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

Combining Multiple Spectroscopic Techniques to Reveal the Effects of Staphylococcus aureus Infection on Human Bone Tissues

Osteomyelitis (OM) and periprosthetic joint infections (PJIs) are major public health concerns in Western countries due to increased life expectancy. Infections usually occur due to bacterial spread through fractures, implants, or blood-borne transmission. The pathogens trigger an inflammatory response that hinders bone tissue regeneration. Treatment requires surgical intervention, which involves the precise removal of infected tissue, wound cleansing, and local and systemic antibiotic administration. Staphylococcus aureus (SA) is one of the most common pathogens causing infection-induced OM and PJIs. It forms antimicrobial-resistant biofilms and is frequently found in healthcare settings. In this proof-of-concept, we present an approach based on multiple spectroscopic techniques aimed at investigating the effects of SA infection on bone tissue, as well as identifying specific markers useful to detect early bacterial colonization on the tissue surface. A cross-section of a human femoral diaphysis, with negative-culture results, was divided into three parts, and the cortical and trabecular regions were separated from each other. Two portions of each bone tissue type were infected with SA for one and seven days, respectively. Multiple techniques were used to investigate the impact of the infection on bone tissue, Brillouin-Raman microspectroscopy and attenuated total reflection Fourier transform infrared spectroscopy were employed to assess and develop a new noninvasive diagnostic method to detect SA by targeting the bone of the host. The results indicate that exposure to SA infection significantly alters the bone structure, especially in the case of the trabecular type, even after just one day. Moreover, Raman spectral markers of the tissue damage were identified, indicating that this technique can detect the effect of the pathogens' presence in bone biopsies and pave the way for potential application during surgery, due to its nondestructive and contactless nature.

Lactobacillus acidophilus VB1 co-aggregates and inhibits biofilm formation of chronic otitis media-associated pathogens

This study aims to evaluate the antibacterial activity of Lactobacillus acidophilus, alone and in combination with ciprofloxacin, against otitis media-associated bacteria. L. acidophilus cells were isolated from Vitalactic B (VB), a commercially available probiotic product containing two lactobacilli species, L. acidophilus and Lactiplantibacillus (formerly Lactobacillus) plantarum. The pathogenic bacterial samples were provided by Al-Shams Medical Laboratory (Baqubah, Iraq). Bacterial identification and antibiotic susceptibility testing for 16 antibiotics were performed using the VITEK2 system. The minimum inhibitory concentration of ciprofloxacin was also determined. The antimicrobial activity of L. acidophilus VB1 cell-free supernatant (La-CFS) was evaluated alone and in combination with ciprofloxacin using a checkerboard assay. Our data showed significant differences in the synergistic activity when La-CFS was combined with ciprofloxacin, in comparison to the use of each compound alone, against Pseudomonas aeruginosa SM17 and Proteus mirabilis SM42. However, an antagonistic effect was observed for the combination against Staphylococcus aureus SM23 and Klebsiella pneumoniae SM9. L. acidophilus VB1 was shown to significantly co-aggregate with the pathogenic bacteria, and the highest co-aggregation percentage was observed after 24 h of incubation. The anti-biofilm activities of CFS and biosurfactant (BS) of L. acidophilus VB1 were evaluated, and we found that the minimum biofilm inhibitory concentration that inhibits 50% of bacterial biofilm (MBIC50) of La-CFS was significantly lower than MBIC50 of La-BS against the tested pathogenic bacterial species. Lactobacillus acidophilus, isolated from Vitane Vitalactic B capsules, demonstrated promising antibacterial and anti-biofilm activities against otitis media pathogens, highlighting its potential as an effective complementary/alternative therapeutic strategy to control bacterial ear infections.

Comparative study of machine-and deep-learning based classification algorithms for biomedical Raman spectroscopy (RS): case study of RS based pathogenic microbe identification

