Lasofoxifene versus fulvestrant for ER+/HER2- metastatic breast cancer with an ESR1 mutation: results from the randomized, phase II ELAINE 1 trial

BACKGROUND

Acquired estrogen receptor alpha (ER/ESR1) mutations commonly cause endocrine resistance in ER+ metastatic breast cancer (mBC). Lasofoxifene, a novel selective ER modulator, stabilizes an antagonist conformation of wild-type and ESR1-mutated ER-ligand binding domains, and has antitumor activity in ESR1-mutated xenografts.

PATIENTS AND METHODS

In this open-label, randomized, phase II, multicenter, ELAINE 1 study (NCT03781063), we randomized women with ESR1-mutated, ER+/human epidermal growth factor receptor 2 negative (HER2-) mBC that had progressed on an aromatase inhibitor (AI) plus a cyclin-dependent kinase 4/6 inhibitor (CDK4/6i) to oral lasofoxifene 5 mg daily or IM fulvestrant 500 mg (days 1, 15, and 29, and then every 4 weeks) until disease progression/toxicity. The primary endpoint was progression-free survival (PFS); secondary endpoints were safety/tolerability.

RESULTS

A total of 103 patients received lasofoxifene (n = 52) or fulvestrant (n = 51). The most current efficacy analysis showed that lasofoxifene did not significantly prolong median PFS compared with fulvestrant: 24.2 weeks (∼5.6 months) versus 16.2 weeks (∼3.7 months; P = 0.138); hazard ratio 0.699 (95% confidence interval 0.434-1.125). However, PFS and other clinical endpoints numerically favored lasofoxifene: clinical benefit rate (36.5% versus 21.6%; P = 0.117), objective response rate [13.2% (including a complete response in one lasofoxifene-treated patient) versus 2.9%; P = 0.124], and 6-month (53.4% versus 37.9%) and 12-month (30.7% versus 14.1%) PFS rates. Most common treatment-emergent adverse events with lasofoxifene were nausea, fatigue, arthralgia, and hot flushes. One death occurred in the fulvestrant arm. Circulating tumor DNA ESR1 mutant allele fraction (MAF) decreased from baseline to week 8 in 82.9% of evaluable lasofoxifene-treated versus 61.5% of fulvestrant-treated patients.

CONCLUSIONS

Lasofoxifene demonstrated encouraging antitumor activity versus fulvestrant and was well tolerated in patients with ESR1-mutated, endocrine-resistant mBC following progression on AI plus CDK4/6i. Consistent with target engagement, lasofoxifene reduced ESR1 MAF, and to a greater extent than fulvestrant. Lasofoxifene may be a promising targeted treatment for patients with ESR1-mutated mBC and warrants further investigation.

Evaluation of the Recommended 30 cc/kg Fluid Dose for Patients With Septic Shock and Hypoperfusion With Lactate Greater Than 4 mmol/L

The Surviving Sepsis Campaign Guidelines recommend fluid administration of 30 cc/kg ideal body weight (IBW) for patients with sepsis and lactate greater than 4 mmol/L within 3 hours of identification. In this study, we explore the impact of fluid dose on lactate normalization, treatment cost, length of stay, and mortality in patients with lactate greater than 4.

DESIGN

Multicenter retrospective observational study.

SETTING

Eight-hospital urban healthcare system in Northeastern United States.

PATIENTS

Patients with sepsis, initial lactate value greater than 4 mmol/L, and received appropriate antibiotics within 3 hours.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We stratified patients into five groups based on the dose of fluid administered within 3 hours after sepsis identification. The groupings were less than 15 cc/kg IBW, 15.1-25 cc/kg IBW, 25.1-35 cc/kg IBW, 35.1-50 cc/kg IBW, and greater than 50 cc/kg IBW. We used the group that received a fluid dose of 25.1-35 cc/kg IBW, as a reference group. The mean age was 66 years, and 56% were male. Three hundred seventy-one (25%) received less than 15 cc/kg of IBW of crystalloid fluid, 278 (17%) received 15-25 cc/kg of IBW, 316 (21%) received 25.1-35 cc/kg of IBW, 319 (21%) received 35.1-50 cc/kg of IBW, and 207 (14%) received greater than 50 cc/kg of IBW. After multilinear regression, there was no significant difference in lactate normalization between the reference group and any of the other fluid groups. We also found no statistically significant difference in the observed/expected cost, or observed/expected length of stay, between the reference group and any of the other fluid groups. Mortality was higher among patients who received greater than 50 cc/kg IBW when compared to the recommended dose.

CONCLUSIONS

In patients with sepsis and lactate value greater than 4 mmol/L, high or low fluid doses were not associated with better lactate clearance or patient outcomes. Greater than 50 cc/kg IBW dose of fluids within 3 hours is associated with higher mortality.

Genome-Wide Association Studies and fine-mapping of genomic loci for n-3 and n-6 Polyunsaturated Fatty Acids in Hispanic American and African American Cohorts

Omega-3 (n-3) and omega-6 (n-6) polyunsaturated fatty acids (PUFAs) play critical roles in human health. Prior genome-wide association studies (GWAS) of n-3 and n-6 PUFAs in European Americans from the CHARGE Consortium have documented strong genetic signals in/near the FADS locus on chromosome 11. We performed a GWAS of four n-3 and four n-6 PUFAs in Hispanic American (n = 1454) and African American (n = 2278) participants from three CHARGE cohorts. Applying a genome-wide significance threshold of P  < 5 x 10 - 8 , we confirmed association of the FADS signal and found evidence of two additional signals (in DAGLA and BEST1 ) within 200 kb of the originally reported FADS signal. Outside of the FADS region, we identified novel signals for arachidonic acid (AA) in Hispanic Americans located in/near genes including TMX2 , SLC29A2 , ANKRD13D and POLD4, and spanning a > 9 Mb region on chromosome 11 (57.5Mb ~ 67.1Mb). Among these novel signals, we found associations unique to Hispanic Americans, including rs28364240, a POLD4 missense variant for AA that is common in CHARGE Hispanic Americans but absent in other race/ancestry groups. Our study sheds light on the genetics of PUFAs and the value of investigating complex trait genetics across diverse ancestry populations.

Impact of 10-Day Fulbright Specialist Program and Project Pink Blue Education Sessions on Medical Oncology Knowledge Among Physicians Who Treat Cancer in Nigeria

Despite an estimated population of over 201 million and over 115,950 yearly diagnosed new cases of cancer, Nigeria does not have dedicated medical oncologists. Most oncology care is delivered through surgical and clinical oncologists, who are trained in both radiation and medical oncology and they number fewer than 50 in the country. With a limited number of oncology professionals, cancer patients in Nigeria experience poor health outcomes, with an estimated cancer mortality rate of 75,000 deaths per year. Participants from 15 Nigerian states were selected to attend the medical oncology training. Through the support of Fulbright Specialist Program and Project PINK BLUE, two of the authors delivered 10 days of lectures based on ASCO, ESMO, and NCCN guidelines. Mean scores of both the pre- and post-course tests as well as a 1-year follow-up test were compared using GraphPad Prism 7.0a by paired t-tests. Forty-four clinical oncologists were selected for participation. Twenty-five (57%) completed the pre- and post-course tests. Of the 25 that completed both tests, percentage of correct answers increased from 45 to 59% (2-sided p-value < 0.0001). Improvements were seen in attending doctors 45 to 59% (p = 0.0046) and resident doctors 45 to 59% (0.0007). Eleven doctors responded to the 1-year follow-up test. Although not statistically significant, a numerical pattern for the benefits was maintained 1 year after the program (45% pre-course versus 52% post-course correct answers, Fisher's exact, p = 0.4185). In the short term, the training improved medical oncology knowledge in Nigeria, regardless of the participant's carrier stage. Long-term benefits were not sustained in a small sample of participants, and continuing education strategies are necessary. Similar models may be employed across Africa.

