Kidney stones are a global problem that cause physical pain and may lead to chronic kidney disease. Recent statistics indicate the incidence of kidney stones is increasing worldwide, and usually varies from 2 to 20% depending on countries¹ and especially on diabetes or obesity incidence in such countries. Intra-operative (i.e. in vivo) characterization of kidney stones is at stake for a better diagnostic management of patients. Such a goal could be achieved by optical methods. The current study aims at evaluating if absorption and scattering coefficients measurements combined to automatic classification based on machine-learning methods could be of interest in assisting urologists with kidney stones characterization. Absorption and scattering coefficients were measured using the inverse adding doubling method (IAD). This method based on solving inverse problem takes as input data measurements acquired on a double integrating spheres optical bench developed in the CRAN laboratory. The dataset is made of absorption and scattering coefficients measured every 10 nm from 535 to 845 nm on 16 kidney stones: 4 kidney stones in each diagnostic class under consideration (1a, 3a, 4c and 5a). Class 3a (5a respectively) kidney stones display the highest (lowest resp.) absorption and scattering coefficients: 3 and 30 mm^-1 (1 and 10 mm^-1 respectively) at 650 nm. Support-vector machine (SVM) and k-nearest neighbors (k-NN) methods were used to perform automatic classification: k-NN reached 98%-accuracy in the four-class confusion matrix when considering both absorption and scattering coefficients. Although a high intra-class variability was observed and may be seen as the main limitation of the study, this good classification rate is worth taking into account to keep on investigating this method on more kidney stones per class as a potential tool for diagnostic assistance for urologists.

The present work shows design, development, and testing of the multimodal optical setup for simultaneous auto-fluorescence imaging, spectroscopy and quantitative phase microscopy (SAF-QPM) from same field of view (FOV). The SAF-QPM combines the capabilities of autofluorescence, offering molecular insights into metabolic activities, with quantitative phase imaging, providing nanometres-level sensitivity to cellular morphology and refractive index distribution. The autofluorescence serves as a biomarker for abnormal cellular changes associated with cancer development. Simultaneous incorporation of quantitative phase microscopy of the same tissue section probes the refractive index-dependent changes in the cellular morphology associated with cancer development. Non-interferometric QPI technique is used to retrieve the phase information of the sample, which overcomes the limitations of instability and coherent noise associated with interferometric QPI techniques. The incorporation of simultaneous AF and QPM from the same field of view enhances the accuracy and specificity of label-free cancer diagnosis.

Non-invasive pulse wave monitoring is now widely used in health and wellness applications. Very often, the use of this measurement to trace physiological parameters is indirect. We propose here a bench for synchronized acquisition of optical and ultrasound signals at high rates (over 100 Hz). Optical acquisition is performed with an in-house system enabling us to perform multispectral PPG. We report here the description of the bench, the performances obtained, and the first bimodal acquisitions made on dynamic phantoms.

Pancreatic ductal adenocarcinoma (PDAC) ranks among the malignancies with the highest fatality and morbidity rates. This is predominantly attributable to an absence of understanding the intricate and diverse microenvironment of the tumor. We use terahertz time-domain spectroscopy (THz-TDS) imaging in transmission geometry to probe ex-vivo the heterogenous microenvironment of the genetically modified murine PDAC tissue that closely resembles the PDAC heterogeneity in human malignancy. We introduced a maximum a-posteriori probability estimation algorithm to objectively the tumor’s heterogenous microenvironment using the average values of refractive index and absorption coefficient within the useable terahertz bandwidth as imaging markers. Direct comparison of stained histopathologic images and the refractive index and the absorption coefficient high-resolution, two-dimensional maps of the same PDAC samples confirms the high potential of the THz-TDS method for tumor tissue characterization.

Each year, about 30% of all newly diagnosed cancer cases in women worldwide are breast cancers [1]. One of the most common techniques for breast cancer diagnosis is mammography. However, this technique provides limited functional information regarding breast tissue morphology. In cases of suspected malignancy invasive techniques such as biopsy are implemented.

In this work an optical deep tissue imaging technique called ultrasound optical tomography (UOT) which combines laser light and ultrasound is implemented for a non-invasive lesion (tumour) characterization in breast tissue.

The experiments were performed using 794 nm laser wavelength, 6 MHz ultrasound frequency and a narrowband spectral filter material, Tm³⁺:LiNbO₃. The measurements were carried out in 5 cm thick agar phantoms using a range of tumor mimicking inclusions of 3 different sizes.

