Deep learning-based 3D quantitative total tumor burden predicts early recurrence of BCLC A and B HCC after resection.

OBJECTIVES: This study aimed to evaluate the potential of deep learning (DL)-assisted automated three-dimensional quantitative tumor burden at MRI to predict postoperative early recurrence (ER) of hepatocellular carcinoma (HCC). MATERIALS AND METHODS: This was a single-center retrospective study enrolling patients who underwent resection for BCLC A and B HCC and preoperative contrast-enhanced MRI. Quantitative total tumor volume (cm3) and total tumor burden (TTB, %) were obtained using a DL automated segmentation tool. Radiologists' visual assessment was used to ensure the quality control of automated segmentation. The prognostic value of clinicopathological variables and tumor burden-related parameters for ER was determined by Cox regression analyses. RESULTS: A total of 592 patients were included, with 525 and 67 patients assigned to BCLC A and B, respectively (2-year ER rate: 30.0% vs. 45.3%; hazard ratio (HR) = 1.8; p = 0.007). TTB was the most important predictor of ER (HR = 2.2; p < 0.001). Using 6.84% as the threshold of TTB, two ER risk strata were obtained in overall (p < 0.001), BCLC A (p < 0.001), and BCLC B (p = 0.027) patients, respectively. The BCLC B low-TTB patients had a similar risk for ER to BCLC A patients and thus were reassigned to a BCLC An stage; whilst the BCLC B high-TTB patients remained in a BCLC Bn stage. The 2-year ER rate was 30.5% for BCLC An patients vs. 58.1% for BCLC Bn patients (HR = 2.8; p < 0.001). CONCLUSIONS: TTB determined by DL-based automated segmentation at MRI was a predictive biomarker for postoperative ER and facilitated refined subcategorization of patients within BCLC stages A and B. CLINICAL RELEVANCE STATEMENT: Total tumor burden derived by deep learning-based automated segmentation at MRI may serve as an imaging biomarker for predicting early recurrence, thereby improving subclassification of Barcelona Clinic Liver Cancer A and B hepatocellular carcinoma patients after hepatectomy. KEY POINTS: Total tumor burden (TTB) is important for Barcelona Clinic Liver Cancer (BCLC) staging, but is heterogenous. TTB derived by deep learning-based automated segmentation was predictive of postoperative early recurrence. Incorporating TTB into the BCLC algorithm resulted in successful subcategorization of BCLC A and B patients.

MRI radiomics based on deep learning automated segmentation to predict early recurrence of hepatocellular carcinoma.

OBJECTIVES: To investigate the utility of deep learning (DL) automated segmentation-based MRI radiomic features and clinical-radiological characteristics in predicting early recurrence after curative resection of single hepatocellular carcinoma (HCC). METHODS: This single-center, retrospective study included consecutive patients with surgically proven HCC who underwent contrast-enhanced MRI before curative hepatectomy from December 2009 to December 2021. Using 3D U-net-based DL algorithms, automated segmentation of the liver and HCC was performed on six MRI sequences. Radiomic features were extracted from the tumor, tumor border extensions (5 mm, 10 mm, and 20 mm), and the liver. A hybrid model incorporating the optimal radiomic signature and preoperative clinical-radiological characteristics was constructed via Cox regression analyses for early recurrence. Model discrimination was characterized with C-index and time-dependent area under the receiver operating curve (tdAUC) and compared with the widely-adopted BCLC and CNLC staging systems. RESULTS: Four hundred and thirty-four patients (median age, 52.0 years; 376 men) were included. Among all radiomic signatures, HCC with 5 mm tumor border extension and liver showed the optimal predictive performance (training set C-index, 0.696). By incorporating this radiomic signature, rim arterial phase hyperenhancement (APHE), and incomplete tumor "capsule," a hybrid model demonstrated a validation set C-index of 0.706 and superior 2-year tdAUC (0.743) than both the BCLC (0.550; p < 0.001) and CNLC (0.635; p = 0.032) systems. This model stratified patients into two prognostically distinct risk strata (both datasets p < 0.001). CONCLUSION: A preoperative imaging model incorporating the DL automated segmentation-based radiomic signature with rim APHE and incomplete tumor "capsule" accurately predicted early postsurgical recurrence of a single HCC. CRITICAL RELEVANCE STATEMENT: The DL automated segmentation-based MRI radiomic model with rim APHE and incomplete tumor "capsule" hold the potential to facilitate individualized risk estimation of postsurgical early recurrence in a single HCC. KEY POINTS: A hybrid model integrating MRI radiomic signature was constructed for early recurrence prediction of HCC. The hybrid model demonstrated superior 2-year AUC than the BCLC and CNLC systems. The model categorized the low-risk HCC group carried longer RFS.

