Study on the pathological and biomedical characteristics of spinal cord injury by confocal Raman microspectral imaging

Studying on the in vivo pathological evolution of spinal cord injury with the rat model by the method of integrated multispectral imaging and Raman spectroscopy

Spinal cord injury (SCI) is a debilitating neurological and pathological condition that results in significant impairments in motor, sensory, and autonomic functions. By integrating multispectral imaging (MSI) with Raman spectroscopy, a label-free optical methodology was developed for achieving a non-invasive in vivo understanding on the pathological features of SCI evolution. Under the guidance of captured the spectral imaging data cube with a rigid endoscope based MSI system, a special designed fiber probe passed through the instrumental channel for acquiring the finger-print spectral information from compression rat SCI models. After identifying the main visual features of injured spinal cord tissue in all Sham, 0-, 3- and 7-days post injury (0 DPI, 3 DPI, and 7 DPI) groups, the blood volume and oxygen content were visualized to describe hemorrhage, hypoxia and inflammatory state after acute injury. The averaged reflectance spectra, which were deduced from MSI data cubes, were utilized for describing oxygen saturation and hemoglobin concentration in living tissue. The results of Raman spectroscopy addressed complex compositional and conformational phenomena during SCI progression, correlated with the well-known event like neuronal apoptosis, hemorrhage, demyelination, and even the upregulation of chondroitin sulfate proteoglycans (CSPGs). A principal component analysis and linear discriminate algorithm (PCA-LDA) based discriminate model was introduced for categorizing spectral features in different injury stages, which was applicable for intraoperative interpretations on the complex pathological courses of SCI and therapeutic outcomes.

Vibrational spectroscopy and multiphoton microscopy for label-free visualization of nervous system degeneration and regeneration

Neurological disorders, including spinal cord injury, peripheral nerve injury, traumatic brain injury, and neurodegenerative diseases, pose significant challenges in terms of diagnosis, treatment, and understanding the underlying pathophysiological processes. Label-free multiphoton microscopy techniques, such as coherent Raman scattering, two-photon excited autofluorescence, and second and third harmonic generation microscopy, have emerged as powerful tools for visualizing nervous tissue with high resolution and without the need for exogenous labels. Coherent Raman scattering processes as well as third harmonic generation enable label-free visualization of myelin sheaths, while their combination with two-photon excited autofluorescence and second harmonic generation allows for a more comprehensive tissue visualization. They have shown promise in assessing the efficacy of therapeutic interventions and may have future applications in clinical diagnostics. In addition to multiphoton microscopy, vibrational spectroscopy methods such as infrared and Raman spectroscopy offer insights into the molecular signatures of injured nervous tissues and hold potential as diagnostic markers. This review summarizes the application of these label-free optical techniques in preclinical models and illustrates their potential in the diagnosis and treatment of neurological disorders with a special focus on injury, degeneration, and regeneration. Furthermore, it addresses current advancements and challenges for bridging the gap between research findings and their practical applications in a clinical setting.

Unveiling osteosarcoma responses to DAPT combined with cisplatin by using confocal Raman microscopy

The aim of this study was to clarify the dose- and time-dependent effect of the γ-secretase inhibitor (DAPT) combined with cisplatin on osteosarcoma (OS) cells, evaluated by confocal Raman microspectral imaging (CRMI) technology. The intracellular composition significantly changed after combined drug action compared with the sole cisplatin treatment, proving the synergistic effect of DAPT combined with cisplatin on OS cells. The principal component analysis-linear discriminant analysis revealed the main compositional variations by distinguishing spectral characteristics. K-means cluster and univariate imaging were used to visualize the changes in subcellular morphology and biochemical distribution. The results showed that the increase of the DAPT dose and cisplatin treatment time in the combination treatment induced the division of the nucleus in OS cells, and other organelles also showed significant physiological changes compared with the effect of sole cisplatin treatment. After understanding the cellular response to the combined drug treatment at a molecular level, the achieved results provide an experimental fact for developing suitable individualized tumor treatment protocols.

