Fusing Positive and Negative CT Contrast Nanoagent for the Sensitive Detection of Hepatoma.

Positive computed tomography (CT) contrast nanoagent has significant applications in diagnosing tumors. However, the sensitive differentiation between hepatoma and normal liver tissue remains challenging. This challenge arises primarily because both normal liver and hepatoma tissues capture the nanoagent, resulting in similar positive CT contrasts. Here, a strategy for fusing positive and negative CT contrast nanoagent is proposed to detect hepatoma. A nanoagent Hf-MOF@AB@PVP initially generates a positive CT contrast signal of 120.3 HU in the liver. Subsequently, it can specifically respond to the acidic microenvironment of hepatoma to generate H2 , further achieving a negative contrast of -96.0 HU. More importantly, the relative position between the negative and positive signals area is helpful to determine the location of hepatoma and normal liver tissues. The distinct contrast difference of 216.3 HU and relative orientation between normal liver and tumor tissues are meaningful to sensitively distinguish hepatoma from normal liver tissue utilizing CT imaging.

Altered functional connectivity within the brain fear circuit in Parkinson's disease with anxiety: A seed-based functional connectivity study.

Objectives: Aimed to investigate whether there are abnormal changes in the functional connectivity (FC) between the amygdala with other brain areas, in Parkinson's disease (PD) patients with anxiety. Methods: Participants were enrolled prospectively, and the Hamilton Anxiety Rating (HAMA) Scale was used to quantify anxiety disorder. Rest-state functional MRI (rs-fMRI) was applied to analyze the amygdala FC patterns among anxious PD patients, non-anxious PD patients, and healthy controls. Results: Thirty-three PD patients were recruited, 13 with anxiety, 20 without anxiety, and 19 non-anxious healthy controls. In anxious PD patients, FC between the amygdala with the hippocampus, putamen, intraparietal sulcus, and precuneus showed abnormal alterations compared with non-anxious PD patients and healthy controls. In particular, FC between the amygdala and hippocampus negatively correlated with the HAMA score (r = -0.459, p = 0.007). Conclusion: Our results support the role of the fear circuit in emotional regulation in PD with anxiety. Also, the abnormal FC patterns of the amygdala could preliminarily explain the neural mechanisms of anxiety in PD.

A novel CEST-contrast nanoagent for differentiating the malignant degree in breast cancer.

Different subtypes of breast cancer (BCC) have variable degrees of malignancy, which is closely related to their extracellular pH (pHe). Therefore, it is increasingly significant to monitor the extracellular pH sensitively to further determine the malignancy of different subtypes of BCC. Here, a l-arginine and Eu3+ assembled nanoparticle Eu3+@l-Arg was prepared to detect the pHe of two breast cancer models (TUBO is non-invasive and 4T1 is malignant) using a clinical chemical exchange saturation shift imaging technique. The experiments in vivo showed that Eu3+@l-Arg nanomaterials could respond sensitively to changes of pHe. In 4T1 models, the CEST signal enhanced about 5.42 times after Eu3+@l-Arg nanomaterials were used to detect the pHe. In contrast, few enhancements of the CEST signal were seen in the TUBO models. This significant difference had led to new ideas for identifying subtypes of BCC with different degrees of malignancy.

A predictive nomogram for intracerebral hematoma expansion based on non-contrast computed tomography and clinical features.

PURPOSE: To develop and validate a new nomogram utilizing non-contrast computed tomography (NCCT) signs and clinical factors for predicting hematoma expansion (HE) in patients with spontaneous intracerebral hemorrhage (ICH). METHODS: HE was defined as > 6 mL or 33% increase in baseline hematoma volume. Multivariable logistic regression analysis was performed to identify the predictors of HE. The discriminatory performance of the proposed model was evaluated via receiver operation characteristic (ROC) analysis, and the predictive accuracy was assessed by a calibration curve. The nomogram was established by R programming language. The decision curve analysis and clinical impact curve were drawn according to the related risk factors. RESULTS: A total of 506 patients with spontaneous ICH were recruited in the development cohort, and 103 patients were registered as the external validation cohort. Among the development cohort, 132 (26.09%) experienced HE. Glasgow coma scale (GCS) (P < 0.001), neutrophil to lymphocyte ratio (NLR) (P < 0.001), blend sign (P < 0.001), swirl sign (P < 0.001), and hypodensities (P = 0.003) were significant predictors of HE, by which were used to establish the nomogram. The model demonstrated good performance with high area under the curve both in the development (AUC = 0.908; 95% confidence interval, 0.880-0.936) and the external validation (AUC = 0.844; 95% confidence interval, 0.760-0.908) cohort. The calibration curve illustrated a high accuracy for HE prediction. CONCLUSION: The nomogram derived from NCCT markers and clinical factors outperformed the NCCT signs-only model in predicting HE for patients with ICH, thus providing an effective and noninvasive tool for the risk stratification of HE.