This PDF file contains the front matter associated with SPIE Proceedings Volume 13006, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

Accelerating the measurement for discrimination of samples, such as classification of cell phenotype, is crucial when faced with significant time and cost constraints. Spontaneous Raman microscopy offers label-free, rich chemical information but suffers from long acquisition time due to extremely small scattering cross-sections. One possible approach to accelerate the measurement is by measuring necessary parts with a suitable number of illumination points. However, how to design these points during measurement remains a challenge. To address this, we developed an imaging technique based on a reinforcement learning in machine learning (ML). This ML approach adaptively feeds back “optimal” illumination pattern during the measurement to detect the existence of specific characteristics of interest, allowing faster measurements while guaranteeing discrimination accuracy. Here accurate discrimination means that a user can determine an allowance error rate δ a priori to ensure that the diagnosis can be accurately accomplished with probability greater than (1 − δ) × 100%. We present our algorithm and our simulation studies using Raman images in the diagnosis of follicular thyroid carcinoma, and show that this protocol can accelerate in speedy and accurate diagnoses faster than the point scanning Raman microscopy that requires the full detailed scanning over all pixels. Given a descriptor based on Raman signals to quantify the degree of the predefined quantity to be evaluated, e.g., the degree of cancers, anomaly or defects of materials, the on-the-fly Raman image microscopy evaluates the upper and lower confidence bounds in addition to the sample average of that quantity based on finite point illuminations, and then the bandit algorithm feedbacks the desired illumination pattern to accelerate the detection of the anomaly, during the measurement to the microscope. Several updated realizations of the programmable illumination microscope using a spatial light modulator and line illumination will be presented.

The current methodology used to assess a drug’s potential to cause severe liver injuries is rather exhaustive and complex. As a result, such injuries are often overlooked during the drug development process, which makes them one of the leading causes of post-market drug withdrawals and restricted use guidelines of drugs. High-content and continuous screening of liver cells with Raman spectroscopy, a non-destructive and label-free technique, presents a promising approach to identify such pathological conditions early in the drug development process. This research investigates the potential of Raman spectroscopy to identify the progression of liver fibrosis, a disease characterized by the excessive formation of scar tissue resulting in liver functions impairment. The main scar-forming cell type in this pathological process is the hepatic stellate cell (HSC). In healthy conditions, those cells retain 80 % of the retinol present in the human body. However, upon liver injury, those HSCs activate, lose their retinoid content, and synthesize an excess of extracellular matrix leading to scar tissue formation. To evaluate HSC activation by Raman spectroscopy, freshly isolated mouse HSCs were cultured in 2D on fused silica substrates for 2, 5 and 10 days. By recording several Raman maps of those cultures, we demonstrated the disappearance of the characteristic retinol peaks upon activation, enabling discrimination between quiescent HSCs, semi-activated and fully activated HSCs using principal component analysis and a standard K-nearest neighbor classifier. This shows that Raman spectroscopy holds promise in the early detection of liver toxicity during drug development by efficiently identifying HSC activation.

Hemoglobinopathies are among the most common inherited diseases worldwide, affecting approximately 7% of the global population. Despite advances in the standardization and harmonization of methods for HbA1c determination, an increasing number of hemoglobinopathies cause false HbA1c results. One of the common techniques for screening hemoglobinopathies is through high-performance liquid chromatography (HPLC) separation, followed by UV–VIS detection. Although UV–VIS can quantify the hemoglobin fractions, it is unable to identify them. In this study, we use Raman spectroscopy to study the fingerprint spectra of hemoglobin fractions based on which the fractions can be identified. To evaluate the potential of Raman spectroscopy in identifying these fractions, we utilize a range of commercially available hemoglobin fractions, including fetal hemoglobin. We automate the classification process with machine learning approaches such as support vector machines (SVM), fully connected neural networks (NN), k-Nearest Neighbors (KNN), Decision Trees (DT), and Bernoulli Naive Bayes (BNB). These models are fine-tuned and optimized to classify the hemoglobin fractions and achieve test accuracies of 98.2% and 98.5%, respectively. Our research highlights the potential of Raman spectroscopy as an identification tool when combined with HPLC.

The huge demand of the market for plastic products together with inappropriate management of plastics waste endanger the environment with microplastics (MPs) pollution. MPs, commonly known as tiny plastic particles between 1 μm – 5 mm, do not always release to the environment with a simple structure made of a single-type polymer. Rather, MPs may release from bulky blended polymers that have enormous industrial applications. In the latter case, the structure of MPs can be very complicated and composed of various types of polymers, thus making their analysis very challenging. Accurate investigation of MPs requires a meticulous analysis approach that provides information about many parameters such as size, type, concentration, number, etc. among others. In this work, we showcase the application of 3-D micro-Raman spectroscopy as a promising approach for the accurate analysis of blended microplastics (B-MPs). Polypropylene (PP) and Low-Density Polyethylene (LDPE), as two widely used polymers, are blended at various weight ratios including 25/75, 50/50, and 75/25, and are thoroughly investigated. Thanks to the high precision of 3-D Raman mapping, not only the types of polymers are distinguished in complex B-MPs but also the morphology of the distribution of polymers as well as their quantitative concentrations are accurately estimated. Subsequently, a parameter defined as the Concentration Estimate Error (CEE) is used to evaluate the performance of the adopted approaches. In the last step, the application of a line-shaped laser line focus is demonstrated for shortening the measurement time of 3-D Raman mapping from 56 to 2 hours, still acquiring valuable information about the morphology and concentration of polymers in B-MPs.

Raman spectroscopy is a powerful technique used across the life sciences to measure the molecular composition of a sample. There has been growing interest to miniaturize Raman imaging devices for endoscopic applications, however typically these probes are based on fiber bundles which increase the overall footprint of the probe. Recent works have shown that by applying a wavefront shaping technique, a single fiber may be transformed into a sub-cellular resolution Raman endoscope. However, a single probe both exciting and collecting the signal leads to an unavoidable large background signal from the fiber itself, masking large portions of the Raman signal from the sample. Here, we adopt a data-driven approach to de-convolve the background signal from the sample. In particular, we demonstrate that by applying PCA and machine learning techniques, sub-cellular resolution Raman images of pharmaceutical clusters can be made with supervision-free analysis.

In this study, we utilized polarization-sensitive holotomography and Raman imaging to analyze prostate healthy (PNT2) and cancer (PC3) cells treated with glucose. After 48-hour incubation, distinct morphological differences were observed in cancer cells, including changes in volume, number, and refractive index of lipid droplets (LDs). Raman imaging confirmed the glucose uptake of these LDs. Cancer cells exhibited larger and more numerous LDs, with higher mean refractive-index and birefringence, compared to healthy cells. The study achieved over 90% accuracy in discriminating between cell types, highlighting its potential in cancer diagnostics. The research contributes to biomedical spectroscopy, offering a valuable tool for understanding cancer cell morphology and enabling early and precise cancer detection.

