofilm related to OM typically contains one or multiple bacterial strains, the most common include Haemophilus influenzae, Streptococcus pneumoniae, Moraxella catarrhalis, Pseudomonas aeruginosa, and Staphylococcus aureus. Optical coherence tomography (OCT) has been used clinically to visualize the presence of bacterial biofilms in the middle ear. This study used OCT to compare microstructural image texture features from primary bacterial biofilms in vitro and in vivo. The proposed method applied supervised machine-learning-based frameworks (SVM, random forest (RF), and XGBoost) to classify and speciate multiclass bacterial biofilms from the texture features extracted from OCT B-Scan images obtained from in vitro cultures and from clinically-obtained in vivo images from human subjects. Our findings show that optimized SVM-RBF and XGBoost classifiers can help distinguish bacterial biofilms by incorporating clinical knowledge into classification decisions. Furthermore, both classifiers achieved more than 95% of AUC (area under receiver operating curve), detecting each biofilm class. These results demonstrate the potential for differentiating OM-causing bacterial biofilms through texture analysis of OCT images and a machine-learning framework, which could provide additional clinically relevant data during real-time in vivo characterization of ear infections.

Effects of phosphate and silicate on stiffness and viscoelasticity of mature biofilms developed with simulated drinking water.

Biofilms, a porous matrix of cells aggregated with extracellular polymeric substances under the influence of chemical constituents in the feed water, can develop a viscoelastic response to mechanical stresses. In this study, the roles of phosphate and silicate, common additives in corrosion control and meat processing, on the stiffness, viscoelasticity, porous structure networks, and chemical properties of biofilm were investigated. Three-year biofilms on PVC coupons were grown from sand-filtered groundwater with or without one of the non-nutrient (silicate) or nutrient additives (phosphate or phosphate blends). Compared with non-nutrient additives, the phosphate and phosphate-blend additives led to a biofilm with the lowest stiffness, most viscoelastic, and more porous structure, including more connecting throats with greater equivalent radii. The phosphate-based additives also led to more organic species in the biofilm matrix than the silicate additive did. This work demonstrated that nutrient additives could promote biomass accumulation but also reduce mechanical stability.

Multimodal Handheld Probe for Characterizing Otitis Media - Integrating Raman Spectroscopy and Optical Coherence Tomography.

Otitis media (OM) is a common disease of the middle ear, affecting 80% of children before the age of three. The otoscope, a simple illuminated magnifier, is the standard clinical diagnostic tool to observe the middle ear. However, it has limited contrast to detect signs of infection, such as clearly identifying and characterizing middle ear fluid or biofilms that accumulate within the middle ear. Likewise, invasive sampling of every subject is not clinically indicated nor practical. Thus, collecting accurate noninvasive diagnostic factors is vital for clinicians to deliver a precise diagnosis and effective treatment regimen. To address this need, a combined benchtop Raman spectroscopy (RS) and optical coherence tomography (OCT) system was developed. Together, RS-OCT can non-invasively interrogate the structural and biochemical signatures of the middle ear under normal and infected conditions.In this paper, in vivo RS scans from pediatric clinical human subjects presenting with OM were evaluated in parallel with RS-OCT data of physiologically relevant in vitro ear models. Component-level characterization of a healthy tympanic membrane and malleus bone, as well as OM-related middle ear fluid, identified the optimal position within the ear for RS-OCT data collection. To address the design challenges in developing a system specific to clinical use, a prototype non-contact multimodal handheld probe was built and successfully tested in vitro. Design criteria have been developed to successfully address imaging constraints imposed by physiological characteristics of the ear and optical safety limits. Here, we present the pathway for translation of RS-OCT for non-invasive detection of OM.

Differentiation of otitis media-causing bacteria and biofilms via Raman spectroscopy and optical coherence tomography.