End-to-end integrated pipeline for underwater optical signal detection using 1D integral imaging capture with a convolutional neural network

Underwater optical signal detection performance suffers from occlusion and turbidity in degraded environments. To tackle these challenges, three-dimensional (3D) integral imaging (InIm) with 4D correlation-based and deep-learning-based signal detection approaches have been proposed previously. Integral imaging is a 3D technique that utilizes multiple cameras to capture multiple perspectives of the scene and uses dedicated algorithms to reconstruct 3D images. However, these systems may require high computational requirements, multiple separate preprocessing steps, and the necessity for 3D image reconstruction and depth estimation of the illuminating modulated light source. In this paper, we propose an end-to-end integrated signal detection pipeline that uses the principle of one-dimensional (1D) InIm to capture angular and intensity of ray information but without the computational burden of full 3D reconstruction and depth estimation of the light source. The system is implemented with a 1D camera array instead of 2D camera array and is trained with a convolutional neural network (CNN). The proposed approach addresses many of the aforementioned shortcomings to improve underwater optical signal detection speed and performance. In our experiment, the temporal-encoded signals are transmitted by a light-emitting diode passing through a turbid and partial occluded environment which are captured by a 1D camera array. Captured video frames containing the spatiotemporal information of the optical signals are then fed into the CNN for signal detection without the need for depth estimation and 3D scene reconstruction. Thus, the entire processing steps are integrated and optimized by deep learning. We compare the proposed approach with the previously reported depth estimated 3D InIm with 3D scene reconstruction and deep learning in terms of computational cost at receiver's end and detection performance. Moreover, a comparison with conventional 2D imaging is also included. The experimental results show that the proposed approach performs well in terms of detection performance and computational cost. To the best of our knowledge, this is the first report on signal detection in degraded environments with computationally efficient end-to-end integrated 1D InIm capture stage with integrated deep learning for classification.

3D object detection through fog and occlusion: passive integral imaging vs active (LiDAR) sensing

In this paper, we address the problem of object recognition in degraded environments including fog and partial occlusion. Both long wave infrared (LWIR) imaging systems and LiDAR (time of flight) imaging systems using Azure Kinect, which combine conventional visible and lidar sensing information, have been previously demonstrated for object recognition in ideal conditions. However, the object detection performance of Azure Kinect depth imaging systems may decrease significantly in adverse weather conditions such as fog, rain, and snow. The concentration of fog degrades the depth images of Azure Kinect camera, and the overall visibility of RGBD images (fused RGB and depth image), which can make object recognition tasks challenging. LWIR imaging may avoid these issues of lidar-based imaging systems. However, due to poor spatial resolution of LWIR cameras, thermal imaging provides limited textural information within a scene and hence may fail to provide adequate discriminatory information to identify between objects of similar texture, shape and size. To improve the object detection task in fog and occlusion, we use three-dimensional (3D) integral imaging (InIm) system with a visible range camera. 3D InIm provides depth information, mitigates the occlusion and fog in front of the object, and improves the object recognition capabilities. For object recognition, the YOLOv3 neural network is used for each of the tested imaging systems. Since the concentration of fog affects the images from different sensors (visible, LWIR, and Azure Kinect depth cameras) in different ways, we compared the performance of the network on these images in terms of average precision and average miss rate. For the experiments we conducted, the results indicate that in degraded environment 3D InIm using visible range cameras can provide better image reconstruction as compared to the LWIR camera and Azure Kinect RGBD camera, and therefore it may improve the detection accuracy of the network. To the best of our knowledge, this is the first report comparing the performance of object detection between passive integral imaging system vs active (LiDAR) sensing in degraded environments such as fog and partial occlusion.

Idiopathic pulmonary fibrosis and lung cancer: future directions and challenges

Idiopathic pulmonary fibrosis (IPF) is a progressive disease of pulmonary scarring. New treatments slow disease progression and allow pulmonary fibrosis patients to live longer. Persistent pulmonary fibrosis increases a patient's risk of developing lung cancer. Lung cancer in patients with IPF differs from cancers that develop in the non-fibrotic lung. Peripherally located adenocarcinoma is the most frequent cell type in smokers who develop lung cancer, while squamous cell carcinoma is the most frequent in pulmonary fibrosis. Increased fibroblast foci in IPF are associated with more aggressive cancer behaviour and shorter doubling times. Treatment of lung cancer in fibrosis is challenging because of the risk of inducing an exacerbation of fibrosis. In order to improve patient outcomes, modifications of current lung cancer screening guidelines in patients with pulmonary fibrosis will be necessary to avoid delays in treatment. 2-fluoro-2-deoxy-d-glucose (FDG) positron emission tomography (PET) computed tomography (CT) imaging can help identify cancer earlier and more reliably than CT alone. Increased use of wedge resections, proton therapy and immunotherapy may increase survival by decreasing the risk of exacerbation, but further research will be necessary.

Information theoretic performance evaluation of 3D integral imaging

Integral imaging (InIm) has proved useful for three-dimensional (3D) object sensing, visualization, and classification of partially occluded objects. This paper presents an information-theoretic approach for simulating and evaluating the integral imaging capture and reconstruction process. We utilize mutual information (MI) as a metric for evaluating the fidelity of the reconstructed 3D scene. Also we consider passive depth estimation using mutual information. We apply this formulation for optimal pitch estimation of integral-imaging capture and reconstruction to maximize the longitudinal resolution. The effect of partial occlusion in integral imaging 3D reconstruction using mutual information is evaluated. Computer simulation tests and experiments are presented.

Automated sickle cell disease identification in human red blood cells using a lensless single random phase encoding biosensor and convolutional neural networks

We present a compact, field portable, lensless, single random phase encoding biosensor for automated classification between healthy and sickle cell disease human red blood cells. Microscope slides containing 3 µl wet mounts of whole blood samples from healthy and sickle cell disease afflicted human donors are input into a lensless single random phase encoding (SRPE) system for disease identification. A partially coherent laser source (laser diode) illuminates the cells under inspection wherein the object complex amplitude propagates to and is pseudorandomly encoded by a diffuser, then the intensity of the diffracted complex waveform is captured by a CMOS image sensor. The recorded opto-biological signatures are transformed using local binary pattern map generation during preprocessing then input into a pretrained convolutional neural network for classification between healthy and disease-states. We further provide analysis that compares the performance of several neural network architectures to optimize our classification strategy. Additionally, we assess the performance and computational savings of classifying on subsets of the opto-biological signatures with substantially reduced dimensionality, including one dimensional cropping of the recorded signatures. To the best of our knowledge, this is the first report of a lensless SRPE biosensor for human disease identification. As such, the presented approach and results can be significant for low-cost disease identification both in the field and for healthcare systems in developing countries which suffer from constrained resources.

COVID-19 detection from red blood cells using highly comparative time-series analysis (HCTSA) in digital holographic microscopy

We present an automated method for COVID-19 screening based on reconstructed phase profiles of red blood cells (RBCs) and a highly comparative time-series analysis (HCTSA). Video digital holographic data -was obtained using a compact, field-portable shearing microscope to capture the temporal fluctuations and spatio-temporal dynamics of live RBCs. After numerical reconstruction of the digital holographic data, the optical volume is calculated at each timeframe of the reconstructed data to produce a time-series signal for each cell in our dataset. Over 6000 features are extracted on the time-varying optical volume sequences using the HCTSA to quantify the spatio-temporal behavior of the RBCs, then a linear support vector machine is used for classification of individual RBCs. Human subjects are then classified for COVID-19 based on the consensus of their cells' classifications. The proposed method is tested on a dataset of 1472 RBCs from 24 human subjects (10 COVID-19 positive, 14 healthy) collected at UConn Health Center. Following a cross-validation procedure, our system achieves 82.13% accuracy, with 92.72% sensitivity, and 73.21% specificity (area under the receiver operating characteristic curve: 0.8357). Furthermore, the proposed system resulted in 21 out of 24 human subjects correctly labeled. To the best of our knowledge this is the first report of a highly comparative time-series analysis using digital holographic microscopy data.

Lowlight object recognition by deep learning with passive three-dimensional integral imaging in visible and long wave infrared wavelengths

Traditionally, long wave infrared imaging has been used in photon starved conditions for object detection and classification. We investigate passive three-dimensional (3D) integral imaging (InIm) in visible spectrum for object classification using deep neural networks in photon-starved conditions and under partial occlusion. We compare the proposed passive 3D InIm operating in the visible domain with that of the long wave infrared sensing in both 2D and 3D imaging cases for object classification in degraded conditions. This comparison is based on average precision, recall, and miss rates. Our experimental results demonstrate that cold and hot object classification using 3D InIm in the visible spectrum may outperform both 2D and 3D imaging implemented in long wave infrared spectrum for photon-starved and partially occluded scenes. While these experiments are not comprehensive, they demonstrate the potential of 3D InIm in the visible spectrum for low light applications. Imaging in the visible spectrum provides higher spatial resolution, more compact optics, and lower cost hardware compared with long wave infrared imaging. In addition, higher spatial resolution obtained in the visible spectrum can improve object classification accuracy. Our experimental results provide a proof of concept for implementing visible spectrum imaging in place of the traditional LWIR spectrum imaging for certain object recognition tasks.