Clinical evidence underscores the intricate interplay between the heart and kidneys, where dysfunction in one organ contributes to progressive failure of both. In our previous work, using vibrational spectroscopy techniques, we demonstrated molecular changes in cardiac tissue post-uninephrectomy (UNX) and ischemia-reperfusion (I/R) surgeries in a rat cardiorenal syndrome (CS) model. It is now imperative to investigate whether structural changes in renal tissue following these two interventions are detectable. Vibrational spectroscopy serves as a powerful analytical technique with a fundamental role in molecular structure analysis. This method provides valuable insights into the intricate architecture of biomolecules, including proteins, lipids, and carbohydrates. Fourier-transform infrared (FTIR) microspectroscopy, in particular, emerges as a potent method for highresolution chemical imaging of various biological tissues, facilitating the analysis of molecular signatures indicative of physiological or pathological states. Despite the recognized utility of FTIR in various biomedical applications, its potential in assessing cardiorenal diseaseinduced lesions in heart and kidney tissues remains underexplored. Therefore, this study aims to bridge this gap by applying FTIR-imaging to identify spectroscopic markers related to renal complications. By characterizing the molecular fingerprints associated with pathological alterations in kidney tissue, this study aims to contribute to the development of non-invasive diagnostic tool for early detection and monitoring of renal dysfunction following surgical interventions. The findings of this study hold promise in advancing our understanding of the molecular mechanisms underlying renal complications, thereby facilitating timely interventions and the development of personalized therapeutic strategies in clinical settings.

Probing volar side fingertip capillary beds with 830 nm light produces remission spectra containing Rayleigh and Raman scattered light, and fluorescence, allowing continuous monitoring of intravascular plasma volume and hematocrit using the FRD-PVOH algorithm. During dialysis, Raman emission by polyatomic electrolytes i.e., phosphate tracks with fluid removal i.e., the change in intravascular plasma volume and in agreement with simultaneous hematocrit measurement of extracorporeal blood in the dialysis unit using the FDA approved CritLine. The variation of Raman features associated with urea in interstitial fluid and plasma suggests urea is involved in chemistry in the skin compartment i.e., in the extravascular space causing its clearance to lag the removal of electrolytes and water. Consistent with known skin conditions induced by chronic kidney disease and dialysis, we speculate that routine excess urea in the interstitial fluid destabilizes hydrogen bonding networks associated with keratin bundles in both viable keratinocytes and stratum corneum, exposing disulfide linkages, making them vulnerable to reduction by other species in the interstitial fluid. Oral administration of furosemide removes more water than electrolytes relative to the proportions removed by dialysis leading to solubility stress and striking variations of the Raman spectra. These results reinforce the notion that the various compartments in the human body do not drain at equal rates during dialysis and that real time Raman and FRD-PVOH monitoring-based feedback during hemodialysis could reduce the frequency of adverse events and thus improve outcomes.

The outbreak of the COVID-19 pandemic, caused by the SARS CoV-2 virus, has underscored the urgent need for rapid and accurate diagnostic techniques. Two cutting-edge spectroscopic methods, Surface Enhanced Raman Scattering (SERS) and Localized Surface Plasmon Resonance (LSPR), have emerged as potential game-changers in the field of viral diagnostics. These technologies offer significant improvements over traditional diagnostic methods, providing enhanced sensitivity, specificity, and speed in the detection of SARS CoV-2. SERS leverages the interaction of viral particles with metallic nanoparticles to produce an amplified Raman scattering signal. This enhancement allows for the detailed characterization of the virus, including its genetic material, through distinct spectral signatures. LSPR utilizes the shift in resonance frequency caused by the binding of viral particles to nanostructured metallic surfaces. This shift serves as a reliable indicator of the virus’s presence, facilitating its rapid detection. We explore three principal approaches to viral detection using SERS and LSPR: direct virus detection, detection via RNA after lysis, and ACE-2 capture. Each approach has its unique advantages, ranging from the ability to detect the virus directly to leveraging specific interactions between the virus and ACE-2 receptors for increased specificity. SERS and LSPR represent significant advancements in the rapid detection of SARS CoV-2. Their deployment could revolutionize clinical diagnostics, offering real-time, accurate results that are critical for effective disease management and control.

As a result of increasing life expectancy, osteomyelitis and periprosthetic joint infections (PJIs) are a major public health problem in Western countries. Infections usually result from bacterial spread through fractures, implants or by blood-borne transmission from surrounding sites. The occurrence of the pathogen leads to excessive inflammatory responses, which reduce the regenerative capacity of bone tissue. Additionally, the treatment of the infection necessitates a surgical approach, as bone tissue is poorly permeable to drug administration. This involves the precise removal of infected tissue, the thorough cleansing of the wound, and the administration of antibiotics directly on-site, complemented by systemic treatment. Despite an accurate surgical procedure, removal and replacement of the medical device is often necessary if it involves an infected prosthesis. Among the various pathogens that can infect bone, Staphylococcus aureus (SA) is the most frequently isolated etiologic agent of infection-induced osteomyelitis and PJIs. This bacterium is common and capable of forming a multilayered antimicrobial-resistant biofilm, frequently found in nosocomial environments. Here, we discuss a methodology for investigating the impact of SA infection on the (i) structure and (ii) chemical composition of the bone tissue, based on the integration of Raman microspectroscopy and AFT-FTIR spectroscopy. We aim to enhance the understanding of SA infection effects on bone tissue and to point out specific markers that can be used to detect the damaged tissue or even the presence of the pathogen with micrometric resolution. Indeed, Raman spectroscopy is a non-destructive, non-contact scattering technique that doesn't require labelling and has the possibility of being utilised for in vivo applications in the future (e.g., helping the surgeon during bone resection or implant revision procedures). On the other hand, ATR-FTIR's rapid measurement speed can be taken advantage of for analysing bone tissue biopsies.

The problem of automated bacterial colony counting is a very relevant one, due to the high importance of bacteriological analysis. Moreover, this automated counting saves biologists time and improves the accuracy of their experiments. This paper has two aims: to investigate the challenges of automated bacterial colony counting, and to address the joint challenges of petri dish localization and bacterial colony reflections in such dishes. These reflections can seriously reduce the accuracy of automated bacterial colony counting. Therefore, the main aim of this paper is to show new methods for detecting and removing bacterial colony reflections in a petri dish by the use of computer vision. It also proposes new methods for petri dish localization and the digital removal of bacterial colony reflections. Additionally, these methods can be implemented on a mobile platform, such as Android and Raspberry Pi. The experimental part of the paper contains the results, and descriptions of petri dish localization, and detecting and removing bacterial colony reflections. The proposed methods and the data obtained from these experiments significantly improve the accuracy of automated bacterial colony counting.

Total internal reflection fluorescence (TIRF) microscopy is a well-known technique allowing to confine the light close to the surface of a glass substrate. This axial confinement is based on the generation of evanescent waves. In TIRF microscopy however, there is no control of the light intensity in the transverse plane. Here, we propose a method to create evanescent patterns, which uses a fast-switching digital micro-mirror device to generate and scan an evanescent spot at multiple positions on the sample plane. In this way, patterns confined in the three dimensions of space can be produced in a fraction of second. This method would allow better spatial control in photo-activation or photo-conversion experiments in living cells, e.g. to target processes located at cell membranes.

