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The emerging demands for healthcare where access is limited due to political, environmental, or socio-economic factors have been driving research into bio-medical devices that perform in both diagnostic and therapeutic roles at lower costs and greater accessibility. Paper microfluidic devices are used in many applications, particularly medical diagnostics and offer an excellent combination of utility and low cost making them particularly valuable in resource-limited applications and point-of-care usage across a wide variety environmental conditions. Microfluidic biological diagnostics continue to mature as researchers discover new ways to exploit the technological possibilities, and address liabilities. The increasing complexity of paper-based microfluidic devices beyond home pregnancy tests is driving the need to produce new tools and methodologies that enable more robust biological diagnostics and potential therapeutic applications. However, the process of developing new paper microfluidic devices is limited due to having to manually design and fabricate designs to research. Often, researchers must design scores of different devices to find a combination of parameters that functions as expected. In this work, a novel software framework to support automated development of paper-based microfluidic devices is introduced to facilitate both research and fabrication to accelerate the investigative process and reduce material utilization and manpower. Unlike to existing lab-on-a-chip technologies, paper-based microfluidics differs in terms of substrate technologies and use a passive flow method to deliver fluids and reagents for assays. While numerous analogies between microfluidics and semiconductor technologies have been espoused, the physical differences between the fluid dynamics and electrical current are significant which suggests that current trends in physical design for microfluidics must change course in order to be of practical use to designers. Within this framework, a methodology is introduced to address design automation such as dynamically placing and routing microfluidic components in a non-discrete design space while avoiding invalid design layouts, accounting for fluid volume usage, surface area utilization, and the timing required to perform specified biological assays and also optimizing device parameters, enabling researchers to focus on the science and thereby accelerating the development of new, low-resource paper microfluidic devices for a developing world.

Environmental toxic pollutants and pathogens that enter the ecosystem are major global issues. Detection of these toxic chemicals/pollutants and the diagnosis of a disease is a first step in efficiently controlling their contamination and spread, respectively. Various analytical techniques are available to detect and determine toxic chemicals/pathogens, including liquid chromatography, HPLC, mass spectroscopy, and enzyme-linked immunosorbent assays. However, these sensing strategies have some drawbacks such as tedious sample pretreatment and preparation, the requirement for skilled technicians, and dependence on large laboratory-based instruments. Alternatively, biosensors, especially paper-based sensors, could be used extensively and are a cost-effective alternative to conventional laboratory testing. They can improve accessibility to testing to identify chemicals and pollutants, especially in developing countries. Due to its low cost, abundance, easy disposal (by incineration, for example) and biocompatible nature, paper is considered a versatile material for the development of environmentally friendly electrochemical/optical (bio) sensor devices. This review presents an overview of sensing platforms constructed from paper, pointing out the main merits and demerits of paper-based sensing systems, their fabrication techniques, and the different optical/electrochemical detection techniques that they exploit.

Significance Prostate cancer is the most common cancer among men. An accurate diagnosis of its severity at detection plays a major role in improving their survival. Recently, machine learning models using biomarkers identified from Raman micro-spectroscopy discriminated intraductal carcinoma of the prostate (IDC-P) from cancer tissue with a ≥85 % detection accuracy and differentiated high-grade prostatic intraepithelial neoplasia (HGPIN) from IDC-P with a ≥97.8 % accuracy. Aim To improve the classification performance of machine learning models identifying different types of prostate cancer tissue using a new dimensional reduction technique. Approach A radial basis function (RBF) kernel support vector machine (SVM) model was trained on Raman spectra of prostate tissue from a 272-patient cohort (Centre hospitalier de l'Universite de Montreal, CHUM) and tested on two independent cohorts of 76 patients [University Health Network (UHN)] and 135 patients (Centre hospitalier universitaire de Quebec-Universite Laval, CHUQc-UL). Two types of engineered features were used. Individual intensity features, i.e., Raman signal intensity measured at particular wavelengths and novel Raman spectra fitted peak features consisting of peak heights and widths. Results Combining engineered features improved classification performance for the three aforementioned classification tasks. The improvements for IDC-P/cancer classification for the UHN and CHUQc-UL testing sets in accuracy, sensitivity, specificity, and area under the curve (AUC) are (numbers in parenthesis are associated with the CHUQc-UL testing set): +4 % (+8 % ), +7 % (+9 % ), +2 % (6%), +9 (+9) with respect to the current best models. Discrimination between HGPIN and IDC-P was also improved in both testing cohorts: +2.2 % (+1.7 % ), +4.5 % (+3.6 % ), +0 % (+0 % ), +2.3 (+0). While no global improvements were obtained for the normal versus cancer classification task [+0 % (-2 % ), +0 % (-3 % ), +2 % (-2 % ), +4 (+3)], the AUC was improved in both testing sets. Conclusions Combining individual intensity features and novel Raman fitted peak features, improved the classification performance on two independent and multicenter testing sets in comparison to using only individual intensity features.