This work is the first deep tissue imaging demonstration using UOT at tissue relevant wavelengths. Current results indicate that the UOT technique can become an important and valuable tool for lesion characterization in breast tissue.

Perceiving the depth of flat and thin adenomas and the extent of invasion is vital in staging, diagnosis, treatment planning, and surgical precision. Raman spectroscopy and its spatially offset version have demonstrated excellent specificity in tissue classification and tumor margin analysis using label-free methods. In this work, we utilize fiber-optic Raman spectroscopy to simultaneously predict the depth and thickness of thin sub-surface tumors using tissue-like multi-layer phantoms. Silicone phantoms incorporating Hydroxyapatite distribution are used as a Raman scatterer to indicate malignant calcifications. The signal intensity for varied tumor depths and thicknesses is also numerically simulated and corroborated with experiments. The high-wavenumber Raman spectrum is captured using a fiber-optic low-resolution spectrometer with an excitation wavelength of 660 nm. The tumor's depth and thickness range from 0.5 mm to 2 mm in 0.5 mm increments. Partial Least Squares Regression (PLSR) analysis is carried out on the acquired dataset for predicting the tumor depth and thickness with a Root Mean Square Error (RMSE) of 0.268 mm (36.33%) and 0.120 mm (12.89%), respectively.

Biophysical properties of living cells largely determine their vital activity and functionality. In cancer, the physical state of the plasma membrane of cells is important for the invasion and metastasis. Cellular-scale viscoelasticity affects cell morphology, motility, interaction with the extracellular matrix, and resistance to mechanical stress. However, the links between membrane fluidity and cellular mechanics are poorly understood. Here, we present the in vitro study of microviscosity and viscoelastic properties of colorectal cancer cells. Measuring microviscosity of membranes at the micrometer scale was performed using fluorescence lifetime imaging microscopy FLIM with a viscosity sensitive probe. Atomic force microscopy AFM was used to evaluate the mechanical properties of cells. Additionally, the lipid profile of cells plasma membranes was analyzed using time-of-flight secondary ion mass spectrometry. A good positive correlation was found between cell stiffness (the Young’s modulus) and the plasma membrane microviscosity of cancer cells. Of the five cell lines, HT29 cells, which has an epithelial phenotype, had the most fluid membranes and the lowest stiffness values; the highest microviscosity and stiffness values were recorded for the SW480 cell line, which is characterized by a mesenchymal phenotype. The obtained results indicate that cell biomechanics is determined by the two sets of parameters that are interconnected in tumor cells and are involved in their migratory behavior.

Photobiomodulation (PBM) utilizes light to stimulate cellular responses in medical and therapeutic fields. This study investigates how light probe design influences tissue penetration. Factors like spot size, geometry, and materials were explored. Monte Carlo simulations and experiments were performed in tissue phantoms and volunteers. Results reveal how probe design optimization can enhance PBM's safety and efficacy, providing insights for personalized treatment protocols. Understanding light interactions in biological tissues is vital for tailoring therapy to individual patients and specific equipment.

Optically induced hyperthermia is an actively developing approach to treating cancer. All-dielectric nanoparticles have established themselves in different biomedical applications, including optical heating and nanothermometry. However, this type of nanoparticles (NPs) do not provide sufficient heating due to the necessity for a narrow size distribution. Thus, size-separation is required. Other method of negating disadvantages of all-dielectric NPs is incorporating plasmonic nanoparticles to create hybrid nanostructure, which would be less sensitive to size distribution, making it great nanoheater and nanothermometer. In this work, we propose a simple way of fabricating hybrid silicon-gold (Si-Au) NPs. We compare hybrid nanoparticles with pristine monodisperse Si NPs. In addition, we perform optical heating and simultaneous nanothermometry inside and outside living B16-F10 melanoma cells. Results reveal, that the hybrid NPs are more efficient in biological environments, since inhomogeneous medium can make it difficult to fulfill the critical coupling conditions.

