DNA Self-Assembly Generated by Aptamer-Triggered Rolling Circle Amplification Cascades for Profiling Colorectal Cancer-Derived Small Extracellular Vesicles.

The analysis of small extracellular vesicles (sEVs) has shown clinical significance in early cancer diagnostics and considerable potential in prognostic assessment and therapeutic monitoring, offering possibilities for precise clinical intervention. Despite recent diagnostic progress based on blood-derived sEVs, the inability to specifically profile multiple parameters of sEVs proteins has hampered advancement in clinical applications. Herein, we report an approach to profile colorectal cancer (CRC)-derived sEVs by using multiaptamer-triggered rolling circle amplification (RCA) cascades.

A clinical-radiomic-pathomic model for prognosis prediction in patients with hepatocellular carcinoma after radical resection.

Purpose: Radical surgery, the first-line treatment for patients with hepatocellular cancer (HCC), faces the dilemma of high early recurrence rates and the inability to predict effectively. We aim to develop and validate a multimodal model combining clinical, radiomics, and pathomics features to predict the risk of early recurrence.

Materials And Methods: We recruited HCC patients who underwent radical surgery and collected their preoperative clinical information, enhanced computed tomography (CT) images, and whole slide images (WSI) of hematoxylin and eosin (H & E) stained biopsy sections.

Enhancing Specific Fluorescence In Situ Hybridization with Quantum Dots for Single-Molecule RNA Imaging in Formalin-Fixed Paraffin-Embedded Tumor Tissues.

Single-molecule fluorescence in situ hybridization (smFISH) represents a promising approach for the quantitative analysis of nucleic acid biomarkers in clinical tissue samples. However, low signal intensity and high background noise are complications that arise from diagnostic pathology when performed with smFISH-based RNA imaging in formalin-fixed paraffin-embedded (FFPE) tissue specimens. Moreover, the associated complex procedures can produce uncertain results and poor image quality.

Radiomics-guided prognostic assessment of early-stage hepatocellular carcinoma recurrence post-radical resection.

Purpose: The prognosis of early-stage hepatocellular carcinoma (HCC) patients after radical resection has received widespread attention, but reliable prediction methods are lacking. Radiomics derived from enhanced computed tomography (CT) imaging offers a potential avenue for practical prognostication in HCC patients.

Methods: We recruited early-stage HCC patients undergoing radical resection.

Detecting urine metabolites of bladder cancer by surface-enhanced Raman spectroscopy.

Aim: Metabolites present in urine reflect the current phenotype of the cancer state. Surface-enhanced Raman spectroscopy (SERS) can be used in urine supernatant or sediment to largely reflect the metabolic status of the body.

Materials & Methods: SERS was performed to detect bladder cancer (BCa) and predict tumour grade from urine supernatant, which contains various system metabolites, as well as from urine sediment, which contains exfoliated tumour cells.

Label-free detection of multiple genitourinary cancers from urine by surface-enhanced Raman spectroscopy.

Detecting cancers through testing biological fluids, namely, "liquid biopsy", is noninvasive and shows great promise in cancer diagnosis, surveillance and screening. Many metabolites that may reflect cancer specificity are concentrated in and excreted through urine. In this study, urine samples were collected from healthy subjects and patients with bladder or prostate cancer.

A surface-enhanced Raman scattering-based probe method for detecting chromogranin A in adrenal tumors.

We aim to demonstrate that a surface-enhanced Raman spectroscopy (SERS) probe can be effectively used for protein detection in adrenal tumors. The SERS probe method, which uses Au@Ag core-shell nanoparticles conjugated with a CgA antibody and a SERS reporter, was applied to detect CgA in adrenal tumors. Our data reveal that the results of the CgA-SERS probe method were almost identical to those of western blot and superior to those of traditional immunohistochemistry.

Assessing the effectiveness of artificial intelligence methods for melanoma: A retrospective review.

Background: Artificial intelligence methods for the classification of melanoma have been studied extensively. However, few studies compare these methods under the same standards.

Objective: To seek the best artificial intelligence method for diagnosis of melanoma.

Establishment and Verification of a Bagged-Trees-Based Model for Prediction of Sentinel Lymph Node Metastasis for Early Breast Cancer Patients.

Lymph node metastasis is a multifactorial event. Several scholars have developed nomograph models to predict the sentinel lymph nodes (SLN) metastasis before operation. According to the clinical and pathological characteristics of breast cancer patients, we use the new method to establish a more comprehensive model and add some new factors which have never been analyzed in the world and explored the prospect of its clinical application.

Analysis and classification of kidney stones based on Raman spectroscopy.

The number of patients with kidney stones worldwide is increasing, and it is particularly important to facilitate accurate diagnosis methods. Accurate analysis of the type of kidney stones plays a crucial role in the patient's follow-up treatment. This study used microscopic Raman spectroscopy to analyze and classify the different mineral components present in kidney stones.