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Theranostics, the combination of diagnosis and therapy, has long held promise as a means to achieving personalised precision cancer treatments. However, despite its potential, theranostics has yet to realise significant clinical translation, largely due the complexity and overriding toxicity concerns of existing theranostic nanoparticle strategies. Methods: Here, we present an alternative nanoparticle-free theranostic approach based on simultaneous Raman spectroscopy and photodynamic therapy (PDT) in an integrated clinical platform for cancer theranostics. Results: We detail the compatibility of Raman spectroscopy and PDT for cancer theranostics, whereby Raman spectroscopic diagnosis can be performed on PDT photosensitiser-positive cells and tissues without inadvertent photosensitiser activation/photobleaching or impaired diagnostic capacity. We further demonstrate that our theranostic platform enables in vivo tumour diagnosis, treatment, and post-treatment molecular monitoring in real-time. Conclusion: This system thus achieves effective theranostic performance, providing a promising new avenue towards the clinical realisation of theranostics.

Establishing the precise timeline of a crime can be challenging as current analytical techniques used suffer from many limitations and are destructive to the body fluids encountered at crime scenes. Raman spectroscopy has demonstrated excellent potential in forensic science as it provides direct information about the structural and molecular changes without the need for processing or extracting samples. However, its current applicability is limited to pure body fluids, as signals from the substrate underlying these fluids greatly influence the current models used for age estimation. In this study, we utilized Raman spectroscopy to identify selective spectral markers that delineate the bloodstain age in the presence of interfering signals from the substrate. The pure bloodstains and the bloodstains on the substrate were aged for two weeks at 21 ± 2 ℃ in the dark. Least absolute shrinkage and selection operator (LASSO) regression was employed to guide the feature selection in the presence of interference from substrates to accurately predict the bloodstain age. Substrate-specific regression models guided by an automated feature selection algorithm yielded low values of predictive root mean square error (0.207, 0.204, 0.222 h in logarithmic scale) and high R2 (0.924, 0.926, 0.913) on test data consisting of blood spectra on floor tile, facial tissue, and linoleum-polymer substrates, respectively. This framework for an automated feature selection algorithm relies entirely on pure bloodstain spectra to train substrate-specific models for estimating the age of composite (blood on substrate) spectra. The model can thus be easily applied to any new composite spectra and is highly scalable to new environments. This study demonstrates that Raman spectroscopy coupled with LASSO could serve as a reliable and nondestructive technique to determine the age of bloodstains on any surface while aiding forensic investigations in real-world scenarios.

Otitis media (OM) is a prevalent disease that is the most frequent cause of acute physician visits and prescription of antibiotics for children. Current methods to diagnose OM and differentiate between the two main types of OM, acute otitis media (AOM) and otitis media with effusion (OME), rely on interpreting symptoms that may overlap between them. Since AOM requires antibiotic treatment and OME does not, there is a clinical need to distinguish between AOM and OME to determine whether antibiotic treatment is necessary and guide future prescriptions. We used an optical spectroscopy technique, Raman spectroscopy (RS), to identify and characterize the biochemical features of the three main pathogens that cause AOM in vitro. A Renishaw inVia confocal Raman microscope at 785 nm was used to spectrally investigate the Raman signatures of Haemophilus influenzae, Moraxella catarrhalis, and Streptococcus pneumoniae. Biochemical features or biomarkers important for classification of each bacterial species were identified and yielded a 97% accuracy of discrimination. To test the effectiveness of Raman-based bacterial classification in a clinical sample, human middle ear effusion (MEE) from patients affected by recurrent AOM was collected, cultured, and measured using RS. The probability of bacterial involvement from each of the three main bacteria that cause AOM was determined from the clinical MEE samples. These results suggest the potential of utilizing RS to aid in accurately diagnosing AOM and providing physicians with bacterial identification to guide treatment.