Photoactivatable Engineering of CRISPR/Cas9-Inducible DNAzyme Probe for In Situ Imaging of Nuclear Zinc Ions.

DNAzyme-based fluorescent probes for imaging metal ions in living cells have received much attention recently. However, employing in situ metal ions imaging within subcellular organelles, such as nucleus, remains a significant challenge. We developed a three-stranded DNAzyme probe (TSDP) that contained a 20-base-pair (20-bp) recognition site of a CRISPR/Cas9, which blocks the DNAzyme activity. When Cas9, with its specialized nuclear localization function, forms an active complex with sgRNA within the cell nucleus, it cleaves the TSDP at the recognition site, resulting in the in situ formation of catalytic DNAzyme structure. With this design, the CRISPR/Cas9-inducible imaging of nuclear Zn2+ is demonstrated in living cells. Moreover, the superiority of CRISPR-DNAzyme for spatiotemporal control imaging was demonstrated by integrating it with photoactivation strategy and Boolean logic gate for dynamic monitoring nuclear Zn2+ in both HeLa cells and mice. Collectively, this conceptual design expands the DNAzyme toolbox for visualizing nuclear metal ions and thus provides new analytical methods for nuclear metal-associated biology.

Efficient Hapten-Specific Biopanning Strategy Based on the Fe3O4@ENR-Functionalized Core-Shell Magnetic Nanoparticles.

The biopanning of target-specific phages is one of the most critical steps in the preparation of single-domain antibodies. In the traditional biopanning of haptens, the nonspecific binding of library phages to macromolecular proteins is one of the most challenging problems in preparing single-domain antibodies. In this research, Fe3O4@ENR-functionalized core-shell magnetic nanoparticles (FMNPs) were silylated and aminated by tetraethyl orthosilicate and (3-aminopropyl)triethoxysilane, and target enrofloxacin was coupled onto the surface by the carbodiimide method. The magnetic nanoparticles were characterized by Fourier transform infrared spectroscopy, particle size distribution, zeta potential, transmission electron microscopy observation, and indirect enzyme-linked immunosorbent assay (ELISA). A biopanning strategy based on Fe3O4@ENR FMNPs was then established to solve the problem in the traditional solid-phase biopanning process. The results showed that a considerable number of enrofloxacin (ENR)-positive phages were screened by only one round of biopanning. Finally, two ENR-specific shark-derived single-domain genes were identified and validated by monoclonal phage ELISA, gene sequencing, and biolayer interferometry technology. Our study provides a new biopanning strategy based on Fe3O4@ENR FMNPs for efficiently providing phages specific to haptens.

Ultralow background one-pot detection of Lead(II) using a non-enzymatic double-cycle system mediated by a hairpin-involved DNAzyme.

A double-cycle system has been developed for specifically detecting trace amounts of Pb2+ by significantly decreasing the background signal. The detection involves two types of RNA cleavage reactions: one using a Pb2+-specific GR5 DNAzyme (PbDz) and the other utilizing a newly constructed 10-23 DNAzyme with two hairpins embedded in its catalytic center (hpDz). The ring-structured hpDz (c-hpDz) exhibits significantly lower activity compared to the circular 10-23 DNAzyme without hairpin structures, which plays a crucial role in reducing the background signal. When Pb2+ is present, PbDz cleaves c-hpDz to its active form, which then disconnects the molecular beacon to emit the fluorescent signal. The method allows for rapid and sensitive Pb2+ detection within 40 min for 10 fM of Pb2+ and even as short as 10 min for 100 nM of Pb2+. Additionally, visual detection is possible through the non-crosslinking assembly of Au nanoparticles. The entire process can be performed in one pot and even one step, making it highly versatile and suitable for a wide range of applications, including food safety testing and environmental monitoring.