Significance: Early detection of oral cancer is vital for high-risk patients, and machine learning-based automatic classification is ideal for disease screening. However, current datasets collected from high-risk populations are unbalanced and often have detrimental effects on the performance of classification. Aim: To reduce the class bias caused by data imbalance. Approach: We collected 3851 polarized white light cheek mucosa images using our customized oral cancer screening device. We use weight balancing, data augmentation, undersampling, focal loss, and ensemble methods to improve the neural network performance of oral cancer image classification with the imbalanced multi-class datasets captured from high-risk populations during oral cancer screening in low-resource settings. Results: By applying both data-level and algorithm-level approaches to the deep learning training process, the performance of the minority classes, which were difficult to distinguish at the beginning, has been improved. The accuracy of “premalignancy” class is also increased, which is ideal for screening applications. Conclusions: Experimental results show that the class bias induced by imbalanced oral cancer image datasets could be reduced using both data- and algorithm-level methods. Our study may provide an important basis for helping understand the influence of unbalanced datasets on oral cancer deep learning classifiers and how to mitigate.

Significance: Oral cancer is among the most common cancers globally, especially in low- and middle-income countries. Early detection is the most effective way to reduce the mortality rate. Deep learning-based cancer image classification models usually need to be hosted on a computing server. However, internet connection is unreliable for screening in low-resource settings. Aim: To develop a mobile-based dual-mode image classification method and customized Android application for point-of-care oral cancer detection. Approach: The dataset used in our study was captured among 5025 patients with our customized dual-modality mobile oral screening devices. We trained an efficient network MobileNet with focal loss and converted the model into TensorFlow Lite format. The finalized lite format model is ∼16.3 MB and ideal for smartphone platform operation. We have developed an Android smartphone application in an easy-to-use format that implements the mobile-based dual-modality image classification approach to distinguish oral potentially malignant and malignant images from normal/benign images. Results: We investigated the accuracy and running speed on a cost-effective smartphone computing platform. It takes ∼300 ms to process one image pair with the Moto G5 Android smartphone. We tested the proposed method on a standalone dataset and achieved 81% accuracy for distinguishing normal/benign lesions from clinically suspicious lesions, using a gold standard of clinical impression based on the review of images by oral specialists. Conclusions: Our study demonstrates the effectiveness of a mobile-based approach for oral cancer screening in low-resource settings.

Significance: To explore brain architecture and pathology, a consistent and reliable methodology to visualize the three-dimensional cerebral microvasculature is beneficial. Perfusion-based vascular labeling is quick and easily deliverable. However, the quality of vascular labeling can vary with perfusion-based labels due to aggregate formation, leakage, rapid photobleaching, and incomplete perfusion. Aim: We describe a simple, two-day protocol combining perfusion-based labeling with a two-day clearing step that facilitates whole-brain, three-dimensional microvascular imaging and characterization. Approach: The combination of retro-orbital injection of Lectin-Dylight-649 to label the vasculature, the clearing process of a modified iDISCO+ protocol, and light-sheet imaging collectively enables a comprehensive view of the cerebrovasculature. Results: We observed ∼threefold increase in contrast-to-background ratio of Lectin-Dylight-649 vascular labeling over endogenous green fluorescent protein fluorescence from a transgenic mouse model. With light-sheet microscopy, we demonstrate sharp visualization of cerebral microvasculature throughout the intact mouse brain. Conclusions: Our tissue preparation protocol requires fairly routine processing steps and is compatible with multiple types of optical microscopy.