Cutaneous pharmacokinetics (cPK) after topical formulation application has been a research area of particular interest for regulatory and drug development scientists to mechanistically understand topical bioavailability (BA). Semi-invasive techniques, such as tape-stripping, dermal microdialysis, or dermal open-flow microperfusion, all quantify macroscale cPK. While these techniques have provided vast cPK knowledge, the community lacks a mechanistic understanding of active pharmaceutical ingredient (API) penetration and permeation at the cellular level. One noninvasive approach to address microscale cPK is coherent Raman scattering imaging (CRI), which selectively targets intrinsic molecular vibrations without the need for extrinsic labels or chemical modification. CRI has two main methods-coherent anti-Stokes Raman scattering (CARS) and stimulated Raman scattering (SRS)-that enable sensitive and selective quantification of APIs or inactive ingredients. CARS is typically utilized to derive structural skin information or visualize chemical contrast. In contrast, the SRS signal, which is linear with molecular concentration, is used to quantify APIs or inactive ingredients within skin stratifications. Although mouse tissue has commonly been utilized for cPK with CRI, topical BA and bioequivalence (BE) must ultimately be assessed in human tissue before regulatory approval. This paper presents a methodology to prepare and image ex vivo skin to be used in quantitative pharmacokinetic CRI studies in the evaluation of topical BA and BE. This methodology enables reliable and reproducible API quantification within human and mouse skin over time. The concentrations within lipid-rich and lipid-poor compartments, as well as total API concentration over time are quantified; these are utilized for estimates of micro- and macroscale BA and, potentially, BE.

Stimulated Raman scattering (SRS) is a nondestructive and rapid technique for imaging of biological and clinical specimens with label-free chemical specificity. SRS spectral imaging is typically carried out either via broadband methods, or by tuning narrowband ultrafast light sources over narrow spectral ranges thus specifically targeting vibrational frequencies. We demonstrate a multi-window sparse spectral sampling SRS (S4RS) approach where a rapidly-tunable dual-output all-fiber optical parametric oscillator is tuned into specific vibrational modes across more than 1400 cm−1 during imaging. This approach is capable of collecting SRS hyperspectral images either by scanning a full spectrum or by rapidly tuning into select target frequencies, hands-free and automatically, across the fingerprint, silent, and high wavenumber windows of the Raman spectrum. We further apply computational techniques for spectral decomposition and feature selection to identify a sparse subset of Raman frequencies capable of sample discrimination. Here we have applied this novel method to monitor spatiotemporal dynamic changes of active pharmaceutical ingredients in skin, which has particular relevance to topical drug product delivery.

The biodistribution and pharmacokinetics of drugs are vital to the mechanistic understanding of their efficacy. Measuring antimicrobial drug efficacy has been challenging as plasma drug concentration is used as a surrogate for tissue drug concentration, yet typically does not reflect that at the intended site(s) of action. Utilizing an image-guided approach, it is feasible to accurately quantify the biodistribution and pharmacokinetics within the desired site(s) of action. We outline imaging modalities used in visualizing drug distribution with examples ranging from in vitro cellular drug uptake to clinical treatment of microbial infections. The imaging modalities of interest are: radio-labeling, magnetic resonance, mass spectrometry imaging, computed tomography, fluorescence, and Raman spectroscopy. We outline the progress, limitations, and future outlook for each methodology. Further advances in these optical approaches would benefit patients and researchers alike, as non-invasive imaging could yield more profound insights with a lower clinical burden than invasive measurement approaches used today.

In vivo Raman spectroscopy has been utilized for the non-invasive, non-destructive assessment of tissue pathophysiology for a variety of applications largely through the use of fiber optic probes to interface with samples of interest. Fiber optic probes can be designed to optimize the collection of Raman-scattered photons from application-dependent depths, and this critical consideration should be addressed when planning a study. Herein we investigate four distinct probe geometries for sensitivity to superficial and deep signals through a Monte Carlo model that incorporates Raman scattering and fluorescence. Experimental validation using biological tissues was performed to accurately recapitulate in vivo scenarios. Testing in biological tissues agreed with modeled results and revealed that microlens designs had slightly enhanced performance at shallow depths (< 1 mm), whereas all of the beampath-modified designs yielded more signal from deep within tissue. Simulation based on fluence maps generated using ray-tracing in the absence of optical scattering had drastically different results as a function of depth for each probe compared to the biological simulation. The contrast in simulation results between the non-scattering and biological tissue phantoms underscores the importance of considering the optical properties of a given application when designing a fiber optic probe. The model presented here can be easily extended for optimization of entirely novel probe designs prior to fabrication, reducing time and cost while improving data quality.

Breast conserving surgery is the preferred treatment for women diagnosed with early stage invasive breast cancer. To ensure successful breast conserving surgeries, efficient tumour margin resection is required for minimizing tumour recurrence. Currently surgeons rely on touch preparation cytology or frozen section analysis to assess tumour margin status intraoperatively. These techniques have suboptimal accuracy and are time-consuming. Tumour margin status is eventually confirmed using postoperative histopathology that takes several days. Thus, there is a need for a real-time, accurate, automated guidance tool that can be used during tumour resection intraoperatively to assure complete tumour removal in a single procedure. In this paper, we evaluate feasibility of a 3-dimensional scanner that relies on Raman Spectroscopy to assess the entire margins of a resected specimen within clinically feasible time. We initially tested this device on a phantom sample that simulated positive tumour margins. This device first scans the margins of the sample and then depicts the margin status in relation to an automatically reconstructed image of the phantom sample. The device was further investigated on breast tissues excised from prophylactic mastectomy specimens. Our findings demonstrate immense potential of this device for automated breast tumour margin assessment to minimise repeat invasive surgeries.

