Injectable Thermosensitive Gel CH-BPNs-NBP for Effective Periodontitis Treatment through ROS-Scavenging and Jaw Vascular Unit Protection

Periodontitis, a prevalent inflammatory condition in the oral cavity, is closely associated with oxidative stress-induced tissue damage mediated by excessive reactive oxygen species (ROS) production. The jaw vascular unit (JVU), encompassing both vascular and lymphatic vessels, plays a crucial role in maintaining tissue fluid homeostasis and contributes to the pathological process in inflammatory diseases of the jaw. This study presents a novel approach for treating periodontitis through the development of an injectable thermosensitive gel (CH-BPNs-NBP). The gel formulation incorporates black phosphorus nanosheets (BPNs), which are notable for their ROS-scavenging properties, and dl-3-n-butylphthalide (NBP), a vasodilator that promotes lymphatic vessel function within the JVU. These results demonstrate that the designed thermosensitive gel serve as a controlled release system, delivering BPNs and NBP to the site of inflammation. CH-BPNs-NBP not only protects macrophages and human lymphatic endothelial cells from ROS attack but also promotes M2 polarization and lymphatic function. In in vivo studies, this work observes a significant reduction in inflammation and tissue damage, accompanied by a notable promotion of alveolar bone regeneration. This research introduces a promising therapeutic strategy for periodontitis, leveraging the unique properties of BPNs and NBP within an injectable thermosensitive gel.

MRI-Based Machine Learning Radiomics for Preoperative Assessment of Human Epidermal Growth Factor Receptor 2 Status in Urothelial Bladder Carcinoma

BACKGROUND

The human epidermal growth factor receptor 2 (HER2) has recently emerged as hotspot in targeted therapy for urothelial bladder cancer (UBC). The HER2 status is mainly identified by immunohistochemistry (IHC), preoperative and noninvasive methods for determining HER2 status in UBC remain in searching.

PURPOSES

To investigate whether radiomics features extracted from MRI using machine learning algorithms can noninvasively evaluate the HER2 status in UBC.

STUDY TYPE

Retrospective.

POPULATION

One hundred ninety-five patients (age: 68.7 ± 10.5 years) with 14.3% females from January 2019 to May 2023 were divided into training (N = 156) and validation (N = 39) cohorts, and 43 patients (age: 67.1 ± 13.1 years) with 13.9% females from June 2023 to January 2024 constituted the test cohort (N = 43).

FIELD STRENGTH/SEQUENCE

3 T, T2-weighted imaging (turbo spin-echo), diffusion-weighted imaging (breathing-free spin echo).

ASSESSMENT

The HER2 status were assessed by IHC. Radiomics features were extracted from MRI images. Pearson correlation coefficient and the least absolute shrinkage and selection operator (LASSO) were applied for feature selection, and six machine learning models were established with optimal features to identify the HER2 status in UBC.

STATISTICAL TESTS

Mann-Whitney U-test, chi-square test, LASSO algorithm, receiver operating characteristic analysis, and DeLong test.

RESULTS

Three thousand forty-five radiomics features were extracted from each lesion, and 22 features were retained for analysis. The Support Vector Machine model demonstrated the best performance, with an AUC of 0.929 (95% CI: 0.888-0.970) and accuracy of 0.859 in the training cohort, AUC of 0.886 (95% CI: 0.780-0.993) and accuracy of 0.846 in the validation cohort, and AUC of 0.712 (95% CI: 0.535-0.889) and accuracy of 0.744 in the test cohort.

DATA CONCLUSION

MRI-based radiomics features combining machine learning algorithm provide a promising approach to assess HER2 status in UBC noninvasively and preoperatively.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 3.