Breast conserving surgery is the preferred treatment for women diagnosed with early stage invasive breast cancer. To ensure successful breast conserving surgeries, efficient tumour margin resection is required for minimizing tumour recurrence. Currently surgeons rely on touch preparation cytology or frozen section analysis to assess tumour margin status intraoperatively. These techniques have suboptimal accuracy and are time-consuming. Tumour margin status is eventually confirmed using postoperative histopathology that takes several days. Thus, there is a need for a real-time, accurate, automated guidance tool that can be used during tumour resection intraoperatively to assure complete tumour removal in a single procedure. In this paper, we evaluate feasibility of a 3-dimensional scanner that relies on Raman Spectroscopy to assess the entire margins of a resected specimen within clinically feasible time. We initially tested this device on a phantom sample that simulated positive tumour margins. This device first scans the margins of the sample and then depicts the margin status in relation to an automatically reconstructed image of the phantom sample. The device was further investigated on breast tissues excised from prophylactic mastectomy specimens. Our findings demonstrate immense potential of this device for automated breast tumour margin assessment to minimise repeat invasive surgeries.

Real-world data sources, including electronic health records (EHRs) and personal digital device data, are increasingly available, but are often siloed and cannot be easily integrated for clinical, research, or regulatory purposes. We conducted a prospective cohort study of 60 patients undergoing bariatric surgery or catheter-based atrial fibrillation ablation at two U.S. tertiary care hospitals, testing the feasibility of using a patient-centered health-data-sharing platform to obtain and aggregate health data from multiple sources. We successfully obtained EHR data for all patients at both hospitals, as well as from ten additional health systems, which were successfully aggregated with pharmacy data obtained for patients using CVS or Walgreens pharmacies; personal digital device data from activity monitors, digital weight scales, and single-lead ECGs, and patient-reported outcome measure data obtained through surveys to assess post-procedure recovery and disease-specific symptoms. A patient-centered health-data-sharing platform successfully aggregated data from multiple sources.

Insulin secretion defects are central to the development of type II diabetes mellitus. Glucose stimulation of insulin secretion has been extensively studied, but its regulation by other stimuli such as incretins and neurotransmitters is not as well understood. We investigated the mechanisms underlying the inhibition of insulin secretion by dopamine, which is synthesized in pancreatic β-cells from circulating L-dopa. Previous research has shown that this inhibition is mediated primarily by activation of the dopamine receptor D3 subtype (DRD3), even though both DRD2 and DRD3 are expressed in β-cells. To understand this dichotomy, we investigated the dynamic interactions between the dopamine receptor subtypes and their G-proteins using two-color fluorescence fluctuation spectroscopy (FFS) of mouse MIN6 β-cells. We show that proper membrane localization of exogenous G-proteins depends on both the Gβ and Gγ subunits being overexpressed in the cell. Triple transfections of the dopamine receptor subtype and Gβ and Gγ subunits, each labeled with a different-colored fluorescent protein (FP), yielded plasma membrane expression of all three FPs and permitted an FFS evaluation of interactions between the dopamine receptors and the Gβγ complex. Upon dopamine stimulation, we measured a significant decrease in interactions between DRD3 and the Gβγ complex, which is consistent with receptor activation. In contrast, dopamine stimulation did not cause significant changes in the interactions between DRD2 and the Gβγ complex. These results demonstrate that two-color FFS is a powerful tool for measuring dynamic protein interactions in living cells, and show that preferential DRD3 signaling in β-cells occurs at the level of G-protein release.

Real-world data sources, including electronic health records (EHR) and personal digital device data, are increasingly available, but are often siloed and cannot be easily integrated for clinical, research, or regulatory purposes. We conducted a prospective cohort study of 60 patients undergoing bariatric surgery or catheter-based atrial fibrillation ablation at two U.S. tertiary care hospitals, testing the feasibility of using a patient-centered health data sharing platform to obtain and aggregate health data from multiple sources. We successfully obtained EHR data for all patients at both hospitals, as well as from 10 additional health systems, which were successfully aggregated with pharmacy data obtained for patients using CVS or Walgreens pharmacies, personal digital device data from activity monitors, digital weight scales, and single-lead ECGs, and patient-reported outcome measure data obtained through surveys to assess post-procedure recovery and disease-specific symptoms. A patient-centered health data sharing platform successfully aggregated data from multiple sources.