Postmenopausal endometrial non-benign lesion risk classification through a clinical parameter-based machine learning model.

OBJECTIVE: This study aimed to develop and evaluate a machine learning model utilizing non-invasive clinical parameters for the classification of endometrial non-benign lesions, specifically atypical hyperplasia (AH) and endometrioid carcinoma (EC), in postmenopausal women. METHODS: Our study collected clinical parameters from a cohort of 999 patients with postmenopausal endometrial lesions and conducted preprocessing to identify 57 relevant characteristics from these irregular clinical data. To predict the presence of postmenopausal endometrial non-benign lesions, including atypical hyperplasia and endometrial cancer, we employed various models such as eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Back Propagation Neural Network (BPNN), as well as two ensemble models. Additionally, a test set was performed on an independent dataset consisting of 152 patients. The performance evaluation of all models was based on metrics including the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, precision, and F1 score. RESULTS: The RF model demonstrated superior recognition capabilities for patients with non-benign lesions compared to other models. In the test set, it attained a sensitivity of 88.1% and an AUC of 0.93, surpassing all alternative models evaluated in this study. Furthermore, we have integrated this model into our hospital's Clinical Decision Support System (CDSS) and implemented it within the outpatient electronic medical record system to continuously validate and optimize its performance. CONCLUSIONS: We have trained a model and deployed a system with high discriminatory power that may provide a novel approach to identify patients at higher risk of postmenopausal endometrial non-benign lesions who may benefit from more tailored screening and clinical intervention.

Metabolism/Immunity Dual-Regulation Thermogels Potentiating Immunotherapy of Glioblastoma Through Lactate-Excretion Inhibition and PD-1/PD-L1 Blockade.

Intrinsic immunosuppressive tumor microenvironment (ITM) and insufficient tumor infiltration of T cells severely impede the progress of glioblastoma (GBM) immunotherapy. In this study, it is identify that inhibiting the expression of glucose transporter 1 (GLUT1) can facilitate the prevention of lactate excretion from tumor glycolysis, which significantly alleviates the lactate-driven ITM by reducing immunosuppressive tumor-associated macrophages (TAMs) and regulatory T cells (Tregs). Simultaneously, the findings show that the generated inflammatory cytokine IFN-γ during immune activation aggravates the immune escape by upregulating immune checkpoint programmed death-ligand 1 (PD-L1) in tumor cells and TAMs. Therefore, an injectable thermogel loaded with a GLUT1 inhibitor BAY-876 and a PD-1/PD-L1 blocker BMS-1 (Gel@B-B) for dual-regulation of metabolism and immunity of GBM is developed. Consequently, in situ injection of Gel@B-B significantly delays tumor growth and prolongs the survival of the orthotopic GBM mouse model. By actively exposing tumor antigens to antigen-presenting cells, the GBM vaccine combined with Gel@B-B is found to significantly increase the fraction of effector T cells (Th1/CTLs) in the tumor microenvironment, thereby remarkably mitigating tumor recurrence long-term. This study may provide a promising strategy for GBM immunotherapy.

Value of Radiomic Analysis Combined With Diffusion Tensor Imaging in Early Diagnosis of HIV-Associated Neurocognitive Disorders.

BACKGROUND: The combination of radiomics and diffusion tensor imaging (DTI) may have potential clinical value in the early stage of HIV-associated neurocognitive disorders (HAND). PURPOSE: To investigate the value of DTI-based radiomics in the early stage of HAND in people living with HIV (PLWH). STUDY TYPE: Retrospective. POPULATION: A total of 138 male PLWH were included, including 68 with intact cognition (IC) and 70 with asymptomatic neurocognitive impairment (ANI). Seventy healthy controls (HCs) were recruited for tract-based spatial statistics (TBSS) analysis. All PLWHs were randomly divided into training and validation cohorts at a 7:3 ratio. FIELD STRENGTH/SEQUENCE: A 3 T, single-shot spin-echo echo planar imaging (EPI). ASSESSMENT: The differences between the PLWH groups were compared using TBSS and region of interest (ROI) analysis. Radiomic features were extracted from the corpus callosum (CC) on DTI postprocessed images, including fractional anisotropy (FA), axial diffusivity (AD), mean diffusivity (MD), and radial diffusivity (RD). The performance of the radiomic signatures was evaluated by ROC curve analysis. The radiomic signature with the highest area under the curve (AUC) was combined with clinical characteristics to construct a nomogram. Decision curve analysis (DCA) was performed to evaluate the ability of different methods in discriminating ANI. STATISTICAL TESTS: Chi-square test, independent-samples t test, Kruskal-Wallis test, Mann-Whitney U test, threshold-free cluster enhancement (TFCE), ROC curve analysis, DCA, multivariate logistic regression analysis, Hosmer-Lemeshow test. P < 0.05 with TFCE corrected and P < 0.0001 without TFCE corrected were considered statistically significant. RESULTS: The ANI group showed lower FA and higher AD than the IC group. In the validation cohort, the AUCs of the FA-, AD-, MD- and RD-based radiomic signatures and the clinicoradiomic nomogram were 0.829, 0.779, 0.790, 0.864, and 0.874, respectively. DCA revealed that the nomogram was of greater clinical value than TBSS analysis, the clinical models, and the RD-based radiomic signature. DATA CONCLUSION: The combination of DTI and radiomics is correlated with early stage of HAND in PLWH. EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 2.