This work demonstrates the fabrication of high-frequency ultrasound transducers, which are capable of sensing high-frequency and broad bandwidth photoacoustics and ultrasound signals. For sensor film fabrication purposes, a piezoelectric composite polymer consisting of PVDF-TrFE (Polyvinylidene difluoride trifluoroethylene) and lab-synthesized single crystal BSTO (Barium Strontium Titanate) nanofiller is used. The piezoelectric efficiency after the addition of BSTO in PVDF-TrFE is characterized using FTIR spectroscopy. The fabricated transducers are tested in the pulse-echo mode and the central frequency is found up to 43 MHz with a bandwidth of 40 MHz (93% at -6dB). Photoacoustic signals of frequency 14.5MHz and bandwidth of more than 100% are detected using the fabricated transducers. The transducers are used for photoacoustic imaging of two photoacoustic sources. The high sensitivity and broad frequency spectrum of the transducers make them suitable for high-resolution photoacoustics and ultrasound imaging for biomedical imaging and nondestructive testing.

Atopic dermatitis, or eczema, is a widespread chronic skin condition mainly treated with steroid creams, which can cause side effects such as skin thinning. Optical coherence tomography (OCT) allows for rapid non-invasive skin examination in the clinic. However, imaging depth at the popular 1300 nm wavelength is limited to ~1 mm due to scattering, making it difficult to assess severely inflamed skin. Skin optical properties, specifically scattering and absorption, vary with wavelength. Longer wavelengths centered at 1600 nm offer potential for deeper penetration due to lower scattering and minimal water absorption. Here we demonstrate a fiber-based, spectral-domain 1600 nm OCT system to study improvements in penetration depth in human skin. We use a supercontinuum laser source optimized for long-wavelength emission ranging from 1446 nm to 1694 nm to achieve high skin penetration depth, while maintaining an axial resolution of ~8-10 μm in the tissue (for the refractive index ranging n = 1.35 to 1.55 for different skin layers). Our system sensitivity is -90 dB at an A-scan rate of 76 kHz and approximately 8 mW of optical power on the skin. Simultaneous B-scans from a semi-transparent tape and human skin were obtained at 1600 nm and 1300 nm wavelengths, demonstrating and quantifying improvements in the imaging depth.

Atopic dermatitis (AD) often induces vasodilation, potentially impacting the velocity of blood flow within capillaries and vessels. To quantify the velocity change, we have developed and tested a 1.67 MHz 1310 nm Fourier-domain mode-locked (FDML) OCT system for measuring the decorrelation coefficient in blood vessels. This system provides an inter-frame time of 0.33 milliseconds and an A-scan spacing of 10 microns. A flow phantom, comprising a glass capillary tube of 80 μm inner diameter infused with unhomogenized milk by a syringe pump, was designed to test our OCT system mimicking the blood vessel. We collected 280 sequential B-scans at the same Y position of the phantom for a number of the velocity values. Based on variable interscan time analysis (VISTA) processing, we observed a strong correlation between the calculated decorrelation coefficients and the predetermined flow velocities, spanning a range from 0.16 mm/s to 30 mm/s. These findings enable us to explore our clinical hypotheses with in vivo tests.

FFOCT (Full-field optical coherence tomography) is a high-resolution optical-sectioning imaging technique for biological tissues. Fractal analysis of the FFOCT image contrast is validated as a quantitative tool to measure the scattering properties of tissue. The defocus is one of the main factors that reduce FFOCT image contrast. This work applies the fractal analysis to model FFOCT image contrast. The contrast correction factor was proposed by considering tissue scattering and the system's defocused coherence transfer function. For each layer of the FFOCT image, the image is transformed to its original defocused amplitude and phase, then corrected with the proposed correction factor. The in-depth fractal dimension and EBCM (Edge-based contrast measurement) are introduced to validate the contrast enhancement quantitively. The result of the mouse organ demonstrated the correction effect quantitatively to a resolution limit, and it may guide a physics-informed interpretation of FFOCT image and augmented microscopy.

We present two neural networks: one capable of processing a raw spectrum into an A-scan with the second-order nonlinearity removed and another for processing a raw spectrum into an A-scan with the third-order nonlinearity removed. An algorithm is also proposed to enable to use these networks in a sequence for removal of both nonlinearities. The presented approaches allow for either independent switching off of each order or the simultaneous removal of all orders, offering a tool for analysing the effects of each nonlinearity order individually or simply for performing all-depth, blind OCT data linearisation.

This report presents a state-of-the-art multimodality imaging device that combines multi-spectral optoacoustic microscopy (OAM) and optical coherence tomography (OCT) to chart absorbers in live tadpoles (Xenopus laevis) accurately. The OAM channel captures maps of five internal contrast agents: melanin, hemoglobin, collagen, glucose, and lipids. A novel method was developed to achieve this by assuming that each voxel in the 3D-OAM image exhibits a single chromophore contributing to the optoacoustic signal. The device is powered by a single optical source (SuperK Compact, NKT Photonics) that operates across an ultra-wide spectral range of 450 to 2400 nm. The set-up was optimized by minimizing optical aberrations and attenuation on optical components to stimulate the sample effectively. Using optical pulses of 2 ns duration and a repetition rate of 20 kHz, the device imaged tadpoles in their embryonic stage at multiple wavelengths, using narrow spectral windows of 25 nm bandwidth within the broad spectrum of the supercontinuum source at a time. In addition, an ultra-high-resolution OCT imaging channel operating at 1300 nm (spectral bandwidth 180 nm) was created and incorporated into the device. The OCT channel, also powered by a commercial supercontinuum source (SuperK EXTREME EXR9, NKT Photonics), was used for guidance purposes and to help determine the location of the chromophores.

Sample handling is an important consideration when aiming for replicating in vivo conditions ex vivo for the sake of validating imaging protocols and identifying biomarkers of disease. We tested five different handling methods: snap frozen in isopentane, directly frozen at -80°C, slowly frozen in a cryobox with and without cryopreservation media, and formalin fixed. The samples were imaged using optical coherence tomography (OCT) for qualitative and quantitative validation based on morphological features and optical properties. All handling methods were compared to fresh tissue samples using OCT-derived optical properties and morphological features. The results indicate a significant difference in the optical attenuation coefficient as well as morphological differences between the five different methods and support the hypothesis that proper sample handling is crucial for obtaining translatable results.

Stratum corneum (SC) biomarkers such as its thickness and hydration level offer valuable information about the state of the skin barrier. A Fourier domain visible light optical coherence tomography (VIS-OCT) system with an axial resolution of ~1 μm in tissue, was used to assess the morphology of the human SC layer under different hydration conditions. 12 human subjects (7 males, 5 females) in an age group of 21-59 were recruited. B-scan images of subjects’ dorsal hands were recorded by the VIS-OCT system and processed in MATLAB and GraphPad. An average SC thickness in the dorsal area before hydration, after hydration in water for 40 minutes and after drying in air for 40 minutes were found to be: 9 μm ± 0.1 μm, 19 μm ± 0.2 μm and 8 μm ± 0.1 μm, correspondingly. SC reflectance, obtained by VIS-OCT, changes at different hydration levels, increasing in a range of |4-12|dB during the hydration phase and decreasing in a range of |4-13|dB during the dehydration phase. That SC reflectance data correlates moderately to strongly, depending on an individual, with SC hydration levels measured by a Corneometer.