In the management of otitis media (OM), identification of causative bacterial pathogens and knowledge of their biofilm formation can provide more targeted treatment approaches. Current clinical diagnostic methods rely on the visualization of the tympanic membrane and lack real-time assessment of the causative pathogen(s) and the nature of any biofilm that may reside behind the membrane and within the middle ear cavity. In recent years, optical coherence tomography (OCT) has been demonstrated as an improved in vivo diagnostic tool for visualization and morphological characterization of OM biofilms and middle ear effusions; but lacks specificity about the causative bacterial species. This study proposes the combination of OCT and Raman spectroscopy (RS) to examine differences in the refractive index, optical attenuation, and biochemical composition of Haemophilus influenzae, Streptococcus pneumoniae, Moraxella catarrhalis, and Pseudomonas aeruginosa; four of the leading otopathogens in OM. This combination provides a dual optical approach for identifying and differentiating OM-causing bacterial species under three different in vitro growth environments (i.e., agar-grown colonies, planktonic cells from liquid cultures, and biofilms). This study showed that RS was able to identify key biochemical variations to differentiate all four OM-causing bacteria. Additionally, biochemical spectral changes (RS) and differences in the mean attenuation coefficient (OCT) were able to distinguish the growth environment for each bacterial species.

Imaging and characterization of transitions in biofilm morphology via anomalous diffusion following environmental perturbation.

Microorganisms form macroscopic structures for the purpose of environmental adaptation. Sudden environmental perturbations induce dynamics that cause bacterial biofilm morphology to transit to another equilibrium state, thought to be related to anomalous diffusion processes. Here, detecting the super-diffusion characteristics would offer a long-sought goal for a rapid detection method of biofilm phenotypes based on their dynamics, such as growth or dispersal. In this paper, phase-sensitive Doppler optical coherence tomography (OCT) and dynamic light scattering (DLS) are combined to demonstrate wide field-of-view and label-free internal dynamic imaging of biofilms. The probability density functions (PDFs) of phase displacement of the backscattered light and the dynamic characteristics of the PDFs are estimated by a simplified mixed Cauchy and Gaussian model. This model can quantify the super-diffusion state and estimate the dynamic characteristics and macroscopic responses in biofilms that may further describe dispersion and growth in biofilm models.

Temporally and spatially adaptive Doppler analysis for robust handheld optical coherence elastography.

Optical coherence elastography (OCE), a functional extension of optical coherence tomography (OCT), can be used to characterize the mechanical properties of biological tissue. A handheld fiber-optic OCE instrument will allow the clinician to conveniently interrogate the localized mechanical properties of in vivo tissue, leading to better informed clinical decision making. During handheld OCE characterization, the handheld probe is used to compress the sample and the displacement of the sample is quantified by analyzing the OCT signals acquired. However, the motion within the sample inevitably varies in time due to varying hand motion. Moreover, the motion speed depends on spatial location due to the sample deformation. Hence, there is a need for a robust motion tracking method for manual OCE measurement. In this study, we investigate a temporally and spatially adaptive Doppler analysis method. The method described here strategically chooses the time interval (δt) between signals involved in Doppler analysis to track the motion speed v(z,t) that varies temporally and spatially in a deformed sample volume under manual compression. Enabled by temporally and spatially adaptive Doppler analysis, we report the first demonstration of real-time manual OCE characterization of in vivo tissue to the best of our knowledge.

Nonlinear characterization of elasticity using quantitative optical coherence elastography.

Optical coherence elastography (OCE) has been used to perform mechanical characterization on biological tissue at the microscopic scale. In this work, we used quantitative optical coherence elastography (qOCE), a novel technology we recently developed, to study the nonlinear elastic behavior of biological tissue. The qOCE system had a fiber-optic probe to exert a compressive force to deform tissue under the tip of the probe. Using the space-division multiplexed optical coherence tomography (OCT) signal detected by a spectral domain OCT engine, we were able to simultaneously quantify the probe deformation that was proportional to the force applied, and to quantify the tissue deformation. In other words, our qOCE system allowed us to establish the relationship between mechanical stimulus and tissue response to characterize the stiffness of biological tissue. Most biological tissues have nonlinear elastic behavior, and the apparent stress-strain relationship characterized by our qOCE system was nonlinear an extended range of strain, for a tissue-mimicking phantom as well as biological tissues. Our experimental results suggested that the quantification of force in OCE was critical for accurate characterization of tissue mechanical properties and the qOCE technique was capable of differentiating biological tissues based on the elasticity of tissue that is generally nonlinear.