Background and objective Soft tissue sarcomas (STS) are a rare and heterogeneous group of malignant tumors that are often treated through surgical resection. Current intraoperative margin assessment methods are limited and highlight the need for an improved approach with respect to time and specificity. Here we investigate the potential of near-infrared Raman spectroscopy for the intraoperative differentiation of STS from surrounding normal tissue. Materials and methods In vivo Raman measurements at 785 nm excitation were intraoperatively acquired from subjects undergoing STS resection using a probe based spectroscopy system. A multivariate classification algorithm was developed in order to automatically identify spectral features that can be used to differentiate STS from the surrounding normal muscle and fat. The classification algorithm was subsequently tested using leave-one-subject-out cross-validation. Results With the exclusion of well-differentiated liposarcomas, the algorithm was able to classify STS from the surrounding normal muscle and fat with a sensitivity and specificity of 89.5% and 96.4%, respectively. Conclusion These results suggest that single point near-infrared Raman spectroscopy could be utilized as a rapid and non-destructive surgical guidance tool for identifying abnormal tissue margins in need of further excision. Lasers Surg. Med. 48:774-781, 2016. © 2016 Wiley Periodicals, Inc.

Monitoring cervical structure and composition during pregnancy has high potential for prediction of preterm birth (PTB), a problem affecting 15 million newborns annually. We use in vivo Raman spectroscopy, a label-free, light-based method that provides a molecular fingerprint to non-invasively investigate normal and impaired cervical remodeling. Prostaglandins stimulate uterine contractions and are clinically used for cervical ripening during pregnancy. Deletion of cyclooxygenase-1 (Cox-1), an enzyme involved in production of these prostaglandins, results in delayed parturition in mice. Contrary to expectation, Cox-1 null mice displayed normal uterine contractility; therefore, this study sought to determine whether cervical changes could explain the parturition differences in Cox-1 null mice and gestation-matched wild type (WT) controls. Raman spectral changes related to extracellular matrix proteins, lipids, and nucleic acids were tracked over pregnancy and found to be significantly delayed in Cox-1 null mice at term. A cervical basis for the parturition delay was confirmed by other ex vivo tests including decreased tissue distensibility, hydration, and elevated progesterone levels in the Cox-1 null mice at term. In conclusion, in vivo Raman spectroscopy non-invasively detected abnormal remodeling in the Cox-1 null mouse, and clearly demonstrated that the cervix plays a key role in their delayed parturition.

Renal mass biopsy is still controversial due to imperfect accuracy. Raman spectroscopy (RS) demonstrated promise as an in vivo real-time, nondestructive diagnostic tool in many malignancies. Short wave infrared (SWIR) RS has the potential to improve on previous RS systems for renal mass diagnosis. The aim of this study is to evaluate a SWIR RS system in differentiating normal and malignant renal samples. Measurements were acquired using a benchtop RS system with excitation wavelength at 1064 nm and an InGaAs array detector. Processed spectra were classified with a Bayesian machine learning algorithm, sparse multinomial logistic regression. Sensitivity and receiver operating characteristic curve analyses evaluated the classifier accuracy. Accuracy of the classifier was 92.5% with sensitivity and specificity of 95.8% and 88.8%, respectively. For posterior probability of malignant class assignment, the area under the ROC curve is 0.94 (95% confidence interval: 0.89-0.99, P < .001). SWIR RS accurately differentiated normal and malignant kidney tumors. RS has the potential to be used as a diagnostic tool in kidney cancer.