Enzyme-like copper-encapsulating magnetic nanoassemblies for switchable T1-weighted MRI and potentiating chemo-/photo-dynamic therapy.

Photodynamic therapy (PDT) has become a promising cancer treatment due to in situ generation of cytotoxic reactive oxygen (ROS); however, it remains limited by the hypoxia of tumor microenvironment (TME) and penetration depth of laser. Herein, we developed a kind of GSH-/H2O2-responsive copper-encapsulating magnetic nanoassemblies (MNSs) for switchable T1-weighted magnetic resonance imaging (MRI) and enzyme-like activity potentiating PDT of cancer. MNSs were rationally constructed using the chelation effect of copper ions (Cu2+) with polyacrylic acid-coated ultrasmall iron oxide nanoparticles (UIONPs). After uptake by tumor cells, the incorporated Cu2+ of MNSs was reduced to Cu+ through the intracellular GSH, which resulted in the disassembly of MNSs accompanied by the "silenced" MR signal shifting to a positive state. Sequentially, the generated Cu+ manifested peroxidase-like activity, catalyzing local H2O2 in TME to cytotoxic ·OH for chemodynamic therapy. Furthermore, Cu2+ and UIONPs could decompose H2O2 to O2, thus providing extra oxygen necessary for enhancing the PDT effect of photosensitizer IR-780. Finally, IR-780-loading MNSs (MNSs@IR-780) under laser irradiation significantly inhibited tumor growth and prolonged the survival of gastric MGC-803 tumor-bearing mice. Therefore, this study provides a versatile nanoplatform as a tumor-responsive theragnostic agent. STATEMENT OF SIGNIFICANCE: Tumor hypoxia and penetration depth of laser severely hindered the PDT of cancer. Valence-convertible metal ions (VCMI, e.g., Cu2+/Cu+, Fe3+/Fe2+) have been reported as Fenton-like agents disintegrating H2O2 to O2 to enhance PDT. Tumor-delivery of VCMI is of essential importance for in situ triggering of a Fenton-like reaction. We thereby developed magnetic nanoassemblies (MNSs) to encapsulate Cu2+ and load photosensitizer (IR-780). Stimulated by GSH and H2O2, MNSs performed catalase/peroxidase-like activity that provided extra O2 for PDT and catalyzed H2O2 to ·OH for CDT. Consequently, IR-780-loading MNSs under laser irradiation significantly inhibit the tumor growth due to effective tumor delivery of Cu2+ and IR-780. This study might offer a feasible nanoplatform for tumor-delivery of metal ions and drugs.

Increased modularity of the resting-state network in children with nonsyndromic cleft lip and palate after speech rehabilitation.

INTRODUCTION: Speech therapy is the primary management followed the physical management through surgery for children with nonsyndromic cleft lip and palate (NSCLP). However, the topological pattern of the resting-state network after rehabilitation remains poorly understood. We aimed to explore the functional topological pattern of children with NSCLP after speech rehabilitation compared with healthy controls. METHODS: We examined 28 children with NSCLP after speech rehabilitation (age = 10.0 ± 2.3 years) and 28 healthy controls for resting-state functional MRI. We calculated functional connections and the degree strength, betweenness centrality, network clustering coefficient (Cp), characteristic path length (Lp), global network efficiency (Eg), local network efficiency (Eloc), modularity index (Q), module number, and participation coefficient for the between-group differences using two-sample t tests (corrected p < .05). Additionally, we performed a correlation analysis between the Chinese language clear degree scale (CLCDS) scores and topological properties in children with NSCLP. RESULTS: We detected significant between-group differences in the areas under the curve (AUCs) of degree strength and betweenness centrality in language-related brain regions. There were no significant between-group differences in module number, participation coefficient, Cp, Lp, Eg, or Eloc. However, the Q (density: 0.05-0.30) and QAUC (t = 2.46, p = .02) showed significant between-group differences. Additionally, there was no significant correlation between topological properties of statistical between-group differences and CLCDS scores. CONCLUSIONS: Although nodal metric differences existed in the language-related brain regions, the children with NSCLP after speech rehabilitation had similar global network properties, module numbers, and participation coefficient, but increased modularity. Our results suggested that children with NSCLP achieved speech rehabilitation through function specialization in the language-related brain regions. The resting-state topology pattern could be of substantive neurobiological importance and potential imaging biomarkers for speech rehabilitation.