Two-Photon-Excited FLIM of NAD(P)H and FAD-Metabolic Activity of Fibroblasts for the Diagnostics of Osteoimplant Survival.

Bioinert materials such as the zirconium dioxide and aluminum oxide are widely used in surgery and dentistry due to the absence of cytotoxicity of the materials in relation to the surrounding cells of the body. However, little attention has been paid to the study of metabolic processes occurring at the implant-cell interface. The metabolic activity of mouse 3T3 fibroblasts incubated on yttrium-stabilized zirconium ceramics cured with aluminum oxide (ATZ) and stabilized zirconium ceramics (Y-TZP) was analyzed based on the ratio of the free/bound forms of cofactors NAD(P)H and FAD obtained using two-photon microscopy. The results show that fibroblasts incubated on ceramics demonstrate a shift towards the free form of NAD(P)H, which is observed during the glycolysis process, which, according to our assumptions, is related to the porosity of the surface of ceramic structures. Consequently, despite the high viability and good proliferation of fibroblasts assessed using an MTT test and a scanning electron microscope, the cells are in a state of hypoxia during incubation on ceramic structures. The FLIM results obtained in this work can be used as additional information for scientists who are interested in manufacturing osteoimplants.

Exploring the future of regenerative medicine: Unveiling the potential of optical microscopy for structural and functional imaging of stem cells.

Regenerative medicine, which utilizes stem cells for tissue and organ repair, holds immense promise in healthcare. A comprehensive understanding of stem cell characteristics is crucial to unlock their potential. This study explores the pivotal role of optical microscopy in advancing regenerative medicine as a potent tool for stem cell research. Advanced optical microscopy techniques enable an in-depth examination of stem cell behavior, morphology, and functionality. The review encompasses current optical microscopy, elucidating its capabilities and constraints in stem cell imaging, while also shedding light on emerging technologies for improved stem cell visualization. Optical microscopy, complemented by techniques like fluorescence and multiphoton imaging, enhances our comprehension of stem cell dynamics. The introduction of label-free imaging facilitates noninvasive, real-time stem cell monitoring without external dyes or markers. By pushing the boundaries of optical microscopy, researchers reveal the intricate cellular mechanisms underpinning regenerative processes, thereby advancing more effective therapeutic strategies. The current study not only outlines the future of regenerative medicine but also underscores the pivotal role of optical microscopy in both structural and functional stem cell imaging.

Review of optical methods for noninvasive imaging of skin fibroblasts-From in vitro to ex vivo and in vivo visualization.

Fibroblasts are among the most common cell types in the stroma responsible for creating and maintaining the structural organization of the extracellular matrix in the dermis, skin regeneration, and a range of immune responses. Until now, the processes of fibroblast adaptation and functioning in a varying environment have not been fully understood. Modern laser microscopes are capable of studying fibroblasts in vitro and ex vivo. One-photon- and two-photon-excited fluorescence microscopy, Raman spectroscopy/microspectroscopy are well-suited noninvasive optical methods for fibroblast imaging in vitro and ex vivo. In vivo staining-free fibroblast imaging is not still implemented. The exception is fibroblast imaging in tattooed skin. Although in vivo noninvasive staining-free imaging of fibroblasts in the skin has not yet been implemented, it is expected in the future. This review summarizes the state-of-the-art in fibroblast visualization using optical methods and discusses the advantages, limitations, and prospects for future noninvasive imaging.

Multimodal Diagnostics of Changes in Rat Lungs after Vaping.

