Intraoperative diagnosis is essential for providing safe and effective care during cancer surgery 1. The existing workflow for intraoperative diagnosis based on hematoxylin and eosin-staining of processed tissue is time-, resource-, and labor-intensive 2,3. Moreover, interpretation of intraoperative histologic images is dependent on a contracting, unevenly distributed pathology workforce 4. Here, we report a parallel workflow that combines stimulated Raman histology (SRH) 5-7 , a label-free optical imaging method, and deep convolutional neural networks (CNN) to predict diagnosis at the bedside in near real-time in an automated fashion. Specifically, our CNN, trained on over 2.5 million SRH images, predicts brain tumor diagnosis in the operating room in under 150 seconds, an order of magnitude faster than conventional techniques (e.g., 20-30 minutes) 2. In a multicenter, prospective clinical trial (n = 278) we demonstrated that CNN-based diagnosis of SRH images was non-inferior to pathologist-based interpretation of conventional histologic images (overall accuracy, 94.6% vs. 93.9%). Our CNN learned a hierarchy of recognizable histologic feature representations to classify the major histopathologic classes of brain tumors. Additionally, we implemented a semantic segmentation method to identify tumor infiltrated, diagnostic regions within SRH images. These results demonstrate how intraoperative cancer diagnosis can be streamlined, creating a complimentary pathway for tissue diagnosis that is independent of a traditional pathology laboratory.

INTRODUCTION Intraoperative diagnosis is essential for providing safe and effective care during cancer surgery. The existing workflow for intraoperative diagnosis based on H&E staining of processed tissue is time-, resource-, and labor-intensive. Moreover, interpretation of intraoperative histologic images is dependent on a pathology workforce that is contracting and unevenly distributed across the centers where cancer surgery is performed worldwide. METHODS We developed an automated workflow, independent of traditional H&E histology, that combines stimulated Raman histology (SRH), a rapid label-free optical imaging method, and deep convolutional neural networks (CNN) to predict diagnosis at the bedside in near real time. Specifically, our CNN, trained on over 2.5 million SRH images, predicts brain tumor diagnosis in the operating room in under 150 s, which is an order of magnitude faster than conventional techniques (eg, 20-30 min). RESULTS To validate our workflow in the clinical setting, we designed a multicenter, prospective, noninferiority clinical trial (N = 204) that compares SRH plus CNN vs traditional H&E histology. Primary endpoint was overall diagnostic accuracy. We show that CNN-based diagnosis of SRH images was noninferior to pathologist-based interpretation of conventional histologic images (overall accuracy, 94.6% vs 95.5%). Additionally, our CNN learned a hierarchy of interpretable histologic feature representations to classify the major histopathologic classes of brain tumors. We then developed and implemented a semantic segmentation method that can identify tumor infiltrated and diagnostic regions within SRH images. Mean intersection over union values was 61 ± 28.6 for ground truth diagnostic class and 86.0 ± 28.6 for tumor-infiltrated regions. CONCLUSION We have demonstrated how combining bedside optical histology with deep learning can result in near real-time intraoperative brain tumor diagnosis. Our workflow provides a means of delivering expert-level intraoperative diagnosis where neuropathology resources are scarce and improve diagnostic accuracy in resource-rich centers.

ObjectiveCold atmospheric plasma (CAP) has recently been shown to selectively target cancer cells with minimal effects on normal cells. We systematically assessed the effects of CAP in the treatment of glioblastoma.MethodsThree glioma cell lines, normal astrocytes, and endothelial cell lines were treated with CAP. The effects of CAP were then characterized for viability, cytotoxicity/apoptosis, and cell cycle effects. Statistical significance was determined with student's t-test.ResultsCAP treatment decreases viability of glioma cells in a dose dependent manner, with the ID50 between 90-120 seconds for all glioma cell lines. Treatment with CAP for more than 120 seconds resulted in viability less than 35% at 24-hours posttreatment, with a steady decline to less than 20% at 72-hours. In contrast, the effect of CAP on the viability of NHA and HUVEC was minimal, and importantly not significant at 90 to 120 seconds, with up to 85% of the cells remained viable at 72-hours post-treatment. CAP treatment produces both cytotoxic and apoptotic effects with some variability between cell lines. CAP treatment resulted in a G2/M-phase cell cycle pause in all three cell lines.ConclusionsThis preliminary study determined a multi-focal effect of CAP on glioma cells in vitro, which was not observed in the non-tumor cell lines. The decreased viability depended on the treatment duration and cell line, but overall was explained by the induction of cytotoxicity, apoptosis, and G2/M pause. Future studies will aim at further characterization with more complex pre-clinical models.

A prototype direct absorption central receiver, called the solid particle receiver (SPR), was built and evaluated on-sun at power levels up to 2.5 MWth at Sandia National Laboratories in Albuquerque, NM. The SPR consists of a 6 m tall cavity through which spherical sintered bauxite particles are dropped and directly heated with concentrated solar energy. In principle, the particles can be efficiently heated to a temperature in excess of 900°C, well beyond the stability limit of existing nitrate salt formulations. The heated particles may then be stored in a way analogous to nitrate salt systems, enabling a dispatchable thermal input to power or fuel production cycles. The focus of this current effort was to provide an experimental basis for the validation of computational models that have been created to support improved designs and further development of the solid particle receiver. In this paper, we present information on the design and construction of the solid particle receiver and discuss the development of a computational fluid dynamics model of the prototype. We also present experimental data and model comparisons for on-sun testing of the receiver over a range of input power levels from 1.58–2.51 MWth. Model validation is performed using a number of metrics including particle velocity, exit temperature, and receiver efficiency. In most cases, the difference between the model predictions and data is less than 10%.

The frequency and gender distribution of thoracic aortic aneurysm as a cardiovascular manifestation of loss-of-function (LOF) X-linked FilaminA (FLNA) mutations are not known. Furthermore, there is very limited cardiovascular morbidity or mortality data in children and adults. We analyzed cardiac data on the largest series of 114 patients with LOF FLNA mutations, both children and adults, with periventricular nodular heterotopia (PVNH), including 48 study patients and 66 literature patients, median age of 22.0 years (88 F, 26 M, range: 0-71 years), with 75 FLNA mutations observed in 80 families. Most (64.9%) subjects had a cardiac anomaly or vascular abnormality (80.8% of males and 60.2% of females). Thoracic aortic aneurysms or dilatation (TAA) were found in 18.4% (n = 21), and were associated with other structural cardiac malformations in 57.1% of patients, most commonly patent ductus arteriosus (PDA) and valvular abnormalities. TAA most frequently involved the aortic root and ascending aorta, and sinus of Valsalva aneurysms were present in one third of TAA patients. Six TAA patients (28.5%) required surgery (median age 37 yrs, range 13-41 yrs). TAA with its associated complications was also the only recorded cause of premature, non-accidental mortality in adults (2 M, 2 F). Two adult patients (1 F, 1 M, median 38.5 yrs), died of spontaneous aortic rupture at aortic dimensions smaller than current recommendations for surgery for other aortopathies. Data from this largest series of LOF FLNA mutation patients underscore the importance of serial follow-up to identify and manage these potentially devastating cardiovascular complications.