Laser safety calculations for optical systems are often based on the assumption of ideal symmetrical retinal images or exposure scenarios. The laser safety standard IEC 60825-1:2014 uses exposure limits from the International Commission on Non-Ionizing Radiation Protection (ICNIRP) guidelines, which refer to symmetrical spots. Further, the standard is premised on retinal damage thresholds of symmetrical exposure scenarios, which are empirically determined in laser damage experiments or simulations. In reality, retinal images of laser systems feature aberration-afflicted, asymmetrical retinal geometries, for example resulting from optical aberrations such as coma and astigmatism. Especially for these asymmetrical retinal exposure scenarios, the laser safety standard ensures that the optical systems are eye-safe by specifying a symmetrization of the retinal image, emission limits and safety factors. In terms of safety and performance of the laser systems, it is particularly important to directly consider asymmetrical retinal images and therefore be able to assess the size of the safety factors. For this consideration, a computer model is recommended, which is an eye and thermal simulation model, handles asymmetrical retinal images and calculates damage thresholds of these exposure scenarios.

A computer model for symmetrical retinal geometries exists, which is validated on experimental data of nonhuman primates (NHP) and uses a finite element method (FEM) simulation to solve the heat transfer equation. Further, it is also used to calculate retinal damage thresholds by inserting the temperature behavior into the Arrhenius equation. The focus of the work presented here is the extension and further development of the computer model and elaborates the difficulties to simulate retinal damage thresholds of asymmetrical exposure scenarios. In particular, the extension of this computer model to asymmetrical retinal images while maintaining validation is addressed. An exemplary case of an asymmetrical retinal image is calculated with the model and the results are presented.

Green synthesis of nanoparticles is widely accepted and appreciated mainly due to non-toxicity and excellent biocompatibility. We report a simple and cost-effective microwave-assisted approach for the fabrication of carbon nanoparticles (NPs) from petal extracts of white and pink Catharanthus roseus (commonly known as Baramasi), hence addressing them as BWNPs and BPNPs respectively. Both procedures resulted giving an average size of 22nm. They both exhibit green fluorescence under exposure to ultraviolet light, giving an absorption peak at 260nm. The topographic details were recorded using Atomic force microscopy, and the quasi-spherical shape and sizes were confirmed using scanning electron microscopy. Various functional groups were identified using Fourier transform infrared spectroscopy, and structural features along with crystallinity were investigated using X-ray diffraction. A time-efficient approach has been highlighted in this paper with the usage of a single solvent. These NPs can be potentially targeted for biomedical and plant health applications.

Transcranial photobiomodulation (tPBM) has emerged as a promising economical point-of-care tool to enhance mitochondrial dynamics, mitigate neuroinflammation, improve sleep and cognitive functions in various CNS disorders. Its propensity to modulate cerebrovascular tone can potentially alter cerebral hemodynamics. We set out to investigate whether tPBM can influence the brain oxygenation as assessed by fNIRS in healthy subjects with a body positional challenge.

Photopletysmographic methods for cardiovascular parameters monitoring have been extensively investigated. Yet, there is still a lack of consensus on the signals origins and sensors are designed based on qualitative intuitions. We present the radiometric calibration of a versatile optical workbench that allows quantitative multi-spectral reflectance measurements on several distances source/detector simultaneously. We propose here a method to retrieve both incident and outgoing radiant fluxes on the studied medium that paves the way for quantitative diffuse optics on-vivo. Compared to existing designs and methods, we believe our protocol matches well the needs of modern simulation tools dedicated to sensor designs.

Neurosurgical treatment is the primary approach for brain cancer, particularly gliomas, posing challenges due to their invasiveness and the imperative to maintain neurological function. Precise delineation of tumor margins becomes crucial to prevent neurological deficits and improve prognosis in neuro-oncological surgery. Intraoperative tumor border visualisation during neurosurgery finds a promising solution in imaging Mueller polarimetry. The development of tumor segmentation algorithms using polarimetric data requires a large and curated database of polarimetric measurement associated with the co-registered ground truth. We developed a neuropathology protocol to gather both histological and polarimetric data. Moreover, we implemented an image processing pipeline to obtain a precise mapping between histological and polarimetric data, allowing histological data to serve as a reliable ground truth for tissue characterisation. However, the histological processing steps, such as the freezing, cryosectioning and thawing of the samples, might alter the tissue microstructure and the polarimetric parameters of brain tissue. In this study, we extend the description of the neuropathology protocol by analysing the effect of the histological processing steps on the polarimetric properties of fresh thick brain specimens. We evaluated and compared polarimetric properties of fresh healthy and neoplastic brain tissue before and after applying the histological processing steps. We found a moderate effect of the latter on the polarimetric properties of both brain tissue types. The contrast in polarimetric parameters observed between different brain tissue types is conserved, as well as the ability to perform fiber tracking. Thus, the protocol facilitates a database of co-registered histological and polarimetric data.