(1) Background: The use of electronic cigarettes has become widespread in recent years. The use of e-cigarettes leads to milder pathological conditions compared to traditional cigarette smoking. Nevertheless, e-liquid vaping can cause morphological changes in lung tissue, which affects and impairs gas exchange. This work studied the changes in morphological and optical properties of lung tissue under the action of an e-liquid aerosol. To do this, we implemented the "passive smoking" model and created the specified concentration of aerosol of the glycerol/propylene glycol mixture in the chamber with the animal. (2) Methods: In ex vivo studies, the lungs of Wistar rats are placed in the e-liquid for 1 h. For in vivo studies, Wistar rats were exposed to the e-liquid vapor in an aerosol administration chamber. After that, lung tissue samples were examined ex vivo using optical coherence tomography (OCT) and spectrometry with an integrating sphere. Absorption and reduced scattering coefficients were estimated for the control and experimental groups. Histological sections were made according to the standard protocol, followed by hematoxylin and eosin staining. (3) Results: Exposure to e-liquid in ex vivo and aerosol in in vivo studies was found to result in the optical clearing of lung tissue. Histological examination of the lung samples showed areas of emphysematous expansion of the alveoli, thickening of the alveolar septa, and the phenomenon of plasma permeation, which is less pronounced in in vivo studies than for the exposure of e-liquid ex vivo. E-liquid aerosol application allows for an increased resolution and improved imaging of lung tissues using OCT. Spectral studies showed significant differences between the control group and the ex vivo group in the spectral range of water absorption. It can be associated with dehydration of lung tissue owing to the hyperosmotic properties of glycerol and propylene glycol, which are the main components of e-liquids. (4) Conclusions: A decrease in the volume of air in lung tissue and higher packing of its structure under e-liquid vaping causes a better contrast of OCT images compared to intact lung tissue.

Diabetes noninvasive diagnostics and monitoring through volatile biomarkers analysis in the exhaled breath using optical absorption spectroscopy.

The review is aimed on the analysis the abilities of noninvasive diagnostics and monitoring of diabetes mellitus (DM) and DM-associated complications through volatile molecular biomarkers detection in the exhaled breath. The specific biochemical reactions in the body of DM patients and their associations with volatile molecular biomarkers in the breath are considered. The applications of optical spectroscopy methods, including UV, IR, and terahertz spectroscopy for DM-associated volatile molecular biomarkers measurements, are described. The applications of similar technique combined with machine learning methods in DM diagnostics using the profile of DM-associated volatile molecular biomarkers in exhaled air or "pattern-recognition" approach are discussed.

A novel Raman spectroscopic method for detecting traces of blood on an interfering substrate.

Traces of body fluids discovered at a crime scene are a primary source of DNA evidence. Raman spectroscopy is a promising universal technique for identifying biological stains for forensic purposes. The advantages of this method include the ability to work with trace amounts, high chemical specificity, no need for sample preparation and the nondestructive nature. However, common substrate interference limits the practical application of this novel technology. To overcome this limitation, two approaches called "Reducing a spectrum complexity" (RSC) and "Multivariate curve resolution combined with the additions method" (MCRAD) were investigated for detecting bloodstains on several common substrates. In the latter approach, the experimental spectra were "titrated" numerically with a known spectrum of a targeted component. The advantages and disadvantages of both methods for practical forensics were evaluated. In addition, a hierarchical approach to reduce the possibility of false positives was suggested.

Implementation of data fusion to increase the efficiency of classification of precancerous skin states using in vivo bimodal spectroscopic technique.

This study presents the results of the classification of diffuse reflectance (DR) spectra and multiexcitation autofluorescence (AF) spectra that were collected in vivo from precancerous and benign skin lesions at three different source detector separation (SDS) values. Spectra processing pipeline consisted of dimensionality reduction, which was performed using principal component analysis (PCA), followed by classification step using such methods as support vector machine (SVM), multilayered perceptron (MLP), linear discriminant analysis (LDA), and random forest (RF). In order to increase the efficiency of lesion classification, several data fusion methods were applied to the classification results: majority voting, stacking, and manual optimization of weights. The results of the study showed that in most of cases the use of data fusion methods increased the average multiclass classification accuracy from 2% up to 4%. The highest accuracy of multiclass classification was obtained using the manual optimization of weights and reached 94.41%.

Quantitative Assessment of Low-Dose Photodynamic Therapy Effects on Diabetic Wound Healing Using Raman Spectroscopy.

