Myocardial infarct border demarcation by dual-wavelength photoacoustic spectral analysis

Selenium-loaded porous silica nanospheres improve cardiac repair after myocardial infarction by enhancing antioxidant activity and mitophagy

Myocardial infarction (MI) is the leading cause of death globally, often resulting to significant loss of cardiac function. A key factor in the pathological progression of MI is the excessive generation of reactive oxygen species (ROS) by dysfunctional mitochondria. However, no antioxidant therapy has been approved for clinical treatment of MI to date. In this study, selenium-loaded porous silica nanospheres (Se@PSN) are synthesized as a novel therapeutic approach for MI. These nanospheres are capable of neutralizing various ROS, thereby reducing hypoxia-induced myocardial cell damage. Additionally, Se@PSN promote the upregulation of antioxidant proteins, providing sustained intracellular ROS scavenging, which helps reduce infarct size and preserve cardiac function post-MI. The sustained antioxidant effects of Se@PSN are attributed to their ability to safeguard mitochondrial function by modulating oxidative phosphorylation, mitochondrial dynamics, and mitophagy. The activation of mitophagy by Se@PSN is achieved through the upregulation of HIF-1α expression. In conclusion, Se@PSN show significant potential for clinical translation as a novel therapeutic strategy for MI.

Photoacoustic imaging of rat kidney tissue oxygenation using second near-infrared wavelengths

Significance

Conventionally, spectral photoacoustic imaging (sPAI) to assess tissue oxygenation (sO 2 ) uses optical wavelengths in the first near-infrared (NIR-I) window. This limits the maximum photoacoustic imaging depth due to the high spectral coloring of biological tissues and has been a major barrier to the clinical translation of the technique.

Aim

We demonstrate the second near-infrared (NIR-II) tissue optical window (950 to 1400 nm) for the assessment of blood and tissuesO 2 .

Approach

The NIR-II PA spectra of oxygenated and deoxygenated hemoglobin were first characterized using a phantom. Optimal wavelengths to minimize spectral coloring were identified. The resulting NIR-II PA imaging methods were then validated in vivo by measuring kidneysO 2 in adult female rats.

Results

sPAI of whole blood, in a phantom, and of blood in kidneys in vivo produced PA spectra proportional to wavelength-dependent optical absorption. Using the NIR-II wavelengths for spectral unmixing resulted in a ∼ 50 % decrease in the error of the estimated bloodsO 2 , compared with conventional NIR-I wavelengths. In vivo measurements of kidneysO 2 validated these findings, with a similar 50% reduction in error when using NIR-II wavelengths versus NIR-I wavelengths at larger illumination depths.

Conclusions

sPAI using NIR-II wavelengths improved the accuracy of tissuesO 2 measurements. This is likely due to reduced scattering, which reduces the attenuation and, therefore, the impact of spectral coloring in this wavelength range. Combined with the increased safe skin exposure fluence limits in this wavelength range, these results demonstrate the potential to use NIR-II wavelengths for quantitative sPAI ofsO 2 from deep heterogeneous tissues.

Detecting Collagen by Machine Learning Improved Photoacoustic Spectral Analysis for Breast Cancer Diagnostics: Feasibility Studies With Murine Models

Collagen, a key structural component of the extracellular matrix, undergoes significant remodeling during carcinogenesis. However, the important role of collagen levels in breast cancer diagnostics still lacks effective in vivo detection techniques to provide a deeper understanding. This study presents photoacoustic spectral analysis improved by machine learning as a promising non-invasive diagnostic method, focusing on exploring collagen as a salient biomarker. Murine model experiments revealed more profound associations of collagen with other cancer components than in normal tissues. Moreover, an optimal set of feature wavelengths was identified by a genetic algorithm for enhanced diagnostic performance, among which 75% were from collagen-dominated absorption wavebands. Using optimal spectra, the diagnostic algorithm achieved 72% accuracy, 66% sensitivity, and 78% specificity, surpassing full-range spectra by 6%, 4%, and 8%, respectively. The proposed photoacoustic methods examine the feasibility of offering valuable biochemical insights into existing techniques, showing great potential for early-stage cancer detection.

Diagnostic and therapeutic optical imaging in cardiovascular diseases

Cardiovascular disease (CVD) is one of the most prevalent health threats globally. Traditional diagnostic methods for CVDs, including electrocardiography, ultrasound, and cardiac magnetic resonance imaging, have inherent limitations in real-time monitoring and high-resolution visualization of cardiovascular pathophysiology. In recent years, optical imaging technology has gained considerable attention as a non-invasive, high-resolution, real-time monitoring solution in the study and diagnosis of CVD. This review discusses the latest advancements, and applications of optical techniques in cardiac imaging. We compare the advantages of optical imaging over traditional modalities and especially scrutinize techniques such as optical coherence tomography, photoacoustic imaging, and fluorescence imaging. We summarize their investigations in atherosclerosis, myocardial infarction, and heart valve disease, etc. Additionally, we discuss challenges like deep-tissue imaging and high spatiotemporal resolution adjustment, and review existing solutions such as multimodal integration, artificial intelligence, and enhanced optical probes. This article aims to drive further development in optical imaging technologies to provide more precise and efficient tools for early diagnosis, pathological mechanism exploration, and treatment of CVD.

