Autophagy inhibition overcomes sorafenib resistance in S45F-mutated desmoid tumors.

BACKGROUND: Desmoid tumors (DTs) are rare and understudied fibroblastic lesions that are frequently recurrent and locally invasive. DT patients often experience chronic pain, organ dysfunction, decrease in quality of life, and even death. METHODS: Sorafenib has emerged as a promising therapeutic strategy, which has led to the first randomized phase 3 clinical trial devoted to DTs. Concurrently, we conducted a comprehensive analysis of sorafenib efficacy in a large panel of desmoid cell strains to probe for response mechanism. RESULTS: We found distinctive groups of higher- and lower-responder cells. Clustering the lower-responder group, we observed that CTNNB1 mutation was determinant of outcome. Our results revealed that a lower dose of sorafenib was able to inhibit cell viability, migration, and invasion of wild-type and T41A-mutated DTs. Apoptosis induction was observed in those cells after treatment with sorafenib. On the other hand, the lower dose of sorafenib was not able to inhibit cell viability, migration, or invasion or to induce apoptosis in the S45F-mutated DTs. The investigation of autophagy showed the dependency of S45F-mutated DTs on this pathway as a part of cell survival mechanism. Significantly, when autophagy was inhibited genetically or pharmacologically in the S45F mutant cell strains, sensitivity to sorafenib was restored. CONCLUSIONS: Our findings suggest that the response to sorafenib differs when comparing S45F-mutated DTs and T41A-mutated or wild-type DTs. Furthermore, the combination of hydroxychloroquine and sorafenib enhances the antiproliferative and proapoptotic effects in S45F-mutated DT cells, suggesting that profiling ß-catenin status could guide clinical management of desmoid patients who are considering sorafenib treatment.

A novel multi-task machine learning classifier for rare disease patterning using cardiac strain imaging data.

To provide accurate predictions, current machine learning-based solutions require large, manually labeled training datasets. We implement persistent homology (PH), a topological tool for studying the pattern of data, to analyze echocardiography-based strain data and differentiate between rare diseases like constrictive pericarditis (CP) and restrictive cardiomyopathy (RCM). Patient population (retrospectively registered) included those presenting with heart failure due to CP (n = 51), RCM (n = 47), and patients without heart failure symptoms (n = 53). Longitudinal, radial, and circumferential strains/strain rates for left ventricular segments were processed into topological feature vectors using Machine learning PH workflow. In differentiating CP and RCM, the PH workflow model had a ROC AUC of 0.94 (Sensitivity = 92%, Specificity = 81%), compared with the GLS model AUC of 0.69 (Sensitivity = 65%, Specificity = 66%). In differentiating between all three conditions, the PH workflow model had an AUC of 0.83 (Sensitivity = 68%, Specificity = 84%), compared with the GLS model AUC of 0.68 (Sensitivity = 52% and Specificity = 76%). By employing persistent homology to differentiate the "pattern" of cardiac deformations, our machine-learning approach provides reasonable accuracy when evaluating small datasets and aids in understanding and visualizing patterns of cardiac imaging data in clinically challenging disease states.

Real-time motion-enabling positron emission tomography of the brain of upright ambulatory humans.

BACKGROUND: Mobile upright PET devices have the potential to enable previously impossible neuroimaging studies. Currently available options are imagers with deep brain coverage that severely limit head/body movements or imagers with upright/motion enabling properties that are limited to only covering the brain surface. METHODS: In this study, we test the feasibility of an upright, motion-compatible brain imager, our Ambulatory Motion-enabling Positron Emission Tomography (AMPET) helmet prototype, for use as a neuroscience tool by replicating a variant of a published PET/fMRI study of the neurocorrelates of human walking. We validate our AMPET prototype by conducting a walking movement paradigm to determine motion tolerance and assess for appropriate task related activity in motor-related brain regions. Human participants (n = 11 patients) performed a walking-in-place task with simultaneous AMPET imaging, receiving a bolus delivery of F18-Fluorodeoxyglucose. RESULTS: Here we validate three pre-determined measure criteria, including brain alignment motion artifact of less than <2 mm and functional neuroimaging outcomes consistent with existing walking movement literature. CONCLUSIONS: The study extends the potential and utility for use of mobile, upright, and motion-tolerant neuroimaging devices in real-world, ecologically-valid paradigms. Our approach accounts for the real-world logistics of an actual human participant study and can be used to inform experimental physicists, engineers and imaging instrumentation developers undertaking similar future studies. The technical advances described herein help set new priorities for facilitating future neuroimaging devices and research of the human brain in health and disease.