Lobar Gross Endobronchial Disease Predicts for Overall Survival and Grade 5 Pulmonary Toxicity in Medically Inoperable Early Stage Non-Small Cell Lung Cancer Patients Treated With Stereotactic Body Radiation Therapy

Purpose

Stereotactic body radiation therapy (SBRT) is considered standard of care for medically inoperable early stage non-small cell lung cancer (ES-NSCLC). Central tumor location is a known risk factor for severe SBRT related toxicity. Bronchoscopy allows for visualization of the central airways prior to treatment. Five fraction SBRT approaches have been advocated to mitigate treatment induced toxicity. In this report, we examine the mature clinical outcomes of a diverse cohort of ES-NSCLC patients with both peripheral and central tumors treated with a conservative 5 fraction SBRT approach and evaluate the role of lobar gross endobronchial disease (LGED) in predicting overall survival and treatment-related death.

Methods

Medically inoperable biopsy-proven, lymph node-negative ES-NSCLC patients were treated with SBRT. Bronchoscopy was completed prior to treatment in all centrally located cases. The Kaplan-Meier method was used to estimate overall survival (OS), local control (LC), regional control (RC), distant metastasis free survival (DMFS) and disease-free survival (DFS). Overall survival was stratified based on clinical stage, histology, tumor location and LGED. Toxicities were scored according to the National Cancer Institute Common Terminology Criteria for Adverse Events, Version 5.0.

Results

From December 2010 to December 2015, 50 consecutive patients were treated uniformly with a 50 Gy in 5 fraction SBRT approach (tumor BED₁₀ ≥ 100 Gy) and followed for a minimum of 5 years or until death. At a median follow up of 42 months for all patients, 3-year OS was 50%. Three-year OS did not statistically differ between stage I and stage II disease (51% vs. 47%; p=0.86), adenocarcinoma and squamous cell carcinoma (50% vs. 45%; p=0.68), or peripheral and central tumors (56% vs. 45%; p=0.46). Five central tumors were found to have LGED, and 3-year OS for this cohort was quite poor at 20%. Cox regression analysis identified LGED as a predictor of OS while controlling for age, stage and location (OR:4.536, p-value=0.038). Despite the relatively low dose delivered, treatment likely contributed to the death of 4 patients with central tumors. Lobar gross endobronchial disease was an independent predictor for grade 5 pulmonary toxicity (n=4, p=0.007). Specifically, 3 of the 5 patients with LGED developed fatal radiation-induced bronchial stricture. Three-year LC, RC, DMFS and DFS results for the group were similar to contemporary studies at 90%, 90%, 82% and 65%.

Conclusions

Central location of ES-NSCLC is a well-established predictor for severe SBRT-related toxicity. Here we identify LGED as a significant predictor of poor overall survival and grade 5 pulmonary toxicity. The relatively high rates of severe treatment-related toxicity seen in patients with central ES-NSCLC may be due in part to LGED. Underlying LGED may cause irreparable damage to the lobar airway, unmitigated by SBRT treatment thus increasing the risk of severe treatment-related toxicity. These findings should be verified in larger data sets. Future prospective central ES-NSCLC clinical trials should require staging bronchoscopy to identify LGED and further assess its clinical significance.

Lung ultrasound volume sweep imaging for respiratory illness: a new horizon in expanding imaging access

Background

Respiratory illness is a leading cause of morbidity in adults and the number one cause of mortality in children, yet billions of people lack access to medical imaging to assist in its diagnosis. Although ultrasound is highly sensitive and specific for respiratory illness such as pneumonia, its deployment is limited by a lack of sonographers. As a solution, we tested a standardised lung ultrasound volume sweep imaging (VSI) protocol based solely on external body landmarks performed by individuals without prior ultrasound experience after brief training. Each step in the VSI protocol is saved as a video clip for later interpretation by a specialist.

Methods

Dyspneic hospitalised patients were scanned by ultrasound naive operators after 2 hours of training using the lung ultrasound VSI protocol. Separate blinded readers interpreted both lung ultrasound VSI examinations and standard of care chest radiographs to ascertain the diagnostic value of lung VSI considering chest X-ray as the reference standard. Comparison to clinical diagnosis as documented in the medical record and CT (when available) were also performed. Readers offered a final interpretation of normal, abnormal, or indeterminate/borderline for each VSI examination, chest X-ray, and CT.

Results

Operators scanned 102 subjects (0–89 years old) for analysis. Lung VSI showed a sensitivity of 93% and a specificity of 91% for an abnormal chest X-ray and a sensitivity of 100% and a specificity of 93% for a clinical diagnosis of pneumonia. When any cases with an indeterminate rating on chest X-ray or ultrasound were excluded (n=38), VSI lung ultrasound showed 92% agreement with chest X-ray (Cohen’s κ 0.83 (0.68 to 0.97, p<0.0001)). Among cases with CT (n=21), when any ultrasound with an indeterminate rating was excluded (n=3), there was 100% agreement with VSI.

Conclusion

Lung VSI performed by previously inexperienced ultrasound operators after brief training showed excellent agreement with chest X-ray and high sensitivity and specificity for a clinical diagnosis of pneumonia. Blinded readers were able to identify other respiratory diseases including pulmonary oedema and pleural effusion. Deployment of lung VSI could benefit the health of the global community.

Optical signal detection in turbid water using multidimensional integral imaging with deep learning

Optical signal detection in turbid and occluded environments is a challenging task due to the light scattering and beam attenuation inside the medium. Three-dimensional (3D) integral imaging is an imaging approach which integrates two-dimensional images from multiple perspectives and has proved to be useful for challenging conditions such as occlusion and turbidity. In this manuscript, we present an approach for the detection of optical signals in turbid water and occluded environments using multidimensional integral imaging employing temporal encoding with deep learning. In our experiments, an optical signal is temporally encoded with gold code and transmitted through turbid water via a light-emitting diode (LED). A camera array captures videos of the optical signals from multiple perspectives and performs the 3D signal reconstruction of temporal signal. The convolutional neural network-based bidirectional Long Short-Term Network (CNN-BiLSTM) network is trained with clear water video sequences to perform classification on the binary transmitted signal. The testing data was collected in turbid water scenes with partial signal occlusion, and a sliding window with CNN-BiLSTM-based classification was performed on the reconstructed 3D video data to detect the encoded binary data sequence. The proposed approach is compared to previously presented correlation-based detection models. Furthermore, we compare 3D integral imaging to conventional two-dimensional (2D) imaging for signal detection using the proposed deep learning strategy. The experimental results using the proposed approach show that the multidimensional integral imaging-based methodology significantly outperforms the previously reported approaches and conventional 2D sensing-based methods. To the best of our knowledge, this is the first report on underwater signal detection using multidimensional integral imaging with deep neural networks.

Spatio-temporal continuous gesture recognition under degraded environments: performance comparison between 3D integral imaging (InIm) and RGB-D sensors

In this paper, we introduce a deep learning-based spatio-temporal continuous human gesture recognition algorithm under degraded conditions using three-dimensional (3D) integral imaging. The proposed system is shown as an efficient continuous human gesture recognition system for degraded environments such as partial occlusion. In addition, we compare the performance between the 3D integral imaging-based sensing and RGB-D sensing for continuous gesture recognition under degraded environments. Captured 3D data serves as the input to a You Look Only Once (YOLOv2) neural network for hand detection. Then, a temporal segmentation algorithm is employed to segment the individual gestures from a continuous video sequence. Following segmentation, the output is fed to a convolutional neural network-based bidirectional long short-term memory network (CNN-BiLSTM) for gesture classification. Our experimental results suggest that the proposed deep learning-based spatio-temporal continuous human gesture recognition provides substantial improvement over both RGB-D sensing and conventional 2D imaging system. To the best of our knowledge, this is the first report of 3D integral imaging-based continuous human gesture recognition with deep learning and the first comparison between 3D integral imaging and RGB-D sensors for this task.

Three-dimensional polarimetric image restoration in low light with deep residual learning and integral imaging

Polarimetric imaging can become challenging in degraded environments such as low light illumination conditions or in partial occlusions. In this paper, we propose the denoising convolutional neural network (DnCNN) model with three-dimensional (3D) integral imaging to enhance the reconstructed image quality of polarimetric imaging in degraded environments such as low light and partial occlusions. The DnCNN is trained based on the physical model of the image capture in degraded environments to enhance the visualization of polarimetric imaging where simulated low light polarimetric images are used in the training process. The DnCNN model is experimentally tested on real polarimetric images captured in real low light environments and in partial occlusion. The performance of DnCNN model is compared with that of total variation denoising. Experimental results demonstrate that DnCNN performs better than total variation denoising for polarimetric integral imaging in terms of signal-to-noise ratio and structural similarity index measure in low light environments as well as low light environments under partial occlusions. To the best of our knowledge, this is the first report of polarimetric 3D object visualization and restoration in low light environments and occlusions using DnCNN with integral imaging. The proposed approach is also useful for 3D image restoration in conventional (non-polarimetric) integral imaging in a degraded environment.