In this study, we applied Multispectral Mueller Matrix Imaging (MMI) at six distinct wavelengths in the visible range to analyze brain structures using lamb cerebral samples. The imaging of several brain sections revealed that white matter (WM) exhibits pronounced depolarization and retardance when contrasted with grey matter (GM), a phenomenon likely attributed to the elevated scattering and anisotropic nature of WM. More precisely, with an increase in wavelength, both depolarization and retardance also increase, suggesting additional penetration into deeper tissue layers. Employing various wavelengths enabled us to trace the shifts in the optical axis of retardance within the brain tissue, offering insights into the morphological changes in WM and GM below the cortical surface. The consistency observed in our results highlights the promise of Multispectral Wide-Field MMI as a non-intrusive, efficacious modality for probing brain architecture.

Investigating optical properties (OPs) is crucial in the field of biophotonics. Various techniques are available for deriving OPs, with inverse Monte Carlo simulations (IMCS) being the most advanced for ex-vivo contexts. However, identifying the spectral behavior of each microscopic absorber and scatterer responsible for generating these OPs requires further experimentation. To tackle this issue, a customized autoencoder neural network (ANN) is suggested. The ANN computes OPs from measurements, where the bottleneck corresponds to the number of absorbers and scatterers. The presented ANN functions asymmetrically and computes the final OPs using a linear combination of absorbers and scatterers. Consequently, the decoder’s weight corresponds to the constituent’s OPs spectral behavior. Validation was conducted by utilizing intralipid as a scatterer and ink as an absorber. The employment of the decoder weights facilitated the successful extraction of the spectral shape of every constituent.

Fluorescence spectroscopy is a technique proposed to improve the detection of tumor boundaries during fluorescence-guided neurosurgery. More sensitive than fluorescence microscopy, this technique can provide spectral measurements of fluorescence that are post-processed to extract qualitative or even quantitative information on fluorophores present in the tissue probed. To obtain quantitative information, this technique requires modeling the propagation of radiation in the brain, considering the optical properties of the tissue as well as the fluorescence phenomenon. The present work is devoted to the development and application of Monte Carlo methods including the fluorescence phenomenon, enabling us to study the effects of optical properties on the measured signal and, consequently, on biomarker quantification. Symbolic Monte Carlo method based on orthogonal polynomial sequences is developed to express a physical observable as a function of the fluorophore concentration in a single simulation. The results obtained for the case of a brain composed of grey matter and different fluorophore concentrations are studied and show good agreement with standard Monte Carlo approaches.

Functional near-infrared spectroscopy (fNIRS) presents an affordable and light-weight method to monitor the cerebral hemodynamics of the brain. However, noise and artefacts hamper the analysis of fNIRS signals. Thus, the signal quality assessment is a crucial step when planning fNIRS experiments. Currently no standardized method exists for the evaluation. Commonly used visual inspection of the signals is time consuming and prone to subjective bias. Recently use of machine learning and deep learning approaches have been applied for the fNIRS signal quality assessment, showing promising results. However, currently there are only a few experiments which have investigated the use of these approaches to evaluate fNIRS signal quality. In this human brain study, we utilized previously developed deep learning approach used for the assessment of PPG signal quality with short-time Fourier transform (STFT) to evaluate the quality of raw fNIRS signals with wavelengths 690 nm, 810 nm, 830 nm and 980 nm. The data was collected from 38 subjects with a two-channel fNIRS device, measured during breath hold protocol in sitting position. A total of 10,144 segments were extracted using a window of 10 seconds length without overlap and annotated for SQA by three independent evaluators. The segments were transformed with STFT, and further processed into 2D images. The images were used as input data for CNN deep learning network, and the output further used to classify the segments as acceptable or unacceptable. The results show high potential of using DL approach for fNIRS signal quality assessment with classification accuracy of 87.89 %.

Colorectal cancer is the second most common cancer and the second with the highest associated deaths in the world. Methods used in clinical practice for colon cancer diagnosis are fairly effective but quite unpleasant and not always applicable in situations where the patient has symptoms of colonic obstruction. This problem can be solved by the use of optical methods that can be applied less invasively.

This study presents the results of classification of cancerous and healthy colon tissue absorption coefficient spectra. The absorption coefficient was measured using direct calculations from the total reflectance and total transmittance spectra obtained ex vivo. Classification was performed using support vector machine, multilayer perceptron and linear discriminant analysis.