Kidney cancer affects 65,000 new patients every. As computerized tomography became ubiquitous, the number of small, incidentally detected renal masses increased. About 6,000 benign cases are misclassified radiographically as malignant and removed surgically. Raman spectroscopy (RS) has been widely demonstrated for disease discrimination, however intense near-infrared auto-fluorescence of certain tissues (e.g kidney) can present serious challenges to bulk tissue diagnosis. A 1064nm excitation dispersive detection RS system demonstrated the ability to collect spectra with superior quality in tissues with strong auto-fluorescence. Our objective is to develop a 1064 nm dispersive detection RS system capable of differentiating normal and malignant renal tissue. We will report on the design and development of a clinical system for use in nephron sparing surgeries. We will present pilot data that has been collected from normal and malignant ex vivo kidney specimens using a benchtop RS system. A total of 93 measurements were collected from 12 specimens (6 Renal Cell Carcinoma, 6 Normal ). Spectral classification was performed using sparse multinomial logistic regression (SMLR). Correct classification by SMLR was obtained in 78% of the trials with sensitivity and specificity of 82% and 75% respectively. We will present the association of spectral features with biological indicators of healthy and diseased kidney tissue. Our findings indicate that 1064nm RS is a promising technique for differentiation of normal and malignant renal tissue. This indicates the potential for accurately separating healthy and cancerous tissues and suggests implications for utilizing RS for optical biopsy and surgical guidance in nephron sparing surgery.

We present a rapidly tunable dual-output all-fiber light source for coherent Raman imaging, based on a dispersively matched mode-locked laser pumping a parametric oscillator. Output pump and Stokes pulses with a maximal power of 170 and 400 mW, respectively, and equal durations of 7 ps could be generated. The tuning mechanism required no mechanical delay line, enabling all-electronic arbitrary wavelength switching across more than 2700 cm−1 in less than 5 ms. The compact setup showed a reliable operation despite mechanical shocks of more than 25 m/s2 and is, thus, well suited for operation in a mobile cart. Imaging mouse and human skin tissue with both the portable light source and a commercial laboratory-bound reference system yielded qualitatively equal results and verified the portable light source being well suited for coherent Raman microscopy.

Inflammatory bowel disease (IBD) is an idiopathic disease that is typically characterized by chronic inflammation of the gastrointestinal tract. Recently much effort has been devoted to the development of novel diagnostic tools that can assist physicians for fast, accurate, and automated diagnosis of the disease. Previous research based on Raman spectroscopy has shown promising results in differentiating IBD patients from normal screening cases. In the current study, we examined IBD patients in vivo through a colonoscope-coupled Raman system. Optical diagnosis for IBD discrimination was conducted based on full-range spectra using multivariate statistical methods. Further, we incorporated several feature selection methods in machine learning into the classification model. The diagnostic performance for disease differentiation was significantly improved after feature selection. Our results showed that improved IBD diagnosis can be achieved using Raman spectroscopy in combination with multivariate analysis and feature selection.

Soft tissue sarcomas (STS) are a rare and heterogeneous group of malignant tumors that are often treated via surgical resection. Inadequate resection can lead to local recurrence and decreased survival rates. In this study, we investigate the hypothesis that near-infrared (NIR) autofluorescence can be utilized for tumor margin analysis by differentiating STS from the surrounding normal tissue. Intraoperative in vivo measurements were acquired from 30 patients undergoing STS resection and were characterized to differentiate between normal tissue and STS. Overall, normal muscle and fat were observed to have the highest and lowest autofluorescence intensities, respectively, with STS falling in between. With the exclusion of well-differentiated liposarcomas, the algorithm's accuracy for classifying muscle, fat, and STS was 93%, 92%, and 88%, respectively. These findings suggest that NIR autofluorescence spectroscopy has potential as a rapid and nondestructive surgical guidance tool that can inform surgeons of suspicious margins in need of immediate re-excision.

Raman spectroscopy has been widely demonstrated for tissue characterization and disease discrimination, however current implementations with either 785 or 830 nm near-infrared (NIR) excitation have been ineffectual in tissues with intense autofluorescence such as the liver. Here we report the use of a dispersive 1064 nm Raman system using a low-noise Indium-Gallium-Arsenide (InGaAs) array to discriminate highly autofluorescent bulk tissue ex vivo specimens from healthy liver, adenocarcinoma, and hepatocellular carcinoma (N = 5 per group). The resulting spectra have been combined with a multivariate discrimination algorithm, sparse multinomial logistic regression (SMLR), to predict class membership of healthy and diseased tissues, and spectral bands selected for robust classification have been extracted. A quantitative metric called feature importance is defined based on classification outputs and is used to guide the association of spectral features with biological indicators of healthy and diseased liver tissue. Spectral bands with high feature importance for healthy and liver tumor specimens include retinol, heme, biliverdin, or quinones (1595 cm(-1)); lactic acid (838 cm(-1)); collagen (873 cm(-1)); and nucleic acids (1485 cm(-1)). Classification performance in both binary (normal versus tumor, 100% sensitivity and 89% specificity) and three-group cases (classification accuracy: normal 89%, adenocarcinoma 74%, hepatocellular carcinoma 64%) indicates the potential for accurately separating healthy and cancerous tissues and suggests implications for utilizing Raman techniques during surgical guidance in liver resection.