One key aspect pushing the frontiers of biomedical RS is dedicated machine- or deep- learning (ML or DL) algorithms. Yet, systematic comparative study between ML and DL algorithms has not been conducted for biomedical RS, largely due to the limited availability of open-source and large Raman spectra dataset. Therefore we compared typical ML partial least square-discriminant analysis (PLS-DA) and DL one dimensional convolution neural network (1D-CNN) based pathogenic microbe identification on 12,000 Raman spectra from six species of microbe (i.e., K. aerogenes (Klebsiella aerogenes), C. albicans (Candida albicans), C. glabrata (Candida glabrata), Group A Strep. (Group A Streptococcus), E. coli1 (Escherichia coli1), E. coli2 (Escherichia coli2)) when 100%, 75%, 50% and 25% of the 12,000 Raman spectra were retained. The total Raman dataset was analyzed with 80% split for training and 20% for testing. The 100% retained testing dataset accuracy, area under curve (AUC) of the receiver operating characteristic (ROC) curve were 95.25% and 0.997 for 1D-CNN, which are higher than those (89.42% and 0.979) of PLS-DA. Yet, PLS-DA outperforms 1D-CNN for 75%, 50% and 25% retained testing dataset. The resultant accuracies and AUCs demonstrated the performance reliance of PLS-DA and 1D-CNN on Raman spectra number. Besides, both loadings on the latent variables of PLS-DA and the saliency maps of 1D-CNN largely captured Raman peaks arising from DNA and proteins with comparable interpretability. The results of the current work indicated that both ML and DL algorithms should be explored for application-wise Raman spectra identification to select whichever with higher accuracies and AUCs.

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

Otitis media (OM), a highly prevalent inflammatory middle-ear disease in children worldwide, is commonly caused by an infection, and can lead to antibiotic-resistant bacterial biofilms in recurrent/chronic OM cases. A biofilm related to OM typically contains one or multiple bacterial species. 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 bacterial biofilms. The proposed method applied supervised machine-learning-based frameworks (SVM, random forest, and XGBoost) to classify multiple species bacterial biofilms from in vitro cultures and clinically-obtained in vivo images from human subjects. Our findings show that optimized SVM-RBF and XGBoost classifiers achieved more than 95% of AUC, 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, offering valuable insights for real-time in vivo characterization of ear infections.

Label-Free Optical Technologies for Middle-Ear Diseases

Medical applications of optical technology have increased tremendously in recent decades. Label-free techniques have the unique advantage of investigating biological samples in vivo without introducing exogenous agents. This is especially beneficial for a rapid clinical translation as it reduces the need for toxicity studies and regulatory approval for exogenous labels. Emerging applications have utilized label-free optical technology for screening, diagnosis, and surgical guidance. Advancements in detection technology and rapid improvements in artificial intelligence have expedited the clinical implementation of some optical technologies. Among numerous biomedical application areas, middle-ear disease is a unique space where label-free technology has great potential. The middle ear has a unique anatomical location that can be accessed through a dark channel, the external auditory canal; it can be sampled through a tympanic membrane of approximately 100 microns in thickness. The tympanic membrane is the only membrane in the body that is surrounded by air on both sides, under normal conditions. Despite these favorable characteristics, current examination modalities for middle-ear space utilize century-old technology such as white-light otoscopy. This paper reviews existing label-free imaging technologies and their current progress in visualizing middle-ear diseases. We discuss potential opportunities, barriers, and practical considerations when transitioning label-free technology to clinical applications.

A growing battlefield in the war against biofilm-induced antimicrobial resistance: insights from reviews on antibiotic resistance

Biofilms are a common survival strategy employed by bacteria in healthcare settings, which enhances their resistance to antimicrobial and biocidal agents making infections difficult to treat. Mechanisms of biofilm-induced antimicrobial resistance involve reduced penetration of antimicrobial agents, increased expression of efflux pumps, altered microbial physiology, and genetic changes in the bacterial population. Factors contributing to the formation of biofilms include nutrient availability, temperature, pH, surface properties, and microbial interactions. Biofilm-associated infections can have serious consequences for patient outcomes, and standard antimicrobial therapies are often ineffective against biofilm-associated bacteria, making diagnosis and treatment challenging. Novel strategies, including antibiotics combination therapies (such as daptomycin and vancomycin, colistin and azithromycin), biofilm-targeted agents (such as small molecules (LP3134, LP3145, LP4010, LP1062) target c-di-GMP), and immunomodulatory therapies (such as the anti-PcrV IgY antibodies which target Type IIIsecretion system), are being developed to combat biofilm-induced antimicrobial resistance. A multifaceted approach to diagnosis, treatment, and prevention is necessary to address this emerging problem in healthcare settings.

ISOM 2023 research Panel 4 - Diagnostics and microbiology of otitis media

OBJECTIVES

To identify and review key research advances from the literature published between 2019 and 2023 on the diagnosis and microbiology of otitis media (OM) including acute otitis media (AOM), recurrent AOM (rAOM), otitis media with effusion (OME), chronic suppurative otitis media (CSOM) and AOM complications (mastoiditis).