Guidelines for the use of the Attend Anywhere Platform for Telecommunications within the Pain Service

Introduction Remote consultation is of growing in importance and gaining popularity in both primary and secondary healthcare settings. Reduced necessity for a physical presence of the patient within the healthcare setting is of particular benefit in the current COVID-19 era. It is also of benefit to a diverse group of patients, for example: those who are geographically distant from the base hospital, those suffering from mobility issues or chronic illness, those who require chaperoning as well as those with limited access to transport. We have developed guidelines for the use of the medical telecommunications platform, Attend Anywhere, which has been utilised across the English and Scottish National Health Services, as well as with the Australian Health service, and is now available in Health Service Executive (HSE) settings. Herein we describe and recommend a process that we have found helpful, and we propose guidelines on how a Health Care Worker (HCW) might consider approaching a virtual consultation when initiating and safely executing a patient encounter on Attend Anywhere, in a secure and efficient manner. The guidelines were created following review of the literature on previous experience by others with this software, as well as recent guidance published by the Irish Medical Council. A proportion of this guidance is transferable to other platforms. Methods We also undertook a short survey of our patients and physicians in Sligo University Hospital, who used Attend Anywhere over a six-week period to gauge their satisfaction levels with the experience., We estimated distance that our patients would have travelled for their appointment had the traditional face-to-face consultation been carried out. We noted whether we considered the medium appropriate for the patient consultations. Results 53 patients took part and satisfaction was rated from satisfied to very satisfied on a 3-point scale for all stakeholders. In addition, we found that remote consultation, when compared to face-to-face consultation, alleviated an average of 144km of unnecessary travel per appointment. Remote consultation was deemed appropriate in all cases and no rescheduled face-to-face appointments were required due to failure of the consultation due to difficulties encountered. Conclusion The authors recommend the implementation of the described guidance, with suggested Checklist, Information leaflet and Consent form, as a means of ensuring the confidentiality of the consultation and to ensure that processes are adhered to that optimise protection for both the patient and the clinician, while reducing the burden of attendance to the healthcare location.

Digital holographic deep learning of red blood cells for field-portable, rapid COVID-19 screening

Rapid screening of red blood cells for active infection of COVID-19 is presented using a compact and field-portable, 3D-printed shearing digital holographic microscope. Video holograms of thin blood smears are recorded, individual red blood cells are segmented for feature extraction, then a bi-directional long short-term memory network is used to classify between healthy and COVID positive red blood cells based on their spatiotemporal behavior. Individuals are then classified based on the simple majority of their cells' classifications. The proposed system may be beneficial for under-resourced healthcare systems. To the best of our knowledge, this is the first report of digital holographic microscopy for rapid screening of COVID-19.

Deep learning polarimetric three-dimensional integral imaging object recognition in adverse environmental conditions

Polarimetric imaging is useful for object recognition and material classification because of its ability to discriminate objects based on polarimetric signatures of materials. Polarimetric imaging of an object captures important physical properties such as shape and surface properties and can be effective even in low light environments. Integral imaging is a passive three-dimensional (3D) imaging approach that takes advantage of multiple 2D imaging perspectives to perform 3D reconstruction. In this paper, we propose a unified polarimetric detection and classification of objects in degraded environments such as low light and the presence of occlusion. This task is accomplished using a deep learning model for 3D polarimetric integral imaging data captured in the visible spectral domain. The neural network system is designed and trained for 3D object detection and classification using polarimetric integral images. We compare the detection and classification results between polarimetric and non-polarimetric 2D and 3D imaging. The system performance in degraded environmental conditions is evaluated using average miss rate, average precision, and F-1 score. The results indicate that for the experiments we have performed, polarimetric 3D integral imaging outperforms 2D polarimetric imaging as well as non-polarimetric 2D and 3D imaging for object recognition in adverse conditions such as low light and occlusions. To the best of our knowledge, this is the first report for polarimetric 3D object recognition in low light environments and occlusions using a deep learning-based integral imaging. The proposed approach is attractive because low light polarimetric object recognition in the visible spectral band benefits from much higher spatial resolution, more compact optics, and lower system cost compared with long wave infrared imaging which is the conventional imaging approach for low light environments.

T-Gene: improved target gene prediction

MOTIVATION

Identifying the genes regulated by a given transcription factor (TF) (its 'target genes') is a key step in developing a comprehensive understanding of gene regulation. Previously, we developed a method (CisMapper) for predicting the target genes of a TF based solely on the correlation between a histone modification at the TF's binding site and the expression of the gene across a set of tissues or cell lines. That approach is limited to organisms for which extensive histone and expression data are available, and does not explicitly incorporate the genomic distance between the TF and the gene.

RESULTS

We present the T-Gene algorithm, which overcomes these limitations. It can be used to predict which genes are most likely to be regulated by a TF, and which of the TF's binding sites are most likely involved in regulating particular genes. T-Gene calculates a novel score that combines distance and histone/expression correlation, and we show that this score accurately predicts when a regulatory element bound by a TF is in contact with a gene's promoter, achieving median precision above 60%. T-Gene is easy to use via its web server or as a command-line tool, and can also make accurate predictions (median precision above 40%) based on distance alone when extensive histone/expression data is not available for the organism. T-Gene provides an estimate of the statistical significance of each of its predictions.

AVAILABILITY AND IMPLEMENTATION

The T-Gene web server, source code, histone/expression data and genome annotation files are provided at http://meme-suite.org.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Abstract PO-064: Applying geospatial strategies to examine tobacco/vape shops and socioecological context with lung cancer and cardiovascular disease outcomes

Background: Although smoking tobacco products has declined, electronic nicotine delivery systems(ENDS) have flooded the marketplace. With increased retailing of carcinogenic tobacco products, there is a need to better understand how they may influence lung cancer and cardiovascular disease outcomes and where they are located. Therefore, the purpose of the study was to examine the association between the location of tobacco/vape shops and lung cancer and cardiovascular disease. Methods: Aggregate data from the U.S. Census, Google Maps, and Yelp was used to determine the number of smoke shops by city in San Gabriel Valley, CA and Antelope Valley, CA. Lung cancer incidence, lung cancer mortality, and cardiovascular disease (CVD) mortality was obtained from the Los Angeles County Department of Public Health. A correlation analysis was conducted and Pearson correlation coefficients with p-values were obtained. Additional socioecological variables were also included for exploration. A p-value ≤0.05 was considered statistically significant. All analyses were conducted using SAS v9.4 software. Results: Data showed that the number of tobacco smoke shops was significantly positively correlated with lung cancer incidence (rho [ρ]= 0.546; p= 0.0027), lung cancer mortality (ρ= 0.517; p=0.0048), and cardiovascular disease mortality (ρ= 0.620; p= 0.0004). Percentage of residents 0-17 years old (ρ=0.532; p= 0.0036), percentage of Black residents (ρ= 0.420; p= 0.0259) and American Indian/Alaskan Native residents (ρ= 0.676; p&lt;.0001); percentage of residents with some college education (ρ= 0.431;p= 0.0222), percentage of residents meeting physical activity guidelines (ρ= 0.444; p= 0.0180), percentage of smokers (ρ= 0.370; p= 0.0529), crime rate (ρ= 0.693; p= &lt;.0001), and percentage of depressed residents (ρ= 0.473; p= 0.0111) were positively correlated with the number of smoke shops. Percentage of residents 65+ years old (ρ= -0.567; p= 0.0017), percentage of Asian residents (ρ= -0.382;p= 0.0450), percentage of residents with a college degree (ρ= -0.407; p=0.0318), and percentage of foreign- born residents (ρ= -0.429; p= 0.0228) were negatively correlated with the number of smoke shops. Conclusion: Findings suggest that the number of tobacco shops is associated with lung cancer incidence and mortality and cardiovascular disease mortality. Neighborhoods with a higher proportion of Black, American Indian/Alaskan Native residents, and depressed residents and high-crime areas may have more smoke shops. Additional studies are needed to verify these results, which may help inform tobacco control policies. Addressing cancer and CVD disparities, particularly tobacco-related diseases, may require stronger laws and licensing regulating the tobacco industry and tobacco/vaping shops to prevent targeting of minorities and other vulnerable populations. This data can help inform the membership of tobacco control task forces and committees at governmental, community, health center and academic sites.

