Residual nonvisible bladder cancer after proper treatment caused by technological and therapeutic limitations is responsible for tumor relapse and progression. This study aimed to demonstrate the feasibility of a solution for simultaneous detection and treatment of bladder cancer lesions smaller than one millimeter. The α5β1 integrin was identified as a specific marker in 81% of human high-grade nonmuscle invasive bladder cancers and used as a target for the delivery of targeted gold nanorods (GNRs). In a preclinical model of orthotopic bladder cancer expressing the α5β1 integrin, the photoacoustic imaging of targeted GNRs visualized lesions smaller than one millimeter, and their irradiation with continuous laser was used to induce GNR-assisted hyperthermia. Necrosis of the tumor mass, improved survival, and computational modeling were applied to demonstrate the efficacy and safety of this solution. Our study highlights the potential of the GNR-assisted theranostic strategy as a complementary solution in clinical practice to reduce the risk of nonvisible residual bladder cancer after current treatment. Further validation through clinical studies will support the findings of the present study.

Fibrillar collagen accumulation emerges as a promising biomarker in several diseases, such as desmoplastic tumors and unstable atherosclerotic plaque. Gold nanorods (GNRs) hold great potential as contrast agents in high-resolution, biomedically safe, and non-invasive photoacoustic imaging (PAI). This study presents the design and characterization of a specialized imaging tool which exploits GNR assisted targeted photoacoustic imaging that is tailored for the identification of fibrillar collagen. In addition to the photoacoustic characterization of collagen in the NIR 1 and 2 regions, we demonstrate the detailed steps of conjugating a decoy to GNRs. This study serves as a proof of concept, that demonstrates that conjugated collagenase-1 (MMP-1) generates a distinct and collagen-specific photoacoustic signal, facilitating real-time visualization in the wavelength range of 700-970 nm (NIR I). As most of the reported studies utilized the endogenous contrast of collagen in the NIR II wavelength that has major limitations to perform deep tissue imaging, the approach that we are proposing is unique and it highlights the promise of MMP-1 decoy-functionalized GNRs as novel contrast agents for photoacoustic imaging of collagen in the NIR 1 region. To our knowledge this is the first time functionalized GNRs are optimized for the detection of fibrillar collagen and utilized in the field of non-invasive photoacoustic imaging that can facilitate a better prognosis of desmoplastic tumors and broken atherosclerotic plaques.

Liver fibrosis, a major global health issue, is marked by excessive collagen deposition that impairs liver function. Noninvasive methods for the direct visualization of collagen content are crucial for the early detection and monitoring of fibrosis progression. This study investigates the potential of spectral photoacoustic imaging (sPAI) to monitor collagen development in liver fibrosis. Utilizing a novel data-driven superpixel photoacoustic unmixing (SPAX) framework, we aimed to distinguish collagen presence and evaluate its correlation with fibrosis progression. We employed an established diethylnitrosamine (DEN) model in rats to study liver fibrosis over various time points. Our results revealed a significant correlation between increased collagen photoacoustic signal intensity and advanced fibrosis stages. Collagen abundance maps displayed dynamic changes throughout fibrosis progression. These findings underscore the potential of sPAI for the noninvasive monitoring of collagen dynamics and fibrosis severity assessment. This research advances the development of noninvasive diagnostic tools and personalized management strategies for liver fibrosis.

Monitoring land use change dynamics is critical for tackling food security, climate change, and biodiversity loss on a global scale. This study is designed to classify land use and land cover in the upper Blue Nile River Basin (BNRB) using a random forest (RF) algorithm. The Landsat images for Landsat 45, Landsat 7, and Landsat 8 are used for classification purposes. The study area is classified into seven land use/land cover classes: cultivated lands, bare lands, built-ups, forests, grazing lands, shrublands, and waterbodies. The accuracy of classified images is 83%, 85%, and 91% using the Kappa index of agreements. From 1983 to 2022 periods, cultivated lands and built-up areas increased by 47541 and 1777 km2, respectively, at the expense of grazing lands, shrublands, and forests. Furthermore, the area of water bodies has increased by 662 km2 due to the construction of small and large-scale irrigation and hydroelectric power generation dams. The main factors that determine agricultural land expansion are related to population growth. Therefore, land use and land cover change detection using a random forest is an important technique for multispectral satellite data classification to understand the optimal use of natural resources, conservation practices, and decision-making for sustainable development.

Early detection and removal of bladder cancer in patients is crucial to prevent tumor recurrence and progression. Because current imaging techniques may fail to detect small lesions of in situ carcinomas, patients with bladder cancer often relapse after initial diagnosis, thereby requiring frequent follow-up and treatments.