Hyperspectral OCT imaging of retina within the visible-near-infrared wavelengths could further improve the achievable axial resolution and harness the spectroscopic information for biomarker of retinal diseases . Previous efforts to develop OCT covering both visible and near-infrared wavelengths were hampered by the challenges in designing broadband spectrometer with adequate performance. Here, we described a design and implementation of a broadband spectrometer that could achieve spectral range of more than 450 nm with central wavelength around 680 nm. With it, we further demonstrate in vivo imaging of mouse retina using a fiber-based OCT system.

Hyperspectral imaging (HSI) is a powerful tool for noninvasive assessment of skin properties, as it can capture the spectral signatures of different skin layers and components. However, HSI also requires efficient and accurate methods for estimating skin parameters, such as the thickness, scattering, and absorption coefficients of each skin layer, from the measured spectra. In recent years, much research has been done regarding the use of machine learning (ML) methods for reducing the time and computational cost required for estimating parameters, compared to classical methods, such as the inverse Monte Carlo (IMC) or the inverse adding-doubling (IAD) algorithm. In this study, we investigated the impact of using random Fourier features (RFF) with a simple linear regression model, as well as with an artificial neural network (ANN), to estimate parameter values directly from the spectra. We compared the proposed models with the ANN and a 1D convolutional neural network (CNN), both trained using the raw spectra as input. All models were trained on simulated data and evaluated on both simulated and in vivo measured spectra using mean absolute error (MAE). We found that even simple linear regression with RFFs performs comparably to the neural networks trained on raw spectra while having much lower training and inference time. The best results were attained with the RFF-based ANN, having an overall MAE of 0.0226, which is an improvement compared to the 1D-CNN, having an MAE of 0.0284.

The aim of this document is to highlight key design techniques and considerations to adopt when designing a readout integrated circuit (ROIC) for InGaAs image sensors. An overall design approach is proposed while considering the conflicting issues inherent to the design requirement tradeoffs. The article covers both system level design and circuit design up until fabrication on silicon and measurement in the Lab.

Carbon nanoparticles (CNPs) are among the most extensively researched and utilized nanomaterials due to a combination of unique optical and electronic properties. This work proposes an inexpensive and time-efficient green synthesis method for synthesizing fluorescent CNPs from the leaf extracts of Murraya koenigii, following a microwave-assisted approach. This work highlights the successful synthesis of CNPs using a single organic solvent throughout the procedure, without the use of any hazardous chemicals. They offer great dispersibility with water, ranging from 20-30 nm in size, as confirmed by scanning electron microscopy (SEM), with the maximum height observed at 1.92 nm, as confirmed by atomic force microscopy (AFM). The derived CNPs exhibited bright red fluorescence emission at 663 nm, as investigated by optical characterization. The chemical functional groups were investigated and interpreted using Fourier transform infrared (FTIR) and X-ray diffraction (XRD) spectroscopy. Further, the antioxidant assay was performed on derived CNPs with different concentrations, which exhibited excellent free radical scavenging properties. Moreover, the anti-bacterial activity was performed with E. coli and S. aureus, along with antioxidant assay. This work demonstrates a non-toxic and straightforward approach to promoting sustainable development by synthesizing CNPs using green leaf extracts. Nanoparticles possessing bright red fluorescence in the near-infrared (NIR) region opens up further scope of this work to contribute towards biomedical and plant health applications such as bioimaging, drug delivery, lateral root growth tests, and therapeutics.

We report a multi-class classification model built using random forest (RF) and synthetic minority oversampling technique (SMOTE) applied to extracted intrinsic fluorescence (IF) data to detect normal, pre-cancer, and cancer samples. Important features in the fluorescence signal often get suppressed by the noise which makes denoising an essential pre-processing step. The proposed algorithm implements a wavelet-based denoising technique as a pre-processing step before data analysis which utilizes the “coif3” mother wavelet function to denoise IF data. Synthetic minority oversampling technique (SMOTE) is utilized to generate a balanced dataset. We achieved the best classification for the denoised balanced dataset with accuracy, sensitivity, and specificity above 90% for normal/pre-cancer and precancer/cancer groups. Further, the receiver operating curve (ROC) shows a clear distinction among three grades with the area under curve (AUC) of 0.96 for normal and precancer samples and 1.00 for cancer samples. The python script prepared for this study is available on GitHub and Signal Science Lab.

We discuss and present preliminary experimental evidence of three novel add-on functionalities that can make Optical Feedback Imaging a truly small footprint and label-free bioimaging technology. The first is single-pixel compressed sensing. Here we report on scanless optical feedback imaging in free space by spatially modulated illumination of the target. The second is chemical sensitivity. Here, we report the identification of several pigments by selective spectral discrimination at three different wavelengths. The third functionality is the integration of OFI in silicon photonic chips. Here we identify the building blocks necessary to implement a scanless imaging system in an integrated photonic chip and show evidence of laser modulation through optical feedback provided by the emitted radiation after passing through a silicon passive integrated waveguide.

Valproic acid (VPA) is an epigenetic regulator used in the treatment of epilepsy and other neurological disorders for the past 50 years. The direct impact of VPA on cells of the immune system has only been explored recently. Immune cells have been relatively less studied in the context of photoacoustic tomography imaging, which has been used in different biological systems with many different applications in biomedical investigations. Therefore, we aimed to perform a comparative analysis of photoacoustic imaging of immune cells in the context of VPA treatment. Four different doses of VPA (1 mM, 2.5 mM, 5 mM, and 7.5 mM) were applied to in vitro models established with human EoL-1 eosinophilic cell lines and human Jurkat T lymphocyte cell lines. Cell viability was assessed by a trypan blue exclusion test. The changes in morphology caused by VPA were examined with a photoacoustic tomography system. Photoacoustic signals measured from the VPA in different immune cells showed similar results obtained from light microscopy and cell viability. Photoacoustic imaging is promising for identifying and distinguishing different cell types and detecting changes at the cellular level.

Traditional methods for measuring blood oxygen use multiple wavelengths, which produces an In the biomedical field, the reemitted light intensity measured from the tissue depends on both scattering and absorption. In order to separate these variables, we use a physical phenomenon discovered in our lab, called the iso-path length (IPL) point. The IPL point is a specific angle around a cylindrical media, where the light intensity is not affected by the scattering and can serve for self-calibration. For a practical use of this concept, we designed an optic biosensor for measuring physiological parameters such as heart rate, oxygen saturation and respiratory rate, in both ordinary and extreme conditions in a hypoxic chamber.