DATA SOURCES

PubMed database of the National Library of Medicine.

REVIEW METHODS

All relevant original articles published in Medline in English between July 2019 and February 2023 were identified. Studies that were reviews, case studies, relating to OM complications (other than mastoiditis), and studies focusing on guideline adherence, and consensus statements were excluded. Members of the panel drafted the report based on these search results.

MAIN FINDINGS

For the diagnosis section, 2294 unique records screened, 55 were eligible for inclusion. For the microbiology section 705 unique records were screened and 137 articles were eligible for inclusion. The main themes that arose in OM diagnosis were the need to incorporate multiple modalities including video-otoscopy, tympanometry, telemedicine and artificial intelligence for accurate diagnoses in all diagnostic settings. Further to this, was the use of new, cheap, readily available tools which may improve access in rural and lowmiddle income (LMIC) settings. For OM aetiology, PCR remains the most sensitive method for detecting middle ear pathogens with microbiome analysis still largely restricted to research use. The global pandemic response reduced rates of OM in children, but post-pandemic shifts should be monitored.

IMPLICATION FOR PRACTICE AND FUTURE RESEARCH

Cheap, easy to use multi-technique assessments combined with artificial intelligence and/or telemedicine should be integrated into future practice to improve diagnosis and treatment pathways in OM diagnosis. Longitudinal studies investigating the in-vivo process of OM development, timings and in-depth interactions between the triad of bacteria, viruses and the host immune response are still required. Standardized methods of collection and analysis for microbiome studies to enable inter-study comparisons are required. There is a need to target underlying biofilms if going to effectively prevent rAOM and OME and possibly enhance ventilation tube retention.

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.

Ensuring the future of otitis media research: Interest in and feasibility of a mentoring network program within the otitis media research community

OBJECTIVES

The goal of this study was to assess the feasibility of the implementation of a mentoring network program based on expressed interest and the diverse composition of the otitis media research community in order to address attrition of the scientific workforce.

METHODS

An online survey was sent to basic, translational, and clinical researchers with a known interest in otitis media.

RESULTS

Of 509 eligible participants, 119 (23.4%) responded to the survey. Survey respondents had a diverse background by completed education, current job description, and membership in an underrepresented group in science. Most faculty respondents (76.4%) were willing to participate in the proposed program as mentors and faculty lecturers, or had early-career researchers or trainees in their research group who were willing to participate as mentees in the mentoring network. Scientific and non-scientific topics for inclusion in the training program were ranked, with immunology and inflammation, microbiology, science leadership and collaboration, mentoring, and grantsmanship as main foci of interest among respondents.

CONCLUSION

Our survey results showed enthusiastic participation among responding otitis media researchers, indicating the feasibility of implementing a mentoring network program that will address workforce attrition, particularly among underrepresented groups in science.

In Vivo Optical Characterization of Middle Ear Effusions and Biofilms During Otitis Media

Otitis media (OM), a common ear infection, is characterized by the presence of an accumulated middle ear effusion (MEE) in a normally air-filled middle ear cavity. While assessing the MEE plays a critical role in the overall management of OM, identifying and examining the MEE is challenging with the current diagnostic tools since the MEE is located behind the semi-opaque eardrum. The objective of this cross-sectional, observational study is to non-invasively visualize and characterize MEEs and bacterial biofilms in the middle ear. A portable, handheld, otoscope-integrated optical coherence tomography (OCT) system combined with novel analytical methods has been developed. In vivo middle ear OCT images were acquired from 53 pediatric subjects (average age of 3.9 years; all awake during OCT imaging) diagnosed with OM and undergoing a surgical procedure (ear tube surgery) to aspirate the MEE and aerate the middle ear. In vivo middle ear OCT acquired prior to the surgery was compared with OCT of the freshly extracted MEEs, clinical diagnosis, and post-operative evaluations. Among the subjects who were identified with the presence of MEEs, 89.6% showed the presence of the TM-adherent biofilm in in vivo OCT. This study provides an atlas of middle ear OCT images exhibiting a range of depth-resolved MEE features, which can only be visualized and assessed non-invasively through OCT. Quantitative metrics of OCT images acquired prior to the surgery were statistically correlated with surgical evaluations of MEEs. Measurements of MEE characteristics will provide new readily available information that can lead to improved diagnosis and management strategies for the highly prevalent OM in children.

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.