One of challenges that faces diabetes is the wound healing process. The delayed diabetic wound healing is caused by a complicated molecular mechanism involving numerous physiological variables. Low-dose photodynamic therapy (LDPDT) provides excellent results in rejuvenation and wound healing. In this study, the LDPDT effect on diabetic wounds in mice was studied using two photosensitizers, 5-aminolevulinic acid and methylene blue, and two laser dose expositions of 1 J/cm2 and 4 J/cm2 by Raman spectroscopy (RS). The latter was used as a noninvasive method, providing specific information about tissue state based on the fundamental vibrational modes of its molecular components. RS allows high spatial resolution acquisition of biochemical and structural information through the generation of point spectra or spectral images. An approach to in vivo quantitative assessment of diabetic wound healing state was developed. This approach is based on an application of the principal component analysis combined with the Mahalanobis metrics to skin Raman spectra, in particular, intensities of the amide I and CH2 bands.

Light distribution in fat cell layers at physiological temperatures.

Adipose tissue (AT) optical properties for physiological temperatures and in vivo conditions are still insufficiently studied. The AT is composed mainly of packed cells close to spherical shape. It is a possible reason that AT demonstrates a very complicated spatial structure of reflected or transmitted light. It was shown with a cellular tissue phantom, is split into a fan of narrow tracks, originating from the insertion point and representing filament-like light distribution. The development of suitable approaches for describing light propagation in a AT is urgently needed. A mathematical model of the propagation of light through the layers of fat cells is proposed. It has been shown that the sharp local focusing of optical radiation (light localized near the shadow surface of the cells) and its cleavage by coupling whispering gallery modes depends on the optical thickness of the cell layer. The optical coherence tomography numerical simulation and experimental studies results demonstrate the importance of sharp local focusing in AT for understanding its optical properties for physiological conditions and at AT heating.

Discovering Glioma Tissue through Its Biomarkers' Detection in Blood by Raman Spectroscopy and Machine Learning.

The most commonly occurring malignant brain tumors are gliomas, and among them is glioblastoma multiforme. The main idea of the paper is to estimate dependency between glioma tissue and blood serum biomarkers using Raman spectroscopy. We used the most common model of human glioma when continuous cell lines, such as U87, derived from primary human tumor cells, are transplanted intracranially into the mouse brain. We studied the separability of the experimental and control groups by machine learning methods and discovered the most informative Raman spectral bands. During the glioblastoma development, an increase in the contribution of lactate, tryptophan, fatty acids, and lipids in dried blood serum Raman spectra were observed. This overlaps with analogous results of glioma tissues from direct Raman spectroscopy studies. A non-linear relationship between specific Raman spectral lines and tumor size was discovered. Therefore, the analysis of blood serum can track the change in the state of brain tissues during the glioma development.

Metal-organic frameworks based surface-enhanced Raman spectroscopy technique for ultra-sensitive biomedical trace detection.

Metal-organic frameworks (MOFs) have attracted widespread interest due to their unique and unprecedented advantages in microstructures and properties. Besides, surface-enhanced Raman scattering (SERS) technology has also rapidly developed into a powerful fingerprint spectroscopic technique that can provide rapid, non-invasive, non-destructive, and ultra-sensitive detection, even down to single molecular level. Consequently, a considerable amount of researchers combined MOFs with the SERS technique to further improve the sensing performance and broaden the applications of SERS substrates. Herein, representative synthesis strategies of MOFs to fabricate SERS-active substrates are summarized and their applications in ultra-sensitive biomedical trace detection are also reviewed. Besides, relative barriers, advantages, disadvantages, future trends, and prospects are particularly discussed to give guidance to relevant researchers.

Predictive models for COVID-19 detection using routine blood tests and machine learning.

The problem of accurate, fast, and inexpensive COVID-19 tests has been urgent till now. Standard COVID-19 tests need high-cost reagents and specialized laboratories with high safety requirements, are time-consuming. Data of routine blood tests as a base of SARS-CoV-2 invasion detection allows using the most practical medicine facilities. But blood tests give general information about a patient's state, which is not directly associated with COVID-19. COVID-19-specific features should be selected from the list of standard blood characteristics, and decision-making software based on appropriate clinical data should be created. This review describes the abilities to develop predictive models for COVID-19 detection using routine blood tests and machine learning.