Citation Format: Christyl T. Dawson, Kimlin T. Ashing, Brian Tiep, Jonjon Macalintal, Sophia Yeung, Gaole Song, Timothy O'Connor, Udochukwu Obodo, Cary Presant. Applying geospatial strategies to examine tobacco/vape shops and socioecological context with lung cancer and cardiovascular disease outcomes [abstract]. In: Proceedings of the AACR Virtual Conference: Thirteenth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2020 Oct 2-4. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(12 Suppl):Abstract nr PO-064.

Signal detection in turbid water using temporally encoded polarimetric integral imaging

To improve signal detection in a turbid medium, we propose temporally encoded single shot polarimetric integral imaging. An optical signal is temporally encoded using gold coded sequences and transmitted through a turbid medium. The encoded signals are captured as a sequence of elemental images by two orthogonal polarized image sensor arrays. Polarimetric and polarization difference imaging are used to suppress the partially polarized and unpolarized background noise such that only the polarized ballistic signal photons are captured at the sensor. Multidimensional integral imaging is used to obtain 4D reconstructed data, and multidimensional nonlinear correlation is performed on the reconstructed data to detect the optical signal. We compare the effectiveness of the proposed polarimetric underwater optical signal detection approach to conventional (non-polarimetric) integral imaging-based and 2D imaging-based signal detection systems. The underwater signal detection capabilities are measured through performance metrics such as receiver operating characteristic (ROC) curves, the area under the curve (AUC), and the number of detection errors. Furthermore, statistical measures, including the Kullback-Leibler divergence, signal-to-noise ratio (SNR), and peak-to-correlation energy (PCE), are also calculated to show the improved performance of the proposed system. Our experimental results show that the proposed polarimetric integral-imaging approach significantly outperforms the conventional imaging-based methods. To the best of our knowledge, this is the first report on temporally encoded single shot polarimetric integral imaging for signal detection in turbid water.

Red blood cell classification in lensless single random phase encoding using convolutional neural networks

Rapid cell identification is achieved in a compact and field-portable system employing single random phase encoding to record opto-biological signatures of living biological cells of interest. The lensless, 3D-printed system uses a diffuser to encode the complex amplitude of the sample, then the encoded signal is recorded by a CMOS image sensor for classification. Removal of lenses in this 3D sensing system removes restrictions on the field of view, numerical aperture, and depth of field normally imposed by objective lenses in comparable microscopy systems to enable robust 3D capture of biological volumes. Opto-biological signatures for two classes of animal red blood cells, situated in a microfluidic device, are captured then input into a convolutional neural network for classification, wherein the AlexNet architecture, pretrained on the ImageNet database is used as the deep learning model. Video data was recorded of the opto-biological signatures for multiple samples, then each frame was treated as an input image to the network. The pre-trained network was fine-tuned and evaluated using a dataset of over 36,000 images. The results show improved performance in comparison to a previously studied Random Forest classification model using extracted statistical features from the opto-biological signatures. The system is further compared to and outperforms a similar shearing-based 3D digital holographic microscopy system for cell classification. In addition to improvements in classification performance, the use of convolutional neural networks in this work is further demonstrated to provide improved performance in the presence of noise. Red blood cell identification as presented here, may serve as a key step toward lensless pseudorandom phase encoding applications in rapid disease screening. To the best of our knowledge this is the first report of lensless cell identification in single random phase encoding using convolutional neural networks.

The right upper lobe bronchus angle: A tool for differentiating fibrotic and non-fibrotic sarcoidosis

Purpose:

To evaluate the Right Upper Lobe Bronchus Angle (RUL-BA) on chest CT in patients with Stage 4 sarcoidosis and compare to others with non-fibrotic sarcoidosis.

Methods:

IRB approval was obtained for review of all chest CT scans performed from January 2015 through December 2017 that contained the word sarcoidosis using the computer program Montage. The most recent CT scans of 633 people were reviewed. The patients’ age and sex at the time of their most recent CT scan were recorded. The radiographic diagnosis and the Right Upper Lobe Bronchus Angle (RUL-BA) were determined by a chest radiologist with 20 years of experience.

Results:

The RUL-BA increased with Stage 4 sarcoidosis, measuring on average 104 degrees, compared to the average angle of 88 degrees for those without fibrotic sarcoid. More often men’s CT scans exhibited the earlier stages of sarcoidosis, and a higher number of women’s scans showed fibrotic sarcoidosis. As would be expected, scans with advanced disease were typically from older patients; however, there was no correlation between age and degree of fibrosis as measured by increasing RUL-BA.

Conclusion:

The RUL-BA assists radiologists in differentiating fibrotic sarcoidosis from non-fibrotic sarcoidosis. Further research will determine if the RUL-BA measurement can help differentiate fibrotic sarcoid from other fibrotic lung diseases and if the angle can be used to follow disease progression. (Sarcoidosis Vasc Diffuse Lung Dis 2020; 37 (2): 99-103)

Using informatics to engage patients

As a specialty, radiology has spent much of the last two decades implementing information systems that improve departmental efficiency and the ordering provider's access to information. While our patients have realized benefits such as improved access to care and reduced turnaround times, there has been little focus on using these information systems to improve patient engagement. In the last decade, society has shifted. Now, consumers in every industry expect to be able to use technology to help them accomplish different tasks from scheduling to communicating. Medicine, in general, has been slow to respond to the concept of the patient as a consumer. In this manuscript we describe some of the informatics efforts we have employed in our department to improve patient engagement. We present these initiatives, corresponding to each aspect of the radiology value stream, from the patient's point of view.

Abstract CT087: A geospatial approach to explore the socioecological context of tobacco and vape shop locations

Introduction: Despite growing public health efforts to discourage teens from using hazardous tobacco products, their use of ENDS (electronic nicotine delivery system) remain piqued due to direct youth targeting by tobacco and vaping companies. ENDs especially e-cigs most often have a fruit or candy flavor that are particularly attractive to young people and may have high nicotine - which is highly addicting. Methods: 1) analyzed national/local nicotine product use in youth by gender, age, and racial/ethnic groups, 2) explored socio-ecological context of tobacco/vape shops via geo-spatial mapping within Los Angeles Service Planning Area 3 (SPA 3), 3) conducted environmental scan of social media platforms combined with Google and Yellow Pages searches. Results: Population level results were drawn from US Census, 2013-2017 American Community Survey (ACS) and California Tobacco Facts and Figures, 2019. 13.5% middle schoolers and 37.7% high schoolers reported ever using e-cigarettes. Use of basic or disposable devices was associated with younger age especially among Latinos (5% reported use in the past month compared to &lt; 3% among other ethnicities). To explore socioecological contexts of tobacco and vape shops in SPA 3. Regardless of income, a higher number of tobacco and vape shops appear to correspond with higher percentage ethnic minorities. Lower income cities tended to have more tobacco and vape shops compared to very high-income cities. Tobacco products cost less in neighborhoods with a lower median household income and in neighborhoods with a higher proportion of Hispanic residents, even after adjusting for store type.Additionally, prior to the presentation, we will conduct geospatial mapping analysis: We will utilize open GIS software (QGIS) and open web map tools (Leaflet) to geocode locations of smoke/vape shops and overlay them with city demography to illustrate their spatial relationships and proximity to various neighborhoods. Discussion: Sustained implementation of population-based tobacco, including vaping, prevention and control strategies, in coordination with the Food and Drug Administration, are critical to reducing tobacco use including vaping and initiation among all U.S. population especially ethnic minorities and youths. There are associations of Tobacco Products marketing (price, placement, promotion, or product availability) with a neighborhood demographic (socioeconomic challenged, minority race/ethnicity and high traffic volume). Vape shops are more likely to be concentrated in low resourced communities with less political clout - these areas tend to be minority dense with fewer restrictions. Findings could inform initiatives aimed at a stronger licensing requirement for vape shops and federal and state-level regulations of this industry to prevent tobacco/vape shop from targeting minority and other socially challenged groups.

Citation Format: Kimlin T. Ashing, Brian Tiep, Jonjon Macalintal, Sophia Yeung, Timothy O'Connor, Bin Xie, Udochukwu Obodo, Ming-Hsiang Tsou, Gaole Song, Faith Abuan, Christyl Dawson. A geospatial approach to explore the socioecological context of tobacco and vape shop locations [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr CT087.