Brain imaging plays an important role in understanding how the human brain functions in both health and disease. However, traditional brain scanners often require people to remain still, limiting the study of the brain in motion, and excluding people who cannot remain still. To overcome this, our team developed an imager that moves with a person's head, which uses a suspended ring of lightweight detectors that fit to the head. Using our imager, we were able to obtain clear brain images of people walking in place that showed the expected brain activity patterns during walking. Further development of our imager could enable it to be used to better understand real-world brain function and behavior, enabling enhanced knowledge and treatment of neurological conditions.

Safety and efficacy of peripheral nerve blocks to treat refractory headaches after aneurysmal subarachnoid hemorrhage - A pilot observational study.

Objectives: Headache after aneurysmal subarachnoid hemorrhage (HASH) is common, severe, and often refractory to conventional treatments. Current treatment standards include medications including opioids, until the pain is mitigated. Peripheral nerve blocks (PNBs) may be an effective therapeutic option for HASH. We conducted a small before-and-after study of PNBs to determine safety, feasibility, and efficacy in treatment of HASH. Methods: We conducted a pilot before-and-after observational study and collected data for 5 patients in a retrospective control group and 5 patients in a prospective intervention PNB group over a 12-month period. All patients received a standard treatment of medications including acetaminophen, magnesium, gabapentin, dexamethasone and anti-spasmodics or anti-emetics as needed. Patients in the intervention group received bilateral greater occipital, lesser occipital, and supraorbital PNBs in addition to medications. The primary outcome was pain severity, measured by Numeric pain rating scale (NPRS). All patients were followed for 1 week following enrollment. Results: The mean ages in the PNB group and control group were 58.6 and 57.4, respectively. One patient in the control group developed radiographic vasospasm. Three patients in both groups had radiographic hydrocephalus and IVH, requiring external ventricular drain (EVD) placement. The PNB group had an average reduction in mean raw pain score of 2.76 (4.68, 1.92 p = 0.024), and relative pain score by 0.26 (0.48, 0.22 p = 0.026), compared to the control group. The reduction occurred immediately after PNB administration. Conclusion: PNB can be a safe, feasible and effective treatment modality for HASH. Further investigations with a larger sample size are warranted.

Ultrasonic Texture Features for Assessing Cardiac Remodeling and Dysfunction.

BACKGROUND: Changes in cardiac size, myocardial mass, cardiomyocyte appearance, and, ultimately, the function of the entire organ are interrelated features of cardiac remodeling that profoundly affect patient outcomes. OBJECTIVES: This study proposes that the application of radiomics for extracting cardiac ultrasonic textural features (ultrasomics) can aid rapid, automated assessment of left ventricular (LV) structure and function without requiring manual measurements. METHODS: This study developed machine-learning models using cardiac ultrasound images from 1,915 subjects in 3 clinical cohorts: 1) an expert-annotated cardiac point-of-care-ultrasound (POCUS) registry (n = 943, 80% training/testing and 20% internal validation); 2) a prospective POCUS cohort for external validation (n = 275); and 3) a prospective external validation on high-end ultrasound systems (n = 484). In a type 2 diabetes murine model, echocardiography of wild-type (n = 10) and Leptr-/- (n = 8) mice were assessed longitudinally at 3 and 25 weeks, and ultrasomics features were correlated with histopathological features of hypertrophy. RESULTS: The ultrasomics model predicted LV remodeling in the POCUS and high-end ultrasound external validation studies (area under the curve: 0.78 [95% CI: 0.68-0.88] and 0.79 [95% CI: 0.73-0.86], respectively). Similarly, the ultrasomics model predicted LV remodeling was significantly associated with major adverse cardiovascular events in both cohorts (P < 0.0001 and P = 0.0008, respectively). Moreover, on multivariate analysis, the ultrasomics probability score was an independent echocardiographic predictor of major adverse cardiovascular events in the high-end ultrasound cohort (HR: 8.53; 95% CI: 4.75-32.1; P = 0.0003). In the murine model, cardiomyocyte hypertrophy positively correlated with 2 ultrasomics biomarkers (R2 = 0.57 and 0.52, Q < 0.05). CONCLUSIONS: Cardiac ultrasomics-based biomarkers may aid development of machine-learning models that provide an expert-level assessment of LV structure and function.