Optical imaging is a marker-free, contactless, and non-invasive technique that is able to monitor hemodynamic and metabolic brain response following neuronal activation during neurosurgery. However, a robust quantification is complicated to perform during neurosurgery due to the critical context of the operating room, which makes the calibration and adjustment of optical devices more complex. To overcome this issue, tissue-simulating objects that mimic the properties of biological tissues are required for the development of detection or diagnostic imaging systems. In this study, we developed a digital instrument simulator to optimize the development of a novel hyperspectral system for application in brain/cortex imaging. This digital phantom is based on white Monte Carlo simulations of the light propagation in tissues. The output of the Monte Carlo simulations are integrated with the key instrument parameters in order to produce realistic images. The results can be beneficial and useful within the framework of our EU-funded HyperProbe project, which aims at transforming neuronavigation during glioma resection using novel hyperspectral imaging technology.

In the field of Biomedical Optics, understanding how transparent layers affect light propagation in tissue is crucial for optimizing therapeutic and diagnostic applications. We employed Monte Carlo simulation to analyze changes in light behavior when introducing a transparent layer onto a multilayer optical skin phantom. Our study revealed that adding a transparent layer alters the illuminated volume, with changes influenced by the refractive index and thickness of the layer. This insight emphasizes the significance of both composition and thickness of transparent materials in influencing light propagation within the skin model. Such knowledge is fundamental for improving light-based therapies and diagnostics in Biomedical Optics.

Photoplethysmography (PPG) is a high-speed optical measurement technique that resolves fast physiological phenomena and offers promising applications in the medical field. In order to have a better understanding of the cardiovascular dynamics through the PPG signal, we propose a comprehensive multilayered and optical biophysical skin model from the wrist that is able to simulate some characteristic physiological properties and the complex cardiovascular dynamics. Thanks to this dynamic optical model, we are able to identify and disentangle the microvascular, arterial and venous contributions retrieved from the PPG signal signature.

Epithelial cancers, constituting the majority of human cancer cases, can be identified by alterations in the biochemical and morphological characteristics of the thin epithelial layer (ranging from 100 μm to 500 μm), serving as an initial indication of the disease. Many researchers have utilised spatially resolved fiber optic probes and fluorescence spectroscopy technique to detect subtle variations in the optical properties of the epithelium layer of tissue. This study explores the impact of the incident and different collection configurations on epithelium layer sensitivity for spatially resolved fluorescence. Monte Carlo simulation reveals that a fiber probe with illumination-collection at 45-degree beveled angle in parallel configuration provides maximum fluorescence from the epithelium layer. This configuration is suitable for both in vitro and in vivo settings for epithelial precancer diagnosis. The efficacy of the 45-degree beveled angle fiber probe for measuring spatially resolved sensitivity has also been validated experimentally using two layer solid tissue-mimicking phantoms which demonstrates strong agreement with the results generated from Monte Carlo simulation. These findings suggest that employing an optimum source detector configuration enables the collection of enhanced spatially resolved fluorescence from the epithelium layer.

Multiple Laser Speckle contrast Imaging (MESI) is an imaging method that provides relative blood flow maps from the statistical analysis of the dynamic speckle patterns observed when a coherent source is used to illuminate a tissue that contains moving scatterers. The gold standard analysis of MESI data is done by pixelwise regression of the experimental images to a theoretical function of the contrast K as a function of the exposure time T and decorrelation time τc. This approach is computer intensive, and the duration required to obtain a single flow map is too long for "real-time" analysis of in vivo hemodynamics. In addition, the mathematical model used relies on assumptions that oversimplify the local flow within the object of study. We have evaluated as an alternative a method based on Convolutional Neural Networks (CNN) to directly infer blood flow maps from MESI data, bypassing the model based fitting procedure. The CNN approach is model-free and delivers blood flow maps several orders of magnitude faster than the classical pixelwise non-linear regression. Here, we have evaluated two different datasets of annotated speckle contrast images to train the neural networks. One is composed of simulated time integrated speckle while the other one is composed of experimental data acquired for microfluidic channels with controlled geometries and flows. The study aims at discussing the assets and limits of both approaches.