Human gesture recognition under degraded environments using 3D-integral imaging and deep learning

In this paper, we propose a spatio-temporal human gesture recognition algorithm under degraded conditions using three-dimensional integral imaging and deep learning. The proposed algorithm leverages the advantages of integral imaging with deep learning to provide an efficient human gesture recognition system under degraded environments such as occlusion and low illumination conditions. The 3D data captured using integral imaging serves as the input to a convolutional neural network (CNN). The spatial features extracted by the convolutional and pooling layers of the neural network are fed into a bi-directional long short-term memory (BiLSTM) network. The BiLSTM network is designed to capture the temporal variation in the input data. We have compared the proposed approach with conventional 2D imaging and with the previously reported approaches using spatio-temporal interest points with support vector machines (STIP-SVMs) and distortion invariant non-linear correlation-based filters. Our experimental results suggest that the proposed approach is promising, especially in degraded environments. Using the proposed approach, we find a substantial improvement over previously published methods and find 3D integral imaging to provide superior performance over the conventional 2D imaging system. To the best of our knowledge, this is the first report that examines deep learning algorithms based on 3D integral imaging for human activity recognition in degraded environments.

Three-dimensional polarimetric integral imaging in photon-starved conditions: performance comparison between visible and long wave infrared imaging

Three-dimensional (3D) polarimetric integral imaging (InIm) to extract the 3D polarimetric information of objects in photon-starved conditions is investigated using a low noise visible range camera and a long wave infrared (LWIR) range camera, and the performance between the two sensors is compared. Stokes polarization parameters and degree of polarization (DoP) are calculated to extract the polarimetric information of the 3D scene while integral imaging reconstruction provides depth information and improves the performance of low-light imaging tasks. An LWIR wire grid polarizer and a linear polarizer film are used as polarimetric objects for the LWIR range and visible range cameras, respectively. To account for a limited number of photons per pixel using the visible range camera in low light conditions, we apply a mathematical restoration model at each elemental image of visible camera to enhance the signal. We show that the low noise visible range camera may outperform the LWIR camera in detection of polarimetric objects under low illumination conditions. Our experiments indicate that for 3D polarimetric measurements under photon-starved conditions, visible range sensing may produce a signal-to-noise ratio (SNR) that is not lower than the LWIR range sensing. We derive the probability density function (PDF) of the 2D and 3D degree of polarization (DoP) images and show that the theoretical model demonstrates agreement to that of the experimentally obtained results. To the best of our knowledge, this is the first report comparing the polarimetric imaging performance between visible range and infrared (IR) range sensors under photon-starved conditions and the relevant statistical models of 3D polarimetric integral imaging.

Phase I study of TRC102 in combination with cisplatin and pemetrexed in patients with advanced solid tumors/Phase II study of TRC102 with pemetrexed in patients with mesothelioma refractory to pemetrexed and cisplatin or carboplatin

9055

Background: Treatment options remain limited in malignant pleural mesothelioma refractory to pemetrexed +/- platinum. TRC102 (methoxyamine hydrochloride) is a novel biochemical inhibitor of the BER pathway. Available data support the hypothesis that TRC102 bound DNA is a substrate for topoisomerase II, which cleaves TRC102-bound DNA sites to produce strand breaks in cancer cells that cause cellular apoptosis and enhance the cytotoxic effects of chemotherapy. Methods: This was a parallel cohort trial of a Phase I of TRC102 in combination with cisplatin (CDDP) and pemetrexed in patients with advanced solid tumors (Arm A) and a Phase II of TRC102 with pemetrexed in patients with mesothelioma refractory to platinum and pemetrexed (Arm B). Results: In Arm A dose escalation, 16 pts (11M/5F) were treated; 9 evaluable through 3 TRC102 dose levels (50, 75, and 100 mg/day, PO), with CDDP 60 mg/m2 and pemetrexed 500 mg/m2 (levels 1- 3); and 5 evaluable at TRC102 100 mg/day PO, CDDP 75 mg/m2, pemetrexed 500 mg/m2 (level 4). Cycles were every 21 days. There were no DLT’s, establishing level 4 as the RP2D. The only grade 4 treatment-related AE was thrombocytopenia on cycle 22 (level 2). Cycle 1 grade 3 AEs were 1 hypophosphatemia (level 1) and 1 leukopenia (level 2). There were 3 PRs (all parotid salivary gland tumors). Median PFS (95%CI) = 7.1% (1.4 – 15.5) mos. Arm B was designed as the first stage of a two stage Gehan design trial of patients with mesothelioma who had progressed on or recurred within 6 months of pemetrexed + platinum frontline treatment. 14 pts were treated with TRC102 50 mg/day D1-4 and pemetrexed 500 mg/m2 every 21 days. There were 2 PRs (both in epithelioid cancer of which 1 was confirmed), meeting the pre-specified criteria for continued interest ( > 0/14). mPFS (95% CI) was 4.3 (1.4 - 6.8) mos. 8 pts had stable disease for at least 1 cycle (4 stable at cycles 6, 9, 10 and 12). There were 1 grade 4 neutropenia and 5 grade 3 AE’s (1 each – anemia, neutropenia, leukopenia, fatigue, hyponatremia). Conclusions: TRC102 in combination with CDDP and pemetrexed exhibited antitumor activity, particularly in salivary gland tumors, and a tolerable safety profile at the doses tested. The combination of TRC102 and pemetrexed demonstrated activity in malignant mesothelioma that progressed on prior pemetrexed. Additional studies are warranted to confirm preliminary signals of activity. Clinical trial information: NCT02535312.

Photon-counting 3D integral imaging with less than a single photon per pixel on average using a statistical model of the EM-CCD camera

We investigate photon-counting 3D integral imaging (PCII) with an electron multiplying charged-coupled device (EM-CCD) camera using dedicated statistical models. Using conventional integral imaging reconstruction methods with this camera in photon-counting conditions may result in degraded reconstructed image quality if multiple photons are detected simultaneously in a given pixel. We propose an estimation method derived from the photon detection statistical model of the EM-CCD to address the problems caused by multiple photons detected at the same pixel and provide improved 3D reconstructions. We also present a simplified version of this statistical method that can be used under the correct conditions. The imaging performance of these methods is evaluated on experimental data by the peak signal-to-noise ratio and the structural similarity index measure. The experiments demonstrate that 3D integral imaging substantially outperforms 2D imaging in degraded conditions. Furthermore, we achieve imaging in photon-counting conditions where, on average, less than a single photon per pixel is detected by the camera. To the best of our knowledge, this is the first report of PCII with the EM-CCD camera employing its statistical model in 3D reconstruction of PCII.

Optical 4D signal detection in turbid water by multi-dimensional integral imaging using spatially distributed and temporally encoded multiple light sources

We propose an underwater optical signal detection system based on multi-dimensional integral imaging with spatially distributed multiple light sources and four-dimensional (4D) spatial-temporal correlation. We demonstrate our system for the detection of optical signals in turbid water. A 4D optical signal is generated from a three-dimensional (3D) spatial distribution of underwater light sources, which are temporally encoded using spread spectrum techniques. The optical signals are captured by an array of cameras, and 3D integral imaging reconstruction is performed, followed by multi-dimensional correlation to detect the optical signal. Inclusion of multiple light sources located at different depths allows for successful signal detection at turbidity levels not feasible using only a single light source. We consider the proposed system under varied turbidity levels using both Pseudorandom and Gold Codes for temporal signal coding. We also compare the effectiveness of the proposed underwater optical signal detection system to a similar system using only a single light source and compare between conventional and integral imaging-based signal detection. The underwater signal detection capabilities are measured through performance-based metrics such as receiver operating characteristic (ROC) curves, the area under the curve (AUC), and the number of detection errors. Furthermore, statistical analysis, including Kullback-Leibler divergence and Bhattacharya distance, shows improved performance of the proposed multi-source integral imaging underwater system. The proposed integral-imaging based approach is shown to significantly outperform conventional imaging-based methods.

Three-dimensional integral imaging in photon-starved environments with high-sensitivity image sensors

Imaging in poorly illuminated environments using three-dimensional (3D) imaging with passive imaging sensors that operate in the visible spectrum is a formidable task due to the low number of photons detected. 3D integral imaging, which integrates multiple two-dimensional perspectives, is expected to perform well in the presence of noise, as well as statistical fluctuation in the detected number of photons. In this paper, we present an investigation of 3D integral imaging in low-light-level conditions, where as low as a few photons and as high as several tens of photons are detected on average per pixel. In the experimental verification, we use an electron multiplying charge-coupled device (EM-CCD) and a scientific complementary metal-oxide-semiconductor (sCMOS) camera. For the EM-CCD, a theoretical model for the probability distribution of the pixel values is derived, then fitted with the experimental data to determine the camera parameters. Likewise, pixelwise calibration is performed on the sCMOS to determine the camera parameters for further analysis. Theoretical derivation of the expected signal-to-noise-ratio is provided for each image sensor and corroborated by the experimental findings. Further comparison between the cameras includes analysis of the contrast-to-noise ratio (CNR) as well as the perception-based image quality estimator (PIQE). Improvement of image quality metrics in the 3D reconstructed images is successfully confirmed compared with those of the 2D images. To the best of our knowledge, this is the first experimental report of low-light-level 3D integral imaging with as little as a few photons detected per pixel on average to improve scene visualization including occlusion removal from the scene.