In this paper, an innovative approach for detecting and analyzing speckle pattern signals is demonstrated, based on dynamic speckle analysis using a low-cost and low-framerate rolling shutter (RS) CMOS image sensor. The row scanning mechanism of a rolling shutter camera samples dynamic speckle patterns at a higher rate than typical Global Shutter (GS) cameras. In this research we demonstrate the detection and analysis of vibration signals that arise from an acoustic signal. We will illustrate the process of reconstructing a voice signal by analyzing a vibrating speckle pattern, with a primary focus on detecting and audibly capturing lung sounds.

Laser-induced hyperthermia (LH) is a treatment technique researched in multiple medical applications, including the treatment of warts and malignant tumors. Optimal laser energy delivery to target tissue is important to achieve an effective LH with minimal damage to healthy tissue. However, this can be challenging because optical properties may vary depending on tissue type and environmental factors. Therefore, real-time measurement of temperature is as important as dosimetry for a successful and safe LH application. In addition, the temperature measurement must be non-contact to minimize the risk of microbial contamination. In this study, we developed a temperature-controlled 808 nm diode-laser system that eliminates the risk of thermal damage and contamination by performing non-contact, real-time temperature measurements of the irradiated surface. The system is composed of an 808 nm c-mount diode laser, an infrared (IR) array sensor for temperature measurement, a PC, and an electronic control unit (ECU). The system was tested on phantoms and ex vivo tissues. According to the results, the temperature-controlled 808 nm diode-laser system could maintain the surface temperature of samples at the target temperature value.

Bacterial biofilms exist as aggregates of bacterial cells enclosed in a polysaccharide matrix, offering them resistance to various mechanical, chemical, antimicrobial, and environmental stress. The formation of biofilm on solid substrates occurs through a series of steps. It is essential to understand the stage of biofilm formation for several biomedical applications, such as assessing the coating efficacy for hard tissue implants, devising treatment strategies to inhibit biofilm formation in thin tissues, and building biocompatible diagnostic devices. In this work, we propose Laser Speckle Image Analysis to evaluate the bacterial biofilm formation stages with the help of different parameters extracted from the laser speckle image. These parameters give insight into the concentration and orientation changes of bacteria through the different stages of biofilm. The formation of biofilm on polypropylene substrate is evaluated and validated with optical microscopy images.

Liquid biopsy is an emerging and promising biomedical tool that aims to the early cancer diagnosis and the definition of personalized therapies in non-invasive and cost-effective way, since it is based on the blood sample analysis. Several strategies have been tested to implement an effective liquid biopsy system. Among them, searching of circulating tumor cells (CTCs) released by the tumor into the bloodstream can be a valid solution. Within a blood sample, CTCs can be considered as rare cells due to their extremely low percentage with respect to white blood cells (WBCs). Therefore, a technology able to perform an advanced single-cell analysis is requested for implementing a CTCs-based liquid biopsy. Recently, tomographic phase imaging flow cytometry (TPIFC) has been developed as a technique for the reconstruction of the 3D volumetric distribution of the refractive indices (RIs) of single cells flowing along a microfluidic channel. Hence, TPIFC allows collecting large datasets of single cells thanks to the flow-cytometry high-throughput property in 3D and quantitative manner. Moreover, TPIFC works in label-free modality as no exogenous marker is employed, thus avoiding the limitations of marker-based techniques. For this reason, here we investigate the possibility of exploiting the 3D dataset of single cells recorded by TPIFC to feed a machine learning model, in order to recognize tumor cells with respect to a background of monocytes, which are the most similar cells among the WBCs in terms of morphology. Reported results aim to emulate a real scenario for the label-free liquid biopsy based on TPIFC.

In this work, efficient light coupling into a microdisk capable of sustaining whispering gallery modes is thoroughly investigated, in order to understand the effect of polymer coating on the coupling efficiency. Light coupling within the microdisk from a tapered single mode optical fiber (SMF) is modelled and simulated, in presence and absence of a 100 nm thick polystyrene (PS) shell. The critical coupling parameters, such as the optical fiber distance to the microresonator (d_x) as well as the fiber optical properties such as cladding material (n_clad) are altered in order to achieve an efficient coupling and accordingly a high-quality factor Q_F in the microresonator. Results show that the Q_F of the resonators can exceed 104 only by tuning the geometrical parameter such as coupling distance d_x where the ideal d_x for uncoated and PS-coated microresonator is 0.55 μm and 0.40 μm respectively. Additionally, the sensitivity of the surrounding medium including the variation of the SMF cladding layer can be improved via using thin PS coatings on the surface of the microresonators.

Polarimetry comprises a set of noninvasive and nondesctructive optical techniques that demonstrated their great interest in biophotonics due to its capability to obtain relevant information from biological samples in a noninvasive and nondestructive way. Various polarimetric observables, derived from the Mueller matrix of a sample, are used to probe the efficacy of these techniques in pathology detection or different biological structures classification. The physical properties of a sample related to polarization can be categorized in three groups: retardance, dichroism and depolarization. In this work, we propose the study of the polarimetric observables linked to these physical properties for the identification of different structures within an ex-vivo cow brain sample by means of different pseudo-coloration methods. In particular, we study pseudo-coloration functions based on the Gaussian and Cauchy probabilistic functions. These probabilistic functions allow us to compute the probability of a given part of a sample to belong to a particular class (i.e. healthy or pathological or different structures inside the same sample) where, this probability depends on the polarimetric observables obtained from the studied sample. Our investigation encompasses a study of different observables and methodologies to find the optimal approach for brain tissue identification (identification of gray and white matter in ex-vivo cow brain) and, which may be of interest in multiple biomedical scenarios such as early pathology detection and diagnosis or enhanced visualization of different structures for clinical applications.

Endoscopes are medical inspection devices allowing doctors to examine internal organs without the need for large incisions. Made of optical fibers and an imaging lens at the tip, one of their most critical parameters is the size of the imaging optical system, limiting the agility and accessibility for clinical applications. Metalens-based fiber-optic endoscopes offer a promising alternative to conventional devices to reduce the size while maintaining the image quality. However, the accurate modelling and analysis required to design these devices can be challenging as they combine nanoscopic elements in a macroscopic optical system. In this work, we present a new multiscale metalens design solution for fiber-optic endoscopes, utilizing full-wave electromagnetic simulations and ray-tracing techniques. The metalens consists of subwavelength scatterers (meta-atoms) characterized individually using Rigorous Coupling Wave Analysis (RCWA). By controlling their distribution in size according to a target phase profile optimized in ray-tracing optics software, one can manipulate the phase, amplitude and polarization of the transmitted light. Smaller scale metalens (~100λ),can be directly simulated and their near-/far-field results can be obtained with the Finite Difference Time Domain (FDTD) method. For larger metalens (≫ 100 λ) stitching the near field or summing up the farfield from individual metaatoms to obtain the overall response of the metalens is more efficient than the direct simulationFinally, we can perform ray-tracing simulations to characterize the full system in a macro-scale environment, utilizing the response of the metalens.