Tumor vasculature plays an essential role in tumor growth and is a potential target for cancer treatment. Monitoring the vasculature during tumor growth, disease progression, and after treatment (e.g., radiotherapy and gene electrotransfer (GET)) could provide valuable diagnostic information and improve knowledge of tumors and their microenvironment. Moreover, it could provide predictive information for tumor treatment and improve therapeutic outcomes. This study combined hyperspectral imaging (HSI) with laser speckle contrast imaging (LSCI) to monitor 4T1 murine mammary carcinomas grown subcutaneously in dorsal skinfold window chambers (DSWCs) over 14 days. Specifically, we utilized a custom-built HSI system with a spectral range of 400–1000 nm and an LSCI system with a 650 nm laser. Using LSCI, we monitored the blood flow in blood vessels and tissue perfusion, while HSI enabled us to detect tumor margins and track oxygenation changes during tumor growth and after electroporation-based therapy. Our findings indicate an immediate >70% reduction in blood flow within tumor vessels after the GET procedure, which could be attributed to vasoconstriction induced by the electrical pulses. Additionally, the overall tumor perfusion decreased by at least 30% post-treatment and gradually increased in the following days. In contrast, a control tumor that received no treatment exhibited a substantial increase in blood flow, possibly linked to an elevated need for oxygen and nutrients due to tumor progression. Our study demonstrates that the combined HSI and LSCI optical imaging techniques effectively monitor blood flow, tumor perfusion, and oxygenation alterations within tumor vessels following electroporation-based therapy.

Elasticity of blood vessels makes them contract or dilate in regulating the body temperature to changes in external temperature changes such as air-conditioning. However, ageing makes them gradually lose elasticity, making it difficult for blood vessels to make adjustments. In this study, we propose the use of biospeckle Optical Coherence Tomography (b-OCT) to visualize the dynamic changes within the skin. A total of 20 subjects with equal number of male and female particpants with ten in their 20’s and the other ten subjects in their 30’s or older were subjected to heating of the palmar forearm of their dominant hand by a USB hot pad (40°C) for five minutes. A swept source OCT (SS-OCT) operating with a central wavelength of 1310nm, a bandwidth of 125 nm and a sweep frequency of 20kHz was used to obtain OCT structural images at 12fps. From the OCT structural images obtained before and after heating, biospeckle contrast was calculated from the temporal variation in the images and compared. Biospeckle contrast results were compared for the depth, gender, and age differences. With heating, a clear difference of increased contrast was observed at shallower depths in comparison to deeper regions for both genders, while, as a whole, a larger contrast difference was observed for male in comparison to female participants. Furthermore, in the age group larger than 30, the contrast change with change in environment was smaller, suggesting the loss of elasticity to adjust to the environmental changes.

Recent advancements in molecular biology have facilitated the routine engineering of artificial human tissues. Typically, DLP bioprinters are utilized for creating 3D matrices that incorporate either specific cell types or transient spheroids/organoids. However, engineering complex neuronal or innervated tissue models necessitates the use of high-precision femtosecond (fs) laser-printing technology, offering nano and micrometer resolutions. Despite the prevalent use of hi-PSC-derived cell lines for human model engineering, the complexity and cost remain significant challenges. In response, we explored the efficacy of a calcium imaging for the non-destructive functional assessment of 3D neuronal networks derived from neural progenitors, focusing on their differentiation into functionally active, post-mitotic neurons. Furthermore, we developed a custom-built dual-mode fluorescence spectroscopy (FS) and Optical Coherence Tomography (OCT) system for evaluating the metabolism and morphology of full-thickness skin equivalents (FSE) cultivated on laser-printed 3D scaffolds. Our findings demonstrate that the integration of calcium and dual-mode FS-OCT systems enables the comprehensive monitoring of functionality, morphology, and metabolism in developing human brain-like and FSE models. Consequently, 3D laser-printed scaffolds, when combined with these innovative monitoring technologies, offer a feasible and efficient approach to engineering human tissue models.