Structured illumination in compact and field-portable 3D-printed shearing digital holographic microscopy for resolution enhancement

A compact and field-portable three-dimensional (3D)-printed structured illumination (SI) digital holographic microscope based on shearing geometry is presented. By illuminating the sample using a SI pattern, the lateral resolution in both reconstructed phase and amplitude images can be improved up to twice the resolution provided by conventional illumination. The use of a 3D-printed system and shearing geometry reduces the complexity of the system, while providing high temporal stability. The experimental results for the USAF resolution target show a resolution improvement of a factor of two which corroborates the theoretical prediction. Resolution enhancement in phase imaging is also demonstrated by imaging a biological sample. To the best of our knowledge, this is the first report of a compact and field-portable SI digital holographic system based on shearing geometry.

Status of the UCN experiment

The neutron is the simplest nuclear system that can be used to probe the structure of the weak interaction and search for physics beyond the standard model. Measurements of neutron lifetime and β-decay correlation coefficients with precisions of 0.02% and 0.1%, respectively, would allow for stringent constraints on new physics. The UCNτ experiment uses an asymmetric magneto-gravitational UCN trap with in situ counting of surviving neutrons to measure the neutron lifetime, τn = 877.7s (0.7s)stat (+0.4/−0.2s)sys. We discuss the recent result from UCNτ, the status of ongoing data collection and analysis, and the path toward a 0.25 s measurement of the neutron lifetime with UCNτ.

CisMapper: predicting regulatory interactions from transcription factor ChIP-seq data

Identifying the genomic regions and regulatory factors that control the transcription of genes is an important, unsolved problem. The current method of choice predicts transcription factor (TF) binding sites using chromatin immunoprecipitation followed by sequencing (ChIP-seq), and then links the binding sites to putative target genes solely on the basis of the genomic distance between them. Evidence from chromatin conformation capture experiments shows that this approach is inadequate due to long-distance regulation via chromatin looping. We present CisMapper, which predicts the regulatory targets of a TF using the correlation between a histone mark at the TF's bound sites and the expression of each gene across a panel of tissues. Using both chromatin conformation capture and differential expression data, we show that CisMapper is more accurate at predicting the target genes of a TF than the distance-based approaches currently used, and is particularly advantageous for predicting the long-range regulatory interactions typical of tissue-specific gene expression. CisMapper also predicts which TF binding sites regulate a given gene more accurately than using genomic distance. Unlike distance-based methods, CisMapper can predict which transcription start site of a gene is regulated by a particular binding site of the TF.

Sickle cell disease diagnosis based on spatio-temporal cell dynamics analysis using 3D printed shearing digital holographic microscopy

We present a spatio-temporal analysis of cell membrane fluctuations to distinguish healthy patients from patients with sickle cell disease. A video hologram containing either healthy red blood cells (h-RBCs) or sickle cell disease red blood cells (SCD-RBCs) was recorded using a low-cost, compact, 3D printed shearing interferometer. Reconstructions were created for each hologram frame (time steps), forming a spatio-temporal data cube. Features were extracted by computing the standard deviations and the mean of the height fluctuations over time and for every location on the cell membrane, resulting in two-dimensional standard deviation and mean maps, followed by taking the standard deviations of these maps. The optical flow algorithm was used to estimate the apparent motion fields between subsequent frames (reconstructions). The standard deviation of the magnitude of the optical flow vectors across all frames was then computed. In addition, seven morphological cell (spatial) features based on optical path length were extracted from the cells to further improve the classification accuracy. A random forest classifier was trained to perform cell identification to distinguish between SCD-RBCs and h-RBCs. To the best of our knowledge, this is the first report of machine learning assisted cell identification and diagnosis of sickle cell disease based on cell membrane fluctuations and morphology using both spatio-temporal and spatial analysis.

Abstract P5-21-08: Tolerability of the combination of lapatinib and trastuzumab in older patients with HER2 positive metastatic breast cancer

Background: Older adults are less likely to be included in clinical trials leading to the approval of novel cancer treatments. The Institute of Medicine and ASCO have identified therapeutic phase II trials as a key research priority to increase the evidence base for older adults with cancer. While targeted therapies may represent a less toxic option for older patients, few trials have studied their tolerability and efficacy in older adults. Here, we present a phase II study (NCT01273610) of the combination of trastuzumab and lapatinib in older patients with HER2+ metastatic breast cancer (MBC), incorporating geriatric oncology principles in the study design.

Methods: Patients age ≥ 60 years with MBC and any number of prior chemotherapy (CT) lines received trastuzumab (either 4mg/kg loading dose followed by 2mg/kg weekly or 8mg/kg followed by 6mg/kg q/3 weeks) plus lapatinib 1000 mg/m2 daily in 21-day cycles. Patients completed a pre-treatment geriatric assessment including measures of function, comorbidity, cognition, nutrition, and psychosocial status. A toxicity risk score developed for older adults receiving cytotoxic CT was calculated for each patient (Hurria et al. JCO 2011 & 2016). Relationships between tolerability (dose reductions and grade (G) ≥ 3 toxicity attributed to treatment) and risk score analyzed using a log2 transformation were assessed using generalized linear models, Student's t tests, and Fisher's exact test. Response rate (RR) and progression free survival (PFS) were evaluated.

Results: 40 patients (mean age 72 [60-92]) were accrued from 04/11 to 05/15. 25% (n = 10) were ≥ 75 years of age. 65% of patients (n = 26) had HR+ tumors and 35% (n = 14) were receiving ≥ 3rd line treatment. Median number of cycles was 4 (0-28). RR was 23% (n = 9, 95% CI 11-38%; 1 complete, 8 partial). 23% (n = 9) achieved stable disease. PFS was 2.7 months (95% CI 2.5-12). Based on the toxicity risk score, 21% (n = 8), 54% (n = 21), and 26% (n = 10) were at low, intermediate, and high risk. 70% (n = 28) of patients had G ≥ 2 toxicities and 20% (n = 8) G ≥ 3 toxicities. G 2 and 3 diarrhea occurred in 28% (n = 11) and 5% (n = 2) respectively. 5% (n = 2) were hospitalized due to treatment-related toxicity. No G ≥ 3 cardiac toxicities were observed. 23% of patients (n = 9) had treatment delays, and 43% (n = 17) required a lapatinib dose reduction. The mean toxicity risk score was higher in patients who required dose reductions (Student's t: p = 0.02). No statistically significant relationship was found between toxicity risk scores and the presence of G ≥ 3 treatment toxicity (logistic regression: OR = 3.08, 95% CI [0.54, 21.2], p = 0.22).

Conclusions: Among older patients with MBC (79% at intermediate or high risk of G ≥ 3 cytotoxic CT toxicity), trastuzumab and lapatinib were well tolerated, with only 20% experiencing G3 toxicities. The toxicity risk score was not found to be significantly related with treatment toxicity, which may be explained by the very low incidence of G3 events. Patients with a low toxicity risk score were not likely to require a lapatinib dose reduction.

Citation Format: O'Connor T, Soto-Perez-de-Celis E, Blanchard S, Chapman A, Kimmick G, Muss H, Luu T, Waisman JR, Li D, Mortimer J, Yuan Y, Somlo G, Stewart D, Katheria V, Levi A, Hurria A. Tolerability of the combination of lapatinib and trastuzumab in older patients with HER2 positive metastatic breast cancer [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P5-21-08.

The impact of geriatric focused nurse assessment and intervention in the emergency department: A systematic review

BACKGROUND

Nursing assessment of elderly patients is imperative in Emergency Departments (ED) while providing interventions that increase independence facilitating discharge to primary healthcare.

AIMS

To systematically review the impact of geriatric focused nurse assessment and intervention in the ED on hospital utilisation in terms of admission rate, ED revisits and length of hospital stay (LOHS).

METHODS

Search strategy used following databases; Cochrane, Medline, CINAHL, Embase, Scopus and Web of Knowledge; And terms; geriatric nurse assessment, nurse discharge planning, geriatric nurse specialist, nurse intervention, emergency department, accident and emergency, patient outcomes, discharge, admissions, readmissions, hospital utilization, hospitalization, length of stay/hospital stay.

RESULTS

Nine studies were included: seven RCTs and two prospective pre/post-intervention designed studies. Geriatric focused nursing assessment and interventions did not have a statistical impact on hospitalization, readmissions, LOHS and ED revisits. Risk screening and comprehensive geriatric assessment extending into primary care may reduce readmission rates but not affect hospitalization. An increase in ED visits in the intervention group at 30 days post-intervention was noted.