We present a study to evaluate and compare three near-infrared (NIR) fluorescence imaging systems designed to provide intraoperative guidance around anatomical structures. The three systems adapted specifically for the application of endogenous NIR fluorescence detection were (1) a photomultiplier tube based NIR viewer, (2) a thermoelectrically cooled electron-multiplying charge-coupled device (CCD) camera, and (3) a clinical endoscope CCD camera system. Each system was evaluated on the basis of ease-of-use, cost, and system performance. The cooled CCD camera showed the highest contrast ratio, but is limited in utility by its bulk interface and high cost. The clinical endoscope camera showed flexibility in its field-of-view and provides the benefit of being sterilized to allow easy integration into the surgical suite; however, it exhibits signal nonlinearity that would distort quantitative analysis. The NIR viewer shows optimal performance, exhibiting high spatial resolution, linearity, and sufficient contrast to differentiate between tissue types. This low-cost design proves to be the optimal system for parathyroid detection, offering ease-of-use in a surgical setting while meeting system performance requirements.

Inflammatory bowel disease (IBD), including ulcerative colitis (UC) and Crohn’s colitis (CC), affects nearly 2 million Americans, and the incidence is increasing worldwide. It has been established that UC and CC are distinct forms of IBD and require different medical care, however the distinction made between UC and CC is based upon inexact clinical, radiological, endoscopic, and pathologic features. A diagnosis of indeterminate colitis occurs in up to 15% of patients when UC and CC features overlap and cannot be differentiated; in these patients, diagnosis relies on long term followup, success or failure of existing treatment, and recurrence of the disease. Thus, there is need for a tool that can improve the sensitivity and specificity for fast, accurate and automated diagnosis of IBD. Here we present colonoscopy-coupled fiber probe-based Raman spectroscopy as a novel in vivo diagnostic tool for IBD. This in vivo study of both healthy control (NC, N=10) and diagnosed IBD patients with UC (N=15) and CC (N=26) aims to characterize spectral signatures of NC, UC, and CC. Samples are correlated with tissue pathology markers and endoscopic evaluation. Optimal collection parameters for detection have been identified based upon the new, application specific instrument design. The collected spectra are processed and analyzed using multivariate statistical techniques to identify spectral markers and discriminate NC, UC, and CC. Development of spectral markers to discriminate disease type is a necessary first step in the development of real-time, accurate and automated in vivo detection of IBD during colonoscopy procedures.

Raman spectroscopy is an established technique for molecularly specific characterization of tissues. However, even with near-infrared (NIR) excitation, some tissues possess background autofluorescence, which can overwhelm Raman scattering. Here, we report collection of spectra from tissues with strong autofluorescence using a 1064 nm system with a high-throughput dispersive spectrometer and deep-cooled InGaAs array. Spectra collected at 1064 nm were compared with those collected at 785 nm in specimens from human breast, liver, and kidney. The results demonstrate superior performance at 1064 nm in the liver and kidney, where NIR autofluorescence is intense. The results indicate the feasibility of new biomedical applications for Raman spectroscopy at 1064 nm in tissues with strong autofluorescence.

Inflammatory bowel disease (IBD), including ulcerative colitis (UC) and Crohn's disease (CD), affects over 1 million Americans and 2 million Europeans, and the incidence is increasing worldwide. While these diseases require unique medical care, the differentiation between UC and CD lacks a gold standard, and therefore relies on long term follow up, success or failure of existing treatment, and recurrence of the disease. Here, we present colonoscopy-coupled fiber optic probe-based Raman spectroscopy as a minimally-invasive diagnostic tool for IBD of the colon (UC and Crohn's colitis). This pilot in vivo study of subjects with existing IBD diagnoses of UC (n = 8), CD (n = 15), and normal control (n = 8) aimed to characterize spectral signatures of UC and CD. Samples were correlated with tissue pathology markers and endoscopic evaluation. The collected spectra were processed and analyzed using multivariate statistical techniques to identify spectral markers and discriminate IBD and disease classes. Confounding factors including the presence of active inflammation and the particular colon segment measured were investigated and integrated into the devised prediction algorithm, reaching 90% sensitivity and 75% specificity to CD from this in vivo data set. These results represent significant progress towards improved real-time classification for accurate and automated in vivo detection and discrimination of IBD during colonoscopy procedures.