Assessing Breast Cancer through Tumor Microenvironment Mapping of Collagen and Other Biomolecule Spectral Fingerprints─A Review

Breast cancer is a major challenge in the field of oncology, with around 2.3 million cases and around 670,000 deaths globally based on the GLOBOCAN 2022 data. Despite having advanced technologies, breast cancer remains the major type of cancer among women. This review highlights various collagen signatures and the role of different collagen types in breast tumor development, progression, and metastasis, along with the use of photoacoustic spectroscopy to offer insights into future cancer diagnostic applications without the need for surgery or other invasive techniques. Through mapping of the tumor microenvironment and spotlighting key components and their absorption wavelengths, we emphasize the need for extensive preclinical and clinical investigations.

Dynamic and Label-Free Sensing of Cardiomyocyte Responses to Nanosized Vesicles for Cardiac Oxidative Stress Injury Therapy

Cardiac oxidative stress is a significant phenotype of myocardial infarction disease, a leading cause of global health threat. There is an urgent need to develop innovative therapies. Nanosized extracellular vesicle (nEV)-based therapy shows promise, yet real-time monitoring of cardiomyocyte responses to nEVs remains a challenge. In this study, a dynamic and label-free cardiomyocyte biosensing system using microelectrode arrays (MEAs) was constructed. Cardiomyocytes were cultured on MEA devices for electrophysiological signal detection and treated with nEVs from E. coli, gardenia, HEK293 cells, and mesenchymal stem cells (MSC), respectively. E. coli-nEVs and gardenia-nEVs induced severe paroxysmal fibrillation, revealing distinct biochemical communication compared to MSC-nEVs. Principal component analysis identified variations and correlations between nEV types. MSC-nEVs enhanced recovery without inducing arrhythmias in a H2O2-induced oxidative stress injury model. This study establishes a fundamental platform for assessing biochemical communication between nEVs and cardiomyocytes, offering new avenues for understanding nEVs' functions in the cardiovascular system.

Identification of different types of tumors based on photoacoustic spectral analysis: preclinical feasibility studies on skin tumors

Significance

Collagen and lipid are important components of tumor microenvironments (TME) and participates in tumor development and invasion. It has been reported that collagen and lipid can be used as a hallmark to diagnosis and differentiate tumors.

Aim

We aim to introduce photoacoustic spectral analysis (PASA) method that can provide both the content and structure distribution of endogenous chromophores in biological tissues to characterize the tumor-related features for identifying different types of tumors.

Approach

Ex vivo human tissues with suspected squamous cell carcinoma (SCC), suspected basal cell carcinoma (BCC), and normal tissue were used in this study. The relative lipid and collagen contents in the TME were assessed based on the PASA parameters and compared with histology. Support vector machine (SVM), one of the simplest machine learning tools, was applied for automatic skin cancer type detection.

Results

The PASA results showed that the lipid and collagen levels of the tumors were significantly lower than those of the normal tissue, and there was a statistical difference between SCC and BCC (p < 0.05 ), consistent with the histopathological results. The SVM-based categorization achieved diagnostic accuracies of 91.7% (normal), 93.3% (SCC), and 91.7% (BCC).

Conclusions

We verified the potential use of collagen and lipid in the TME as biomarkers of tumor diversity and achieved accurate tumor classification based on the collagen and lipid content using PASA. The proposed method provides a new way to diagnose tumors.

Molecular breast cancer subtype identification using photoacoustic spectral analysis and machine learning at the biomacromolecular level

Breast cancer threatens the health of women worldwide, and its molecular subtypes largely determine the therapy and prognosis of patients. However, an uncomplicated and accurate method to identify subtypes is currently lacking. This study utilized photoacoustic spectral analysis (PASA) based on the partial least squares discriminant algorithm (PLS-DA) to identify molecular breast cancer subtypes at the biomacromolecular level in vivo. The area of power spectrum density (APSD) was extracted to semi-quantify the biomacromolecule content. The feature wavelengths were obtained via the variable importance in projection (VIP) score and the selectivity ratio (Sratio), to identify the biomarkers. The PASA achieved an accuracy of 84%. Most of the feature wavelengths fell into the collagen-dominated absorption waveband, which was consistent with the histopathological results. This paper proposes a successful method for identifying molecular breast cancer subtypes and proves that collagen can be treated as a biomarker for molecular breast cancer subtyping.