Cancer is one of the leading causes of death, thereby, contributing to their quick diagnosis or treatment is of greatest importance. Nowadays, tumours are mainly diagnosed and graded histologically using biopsies. Since the images need to be sharp to distinguish biological structures, samples are thinly sliced (3-5 μm) to avoid scattering and contrast is obtained using highly absorbance dyes (e.g., Haematoxylin and Eosin (H&E)). RGB (Red-Green-Blue) cameras have been widely employed to acquire those images, while new approaches, such as Hyperspectral (HS) Imaging (HSI), have been arising to obtain a greater amount of spectral information from the samples. However, in order to have diffuse light for the HS cameras to capture it, the thickness of the sample should be bigger than the ones employed in conventional microscopy. This work aims to characterize the influence of tissue thickness of histology breast samples sectioned at 2 and 3 μm on their spectral signatures. Based on the H&E transmittance spectra peaks, HS images were segmented into three structures: stroma (eosin-stained), nuclei (haematoxylin-stained), and background (non-stained). Results show that, spatially, in 3 μm samples there are more cells imaged than in 2 μm samples. Moreover, spectrally, 3 μm samples proportionate higher spectral contrast than 2 μm samples due the greater interaction of light with tissue, denoting them as more suitable for microscopic HSI.

We demonstrate a Bayesian statistics-based outlier separation algorithm, which clearly distinguishes microscope captured images of unstained human cervical tissue sections of normal and different grades of precancerous tissues. The semi-automated global and adaptive method implements outlier separation based on the statistical characterization of the image histogram distribution. This multi-level thresholding achieves an effective image quantization of the high cell density domain, most affected in the progression of the disease, which yields a precise visualization of the lesions in the epithelium cellular structures, revealing their temporal changes with the progression of the disease. The pixel count ratio of the quantized high cell density region, below a statistically well-defined threshold, quantitatively discriminates different grades of precancer tissues through Receiver Operating Characteristics.

In cancer research, accurate characterization of the tumor microenvironment (TME) components is pivotal for diagnosis and treatment. In cancer progression, desmoplasia (a cancer-specific type of fibrosis mainly due to collagen overproduction) plays a crucial role and a specific barrier to treatment efficacy. Collagen, a key extracellular matrix component, plays a significant role in cancer progression, making its identification vital for understanding tumor behavior. This study presents a fiber optic approach utilizing adaptive focus light and fluorescence sensing techniques to detect collagen within fresh cancer specimens. Conventional methods often face challenges in precisely distinguishing collagen amidst complex TME. We demonstrate the efficacy of our approach through comprehensive experiments involving diverse cancer tissue samples. We accurately detect and characterize collagen by employing fluorescence sensing, providing invaluable insights into the TME. The adaptive focus light system optimizes imaging conditions and ensures high-resolution collagen identification. Subsequently, the proposed method simplifies the further analysis of the samples when subjected to Atomic Force Microscopy (AFM) to characterize their mechanical characteristics. The proposed techniques can offer a multimodal approach to characterizing fresh tissue biopsies, including quantitated collagen characterization through fluorescence measurements and AFM nanomechanical characterization. Our research signifies a paradigm shift in cancer tissue analysis, offering a potent toolset for researchers and clinicians alike. By enhancing our understanding of the intricate interplay between collagen and cancer cells, this innovative approach paves the way for targeted therapies and personalized interventions, ultimately advancing the forefront of cancer diagnostics and treatment.

The capability of Raman spectroscopy for biological cell classification has been previously reported and is shown to be well suited for research purposes. The implementation in the clinical setting for such tasks as cell counting and pathology is prohibited by the required acquisition time due to the low scattering cross section present. In this work, we present a study on the capability of broadband coherent anti-Stokes Raman scattering (BCARS) using a fiber laser, for white blood cell analysis. The improvements in acquisition time afforded by the coherent process in BCARS could potentially allow for hyperspectral imaging and cell classification or cellomics, but there are known drawbacks in BCARS such as the quadratic concentration dependence and nonresonant background. We provide some initial results on comparing the spontaneous Raman spectrum of a plasmacytoid dendritic cell line, with the corresponding BCARS spectrum. We offer an approach whereby a single BCARS spectrum can be obtained for a single cell from a hyperspectral image, for the purpose of a potential downstream cell classification.

Label-free infrared (IR) imaging reveals chemical bonds in specimens, but its limitations include a spatial resolution of approximately 5 μm. The advent of optical photothermal infrared (OPTIR) microscopy extends molecular fingerprint imaging by one order of magnitude near 500 nm. However, traditional scanning OPTIR faces a challenge of imaging speed. To improve speed and throughput, we employ pulsed visible probe light for widefield detection of transient photothermal responses induced by mid-infrared pulses. Our time-gated camera technique achieves sub-microsecond temporal resolution. We successfully imaged polystyrene beads, submicron SU8 polymer etchings, and mouse brain tissue samples using our widefield OPTIR microscope. The system excels in probe-dependent temporal and submicron spatial resolution, operating at 100 Hz over a 50 μm diameter field of view, while maintaining reasonable spectral fidelity. This enhanced widefield OPTIR microscopy promises rapid, label-free chemical imaging for biological samples and high-throughput screening applications.

Atopic dermatitis, characterized by itchiness and inflammation, often results in increased skin thickness. Traditional treatments with topical corticosteroids may compound this effect. Addressing the need for accurate epidermal measurement and the slow acquisition times of previous methods, we have developed a high-speed OCT system utilizing a 1.67 MHz Fourier-domain Mode-locked (FDML) and a MEMS scanner, providing a 3 kHz frame rate. The measured axial and lateral resolutions are 13-14 μm and 35 μm in air, respectively. We have tested our system on the dorsal skin of human hands in vivo, targeting a volume scan of 2.8 x 2.8 x 5 mm³. The acquisition from the digitizer to PC memory only takes 0.1 seconds. To assess the epidermal thickness, we have developed an automatic segmentation algorithm for the detection of the skin surface and epidermal-dermal junction. The results indicate that the epidermal thickness is mostly between 110 to 150 μm on healthy dorsal hand skin. Additionally, we have generated an epidermal thickness map overlaying the enface skin image, providing a comprehensive view of the skin's structural integrity.

Astrocytes, essential components of the central nervous system (CNS), perform diverse functions such as providing structural support, maintaining ion homeostasis, forming a glial scar after injury, contributing to the blood-brain barrier, and providing neuroprotection, tasks that are critical to proper CNS function. Astrocytes comprise of the soma with radially arranged extendable processes. They display distinctive morphological characteristics to perform their specific functions successfully. Abnormalities in astrocyte morphology have been linked to various neurological disorders. Despite their acknowledged significance, our comprehension of astrocytes remains incomplete, particularly regarding their intricate morphology. In the past, astrocytes were visualized using fluorescence microscopy. Using dye has several disadvantages, including increased chances of photobleaching, perturbations to the system, and not allowing continuous monitoring. This greatly limits the amount of morphological information that can be extracted. To address these challenges, we utilized quantitative phase imaging (QPI), a label-free imaging method that produces 2D and 3D refractive index profiles, allowing us to extract and quantify a plethora of morphological information. In our study, we investigated the impact of silicon nanowire (SiNW) substrates on rat cortical astrocyte morphology, aiming to understand how this substrate influences astrocyte morphology compared to traditional glass substrates. The novelty lies in utilizing QPI to image astrocytes on nanostructured substrates such as SiNW substrates. Astrocytes cultured on SiNW substrates displayed a “star-like” morphology typically found in vivo. This leads to several opportunities for future studies such as quantification of morphological features of astrocytes on SiNW substrates.