SignificanceAutomated understanding of human embryonic stem cell (hESC) videos is essential for the quantified analysis and classification of various states of hESCs and their health for diverse applications in regenerative medicine.AimThis paper aims to develop an ensemble method and bagging of deep learning classifiers as a model for hESC classification on a video dataset collected using a phase contrast microscope.ApproachThe paper describes a deep learning-based random network (RandNet) with an autoencoded feature extractor for the classification of hESCs into six different classes, namely, (1)cell clusters, (2)debris, (3)unattached cells, (4)attached cells, (5)dynamically blebbing cells, and (6)apoptotically blebbing cells. The approach uses unlabeled data to pre-train the autoencoder network and fine-tunes it using the available annotated data.ResultsThe proposed approach achieves a classification accuracy of 97.23 ± 0.94 % and outperforms the state-of-the-art methods. Additionally, the approach has a very low training cost compared with the other deep-learning-based approaches, and it can be used as a tool for annotating new videos, saving enormous hours of manual labor.ConclusionsRandNet is an efficient and effective method that uses a combination of subnetworks trained using both labeled and unlabeled data to classify hESC images.

Significance: The ability to detect and localize specific molecules through tissue is important for elucidating the molecular basis of disease and treatment. Unfortunately, most current molecular imaging tools in tissue either lack high spatial resolution (e.g., diffuse optical fluorescence tomography or positron emission tomography) or lack molecular sensitivity (e.g., micro-computed tomography, μCT). X-ray luminescence imaging emerged about 10 years ago to address this issue by combining the molecular sensitivity of optical probes with the high spatial resolution of x-ray imaging through tissue. In particular, x-ray luminescence computed tomography (XLCT) has been demonstrated as a powerful technique for the high-resolution imaging of deeply embedded contrast agents in three dimensions (3D) for small-animal imaging. Aim: To facilitate the translation of XLCT for small-animal imaging, we have designed and built a small-animal dedicated focused x-ray luminescence tomography (FXLT) scanner with a μCT scanner, synthesized bright and biocompatible nanophosphors as contrast agents, and have developed a deep-learning-based reconstruction algorithm. Approach: The proposed FXLT imaging system was designed using computer-aided design software and built according to specifications. NaGdF4 nanophosphors doped with europium or terbium were synthesized with a silica shell for increased biocompatibility and functionalized with biotin. A deep-learning-based XLCT image reconstruction was also developed based on the residual neural network as a data synthesis method of projection views from few-view data to enhance the reconstructed image quality. Results: We have built the FXLT scanner for small-animal imaging based on a rotational gantry. With all major imaging components mounted, the motor controlling the gantry can be used to rotate the system with a high accuracy. The synthesized nanophosphors displayed distinct x-ray luminescence emission, which enables multi-color imaging, and has successfully been bound to streptavidin-coated substrates. Lastly, numerical simulations using the proposed deep-learning-based reconstruction algorithm has demonstrated a clear enhancement in the reconstructed image quality. Conclusions: The designed FXLT scanner, synthesized nanophosphors, and deep-learning-based reconstruction algorithm show great potential for the high-resolution molecular imaging of small animals.

