Dual Stealth Functional Immuno-magnetic Nanoparticles for High-Performance Isolation of Circulating Tumor Cells.

As a biomarker of hepatocellular carcinoma (HCC) biopsy, circulating tumor cells (CTCs) are often used in the diagnosis of cancer and treatment guidance. For CTCs detection, immuno-magnetic nanoparticles (IMNs) are one of the most commonly used platforms. However, the nonspecific adsorption of proteins and non-tumor cells weakens the performance of IMNs to capture CTCs. In this work, we developed an IMNs platform which was constructed by a biomimetic protein corona precoating and a polyethylene glycol (PEG) spacer to form the PEG and corona-coated IMNs (IP-CMNs). Due to the dual stealth effect of protein corona precoating and PEG spacer, the nonspecific protein adsorption and cell binding of P-CMNs could reduce by ∼5.5- and ∼5.4-fold, respectively, compared with those of unmodified particles. Furthermore, the PEG spacer could not only reduce the interaction between IP-CMNs and leukocytes but also enhance the capture performance toward tumor cells. By using artificial blood samples, the capture efficiency of IP-CMNs toward rare CTCs was found to be 88.3%, while it was 70.5% by using commercial IMNs. Finally, CTCs were successfully isolated in all HCC patient blood samples (7/7) using IP-CMNs. These results provide insight into the use of the multifunctional nanoplatform as a useful tool for CTCs detection.

Establishment and validation of an AI-aid method in the diagnosis of myocardial perfusion imaging.

BACKGROUND: This study aimed to develop and validate an AI (artificial intelligence)-aid method in myocardial perfusion imaging (MPI) to differentiate ischemia in coronary artery disease. METHODS: We retrospectively selected 599 patients who had received gated-MPI protocol. Images were acquired using hybrid SPECT-CT systems. A training set was used to train and develop the neural network and a validation set was used to test the predictive ability of the neural network. We used a learning technique named "YOLO" to carry out the training process. We compared the predictive accuracy of AI with that of physician interpreters (beginner, inexperienced, and experienced interpreters). RESULTS: Training performance showed that the accuracy ranged from 66.20% to 94.64%, the recall rate ranged from 76.96% to 98.76%, and the average precision ranged from 80.17% to 98.15%. In the ROC analysis of the validation set, the sensitivity range was 88.9 ~ 93.8%, the specificity range was 93.0 ~ 97.6%, and the AUC range was 94.1 ~ 96.1%. In the comparison between AI and different interpreters, AI outperformed the other interpreters (most P-value < 0.05). CONCLUSION: The AI system of our study showed excellent predictive accuracy in the diagnosis of MPI protocols, and therefore might be potentially helpful to aid radiologists in clinical practice and develop more sophisticated models.

Positron emission tomography/computed tomography findings of multiple cystic lymphangiomas in an adult: A case report.

BACKGROUND: Lymphangioma is a rare benign cystic tumor believed to be a proliferation of heterotopic lymphocytes. It is caused by congenital lymphatic dysplasia or other acquired factors related to surgery, trauma, infection, or cancer. In this article, we present the case of an adult patient who underwent multi-modal imaging and whose condition was finally confirmed to be multiple cystic lymphangiomas by pathological examination. CASE SUMMARY: A 61-year-old man was referred to our hospital for having suffered from painless gross hematuria for 2 wk. Multiple masses rising from the retroperitoneum and pelvis were found incidentally by urinary ultrasonography. Contrast-enhanced abdominal-pelvic computed tomography showed multiple well-defined hypodense cystic lesions without enhancement. The lesions showed no uptake on F-18-fluorodeoxyglucose positron emission tomography/computed tomography images. Exploratory laparotomy was performed, and the case was confirmed as multiple cystic lymphangiomas. CONCLUSION: When retroperitoneal and pelvic masses are found, clinicians should always consider cystic lymphangioma when making a differential diagnosis.