Studying the pathological and biochemical features in breast cancer progression by confocal Raman microspectral imaging of excised tissue samples

Confocal Raman microspectral imaging (CRMI) has been used to detect the spectra-pathological features of ductal carcinoma in situ (DCIS) and lobular hyperplasia (LH) compared with the heathy (H) breast tissue. A total of 15-20 spectra were measured from healthy tissue, LH tissue, and DCIS tissue. One-way ANOVA and Tukey's honest significant difference (HSD) post hoc multiple tests were used to evaluate the peak intensity variations in all three tissue types. Besides that, linear discrimination analysis (LDA) algorithm was adopted in combination with principal component analysis (PCA) to classify the spectral features from tissues at different stages along the continuum to breast cancer. Moreover, by using the point-by-point scanning methodology, spectral datasets were obtained and reconstructed for further pathologic visualization by multivariate imaging methods, including K-mean clustering analysis (KCA) and PCA. Univariate imaging of individual Raman bands was also used to describe the differences in the distribution of specific molecular components in the scanning area. After a detailed spectral feature analysis from 800 to 1800 cm^-1 and 2800 to 3000 cm^-1 for all the three tissue types, the histopathological features were visualized based on the content and structural variations of lipids, proteins, phenylalanine, carotenoids and collagen, as well as the calcification phenomena. The results obtained not only allowed a detailed Raman spectroscopy-based understanding of the malignant transformation process of breast cancer, but also provided a solid spectral data support for developing Raman based breast cancer clinical diagnostic techniques.

Design of a Multimodal Imaging System and Its First Application to Distinguish Grey and White Matter of Brain Tissue. A Proof-of-Concept-Study

Multimodal imaging gains increasing popularity for biomedical applications. This article presents the design of a novel multimodal imaging system. The centerpiece is a light microscope operating in the incident and transmitted light mode. Additionally, Raman spectroscopy and VIS/NIR reflectance spectroscopy are adapted. The proof-of-concept is realized to distinguish between grey matter (GM) and white matter (WM) of normal mouse brain tissue. Besides Raman and VIS/NIR spectroscopy, the following optical microscopy techniques are applied in the incident light mode: brightfield, darkfield, and polarization microscopy. To complement the study, brightfield images of a hematoxylin and eosin (H&E) stained cryosection in the transmitted light mode are recorded using the same imaging system. Data acquisition based on polarization microscopy and Raman spectroscopy gives the best results regarding the tissue differentiation of the unstained section. In addition to the discrimination of GM and WM, both modalities are suited to highlight differences in the density of myelinated axons. For Raman spectroscopy, this is achieved by calculating the sum of two intensity peak ratios (I2857 + I2888)/I2930 in the high-wavenumber region. For an optimum combination of the modalities, it is recommended to apply the molecule-specific but time-consuming Raman spectroscopy to smaller regions of interest, which have previously been identified by the microscopic modes.

Identification and classification of pneumonia disease using a deep learning-based intelligent computational framework

Pneumonia is one of the hazardous diseases that lead to life insecurity. It needs to be diagnosed at the initial stages to prevent a person from more damage and help them save their lives. Various techniques are used to identify pneumonia, including chest X-ray, blood culture, sputum culture, fluid sample, bronchoscopy, and pulse oximetry. Chest X-ray is the most widely used method to diagnose pneumonia and is considered one of the most reliable approaches. To analyse chest X-ray images accurately, an expert radiologist needs expertise and experience in the desired domain. However, human-assisted approaches have some drawbacks: expert availability, treatment cost, availability of diagnostic tools, etc. Hence, the need for an intelligent and automated system comes into place that operates on chest X-ray images and diagnoses pneumonia. The primary purpose of technology is to develop algorithms and tools that assist humans and make their lives easier. This study proposes a scalable and interpretable deep convolutional neural network (DCNN) to identify pneumonia using chest X-ray images. The proposed modified DCNN model first extracts useful features from the images and then classifies them into normal and pneumonia classes. The proposed system has been trained and tested on chest X-ray images dataset. Various performance metrics have been utilized to inspect the stability and efficacy of the proposed model. The experimental result shows that the proposed model's performance is greater compared to the other state-of-the-art methodologies used to identify pneumonia.

