The first generation ligands for prostate-specific membrane antigen (PSMA)–targeted radio- and fluorescence-guided surgery followed by adjuvant photodynamic therapy (PDT) have already shown the potential of this approach. Here, we developed three new photosensitizer-based dual-labeled PSMA ligands by crucial modification of existing PSMA ligand backbone structures (PSMA-1007/PSMA-617) for multimodal imaging and targeted PDT of PCa.
Various new PSMA ligands were synthesized using solid-phase chemistry and provided with a DOTA chelator for 111In labeling and the fluorophore/photosensitizer IRDye700DX. The performance of three new dual-labeled ligands was compared with a previously published first-generation ligand (PSMA-N064) and a control ligand with an incomplete PSMA-binding motif. PSMA specificity, affinity, and PDT efficacy of these ligands were determined in LS174T-PSMA cells and control LS174T wildtype cells. Tumor targeting properties were evaluated in BALB/c nude mice with subcutaneous LS174T-PSMA and LS174T wildtype tumors using µSPECT/CT imaging, fluorescence imaging, and biodistribution studies after dissection.
In order to synthesize the new dual-labeled ligands, we modified the PSMA peptide linker by substitution of a glutamic acid into a lysine residue, providing a handle for conjugation of multiple functional moieties. Ligand optimization showed that the new backbone structure leads to high-affinity PSMA ligands (all IC50 < 50 nM). Moreover, ligand-mediated PDT led to a PSMA-specific decrease in cell viability in vitro (P < 0.001). Linker modification significantly improved tumor targeting compared to the previously developed PSMA-N064 ligand (≥ 20 ± 3%ID/g vs 14 ± 2%ID/g, P < 0.01) and enabled specific visualization of PMSA-positive tumors using both radionuclide and fluorescence imaging in mice.
The new high-affinity dual-labeled PSMA-targeting ligands with optimized backbone compositions showed increased tumor targeting and enabled multimodal image-guided PCa surgery combined with targeted photodynamic therapy.
The visual systems found in nature rely on capturing light under different modalities, in terms of spectral sensitivities and polarization sensitivities. Numerous imaging techniques are inspired by this variety, among which, the most famous is color imaging inspired by the trichromacy theory of the human visual system. We investigate the spectral and polarimetric properties of biological imaging systems that will lead to the best performance on scene imaging through haze, i.e., dehazing. We design a benchmark experiment based on modalities inspired by several visual systems, and adapt state-of-the-art image reconstruction algorithms to those modalities. We show the difference in performance of each studied systems and discuss it in front of our methodology and the statistical relevance of our data.
Aortic valve-in-valve (ViV) procedure is a valid treatment option for patients affected by bioprosthetic heart valve (BHV) degeneration. However, ViV implantation is technically more challenging compared to native trans-catheter aortic valve replacement (TAVR). A deep knowledge of the mechanism and features of the failed BHV is pivotal to plan an adequate procedure. Multimodal imaging is fundamental in the diagnostic and pre-procedural phases. The main challenges associated with ViV TAVR consist of a higher risk of coronary obstruction, severe post-procedural patient-prosthesis mismatch, and a difficult coronary re-access. In this review, we describe the principles of ViV TAVR.
This study aimed to analyze the diagnostic value of multimodal images based on artificial intelligence target detection algorithms for early breast cancer, so as to provide help for clinical imaging examinations of breast cancer. This article combined residual block with inception block, constructed a new target detection algorithm to detect breast lumps, used deep convolutional neural network and ultrasound imaging in diagnosing benign and malignant breast lumps, took breast density grading with mammography, compared the convolutional neural network (CNN) algorithm with the proposed algorithm, and then applied the proposed algorithm to the diagnosis of 120 female patients with breast lumps. According to the results, accuracy rates of breast lump detection (94.76%), benign and malignant breast lumps diagnosis (98.22%), and breast grading (93.65%) with the algorithm applied in this study were significantly higher than those (75.67%, 87.23%, and 79.54%) with CNN algorithm, and the difference was statistically significant (
< 0.05); among 62 patients with malignant breast lumps of the 120 patients with breast lumps, 37 were patients with invasive ductal carcinoma, 8 with lobular carcinoma in situ, 16 with intraductal carcinoma, and 4 with mucinous carcinoma; among the remaining 58 patients with benign breast lumps, 28 were patients with fibrocystic breast disease, 17 with intraductal papilloma, 4 with breast hyperplasia, and 9 with adenopathy; the differences in shape, growth direction, edge, and internal echo of multimodal ultrasound imaging of patients with benign and malignant breast lumps had statistical significance (
< 0.05); the malignant constituent ratios of patients with breast density grades I to IV were 0%, 7.10%, 80.40%, and 100%, respectively. In short, the multimodal imaging diagnosis under the algorithm in this article was superior to CNN algorithm in all aspects; according to the judgment on benign and malignant breast lumps and breast density with multimodal imaging features, the higher the breast density, the higher the probability of breast cancer.