Our previous studies have shown that laser speckle imaging with sensitive subpixel correlation analysis is able to detect bacterial growth activity and the pattern of colony growth. In the current study, we demonstrate the potential of this method to analyze fungal growth. We compare the characteristics of the signals obtained from bacteria and fungi. The obtained results will help to improve the parameters of the speckle image acquisition system and the signal processing algorithms useful for microorganism (both eukaryotic and prokaryotic) growth analyses and speeding up and facilitating microbiological diagnostics.

The aim of the study was to combine an X-ray micro-computed tomography (μCT), enhanced with convolutional neural network (CNN) assisted voxel classification and volume segmentation, with photoluminescence (PL) and micro-Raman spectroscopy (μ-RS) for tooth structural integrity evaluation at the microcrack (MC) site of the extracted human teeth. Four maxillary premolars with visible enamel MCs were first examined utilizing an X-ray μCT and segmented with CNN to identify enamel, dentin, and cracks. Secondly, buccal and palatal teeth surfaces with MCs and sound areas were used to obtain fluorescence spectra illuminated with laser exposure wavelengths of 325 nm (CW) and 266 nm (0.5 ns pulsed), spot diameter ~ 80 μm. Thirdly, chemical composition inside the crack and the difference from the sound area were determined utilizing μ-RS method with a 785 nm laser (CW), spot diameter ∼ 3 μm. The proposed approach, which sequentially integrates X-ray μCT in combination with CNN assisted segmentation, PL, and μ-RS, revealed variations in the material composition along the crack line compared to the sound enamel. This includes alterations in the hydroxyapatite crystals’ quantity and/or quality at the sites of cracks versus uncracked enamel, suggesting a potential compromise in the structural integrity of the tooth in the areas affected by MCs.

This work focuses on enhancing the quality of A- and B-scans of a novel linear optical coherence tomography system (LOCT), addressing the image degradation caused by noise and the blurring characteristics of the system’s three-dimensional point spread function. The enhancement procedure includes an initial spatial and frequencybased pre-filtering that is applied to the measured interference pattern. Subsequently, a more robust envelope detection technique based on the Hilbert transform is employed. Lastly, image structures are reconstructed using a deconvolution algorithm based on maximum likelihood estimation, tailored to meet our unique requirements by adapting it to Rician distributed intensity values and employing a sparseness regularization term. For the deconvolution, both the lateral and axial blur of the system are considered. Emphasis is placed on the optimization of signal detection in high-noise regions, while simultaneously preventing image boundary artifacts. The efficacy of this approach is demonstrated across multiple types of measurement objects, including both artificial and biological samples. All results show a significant reduction in noise as well as enhanced resolution. Structure distinguishability is also increased, which plays a crucial role in tomography applications. In summary, the proposed enhancement method substantially improves image quality. This is achieved by still using the same initial measurement data, but incorporating prior knowledge and maximizing the amount of extracted information. Although initially designed for LOCT systems, the processing steps have potential for broader application in other types of optical coherence tomography and imaging systems.

This publication introduces a prototype of a fiber-based linear optical coherence tomography system (LOCT) that can be used for economical retinal screening in ophthalmology. The system uses standard off-the-shelf components to reduce production costs, complexity, and adjustment efforts while providing high-quality imaging of artificial retinal structures. We present the results of A- and B-scans of technical samples and an artificial eye model that was conducted to assess the system’s performance regarding axial resolution, imaging depth, and dispersion compensation. The study’s findings suggest that LOCT is a cost-effective solution for ophthalmology and shows great potential for monitoring the progression of retina-related diseases such as glaucoma or age-related macular degeneration.

The escalating threat of antimicrobial resistance (AMR) underscores the critical role of accurate AMR tests in healthcare. Disk-diffusion tests, a cornerstone in determining bacterial susceptibility require accurate inhibition zone measurement. Automatic inhibition zone measurement using computer vision offers significant advantages for assessing bacterial susceptibility in disk-diffusion tests. This method enhances accuracy, as manual measurements can vary between technicians. Automation ensures precise, consistent results by employing image analysis to gauge inhibition zones. It’s a time-saver, enabling rapid processing of large sample volumes — crucial for busy labs. The integration of these systems with existing databases means that data is captured and stored systematically, leading to efficient record-keeping. By standardizing the measurement process, results from different tests and labs can be reliably compared, aiding in the robust analysis of bacterial resistance patterns. Moreover, with the reduction of hands-on handling, the risk of exposure to infectious agents decreases, promoting a safer work environment. The proposed algorithm showcases enhanced sensitivity, highlighting subtle differences that might go unnoticed by the human eye, thereby ensuring more accurate interpretations. A comparative analysis with existing programs will highlight the efficacy of the new algorithm, emphasizing its advantages in precision and reliability. Proposed algorithm addresses challenges low contrast and indistinct zone boundaries through sophisticated image pre-processing. This advanced approach allows for accurate measurement of non-circular or overlapping zones — a task that can prove difficult for manual methods. This advancement in microbial testing technology contributes to more effective patient treatment, addressing the growing importance of bacteriological analysis in healthcare.

This paper presents an innovative super-resolution (SR) method for Optical Coherence Tomography (OCT), enhancing image resolution and reducing noise without retraining for different scales. Traditional SR techniques, interpolation, reconstruction, and learning-based, are surpassed by our approach, which combines a "shifted steered mixture of experts" with an autoencoder. This method outperforms the latest algorithms in subjective and objective evaluations, including PSNR and perceptual metrics. A distinctive feature is the adjustable sharpness, enabling targeted edge sharpening or defocusing through kernel experts’ bandwidth adjustments. This adaptability negates the need for data-specific retraining, offering a robust solution to improve OCT image quality and medical imaging analysis.

project, which is funded by the German Federal Ministry of Education and Research, attempts to improve the accessibility of diagnostic instruments for glaucoma screening. The presented approach aims to realize real-time nearinfrared video fundus imaging that enables the use of targeted fixation stimuli to ensure continuous imaging. The integration of near-infrared illumination with a wavelength of 780 nm not only avoids pupil constriction, but also enables mesopic imaging in darkened ambient light, ensuring optimal visualization of the retinal structure. This innovative system achieves nearly reflection-free imaging through polarized illumination with polarization-dependent beam paths. Its primary aim is to capture extensive fundus areas to facilitate correlations with linear optical coherence tomography (LOCT) measurements. In the future, the fundus setup will be integrated into the LOCT setup. In this research project, the primary aim is to generate images of the optic nerve, but it is also possible to carry out examinations of the macula. Unlike from traditional fundus cameras, this system has a controllable screen for generating individual fixation stimuli, which creates continuous eye movements and enables controlled imaging. The main objective is to capture large fundus areas and track eye positions to combine this information with the LOCT measurements A-scan positions, which enables the creation of B-scans with irregular geometries. This approach replaces the need for complex scanning systems by leveraging natural eye movements. The approach can thus be used to detect retinal pathologies in a different way and could therefore be used for more comprehensive diagnostic and scientific applications.