Optical clearing and testing of lung tissue using inhalation aerosols: prospects for monitoring the action of viral infections.

Optical clearing of the lung tissue aims to make it more transparent to light by minimizing light scattering, thus allowing reconstruction of the three-dimensional structure of the tissue with a much better resolution. This is of great importance for monitoring of viral infection impact on the alveolar structure of the tissue and oxygen transport. Optical clearing agents (OCAs) can provide not only lesser light scattering of tissue components but also may influence the molecular transport function of the alveolar membrane. Air-filled lungs present significant challenges for optical imaging including optical coherence tomography (OCT), confocal and two-photon microscopy, and Raman spectroscopy, because of the large refractive-index mismatch between alveoli walls and the enclosed air-filled region. During OCT imaging, the light is strongly backscattered at each air-tissue interface, such that image reconstruction is typically limited to a single alveolus. At the same time, the filling of these cavities with an OCA, to which water (physiological solution) can also be attributed since its refractive index is much higher than that of air will lead to much better tissue optical transmittance. This review presents general principles and advances in the field of tissue optical clearing (TOC) technology, OCA delivery mechanisms in lung tissue, studies of the impact of microbial and viral infections on tissue response, and antimicrobial and antiviral photodynamic therapies using methylene blue (MB) and indocyanine green (ICG) dyes as photosensitizers.

A Criterion of Colorectal Cancer Diagnosis Using Exosome Fluorescence-Lifetime Imaging.

This study was aimed to investigate the applicability of the exosome fluorescence-lifetime imaging microscopy (FLIM) for colorectal cancer (CRC) diagnosis. Differential ultra-centrifugation was used to extract exosomes from the blood plasma of 11 patients with colon polyps (CPs) and 13 patients with CRC at the T2-4, N0-3, and M0-1 stages. Analysis was performed using a two-photon FLIM device. In total, 165 and 195 FLIM images were recorded for the CP and CCR patient groups, respectively. Two classes of exosomes differentiated by autofluorescence average lifetime tm were discovered in the samples. The first class of exosomes with tm = (0.21 ± 0.06) ns was mostly found in samples from CRC patients. The second class with tm = (0.43 ± 0.19) ns was mostly found in samples from CP patients. The relative number of "CRC-associated" exosomes Nch in the FLIM dataset was shown to be very small for the CP patient group and large for the CRC patient group. This difference was statistically significant. Therefore, the suggested CRS diagnostics criterion can be as follows. If Nch > 0.5, the probability of CRC is high. If Nch < 0.3, the probability of CRC is low.

Label-free laser spectroscopy for respiratory virus detection: A review.

Infectious diseases are among the most severe threats to modern society. Current methods of virus infection detection based on genome tests need reagents and specialized laboratories. The desired characteristics of new virus detection methods are noninvasiveness, simplicity of implementation, real-time, low cost and label-free detection. There are two groups of methods for molecular biomarkers' detection and analysis: (i) a sample physical separation into individual molecular components and their identification, and (ii) sample content analysis by laser spectroscopy. Variations in the spectral data are typically minor. It requires the use of sophisticated analytical methods like machine learning. This review examines the current technological level of laser spectroscopy and machine learning methods in applications for virus infection detection.

Types of spectroscopy and microscopy techniques for cancer diagnosis: a review.