CONCLUSION

Inconsistencies in assessment and interventions for the older person in ED are apparent. Further research evaluating a standardised risk assessment tool and innovative interventions extending into primary healthcare is required.

Combined use of modulated ultrasound and electric current stimulation for diabetic foot ulcers: a case series

OBJECTIVE

Diabetic foot ulcers (DFUs) are a significant challenge in wound care practice. Our aim was to evaluate the combined use of of two therapies, ultrasound and electrostimulation, in the treatment of DFUs.

METHOD

This study employed a prospective, non-comparative, case series design, undertaken in a podiatry-led diabetic foot clinic, in an acute hospital setting, in an urban location in Ireland. We recruited patinets with hard-to-heal DFUs who were treated twice a week with combined modulated ultrasound and electric current stimulation.

RESULTS

We recruited seven patients with eight chronic DFUs. A mean wound size reduction of 71% was achieved and there were no adverse reactions to the therapy.

CONCLUSION

The results of this small case series indicate that combined modulated ultrasound and electric current stimulation offers promise as an adjunct therapy for DFUs. Further large scale trials are now warranted.

Measurement of pH, exudate composition and temperature in wound healing: a systematic review

OBJECTIVE

To assess the potential of measurements of pH, exudate composition and temperature in wounds to predict healing outcomes and to identify the methods that are employed to measure them.

METHOD

A systematic review based on the outcomes of a search strategy of quantitative primary research published in the English language was conducted. Inclusion criteria limited studies to those involving in vivo and human participants with an existing or intentionally provoked wound, defined as 'a break in the epithelial integrity of the skin', and excluded in vitro and animal studies. Data synthesis and analysis was performed using structured narrative summaries of each included study arranged by concept, pH, exudate composition and temperature. The Evidence Based Literature (EBL) Critical Appraisal Checklist was implemented to appraise the quality of the included studies.

RESULTS

A total of 23 studies, three for pH (mean quality score 54.48%), 12 for exudate composition (mean quality score 46.54%) and eight for temperature (mean quality score 36.66%), were assessed as eligible for inclusion in this review. Findings suggest that reduced pH levels in wounds, from alkaline towards acidic, are associated with improvements in wound condition. Metalloproteinase-9 (MMP-9), matrix metalloproteinase-2 (MMP-2), tissue inhibitor of metalloproteinase (TIMP), neutrophil elastase (NE) and albumin, in descending order, were the most frequently measured analytes in wounds. MMP-9 emerged as the analyte which offers the most potential as a biomarker of wound healing, with elevated levels observed in acute or non-healing wounds and decreasing levels in wounds progressing in healing. Combined measures of different exudate components, such as MMP/TIMP ratios, also appeared to offer substantial potential to indicate wound healing. Finally, temperature measurements are highest in non-healing, worsening or acute wounds and decrease as wounds progress towards healing. Methods used to measure pH, exudate composition and temperature varied greatly and, despite noting some similarities, the studies often yielded significantly contrasting results. Furthermore, a limitation to the generalisability of the findings was the overall quality scores of the research studies, which appeared suboptimal.

CONCLUSION

Despite some promising findings, there was insufficient evidence to confidently recommend the use of any of these measures as predictors of wound healing. pH measurement appeared as the most practical method for use in clinical practice to indicate wound healing outcomes. Further research is required to increase the strength of evidence and develop a greater understanding of wound healing dynamics.

Non-human primates avoid the detrimental effects of prenatal androgen exposure in mixed-sex litters: combined demographic, behavioral, and genetic analyses

Producing single versus multiple births has important life history trade-offs, including the potential benefits and risks of sharing a common in utero environment. Sex hormones can diffuse through amniotic fluid and fetal membranes, and females with male littermates risk exposure to high levels of fetal testosterone, which are shown to have masculinizing effects and negative fitness consequences in many mammals. Whereas most primates give birth to single offspring, several New World monkey and strepsirrhine species regularly give birth to small litters. We examined whether neonatal testosterone exposure might be detrimental to females in mixed-sex litters by compiling data from long-term breeding records for seven primate species (Saguinus oedipus; Varecia variegata, Varecia rubra, Microcebus murinis, Mirza coquereli, Cheirogaleus medius, Galago moholi). Litter sex ratios did not differ from the expected 1:2:1 (MM:MF:FF for twins) and 1:2:2:1 (MMM:MMF:MFF:FFF for triplets). Measures of reproductive success, including female survivorship, offspring-survivorship, and inter-birth interval, did not differ between females born in mixed-sex versus all-female litters, indicating that litter-producing non-human primates, unlike humans and rodents, show no signs of detrimental effects from androgen exposure in mixed sex litters. Although we found no evidence for CYP19A1 gene duplications-a hypothesized mechanism for coping with androgen exposure-aromatase protein evolution shows patterns of convergence among litter-producing taxa. That some primates have effectively found a way to circumvent a major cost of multiple births has implications for understanding variation in litter size and life history strategies across mammals.

Has the Bachelor of Surgery Left Medical School?-A National Undergraduate Assessment

INTRODUCTION

Nearly all trainee doctors would undertake a surgical placement in their clinical training; however, there is anecdotal evidence of variability in undergraduate surgical teaching across the UK. We set out to describe the provision of undergraduate surgery and report graduating students' opinions of aspects of this.

METHODS

We undertook a cross-sectional questionnaire of medical students graduating in 2014 from UK medical schools. An online electronic questionnaire was used to capture demographics, career intentions, and individual's undergraduate experience of surgery. A separate questionnaire was sent to medical schools to assess time devoted to surgical placements and how surgical sciences were taught and assessed.

RESULTS

From 483 responses covering 31 UK medical schools, there were 328 completed student questionnaires. A third of respondents felt that teaching of surgical sciences was inadequate. Medical schools reported time allocated to surgical specialties ranging from 4 to 21 weeks (median 13 weeks). Among all, 1 medical school offered a basic surgical skills course and 1 medical school specifically assessed surgical-related skills. Overall, 65% of medical students felt prepared for a surgical foundation placement and 88% felt prepared for a medical foundation placement. In total, 78% felt ready to participate in an acute medicine on-call and 48% felt ready for emergency surgery on-call. There was a positive association between time dedicated to undergraduate surgery and reported preparedness for a foundation surgical job.

CONCLUSIONS

UK medical students reported uniformly low rates of satisfaction with surgical science teaching. Students studying at medical schools with more time in the curriculum dedicated to surgery reported higher levels of preparedness for surgical foundation jobs. There were differences in the rates of perceived preparedness for surgical posts and for emergency surgery. There is a clear need to review undergraduate surgical provision to ensure that students are equipped for safe practice in junior surgical foundation jobs.

Antiviral Properties of the HIV-1 Proteinase Inhibitor RO 31-8959

The peptide derivative Ro 31-8959 has been shown to be a potent inhibitor of HIV proteinase with an IC50 of 2 × 10−9M, against HIV-1RF in acutely infected lymphoblastoid cells. This inhibition was not overcome by increasing the infectious dose or by extending the culture time. Similar antiviral activity was also obtained against HIV-2, SIV and several AZT-resistant strains of HIV-1. The time of addition of the inhibitor could be delayed for 22 h without significant loss of activity, supporting its mode of action as taking place late in the replication cycle of HIV-1. Ro 31-8959 also showed activity against chronically infected cells.

Screening of Compounds for Activity against HIV: A Collaborative Study

The collaborative study was undertaken to examine the sensitivity of a range of tests used in assessing the antiviral activities of compounds against human immunodeficiency virus (HIV). A panel of 20 compounds with diverse antiviral activities against HIV were tested under code at three antiviral testing centres supported by the Medical Research Council's AIDS Directed Programme and at the European Community Centralised Facility (ECCF) for New Antiviral Compounds against AIDS in Belgium. Compounds known to have major anti-HIV activity ranked high in all assays, with the exception of the glucosidase inhibitors and certain nucleoside analogues. Results of two assays based on MT4 cells (centre IV) showed a high degree of similarity, despite the use of distinct HIV-1 (HTLV-IIIB) and HIV-2 (ROD) viruses. Considerable similarity was also observed between the assays based on HTLV-IIIRF in C8166 cells (centres I and II). Other assays performed at centre II and at centre III had enhanced sensitivity for glycosidase inhibitors. The differences in anti-HIV activity that were observed may be attributable to specific properties of the cell lines used and particular testing methodologies. The use of more than one type of assay is advisable in order not to miss compounds with low to moderate activity against HIV.