Identification of after-culture microbial species using a low-cost fully automated system would strongly benefit Low Income Countries, as traditional culture plate reading for the purpose of species identification currently requires trained professionals and remains mostly manual, while more recent automated identification methods are costly. Our application is the label-free identification of uropathogens from bacterial colonies images directly on a non-chromogenic culture medium. With a frugal innovation mindset, we are developing a simple diagnostic system based on a filter wheel and a smartphone-driven CMOS camera. We are reporting performance of identification for true clinical samples and compare it to samples issued from a strain collection. Also, the capability to classify the five most-prevalent uropathogens (MPU) in presence of low-prevalent uropathogens (LPU) is evaluated. Two machine learning classification approaches are compared: classical or probabilistic support vector machine SVM (Platt method) using solely a 1-D vector of intensities without any morphological predictors at this stage. Some apparent shortcomings of the logistic regression approach are highlighted for the probabilistic SVM approach.

Evaluating endogenous hyaluronic acid (HA) in the skin is essential to understanding the ageing process and analysing the effectiveness of dermocosmetics. In this sense, this study evaluated the skin of 10 individuals between 51 and 65 years old before and after applying a dermocosmetic to stimulate HA production. For this, Gen2 confocal Raman (Rivers Diagnostic) coupled to a 785 nm laser was used. Participants underwent a 7-day washout and then had their skin measured (T0 - control). After the control measure, they used the product for 60 days, applying it twice a day (morning and evening). Measurements were made at times T0, T30 and T60 days in the spectral range of 400 to 1800 cm^-1. Spectral data was processed by removing the baseline, smoothing and vector normalization. The T30 and T60 spectra were subtracted from the control measurement (T0) and compared with the second derivative spectrum of hyaluronic acid. It was observed that the main peaks of HA were presented in the difference between the spectra before and after using the dermocosmetic. The main peaks observed were 1076, 1084, 1096, 1126, 1132 and 1208 cm^-1. The difference between these peaks increased after 30 and 60 days of using the dermocosmetic product, indicating an increase in the concentration of HA in the skin. Therefore, the in vivo assessment of cutaneous HA by confocal Raman spectroscopy can be used to understand the role of dermocosmetics in skin ageing, allowing for more effective treatments.

This study investigates an indirect measurement of the antibiotic susceptibility of Escherichia coli (E. coli) strain ATCC 25922 by analyzing sample background fluorescence at 455 nm excitation and 530 nm emission wavelengths. The study takes a practical approach by exposing the bacterial culture, suspended in Luria-Bertani (LB) broth medium, to various concentrations of ampicillin and analyzing the corresponding fluorescence intensities of the inhibitory and non-inhibitory ranges of antibiotic concentrations. The variations in fluorescence intensities indicate probable antibiotic sensitivity. The results are validated with gold standards such as the broth microdilution method.

Antimicrobial Resistant (AMR) fungal pathogens do not respond to conventional treatments, causing lethal infections, especially in immunocompromised people. The urgent need for fast, reliable, and highly specific diagnostic methods to control this silent pandemic is evident. Raman Spectroscopy methods have great potential for the detection and identification of microbial pathogens, either label- free or using specific Raman tags and probes. We are developing carbohydrate-based Raman probes aiming to achieve selective pathogen detection inspired by the first steps of infection, during which pathogens adhere to the surface of host cells via carbohydrateprotein interactions. Previously, our group identified an aromatic-core divalent galactoside, that mimics host cell carbohydrates and recognizes Candida albicans, a critical priority fungal pathogen. We have synthesized thiol-bearing derivatives of this compound, which are attached to the surface of gold nanoparticles to create novel Raman glycoprobes capable of binding C. albicans. These novel glycoprobes will be studied for the capture, detection and chemical imaging of fungal pathogens, such as C. albicans and Aspergillus fumigatus using coherent Raman spectroscopic techniques. Ultimately, we aim to optimize this approach for the capture, imaging and identification of multiple pathogens in a biological sample. Herein, we present our current progress.

The IR-ATR, IR transmittance and Raman spectroscopy techniques were used in monitoring the immobilization of newly synthesized compounds (4-O-acetyl ferulato)triphenyltin(IV) and (fenoprofenato)tributyltin(IV), showing anticancer activity, on MCM-41 and SBA-15 mesoporous silica nanostructures (MSN). The IR analysis of the synthesized tin(IV) compounds proved a monodentate binding of the carboxylic group to the Sn atom, using the splitting of the ν(COO) band in the IR spectrum. The IR-ATR spectra of the synthesized MSN were used to check the porosity of the synthesized materials on the basis of the changes of the Si-OH(H₂O) and Si-O stretching vibration band profile. The immobilized organotin(IV) compounds in both MSN were analyzed and the success of the immobilization was monitored and discussed through the changes in the IR and Raman spectra.

This study explored the relationship between the structure similarity coefficient extracted from time-lapse monitoring videos of embryos and the embryonic developmental process. It applied the K-means clustering method to analyze the clustering of structure similarity coefficients of different samples, and to investigate the correlation between the clusters with the ability of embryonic cells to develop into blastocysts. The proposed method started to work by using the Hough circle transform to detect cell contours and eliminate image impurities. It further investigated the correlation between the structure similarity coefficient calculated from the time-lapse imaging frames and the embryonic developmental process. In this study, the calculation of the structural similarity coefficient only considered the measure of structural contrast, which accurately reflected the disparity in gray distribution between two images. Normalization was employed to eliminate any influence from brightness and image contrast on the results. After considering the non-uniform distribution of statistical characteristics within an image, local windows were utilized to calculate both mean and variance. We found that the significant decline in the structure similarity coefficient curve corresponds to the event of cell division. The proposed method performed PCA dimensionality reduction on the structure similarity coefficients and applied the K-means clustering to analyze the clustering of sample data. Finally, it explored the relationship between the clustered groups and the ability of embryonic cells to develop into blastocysts. This study generated an effective predictive marker for morphological changes in embryonic cell development, contributing to the prediction of the developmental potential of embryonic cells.

Diabetic foot ulcers (DFU) are open sores or wounds that develop on the feet of people with diabetes. They are a serious complication and often occur on the bottom of the foot. DFU treatment in the field of medical sciences is an advanced field of study. Patients with DFU have a five-year death rate of approximately 40%. Age, gender, medical history, vascular diseases, and renal illness are major risk factors for mortality. While 90% of people with diabetes worldwide have type 2 diabetes mellitus, accounting for 463 million cases of the disease. DFU diagnosis and treatment has been performed with Laser Speckle Contrast Imaging (LSCI) which is a non-invasive imaging technology. LSCI is becoming widely recognized as a vital technique for evaluating the impacts and implications of this disease. Major types of LSCI has been studied for the application of laser speckle technology in medical diagnosis. Region of Interest (ROI) and Multi exposure based LSCI applications and implementations has been reviewed in this study. Along with the application of conventional LSCI, Artificial Intelligence (AI) tools has been studied for robust results to combat issues associated with diabetes.