Raman spectroscopy, a non-invasive analytical method, offers insights into molecular structures and interactions in various liquid and solid samples with applications ranging from material science, and chemical analysis to medical diagnostics. Preprocessing of Raman spectra is vital to remove interferences like background signals and calibration errors, ensuring precise data extraction. Artificial intelligence, particularly machine learning (ML), aids in extracting valuable information from complex datasets. However, effective data preprocessing proves to be crucial as it can influence model robustness. This study addresses the integration of preprocessing and ML algorithms, often treated as distinct identities despite their intrinsic interconnection, in Raman spectra of blood samples from patients suffering from ovarian cancer. Optimal preprocessing configuration may not always be evident due to the complexity of spectral data. There are numerous options available for background corrections, normalization, outlier removal, noise filtering, and dimension reduction algorithms for Raman spectra. Moreover, hyperparameter tuning is required to detect the best choices for the preprocessing steps. In this work, we present a pipeline to co-optimize preprocessing techniques and ML classification methods to promote objective selection and minimize processing time. In our approach, preprocessing methods are not chosen arbitrarily but rather systematically evaluated to enhance the robustness of the models. These criteria focus on ensuring that the model performs well not only on the training data but also on unseen data, thus reducing the risk of overfitting and improving the generalization capability of the model. This systematic approach would reduce the time for new studies by detecting the most suitable preprocessing steps and hyperparameters needed and building a robust model for the task.
In this work, we aim to develop a virtual platform to compare the performance of the different manifestations of photon Time of Flight Spectroscopy namely Direct, Indirect and Interferometric photon Time of Flight Spectroscopy (pToFS). Extending the comparison over a range of scenarios, defined by a matrix of optical properties (dubbed here as Virtual Tissue), allows for the definition of different use cases for each of these techniques. The effect of parameters like temporal drift, exposure time and background noise will also be studied.
Oral cancer (OC) is one of the most common oral malignancies. Despite significant advances in medical devices, the five-year survival rate of OC remains low. Current technologies based on tissue pathology are insufficient to diagnose OC at early stages. Molecular sensitive technique such as optical spectroscopy, on the other hand, has the potential for early-stage diagnostics and non-invasive tissue interrogation. Raman spectroscopy (RS), for instance, is a powerful vibrational spectroscopy that allows highly sensitive detection of low concentration analytes, as well as molecular fingerprints of bio samples to be studied non-invasively. Additionally, higher spatial resolution, narrow peaks, better sensitivity and minimal sample preparation makes RS a potential tool for analysing oral cancer in a clinical setting. In this study, we will validate the potential of Raman spectroscopy (RS) and surface enhanced Raman spectroscopy (SERS) for oral cancer diagnostics. Patients having biopsy and histopathological examination were involved in this study. Ex vivo measurements were performed on saliva specimen using SERS while in-vivo analysis was performed by RS. Integration of in vivo tissue and ex vivo sample analysis could potentially improve early-stage OC detection, and hence the overall survival rate of OC.
Endoscopes have been widely used for biomedical imaging applications like surgical guidance and diagnosis. In this project, we demonstrated a beam-shaping system to manipulate the illumination patterns at the distal tip of the multimode fiber by using the real-valued intensity transmission matrix of the MMF for endoscopic applications, which provides the potential to miniaturize the footprint of the structured illumination system and the endoscope geometry.
KEYWORDS: Short wave infrared radiation, Hypoxia, Fetus, Near infrared spectroscopy, Spectroscopy, Monte Carlo methods, Reflectivity, Sensors, Reflectance spectroscopy, Photon transport, Near infrared
Intra-partum hypoxia is the principal cause of death for every 2 in 10000 infants. Monitoring hypoxia during child-birth will not only prevent infant mortality, but also help prevent cerebral palsy in 10-20% of the surviving babies. Current monitoring techniques either use an indirect biomarker (heart-rate in cardiotocograph) or measure downstream biomarkers intermittently and invasively (fetal blood sampling). For complete fetal wellbeing monitoring, a continuous non-invasive assessment of multiple biomarkers is needed during birth. To address this gap we are developing a noninvasive, continuous sensor based on long wavelength near infrared (LW-NIR) spectroscopic technique for the detection of fetal hypoxia through multiple biomarkers. For specific hypoxia assessment we have identified key optical spectroscopy compatible biomarkers from a list of various biomarkers effected in the physiological processes leading to the development of hypoxia. The key biomarkers identified are – cytochrome-C oxidase, oxygenated and deoxygenated hemoglobin, lactate, pyruvate and pH in the connective tissue in presence of other interferences such as lipids, proteins and other sugars. To translate these biomarkers into a viable diffuse-reflectance probe we assessed the light-tissue interaction in the low-scattering, water-absorption dominated LW-NIR window of 1350-2500 nm using Monte Carlo photon migration model and experimentally verified the penetration depth achievable in fetal tissue phantom to ~0.5 mm, only targeting the capillary bed.
