DNA-Based Near-Infrared Voltage Sensors.

Indocyanine green (ICG) is an FDA approved dye widely used for fluorescence imaging in research, surgical navigation, and medical diagnostics. However, ICG has a few drawbacks, such as concentration-dependent aggregation and absorbance, nonspecific cellular targeting, and rapid photobleaching. Here, we report a novel DNA-based nanosensor platform that utilizes monomers of ICG and cholesterol. Using DNA origami, we can attach ICG to a DNA structure, maintaining its concentration, preserving its near-infrared (NIR) absorbance, and allowing attachment of targeting moieties. We characterized the nanosensors' absorbance, stability in blood, and voltage sensing in vitro. This study presents a novel DNA-based ICG nanosensor platform for cellular voltage sensing for future in vivo applications.

NIR-II Nanoprobes: A Review of Components-Based Approaches to Next-Generation Bioimaging Probes.

Fluorescence and photoacoustic imaging techniques offer valuable insights into cell- and tissue-level processes. However, these optical imaging modalities are limited by scattering and absorption in tissue, resulting in the low-depth penetration of imaging. Contrast-enhanced imaging in the near-infrared window improves imaging penetration by taking advantage of reduced autofluorescence and scattering effects. Current contrast agents for fluorescence and photoacoustic imaging face several limitations from photostability and targeting specificity, highlighting the need for a novel imaging probe development. This review covers a broad range of near-infrared fluorescent and photoacoustic contrast agents, including organic dyes, polymers, and metallic nanostructures, focusing on their optical properties and applications in cellular and animal imaging. Similarly, we explore encapsulation and functionalization technologies toward building targeted, nanoscale imaging probes. Bioimaging applications such as angiography, tumor imaging, and the tracking of specific cell types are discussed. This review sheds light on recent advancements in fluorescent and photoacoustic nanoprobes in the near-infrared window. It serves as a valuable resource for researchers working in fields of biomedical imaging and nanotechnology, facilitating the development of innovative nanoprobes for improved diagnostic approaches in preclinical healthcare.

Automated Classification of Lung Cancer Subtypes Using Deep Learning and CT-Scan Based Radiomic Analysis.

Artificial intelligence and emerging data science techniques are being leveraged to interpret medical image scans. Traditional image analysis relies on visual interpretation by a trained radiologist, which is time-consuming and can, to some degree, be subjective. The development of reliable, automated diagnostic tools is a key goal of radiomics, a fast-growing research field which combines medical imaging with personalized medicine. Radiomic studies have demonstrated potential for accurate lung cancer diagnoses and prognostications. The practice of delineating the tumor region of interest, known as segmentation, is a key bottleneck in the development of generalized classification models. In this study, the incremental multiple resolution residual network (iMRRN), a publicly available and trained deep learning segmentation model, was applied to automatically segment CT images collected from 355 lung cancer patients included in the dataset "Lung-PET-CT-Dx", obtained from The Cancer Imaging Archive (TCIA), an open-access source for radiological images. We report a failure rate of 4.35% when using the iMRRN to segment tumor lesions within plain CT images in the lung cancer CT dataset. Seven classification algorithms were trained on the extracted radiomic features and tested for their ability to classify different lung cancer subtypes. Over-sampling was used to handle unbalanced data. Chi-square tests revealed the higher order texture features to be the most predictive when classifying lung cancers by subtype. The support vector machine showed the highest accuracy, 92.7% (0.97 AUC), when classifying three histological subtypes of lung cancer: adenocarcinoma, small cell carcinoma, and squamous cell carcinoma. The results demonstrate the potential of AI-based computer-aided diagnostic tools to automatically diagnose subtypes of lung cancer by coupling deep learning image segmentation with supervised classification. Our study demonstrated the integrated application of existing AI techniques in the non-invasive and effective diagnosis of lung cancer subtypes, and also shed light on several practical issues concerning the application of AI in biomedicine.

Cerium Oxide Nanoparticles Improve Outcome after In Vitro and In Vivo Mild Traumatic Brain Injury.

Mild traumatic brain injury results in aberrant free radical generation, which is associated with oxidative stress, secondary injury signaling cascades, mitochondrial dysfunction, and poor functional outcome. Pharmacological targeting of free radicals with antioxidants has been examined as an approach to treatment, but has met with limited success in clinical trials. Conventional antioxidants that are currently available scavenge a single free radical before they are destroyed in the process. Here, we report for the first time that a novel regenerative cerium oxide nanoparticle antioxidant reduces neuronal death and calcium dysregulation after in vitro trauma. Further, using an in vivo model of mild lateral fluid percussion brain injury in the rat, we report that cerium oxide nanoparticles also preserve endogenous antioxidant systems, decrease macromolecular free radical damage, and improve cognitive function. Taken together, our results demonstrate that cerium oxide nanoparticles are a novel nanopharmaceutical with potential for mitigating neuropathological effects of mild traumatic brain injury and modifying the course of recovery.