Nanoparticles (NPs) have become more prevalent in the agricultural, industrial, and medicinal fields. There is rising interest in how nanomaterials interact with plants since they influence plants and seeds differently depending on their size, shape, and dose. The techniques to monitor the response of plants to NPs are crucial since the effects of nanomaterials on seed germination and plant growth are uncertain. In this study, a highly sensitive, real-time, non-invasive novel technique called Biospeckle optical coherence tomography (bOCT) is used to examine the size-dependent impact of metal oxides NPs and microparticles (MPs) like Zinc Oxide (ZnO) with a size less than 50 nm, and 45μm and Titanium dioxide (TiO₂) with a size 21 nm and <5μm at concentrations of 25mg/L and 100mg/L on the internal activity of lentil seeds before germination. The results showed that ZnO NPs had an adverse effect at both higher and lower concentrations on the internal activity of lentil seeds, while MPs of 45μm had significantly positive effects even with higher concentrations. However, TiO₂ MPs and NPs showed a significant positive effect on Lentil seed’s internal activity at both concentrations in comparison to control. The proposed method was able to detect the response of Lentil seed’s internal activities to different concentrations and sizes of metal oxides NPs and MPs at an early stage just after 5 hours of exposure before the germination. On the other hand, the conventional physiological methods required a week for the effects to be detected, and the results from bOCT after 5 hours were consistent with those obtained by conventional measures. Because of the non-invasive nature of bOCT, requiring only tens of seconds of measurement with an intact. Furthermore, the technique is capable of monitoring internal biological activities while the conventional OCT monitors just structural images. It has not only the potential to screen but could also constantly monitor long-term changes, thus contributing to the study of the effects of nanomaterials on plants.

Optical imaging is the simplest modality for any imaging applications. Also, microscopy has the best resolution among all the imaging modalities used for clinical diagnosis and biomedical research. However, detection of inclusions by optical imaging in a biological tissue might be challenging because of the scattering and absorption inside the tissue. Due to the scattering, an optical image of an object inside a turbid medium yields a scattering-induced blur, which increases with the depth of the object from the surface of the medium. This correlation has been shown in literature to be used for depth estimation from single trans-illumination images. In a similar way, the scattering-blur also changes while varying the focal plane of the imaging system. This gradual change in the contrast can be utilised to determine the actual position of the object from a stack of multiple focus-shifted images. Here, we present a proof of concept of a deep-learning based method for determining the location of objects inside turbid media, from a stack of blurred images. Since trans-illumination is not applicable for large body-parts, epi-illumination setup was used for imaging. For this preliminary study, a U-Net neural network regression model was trained and tested under simplified conditions. It takes 3D image data as input and gives a 2D depth matrix as output. Black PU structures of simple geometrical shapes were used as absorbing objects. An intralipid solution of 0.7% concentration was used as the scattering substrate. The black absorbers, immersed in the substrate, were imaged with microscope by varying the focal plane to obtain the image-stacks. The actual depth profile of the absorbers was measured with a 3D profilometer, which was used as corresponding ground truth for training. The predicted results from the trained model show good agreement with the ground truth for testing data.

Information on skin phototype and ages is of cosmetic and medical interest in some procedures like objective evaluation of cosmetic treatments effectiveness, laser wavelength choice, risk of skin cancer recurrence and skin evaluation before cosmetic surgeries. Phototype may be evaluated using the Fitzpatrick questionnaire whose results are impaired by patients’ subjective answers; melanometers may be used but are not always available in dermatology practice. Tewameter, corneometer or cutometer are used to evaluate skin features that may be related to skin age but they lack evaluation of skin internal structure directly related to skin age (fibrosis, elastosis, etc.). Optical spectroscopy combining autofluorescence (AF) and diffuse reflectance (DR) may be a promising and non-invasive alternative to these tests.

In the current study, a bimodal spectroscopic device was used to obtain in vivo spatially resolved AF and DR spectra of skin in the visible range. Five LEDs featuring wavelength peaks at 365, 385, 395, 400 and 415 nm and a xenon lamp featuring a 350-800 nm spectral emission were used as light sources. Four source-detector separation (SDS) were used: 400, 600, 800, and 1000 μm.

Spectra were taken in different anatomical sites on 131 patients of different age and gender during a clinical study. Spectra were analysed using classification (support vector machine and multilayer perceptron) and regression (multilayer perceptron, linear, kernel ridge and Lasso) methods. Results of skin phototype and age estimation from AF and DR spectra obtained in vivo using machine learning methods will be presented and discussed.