Oral cancer is one of the most malignant cancers in the world. Early-stage diagnosis of oral cancer is complex process due to the multifocal unspecific development of non-malignant lesions into cancer and impossibility to take biopsy of every lesion. The aim of this study is to develop a screening method for oral cancer diagnosis at early stages using surface enhanced Raman spectroscopy (SERS) and validate the performance of a multimodal system including Raman spectroscopic (RS) and diffuse reflectance spectroscopy (DRS) oral cancer diagnosis and accurate margin detection. The study will involve the identification and integration of spectral biomarkers involved in the carcinogenesis process from different modalities. Each modality SERS, RS and DRS is calibrated and standardized individually. Patients suffering from oral squamous cell carcinoma and other malignant diseases going through biopsy or histopathological examination are enrolled in this study. Ex vivo study involves the SERS analysis of saliva specimen and in vivo analysis will involve measurements on various tissue types, including malignant tissue and healthy contralateral site to evaluate the reproducibility and signal-to-noise ratio using fiber-optic probes for Raman and DRS systems. Feature selection methods and further machine learning tools will be used to discriminate between healthy, benign and cancer lesions based on spectral information and to identify important biomarkers. After data collection, clinician will perform a normal biopsy procedure and histopathological analysis, which will serve as gold standard to determine the sensitivity and specificity of the spectroscopy techniques.
Osteoporosis is a disease that weakens bones increasing the possibility of bone fracture. The gold standard to diagnose osteoporosis is measuring bone mineral density (BMD). Since BMD only partly determines the strength of the bone, more information on chemical composition and microstructure is needed. Here, we implemented a novel dual-wavelength inverse Spatially Offset Raman Spectroscopy (SORS) to characterize tissue chemical composition covering both the fingerprint and high-wavenumber regions. This system provides a greater probing depth keeping the spectrometer setting constant. The results from hydroxyapatite (HA) and water phantom demonstrate the potential of the Raman system to assess bone mineral and matrix quality in-vivo.
The Oral Squamous Cell carcinoma (OSCC) is one of the most common and aggressive oral malignancies. Despite all significant advances in medicine, five-year survival rate is still low. This study aims to develop a full scheme for diagnosing oral cancer in early stages by using Raman spectroscopy. Patients undergoing biopsy or histopathological examination will be enrolled in this study. Ex vivo measurement will be carried out using saliva specimens and in vivo analysis will involve measurements taken on healthy and malignant tissue. In the future, this optical diagnostic approach using Raman spectroscopy and SERS can help in improving diagnostic accuracy and the survival rate by affecting the treatment outcome via early stage detection of oral cancer.
Combined fingerprint and high wavenumber spatially offset Raman spectroscopy was implemented for depth-dependent biochemical characterization. Quantitative spectral analysis of water and other components was conducted in layered optical phantoms.
The ability to use a wide range of wavelengths for deep penetration is important in order to target or avoid absorption bands of the biological media. By utilizing the nonlinear optical effect in the scattering bio-soft-matter, we demonstrate the self-trapping and guiding of light in sheep red blood cell suspensions for a range of different wavelengths. By pump-probe type coupling, biological waveguides formed at one wavelength can effectively guide a wide spectrum of light at low power. Finally, we investigate propagation and guiding of non-Gaussian beams in biological suspensions.
Using light, living cells can be manipulated to form several centimeter long waveguide structures, capable of guiding light through scattering media. Here, we will discuss some results of self-trapping and guiding of light in biological suspensions of different cells, including cyanobacteria, E. coli, and red blood cells. A forward-scattering theoretical model is developed which helps understand the experimental observations. Formed waveguides can provide effective guidance for weaker light through scattered bio-soft-matter. The ability to transmit light through turbid fluids with low loss could open up the possibilities for deep-tissue imaging, as well as noninvasive treatment and diagnostics.