Unveiling dose- and time-dependent osteosarcoma cell responses to the γ-secretase inhibitor, DAPT, by confocal Raman microscopy

Using confocal Raman micro-spectroscopy, this study aims to elucidate the cellular responses of the γ-secretase inhibitor, N-[N-(3,5-difluorophenacetyl)-L-alanyl]-S-phenylglycine t-butyl ester (DAPT), in osteosarcoma (OS) cells in a dose- and time-dependent manner. The K7M2 murine OS cell line was treated with different DAPT doses (0, 10, 20, and 40 μM) for 24 and 48 hours before investigations. Significant compositional changes (nucleic acids, protein and lipid) after DAPT treatment were addressed, which testified inhibitory effect of DAPT on the growth of OS cells. Moreover, both partial least squares-discriminant analysis (PLS-DA) and principal component analysis-linear discriminant analysis (PCA-LDA) analyses revealed governing composition variations among groups by distinguishing their spectral characteristics. Furthermore, by adopting leave-one-out cross validation method, it is shown that PLS-DA exhibited more classification capacity than PCA-LDA algorithm. Hence, by understanding the DAPT-based cellular variations, the achieved results provided an experimental foundation to establish new DAPT-based anticancer therapeutic strategies, and preclinical Raman analytical methodologies on drug-cell interactions.

MiR-34a Inhibits Spinal Cord Injury and Blocks Spinal Cord Neuron Apoptosis by Activating Phatidylinositol 3-kinase (PI3K)/AKT Pathway Through Targeting CD47

OBJECTIVE

Dysregulation of miR-34a has been reported for its implication in neuronal development. This study aims to explore the effect and possible mechanism of miR-34a on neuron apoptosis induced by Spinal Cord Injury (SCI).

MATERIALS AND METHODS

SCI model was established using Allen's weight-drop method and rats in the sham group were performed with laminectomy without weight-drop injury. Basso Bcattie Bresnahan (BBB) rating scale was applied to evaluate the locomotor function of rats. Pathological changes of spinal cord tissues in SCI rats were observed after hematoxylin and eosin (HE) staining. Rats were separately injected with miR-34a agomir, miR-34a agomir NC, si-CD47 and si- CD47 NC before their spinal cord tissues were collected for terminal-deoxynucleoitidyl Transferase Mediated nick end labeling (TUNEL) staining. Expressions of miR-34a, si-CD47, apoptosis related proteins and AKT pathway related proteins were measured by quantitative reverse transcription- polymerase chain reaction (qRT-PCR) and western blot.

RESULTS

SCI rat models were successfully established evidenced by decreased BBB scores and HE staining. Injection of miR-34a agomir and/or si-CD47 could suppress neuron cell apoptosis, with deceased apoptotic index (AI) and pro-apoptotic protein (cleaved caspase-3 and Bax) levels, and increased expressions of anti-apoptotic proteins (Bcl-2 and Mcl-1). Phosphorylated levels of phatidylinositol 3-kinase (PI3K) and AKT were further increased in rats injected with miR-34a agomir and si-CD47, compared with miR-34a agomir or si-CD47 injection alone.

CONCLUSION

MiR-34a can downregulate CD47 expression to activate PI3K/AKT signal pathway, and thus inhibit SCI induced spinal neuron apoptosis.

Study on the biochemical mechanisms of the micro-wave ablation treatment of lung cancer by ex vivo confocal Raman microspectral imaging