Introduction: Presently, the diagnosis of cataracts relies upon the utilization of a slit lamp. This method of examination is inherently subjective, contingent upon the level of expertise possessed by the attending ophthalmologist. That is why our aim was to evaluate lens thickness parameter’s utility for cataract diagnoses. Method: We investigated 1750 patients (age 40 till 75) which were separated in two groups: cataract patients (n=750) and control group (n = 1000). The lens thickness parameter (LT) and anterior chamber depth (ACD) were measured with IOLMaster^® 700 (Zeiss) and analyzes with ANOVA test, The IOLMaster^® 700 is a frequently used device which is utilized for intraocular lens (IOL) measurements. Results: Overall results demonstrates that the cataract groups lens thickness parameter (mean 4.89 ± 0.11mm) was greater than control groups (mean 4.69 ± 0.17mm) and there was a statistically significance between the results (p = 0,01). Control groups ACD results showed similarities with cortical cataract results and there were overlapping (p = 0.15). Conclusion: Lens thickness parameter can be used as an additional criterion to differentiate cataract diagnoses.

The capabilities of ultrasound optical tomography (UOT) is investigated through Monte Carlo simulations on realistic breast tissue phantoms constructed using the OpenVCT platform. This work indicates that UOT is a method capable of distinguishing malignant tumor tissue from benign glandular tissue deep inside the breast, despite the natural variations of adipose and glandular compartments within a breast or between breasts with different volume breast densities.

Fundus imaging is a great tool for the detection of diabetic retinopathy; however, it often suffers from poor image quality and fails to show the vascular information which is crucial for precise diagnosis. Photoacoustic (PA) imaging is a recently developed non-invasive bioimaging technique that illuminates tissues using nanosecond laser pulses to generate acoustic waves to obtain deep tissue images with optical imaging resolution. In this study, we synthesize PA images from normal and abnormal (glaucoma-affected) retinal fundus images. One of the major limitations of synthetic vascular PA images is noise. To alleviate this problem, we propose to use a dictionary learning-based denoising technique i.e., the K-Singular Value Decomposition (K-SVD). Results are compared with several standard denoising approaches such as the Median filter, Jerman filter, and Frangi filter together with the other learning-based approaches, e.g., orthogonal matching pursuit (OMP), and sequential generalized K-means algorithms (SGK). Our results demonstrate that the K-SVD denoising method exhibits superior performance in denoising glaucoma-affected abnormal retina PA images and normal retina PA images, offering better reconstruction image quality and noise removal.

Skin cancer, one of the most prevalent forms of cancer worldwide, poses a significant health threat, emphasizing the need for accurate and efficient diagnostic tools. Fourier Domain Optical Coherence Tomography (FD-OCT) has emerged as a non-invasive imaging technique, offering high-resolution visualization of skin tissue structures for early detection and precise characterization of skin cancer lesions. Our study focuses on the modeling of FD-OCT for skin cancer diagnosis, highlighting its potential to improve diagnostic precision and treatment outcomes. We have simulated the light-tissue interactions and employed sophisticated Fourier domain signal processing models that reconstruct detailed cross-sectional skin images with enhanced spatial resolution. This proposed approach can accurately detect minute fluctuations in tissue morphology, thereby assisting in the detection of critical diagnostic indicators for various forms of skin cancer. FD-OCT enhances the ability to observe cellular and morphological intricacies, allowing for precise differentiation of benign from malignant skin lesions through the examination of characteristics including epidermal thickness, integrity of the dermal-epidermal junction, and the existence of aberrant structures beneath the skin layers. Real-time evaluation is made possible by the non-invasive nature of the proposed FD-OCT imaging, which eliminates the need for invasive biopsies and reduces patient distress. Our findings underscored the potential of FD-OCT as an early-stage skin cancer detection tool, facilitating timely treatment strategies and contributing to improved patient prognosis and survival rates. In conclusion, FD-OCT offers a promising avenue for enhancing the accuracy, efficiency, and accessibility of skin cancer diagnosis, emphasizing its crucial role in advancing personalized and effective dermatological care.

Photodynamic therapy is an effective modality for treating advanced melanoma. However, melanoma's inherent resistance to laser radiation hinders its widespread clinical application. The near-infrared laser radiation range of 1264-1270 nm offers unique properties: firstly, its ability to penetrate melanin-producing cells, and secondly, its capability to generate singlet oxygen without xenobiotics. We assess the impact of continuous wave 1265 nm laser radiation on an antioxidant defense system in melanoma B16-F10 and normal CHO-K1 cells. We observe a time-dependent increase in superoxide dismutase and glutathione-S-transferase activities, fluctuations in reduced glutathione levels, as well as a simultaneous increase in melanoma cell proliferation and cell death. We hypothesize that the differential activation of cellular antioxidant defense mechanisms contributes to melanoma cells' resilience to laser radiation.