Stimulated Raman scattering (SRS) offers a drastic speed advantage over conventional vibrational spectroscopic imaging techniques – making it ideal for studying fast biochemical dynamics. We developed an experimental paradigm that applies spectral stimulated Raman scattering (SRS) imaging to study the mechanisms of infrared (IR) photostimulation of neuronal cells. Infrared neural stimulation (INS) is a label-free optical neuromodulation technique with high spatial and temporal precision. Using SRS, changes in lipid and water vibrational signatures in live cells during INS were observed, suggesting that lipid membrane deformation accompanies IR exposure. The speeds afforded by SRS enables unprecedented observation of fast cellular biophysical dynamics.
Preterm birth (PTB), when defined as labor before 37 weeks of gestation, affects approximately 1 out of every 10 births in the United States, leading to high rates of mortality. Complete understanding of the mechanism of PTB requires non-invasive, multi-modal techniques that can provide information about the cascade of labor onset. This study compares the cervical remodeling in wild-type term and induced preterm mouse models using Raman spectroscopy. This study demonstrates the potential of Raman spectroscopy as a non-invasive, real-time in-vivo modality to understand cervix remodeling, thus guiding future studies to improve reproductive and neonatal outcomes.
Biological samples often have various absorption bands that need to be either targeted or avoided in opto-fluidic micromanipulation or biomedical imaging. With nonlinear optics, it is possible for light to self-induce a waveguide. However, the desired wavelengths may not be suitable to exhibit nonlinear self-guiding due to the absorption bands or the light-bioparticle interaction is not strong enough. Here we study formation of waveguides in red blood cell suspensions for a range of different wavelengths. We utilize nonlinear optical response for self-trapping of a laser beam, forming light guides in RBCs suspended in a phosphate buffer solution. To improve the number of usable light wavelengths over purely self-guided propagation, we use the master-slave relation, in a manner similar to the pump-probe experiment: a master beam creates a waveguide first in a scattering bio-soft-matter suspension over a few centimeters, and then a “slave” beam uses this waveguide to propagate through the medium. The slave beam, injected simultaneously, has no appreciable nonlinear self-action itself but experiences the master waveguide akin to an optical fiber. This new approach can provide a path to guide a wide range of wavelengths, including those in the absorption bands at lower power so as not to damage the sample. The fact that we can guide a wide range of wavelengths may bring about new applications in medicine and biology, for instance, in developing alternative solutions to transmit energy and information through scattering media, as needed in deep-tissue imaging, treatment and diagnostics.
In the recent past, there have been enormous efforts to understand effect of drugs on human body. Prior to
understand the effect of drugs on human body most of the experiments are carried out on cells or model organisms. Here
we present our study on the effect of chemotherapeutic drugs on cancer cells and the acetaminophen (APAP) induced
hepatotoxicity in mouse model. Histone deacetylase inhibitors (HDIs) have attracted attention as potential drug
molecules for the treatment of cancer. These are the chemotherapeutic drugs which have indirect mechanistic action
against cancer cells via acting against histone deacetylases (HDAC). It has been known that different HDAC enzymes
are over-expressed in various types of cancers for example; HDAC1 is over expressed in prostate, gastric and breast
carcinomas. Therefore, in order to optimise chemotherapy, it is important to determine the efficacy of various classes of
HDAC inhibitor drugs against variety of over-expressed HDAC enzymes. In the present study, FTIR microspectroscopy
has been employed to predict the acetylation and propionylation brought in by HDIs.
The liver plays an important role in cellular metabolism and is highly susceptible to drug toxicity. APAP which
is an analgesic and antipyretic drug is extensively used for therapeutic purposes and has become the most common cause
of acute liver failure (ALF). In the current study, we have focused to understand APAP induced hepatotoxicity using
FTIR microspectroscopy. In the IR spectrum the bands corresponding to glycogen, ester group and were found to be
suitable markers to predict liver injury at early time point (0.5hr) due to APAP both in tissue and serum in comparison to
standard biochemical assays. Our studies show the potential of FTIR spectroscopy as a rapid, sensitive and non invasive
detection technique for future clinical diagnosis.
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