Colorectal cancer is the third most common cancer worldwide. Despite recent advances in the treatment of this pathology, which include a personalized approach using radio- and chemotherapies in combination with advanced surgical techniques, it is imperative to enhance the performance of these treatments and decrease their detrimental side effects on patients’ health. Nanomedicine is likely the pathway towards solving this challenge by enhancing both the therapeutic and diagnostic capabilities. In particular, plasmonic nanoparticles show remarkable potential due to their dual therapeutic functionalities as photothermal therapy agents and as radiosensitizers in radiotherapy. Their dual functionality, high biocompatibility, easy functionalization, and targeting capabilities make them potential agents for inducing efficient cancer cell death with minimal side effects. This review aims to identify the main challenges in the diagnosis and treatment of colorectal cancer. The heterogeneous nature of this cancer is also discussed from a single-cell point of view. The most relevant works in photo- and radiotherapy using nanotechnology-based therapies for colorectal cancer are addressed, ranging from in vitro studies (2D and 3D cell cultures) to in vivo studies and clinical trials. Although the results using nanoparticles as a photo- and radiosensitizers in photo- and radiotherapy are promising, preliminary studies showed that the possibility of combining both therapies must be explored to improve the treatment efficiency.

Brillouin spectroscopy has recently gained considerable interest within the biomedical field as an innovative tool to study mechanical properties in biology. The Brillouin effect is based on the inelastic scattering of photons caused by their interaction with thermodynamically driven acoustic modes or phonons and it is highly dependent on the material’s elasticity. Therefore, Brillouin is a contactless, label-free optic approach to elastic and viscoelastic analysis that has enabled unprecedented analysis of ex vivo and in vivo mechanical behavior of several tissues with a micrometric resolution, paving the way to a promising future in clinical diagnosis. Here, we comprehensively review the different studies of this fast-moving field that have been performed up to date to provide a quick guide of the current literature. In addition, we offer a general view of Brillouin’s biomedical potential to encourage its further development to reach its implementation as a feasible, cost-effective pathology diagnostic tool.

Cutaneous melanoma is one of the most aggressive and deadliest cancers in human beings due to its invasiveness and other factors. Histopathological analysis is crucial for a proper diagnosis. Optical tissue clearing is a novel field that allows 3D image acquisition of large-scale biological tissues. Optical clearing and immunolabeling for 3D fluorescence imaging has yet to be extensively applied to melanoma. In the present manuscript, we establish, for the first time, an optical clearing and immunostaining procedure for human melanoma and human cell line-derived melanoma xenograft models using the CUBIC (clear, unobstructed brain imaging cocktails) technique. We have successfully cleared the samples and achieved 3D volumetric visualization of the tumor microenvironment, vasculature, and cell populations.

Diagnostic imaging is essential in veterinary practice, and cone-beam computed tomography (CBCT) has emerged as a promising tool to complement radiography. This study aimed to optimize the image quality of a novel multimodality veterinary X-ray prototype integrating direct digital radiography, fluoroscopy and CBCT, and to assess its potential clinical applications, focusing on the CBCT component. The study was conducted in three phases: optimization of CBCT image quality using postmortem samples, comparison of CBCT and 16-slice multidetector CT (MDCT) images of four cadavers (two dogs and two cats), and potential clinical applications in 24 live patients. Comparative evaluation in postmortem scans revealed that CBCT achieved equal quality in 65% of bone compared to MDCT and a slightly inferior quality in 90% of soft-tissue structures using the bone reconstruction protocol, with beam hardening as the main limiting factor. Clinical validation showed that CBCT was particularly useful for identifying small fractures and mineralized structures, providing diagnostic information not clearly visible on radiographs. Integration of radiography, fluoroscopy, and CBCT in a single device facilitated workflow and allowed a more precise diagnosis in most of the patients examined with the prototype, which demonstrated promising diagnostic performance in small-animal and exotic veterinary practice.

Conventional strategies aimed at mitigating beam-hardening artifacts in computed tomography (CT) can be categorized into two main approaches: (1) postprocessing following conventional reconstruction and (2) iterative reconstruction incorporating a beam-hardening model. While the former fails in low-dose and/or limited-data cases, the latter substantially increases computational cost. Although deep learning-based methods have been proposed for several cases of limited-data CT, few works in the literature have dealt with beam-hardening artifacts, and none have addressed the problems caused by randomly selected projections and a highly limited span. We propose the deep learning-based prior image constrained (PICDL) framework, a hybrid method used to yield CT images free from beam-hardening artifacts in different limited-data scenarios based on the combination of a modified version of the Prior Image Constrained Compressed Sensing (PICCS) algorithm that incorporates the L2 norm (L2-PICCS) with a prior image generated from a preliminary FDK reconstruction with a deep learning (DL) algorithm. The model is based on a modification of the U-Net architecture, incorporating ResNet-34 as a replacement of the original encoder. Evaluation with rodent head studies in a small-animal CT scanner showed that the proposed method was able to correct beam-hardening artifacts, recover patient contours, and compensate streak and deformation artifacts in scenarios with a limited span and a limited number of projections randomly selected. Hallucinations present in the prior image caused by the deep learning model were eliminated, while the target information was effectively recovered by the L2-PICCS algorithm.

: Optical tissue clearing techniques can render large-scale tissue samples and whole organs transparent to apply cell-resolution 3D imaging techniques, revolutionizing the fields of biological research and pathological diagnosis, and providing never-before seen structural information. There are several widely used protocols, but they have shortcomings, both in complexity of use and in suboptimal results in certain tissues and organs. Here, we describe a new method dubbed “White and Clear” (WaC) using 2,2’-thiodiethanol (TDE) as a dynamic Refractive Index (RI)-matching reagent alongside a lipid-extraction and a pigment-bleaching step using dilute hydrogen peroxide (H2O2), to provide ease of use and enhanced clearing effect on all kinds of samples and allow for the clearing parameters to be finely adjusted in order to obtain the best results in each situation.