Author Correction: MRgFUS subthalamotomy in Parkinson’s disease: an approach aimed at minimizing Lesion Volume
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Endometriosis is an enigmatic disease whose diagnosis and management are being transformed through innovative surgical, molecular, and computational technologies. Integrating single-cell and other omic disease data with clinical and surgical metadata can identify multiple disease subtypes with translation to novel diagnostics and therapeutics. Herein, we present real-world perspectives on endometriosis and the importance of multidisciplinary collaboration in informing molecular, epidemiologic, and cell-specific data in the clinical and surgical contexts.
An emerging role for neutrophils in the pathogenesis of endometriosis
Endometriosis is a chronic gynecological disease negatively impacting the health of women and is characterized by the presence of ectopic endometrial-like lesions. The immune system is implicated in endometriosis pathogenesis by promoting endometrial cell survival and creating a microenvironment for lesion development and growth. Neutrophils are phagocytic cells that degranulate, form neutrophil extracellular traps, and recruit immune cells to lesions. Herein we discuss the roles of neutrophils in endometriosis pathogenesis.
Immunomodulation by the combination of statin and matrix-bound nanovesicle enhances optic nerve regeneration
Modulating inflammation is critical to enhance nerve regeneration after injury. However, clinically applicable regenerative therapies that modulate inflammation have not yet been established. Here, we demonstrate synergistic effects of the combination of an HMG-CoA reductase inhibitor, statin/fluvastatin and critical components of the extracellular matrix, Matrix-Bound Nanovesicles (MBV) to enhance axon regeneration and neuroprotection after mouse optic nerve injury. Mechanistically, co-intravitreal injections of fluvastatin and MBV robustly promote infiltration of monocytes and neutrophils, which lead to RGC protection and axon regeneration. Furthermore, monocyte infiltration is triggered by elevated expression of CCL2, a chemokine, in the superficial layer of the retina after treatment with a combination of fluvastatin and MBV or IL-33, a cytokine contained within MBV. Finally, this therapy can be further combined with AAV-based gene therapy blocking anti-regenerative pathways in RGCs to extend regenerated axons. These data highlight novel molecular insights into the development of immunomodulatory regenerative therapy.
Interventional real-time molecular MRI for targeting early myocardial injury in a pig model
Myocardial ischemia induces tissue injury with subsequent inflammation and recruitment of immune cells. Besides myocardial tissue characterization, magnetic resonance imaging (MRI) allows for functional assessment using molecular imaging contrast agents. Here, we assessed ischemic cardiac lesions non-invasively directly after ischemia/reperfusion (I/R) in a porcine model by advanced MRI techniques and molecular imaging, targeting the cell adhesion molecule P-selectin functionalized with microparticles of iron oxide (MPIO). We used a closed-chest model of I/R by temporary coronary balloon-occlusion, real time 3T MRI-guided coronary injection of MPIO-based contrast agents, as well as injury, edema and iron-sensitive MRI. Within the first hours after I/R, we found T1 mapping to be most sensitive for tissue injury, with no changes in edema-sensitive MRI. Intriguingly, P-selectin MPIO contrast agent selectively enhanced the ischemic area in iron-sensitive MRI. In conclusion, this approach allows for sensitive detection of early myocardial inflammation beyond traditional edema-sensitive imaging.
Deep learning for detecting and early predicting chronic obstructive pulmonary disease from spirogram time series
Chronic Obstructive Pulmonary Disease (COPD) is a chronic lung condition characterized by airflow obstruction. Current diagnostic methods primarily rely on identifying prominent features in spirometry (Volume-Flow time series) to detect COPD, but they are not adept at predicting future COPD risk based on subtle data patterns. In this study, we introduce a novel deep learning-based approach, DeepSpiro, aimed at the early prediction of future COPD risk. DeepSpiro consists of four key components: SpiroSmoother for stabilizing the Volume-Flow curve, SpiroEncoder for capturing volume variability-pattern through key patches of varying lengths, SpiroExplainer for integrating heterogeneous data and explaining predictions through volume attention, and SpiroPredictor for predicting the disease risk of undiagnosed high-risk patients based on key patch concavity, with prediction horizons of 1–5 years, or even longer. Evaluated on the UK Biobank dataset, DeepSpiro achieved an AUC of 0.8328 for COPD detection and demonstrated strong predictive performance for future COPD risk (p-value < 0.001). In summary, DeepSpiro can effectively predict the long-term progression of COPD disease.
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