Cancer is a life-threatening disease that has claimed the lives of many people worldwide. With the current diagnostic methods, it is hard to determine cancer at an early stage, due to its versatile nature and lack of genomic biomarkers. The rapid development of biophotonics has emerged as a potential tool in cancer detection and diagnosis. Using the fluorescence, scattering, and absorption characteristics of cells and tissues, it is possible to detect cancer at an early stage. The diagnostic techniques addressed in this review are highly sensitive to the chemical and morphological changes in the cell and tissue during disease progression. These changes alter the fluorescence signal of the cell/tissue and are detected using spectroscopy and microscopy techniques including confocal and two-photon fluorescence (TPF). Further, second harmonic generation (SHG) microscopy reveals the morphological changes that occurred in non-centrosymmetric structures in the tissue, such as collagen. Again, Raman spectroscopy is a non-destructive method that provides a fingerprinting technique to differentiate benign and malignant tissue based on Raman signal. Photoacoustic microscopy and spectroscopy of tissue allow molecule-specific detection with high spatial resolution and penetration depth. In addition, terahertz spectroscopic studies reveal the variation of tissue water content during disease progression. In this review, we address the applications of spectroscopic and microscopic techniques for cancer detection based on the optical properties of the tissue. The discussed state-of-the-art techniques successfully determines malignancy to its rapid diagnosis.

In Vivo Quantification of the Effectiveness of Topical Low-Dose Photodynamic Therapy in Wound Healing Using Two-Photon Microscopy.

The effect of low-dose photodynamic therapy on in vivo wound healing with topical application of 5-aminolevulinic acid and methylene blue was investigated using an animal model for two laser radiation doses (1 and 4 J/cm2). A second-harmonic-generation-to-auto-fluorescence aging index of the dermis (SAAID) was analyzed by two-photon microscopy. SAAID measured at 60-80 µm depths was shown to be a suitable quantitative parameter to monitor wound healing. A comparison of SAAID in healthy and wound tissues during phototherapy showed that both light doses were effective for wound healing; however, healing was better at a dose of 4 J/cm2.

The In Vivo Quantitative Assessment of the Effectiveness of Low-Dose Photodynamic Therapy on Wound Healing Using Optical Coherence Tomography.

The effect of low-dose photodynamic therapy on in vivo wound healing was investigated using optical coherence tomography. This work aims to develop an approach to quantitative assessment of the wound's state during wound healing including the effect of low-dose photodynamic therapy using topical application of two different photosensitizers, 5-aminolevulinic acid and methylene blue, and two laser doses of 1 J/cm2 and 4 J/cm2. It was concluded that the laser dose of 4 J/cm2 was better compared to 1 J/cm2 and allowed the wound healing process to accelerate.

Application of machine learning and laser optical-acoustic spectroscopy to study the profile of exhaled air volatile markers of acute myocardial infarction.

Conventional acute myocardial infarction (AMI) diagnosis is quite accurate and has proved its effectiveness. However, despite this, discovering more operative methods of this disease detection is underway. From this point of view, the application of exhaled air analysis for a similar diagnosis is valuable. The aim of the paper is to research effective machine learning algorithms for the predictive model for AMI diagnosis constructing, using exhaled air spectral data. The target group included 30 patients with primary myocardial infarction. The control group included 42 healthy volunteers. The 'LaserBreeze' laser gas analyzer (Special Technologies Ltd, Russia), based on the dual-channel resonant photoacoustic detector cell and optical parametric oscillator as the laser source, had been used. The pattern recognition approach was applied in the same manner for the set of extracted concentrations of AMI volatile markers and the set of absorption coefficients in a most informative spectral range 2.900 ± 0.125µm. The created predictive model based on the set of absorption coefficients provided 0.86 of the mean values of both the sensitivity and specificity when linear support vector machine (SVM) combined with principal component analysis was used. The created predictive model based on using six volatile AMI markers (C5H12, N2O, NO2, C2H4, CO, CO2) provided 0.82 and 0.93 of the mean values of the sensitivity and specificity, respectively, when linear SVM was used.

Application of multiphoton imaging and machine learning to lymphedema tissue analysis.

The results of in-vivo two-photon imaging of lymphedema tissue are presented. The study involved 36 image samples from II stage lymphedema patients and 42 image samples from healthy volunteers. The papillary layer of the skin with a penetration depth of about 100 µm was examined. Both the collagen network disorganization and increase of the collagen/elastin ratio in lymphedema tissue, characterizing the severity of fibrosis, was observed. Various methods of image characterization, including edge detectors, a histogram of oriented gradients method, and a predictive model for diagnosis using machine learning, were used. The classification by "ensemble learning" provided 96% accuracy in validating the data from the testing set.