As a highly invasive and the most prevalent malignancy, lung cancer remains the leading cause of cancer-associated mortality worldwide, especially in China. Microwave ablation (MWA) is an effective, safe, and the least invasive ablative treatment modality, which has been increasingly used for the management of unrespectable lung tumors. However, the underlying biochemical mechanisms of MWA treatment remain to be incompletely elucidated. Therefore, to illustrate the complex biochemical responses of lung squamous cell carcinoma (LSCC) to MWA treatment, confocal Raman micro-spectral imaging (CRMI) was applied in combination with multivariate analysis. A total of twelve LSCC tissues were acquired from patients undergoing clinical treatment, and their spectral characteristics were analyzed to determine significant spectral variations following cancer progression and MWA treatment in comparison with healthy lung tissues. Point-scanned Raman datasets were acquired from sectioned tissue samples in both pre-therapy (Pre-MWA group) and post-therapy groups (Post-MWA group) and further analyzed using K-means cluster analysis (KCA) and principal component analysis (PCA) to highlight the detailed compositional variations of the biochemical constituents. The spectral variations of essential amino acids (such as phenylalanine and tryptophan), collagen, and nucleic acids in the cancerous tissues of the Post-MWA group were significantly enhanced compared to those in the Pre-MWA group. The acquired information further confirmed a remarkable increase in the content of nucleic acid, protein, and lipid in the cancerous tissue following MWA treatment and, a comparative spectral imaging investigation indicated that MWA had no noticeable adverse effects on the paracancerous tissues. Thus, the findings not only illustrated the underlying biochemical variability in lung cancer during MWA treatment but also further confirmed the feasibility of a combined analytical procedure for assessing the biochemical responses during thermal ablation, which could be applied to prominently enhance the effectiveness of MWA in lung cancer treatment in clinical settings.

Confocal Raman Spectral Imaging Study of DAPT, a γ-secretase Inhibitor, Induced Physiological and Biochemical Reponses in Osteosarcoma Cells

Confocal Raman microspectral imaging was adopted to elucidate the cellular drug responses of osteosarcoma cells (OC) to N-[N-(3, 5-difluorophenyl acetyl)-L-alanyl]-sphenylglycine butyl ester (DAPT), a γ-secretase inhibitor, by identifying the drug induced subcellular compositional and structural changes. Methods: Spectral information were acquired from cultured osteosarcoma cells treated with 0 (Untreated Group, UT), 10 (10 μM DAPT treated, 10T), 20 μM (20 μM DAPT treated, 20T) DAPT for 24 hours. A one-way ANOVA and Tukey's honest significant difference (HSD) post hoc multiple test were sequentially applied to address spectral features among three groups. Multivariate algorithms such as K-means clustering analysis (KCA) and Principal component analysis (PCA) were used to highlight the structural and compositional differences, while, univariate imaging was applied to illustrate the distribution pattern of certain cellular components after drug treatment. Results: Major biochemical changes in DAPT-induced apoptosis came from changes in the content and structure of proteins, lipids, and nucleic acids. By adopted multivariate algorithms, the drug induced cellular changes was identified by the morphology and spectral characteristics between untreated cells and treated cells, testified that DAPT mainly acted in the nuclear region. With the increase of the drug concentration, the content of main subcellular compositions, such nucleic acid, protein, and lipid decreased. In an addition, DAPT-induced nuclear fragmentation and apoptosis was depicted by the univariate Raman image of major cellular components (nucleic acids, proteins and lipids). Conclusions: The achieved Raman spectral and imaging results illustrated detailed DAPT-induced subcellular compositional and structural variations as a function of drug dose. Such observations can not only explain drug therapeutic mechanisms of OC DAPT treatment, and also provide new insights for accessing the medicine curative efficacy and predicting prognosis.

Raman spectroscopy combined with multivariate analysis to study the biochemical mechanism of lung cancer microwave ablation

Lung cancer is the leading cause of death in cancer patients, and microwave ablation (MWA) has been extensively used in clinical treatment. In this study, we characterized the spectra of MWA-treated and untreated lung squamous cell carcinoma (LSCC) tissues, as well as healthy lung tissue, and conducted a preliminary analysis of spectral variations associated with MWA treatment. The results of characteristic spectral analysis of different types of tissues indicated that MWA treatment induces an increase in the content of nucleic acids, proteins, and lipid components in lung cancer tissues. The discriminant model based on the principal component analysis - linear discriminant analysis (PCA-LDA) algorithm together with leave-one-out cross validation (LOOCV) method yield the sensitivities of 90%, 80%, and 96%, and specificities of 86.2%, 93.8%, and 100% among untreated and MWA-treated cancerous tissue, and healthy lung tissue, respectively. These results indicate that Raman spectroscopy combined with multivariate analysis techniques can be used to explore the biochemical response mechanism of cancerous tissue to MWA therapy.