This PDF file contains the front matter associated with SPIE Proceedings Volume 12358, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

Effective and early cancer and infection diagnosis, as well as personalized therapy, necessitate new methods of differential diagnosis and represent an outstanding medical task. Within this contribution we will highlight our recent efforts in translating spectroscopic approaches with focus on Raman spectroscopy towards routine clinical applications. In the first part of this contribution, a series of innovative multi-contrast marker free spectroscopy approaches (both microscopy and endoscopy based) for a precise intraoperative tumor margin control and reliable tumor classification to initiate an individualized therapy plan as quickly as possible. The second part highlights our most recent efforts to use Raman spectroscopy for diagnosing infectious diseases. These efforts include (I) predicting the immune response based on the patient's health, (II) quickly identifying the infection-causing pathogen and, in the case of bacterial infections, its resistance pattern; and (III) assessing the patient's response to treatment. An essential, often neglected point in the research of novel diagnostic methods is the sample preparation. Therefore, promising techniques based on particles and chips focusing on the enrichment of bacteria will be presented. The introduced approaches comprise the entire process chain i.e. from sampling to the final diagnostic result, and have a high potential to significantly reduce the critical parameter ‘time’ to initiate a personalized lifesaving therapy as compared to the gold standard microbiology. Novel, multi-user infrastructures are needed to bring these recent advances to patients faster. The Leibniz Centre for Photonics in Infection Research developing market-ready light-based diagnostic devices and novel infectious disease treatments is introduced.

The conventional methods used for the diagnostics of viral infection are either expensive and time-consuming or not accurate enough and dependent on consumable reagents. In the presence of pandemics, a fast and reagent-free solution is needed for mass screening. Recently, the diagnosis of viral infections using infrared spectroscopy has been reported as a fast and low-cost method. In this work a fast and low-cost solution for corona viral detection using infrared spectroscopy based on a compact micro-electro-mechanical systems (MEMS) device and artificial intelligence (AI) suitable for mass deployment is presented. Among the different variants of the corona virus that can infect people, 229E is used in this study due to its low pathogeny. The MEMS ATR-FTIR device employs a 6 reflections ZnSe crystal interface working in the spectral range of 2200-7000 cm^-1. The virus was propagated and maintained in a medium for long enough time then cell supernatant was collected and centrifuged. The supernatant was then transferred and titrated using plaque titration assay. Positive virus samples were prepared with a concentration of 10⁵ PFU/mL. Positive and negative control samples were applied on the crystal surface, dried using a heating lamp and the spectrum was captured. Principal component analysis and logistic regression were used as simple AI techniques. A sensitivity of about 90 % and a specificity of about 80 % were obtained demonstrating the potential detection of the virus based on the MEMS FTIR device.

Our research should be seen in the light of the worldwide increase of antimicrobial resistance (AMR), which is a serious threat to human health. To prevent the spread of AMR, fast reliable diagnostics tools that facilitate optimal antibiotic stewardship are of urgent need. Raman spectroscopy (RS) is a promising tool for rapid label- and culture-free identification and antimicrobial susceptibility testing (AST) in a single step. To take full advantage of RS for bacterial identification machine learning (ML) analysis is essential. Many limitations must be addressed before RS will be a practical platform for point-of-care diagnostics applications in clinics and hospitals. RS is sensitive to factors such as the growth stage, changes in measurement environment and inconsistency in sample preparation. We address the issues of sample preparation, changes in measurement environment and limited data availability. We reduce sample preparation to merely transferring the bacteria to the measurement environment, hereby minimizing the issue of sample inconsistency and the additional benefit of removing sample preparation. To alleviate the situation of limited data availability for ML model training, we have developed a novel spectral transformer (ST) ML model that is efficient after training on both small- and large RS bacteria datasets. We explicit demonstrated that our ST outperforms a state-of-the-art domain-specific residual CNN both in terms of accuracy with 7.5%. Where we attain more than 96% classification accuracy on a dataset consisting of 15 different classes and 95.6% classification accuracy for six MR–MS bacteria species.

Deep tissue abscesses are inflammatory, purulent lesions encased in a fibrin-rich pseudocapsule that include multiple bacterial and fungal species. We have initiated a Phase 1 clinical trial exploring the safety and feasibility of methylene blue photodynamic therapy (MB-PDT) at the time of abscess drainage. To optimize treatment parameters for future clinical applications, our goal is to generate physically accurate three-dimensional (3D) abscess models upon which bacteria can be grown. Here, we report results of MB-PDT against four representative bacterial species found in human abscesses in planktonic culture, as biofilms on silicone, and pilot results in 3D silicone molds derived from human abscess computed tomography (CT) images. In all cases, MB-PDT was performed with 665 nm light at a fluence rate of 4 mW/cm² for 30 minutes, resulting in a fluence of 7.2 J/cm². In planktonic cultures, MB-PDT was effective against Escherichia coli, Enterococcus faecalis, and methicillin-resistant Staphylococcus aureus (MRSA) (4- to 7-fold log CFU reduction). For Klebsiella pneumoniae, increased fluence was required to achieve comparable efficacy. When bacteria were grown as biofilms on silicone, MB-PDT efficacy was reduced (1- to 2-fold CFU reduction). A 3D silicone model was generated based on pelvic abscess CT images, and MRSA was grown in this model for six days. Crystal violet staining showed abundant growth on the silicone, without penetration into the model. These results motivate exploration of both light and drug dose ranging for biofilm samples. Future experiments will additionally focus on MB-PDT of bacteria grown on 3D silicone surfaces.

In orthopaedic trauma surgery, biofilms account for up to 65% of all infections, typically showing increased resistance to antibiotics, and thus novel anti-biofilm approaches need to be developed. Antimicrobial photodynamic therapy (PDT) had been recently proposed to combat clinically relevant biofilms using photosensitizers to kill bacteria with light-induced reactive oxygen species. In the first stage of the study reported here, we assessed the efficacy of this treatment type in eradication of biofilms typically present on surfaces of orthopaedic devices (e.g., intramedullary nails and osseointegrated prosthetic implants) by growing them in vitro inside soft lithography-fabricated microfluidic chips, treating them with 5- Aminolevulinic acid-based PDT and evaluating treatment efficacy with optical coherence tomography. PDT outcomes were compared to biofilm response to clinical antibiotic treatment (Vancomycin/Tobramycin 1:1 mixture). The antibacterial efficiency of 5-Aminolevulinic acid (5-ALA)-based PDT was found to be nonlinear dependent on the photosensitizer concentration and the light power density, with lowest parameters still being 17 times more effective than antibiotic-treated groups, reaching 99.98% bacteria kill at 250 mW/cm² light power density, 100 mg/mL 5-ALA concentration setting. Performed experiments enable the translation of the developed portable treatment/imaging platform to the second phase of the study: PDT treatment response assessment of biofilms naturally grown on orthopaedic devices of clinical patients.