Synthesis of Renal-Clearable Multicolor Fluorescent Silicon Nanodots for Tumor Imaging and In Vivo H2O2 Profiling.

Fluorescent silicon nanodots have shown great prospects for bioimaging and biosensing applications. Although various fluorescent silicon-containing nanodots (SiNDs) have been developed, there are few reports about renal-clearable multicolor SiNDs. Herein, renal-clearable multicolor fluorescent SiNDs are synthesized by using silane molecules and organic dyes through a facile one-step hydrothermal method. The fluorescence of the resulting SiNDs can be tuned to blue (bSiNDs), green (gSiNDs), and red (rSiNDs) by simply changing the categories of silane reagents or dye molecules. The as-prepared SiNDs exhibit strong fluorescence with a quantum yield up to 72%, excellent photostability, and good biocompatibility with 12 h renal clearance rate as high as 86% ID. These properties enabled the SiNDs for tumor fluorescence imaging and H2O2 imaging in living cells and tissue through in situ reduction reaction-lighted fluorescence of the nanoprobe. Our results provide an invaluable methodology for the synthesis of renal-clearable multicolor SiNDs and their potential applications for fluorescence imaging and biomarker sensing. These SiNDs are also promising for various biological and biomedical applications.

Near infrared imaging of intracellular GSH by AuNCs@MnO2 core-shell nanoparticles based on the absorption competition mechanism.

Dynamically monitoring intracellular glutathione (GSH), a crucial biomarker of oxidative stress, is of significance for the diagnosis and treatment of certain diseases. Although manganese dioxide (MnO2) based GSH fluorescent sensors have exhibited high sensitivity and good selectivity owing to the specific reactivity between GSH and MnO2, near-infrared (NIR) MnO2 based nanoprobes for GSH detection are scarce. Herein, we have developed a NIR activatable fluorescence nanoprobe for the imaging and determination of intracellular GSH based on a core-shell nanoparticle, consisting of NIR emitted gold nanocluster doped silica as the fluorescent core and manganese dioxide as the GSH-responsive shell (named AuNCs@MnO2). Due to the absorption competition mechanism, the outer MnO2 shell rather than the inner AuNCs core preferentially absorbed the excitation light, thus leading to fluorescence quenching of the inner AuNCs core. Upon addition of GSH, the fluorescence of the nanoprobe restored along with the reduction of MnO2 to Mn2+ because of the absorption competition disappearance-induced emission. The activatable fluorescence linearly increased upon changing the GSH concentration in the range of 2 to 5000 µM with a detection limit of 0.67 µM. The cytotoxicity test shows that the AuNCs@MnO2 nanoprobes have a good biocompatibility. After entering the cancer cells, the intracellular GSH degraded the outermost MnO2 shell and initiated the NIR fluorescence restoration of AuNCs, which can be used to monitor the dynamic change of intracellular GSH. This strategy provides an NIR-activatable way to detect GSH levels in living cells and offers a promising platform for the diagnosis and treatment of GSH-related diseases.

Nanomediator-Effector Cascade Systems for Amplified Protein Kinase Activity Imaging and Phosphorylation-Induced Drug Release In Vivo.

Protein kinases constitute a rich pool of biomarkers and therapeutic targets of tremendous diseases including cancer. However, sensing kinase activity in vivo while implementing treatments according to kinase hyperactivation remains challenging. Herein, we present a nanomediator-effector cascade system that can in situ magnify the subtle events of kinase-catalyzed phosphorylation via DNA amplification machinery to achieve kinase activity imaging and kinase-responsive drug release in vivo. In this cascade, the phosphorylation-mediated disassembly of DNA/peptide complex on the nanomediators initiated the detachment of fluorescent hairpin DNAs from the nanoeffectors via hybridization chain reaction (HCR), leading to fluorescence recovery and therapeutic cargo release. We demonstrated that this nanosystem simultaneously enabled trace protein kinase A (PKA) activity imaging and on-demand drug delivery for inhibition of tumor cell growth both in vitro and in vivo, affording a kinase-specific sense-and-treat paradigm for cancer theranostics.