Gaussian mixture model-based cluster analysis of apparent diffusion coefficient values: a novel approach to evaluate uterine endometrioid carcinoma grade

Integrated machine learning-driven disulfidptosis profiling: CYFIP1 and EMILIN1 as therapeutic nodes in neuroblastoma

BACKGROUND

Neuroblastoma (NB), a prevalent pediatric solid tumor, presents formidable challenges due to its high malignancy and intricate pathogenesis. The role of disulfidptosis, a novel form of programmed cell death, remains poorly understood in the context of NB.

METHODS

Gaussian mixture model (GMM)-identified disulfidptosis-related molecular subtypes in NB, differential gene analysis, survival analysis, and gene set variation analysis were conducted subsequently. Weighted gene co-expression network analysis (WGCNA) selected modular genes most relevant to the disulfidptosis core pathways. Integration of machine learning approaches revealed the combination of the Least absolute shrinkage and selection operator (LASSO) and Random Survival Forest (RSF) provided optimal dimensionality reduction of the modular genes. The resulting model was validated, and a nomogram assessed disulfidptosis characteristics in NB. Core genes were filtered and subjected to tumor phenotype and disulfidptosis-related experiments.

RESULTS

GMM clustering revealed three distinct subtypes with diverse prognoses, showing significant variations in glucose metabolism, cytoskeletal structure, and tumor-related pathways. WGCNA highlighted the red module of genes highly correlated with disulfide isomerase activity, cytoskeleton formation, and glucose metabolism. The LASSO and RSF combination yielded the most accurate and stable prognostic model, with a significantly worse prognosis for high-scoring patients. Cytological experiments targeting core genes (CYFIP1, EMILIN1) revealed decreased cell proliferation, migration, invasion abilities, and evident cytoskeletal deformation upon core gene knockdown.

CONCLUSIONS

This study showcases the utility of disulfidptosis-related gene scores for predicting prognosis and molecular subtypes of NB. The identified core genes, CYFIP1 and EMILIN1, hold promise as potential therapeutic targets and diagnostic markers for NB.

Predicting Mechanical Properties of Cold-Rolled Steel Strips Using Micro-Magnetic NDT Technologies

Multiple micro-magnetic non-destructive testing (NDT) technologies are suitable candidates for predicting the mechanical properties of cold-rolled steel strips. In this work, based on magnetic domain dynamics behavior and magnetization theory, the correlation between electromagnetic characteristics extracted by multiple micro-magnetic NDT technologies and the influence factors was investigated. It was found that temperature and tension can subsequently affect the electromagnetic parameters by altering the domain structure and domain walls’ motion properties. Pearson’s correlation coefficients were employed to reflect the dependence of micromagnetic characteristics on influencing factors. The lift-off was determined as the largest influence factor among influence factors. A pseudo-static detection was reached by polynomial fitting, which could eliminate the influence of lift-off on the detection results. The number of training models was optimized, and the detection accuracy was improved via the improved Generalized Regression Neural Network (GRNN) model, based on the Gaussian Mixture Clustering (GMC) algorithm.

Cryo-surgery for symptomatic extra-abdominal desmoids. A proof of concept study

BACKGROUND AND OBJECTIVE

Extra abdominal desmoid tumors are rare, highly aggressive, and invasive benign soft tissue tumors. Current treatment modalities show high levels of recurrence and comorbidities. Cryo-surgery as an alternative was subsequently investigated.

METHODS

In this retrospective, single center study 11 patients showing symptomatic tumors were treated with individualized cryo-surgery. Treatment protocol included preoperative planning using computer rendered 3D models, intraoperative navigation and execution using cone beam guidance, and postoperative magnetic resonance imaging image analysis using a gaussian mixture model software. Subjective outcomes were reported using Short Form Health Survey (SF-36) questionnaires.

RESULTS

Sixteen ablations were performed, each demonstrating a complete match with the determined preoperative plan and model. A total of 9/11 (82%) of patients showed improvements in symptoms and a reduction in tumor volume while 2/11 (18%) did not. Average reduction in tumor volume and viable segments were 36.7% (p = 0.0397) and 63.3% (p = 0.0477), respectively. Mild complications according to the SIR Adverse Event Classification Guidelines were experienced in 3/16 (19%) ablations. SF-36 scores showed a statistically significant improvement (p = 0.0194) in the mental health category and a nonsignificant (p = 0.8071) improvement in the physical health category.

CONCLUSION

Cryo-surgery using the three-phase protocol as described may improve the overall outcome of future ablation procedures.