Significance: Current imaging paradigms for differential diagnosis of suspicious breast lesions suffer from high false positive rates that force patients to undergo unnecessary biopsies. Diffuse optical spectroscopic imaging (DOSI) noninvasively probes functional hemodynamic and compositional parameters in deep tissue and has been shown to be sensitive to contrast between normal and malignant tissues. Aim: DOSI methods are under investigation as an adjunct to mammography and ultrasound that could reduce false positive rates and unnecessary biopsies, particularly in radiographically dense breasts. Methods: We performed a retrospective analysis of 212 subjects with suspicious breast lesions who underwent DOSI imaging. Physiological tissue parameters were z-score normalized to the patient’s contralateral breast tissue and input to univariate logistic regression models to discriminate between malignant tumors and the surrounding normal tissue. The models were then used to differentiate malignant lesions from benign lesions. Results: Models incorporating several individual hemodynamic parameters were able to accurately distinguish malignant tumors from both the surrounding background tissue and benign lesions with area under the curve (AUC) ≥0.85. Z-score normalization improved the discriminatory ability and calibration of these predictive models relative to unnormalized or ratio-normalized data. Conclusions: Findings from a large subject population study show how DOSI data normalization that accounts for normal tissue heterogeneity and quantitative statistical regression approaches can be combined to improve the ability of DOSI to diagnose malignant lesions. This improved diagnostic accuracy, combined with the modality’s inherent logistical advantages of portability, low cost, and nonionizing radiation, could position DOSI as an effective adjunct modality that could be used to reduce the number of unnecessary invasive biopsies.

Significance: Spatial frequency domain imaging (SFDI), a noncontact wide-field imaging technique using patterned illumination with multiple wavelengths, has been used to quantitatively measure structural and functional parameters of in vivo tissue. Using SFDI in a porcine model, we previously found that scattering changes in skin could potentially be used to noninvasively assess burn severity and monitor wound healing. Translating these findings to human subjects necessitates a better understanding of the variation in “baseline” human skin scattering properties across skin types and anatomical locations. Aim: Using SFDI, we aim to characterize the variation in the reduced scattering coefficient (μs′) for skin across a range of pigmentation and anatomic sites (including common burn locations) for normal human subjects. These measurements are expected to characterize baseline human skin properties to inform our use of SFDI for clinical burn severity and wound healing assessments. Approach: SFDI was used to measure μs′ in the visible- and near-infrared regime (471 to 851 nm) in 15 subjects at 10 anatomical locations. Subjects varied in age, gender, and Fitzpatrick skin type. Results: For all anatomical locations, the coefficient of variation in measured μs′ decreased with increasing wavelength. High intersubject variation in μs′ at visible wavelengths coincided with large values of the melanin extinction coefficient at those wavelengths. At 851 nm, where intersubject variation in μs′ was smallest for all anatomical locations and absorption from melanin is minimal, significant intrasubject differences in μs′ were observed at the different anatomical locations. Conclusions: Our study is the first report of wide-field mapping of human skin scattering properties across multiple skin types and anatomical locations using SFDI. Measured μs′ values varied notably between skin types at wavelengths where absorption from melanin was prominent. Additionally, μs′ varied considerably across different anatomical locations at 851 nm, where the confounding effects from melanin absorption are minimized.

Significance: Extremity injury represents the leading cause of trauma hospitalizations among adults under the age of 65 years, and long-term impairments are often substantial. Restoring function depends, in large part, on bone and soft tissue healing. Thus, decisions around treatment strategy are based on assessment of the healing potential of injured bone and/or soft tissue. However, at the present, this assessment is based on subjective clinical clues and/or cadaveric studies without any objective measure. Optical imaging is an ideal method to solve several of these issues. Aim: The aim is to highlight the current challenges in assessing bone and tissue perfusion/viability and the potentially high impact applications for optical imaging in orthopaedic surgery. Approach: The prospective will review the current challenges faced by the orthopaedic surgeon and briefly discuss optical imaging tools that have been published. With this in mind, it will suggest key research areas that could be evolved to help make surgical assessments more objective and quantitative. Results: Orthopaedic surgical procedures should benefit from incorporation of methods to measure functional blood perfusion or tissue metabolism. The types of measurements though can vary in the depth of tissue sampled, with some being quite superficial and others sensing several millimeters into the tissue. Most of these intrasurgical imaging tools represent an ideal way to improve surgical treatment of orthopaedic injuries due to their inherent point-of-care use and their compatibility with real-time management. Conclusion: While there are several optical measurements to directly measure bone function, the choice of tools can determine also the signal strength and depth of sampling. For orthopaedic surgery, real-time data regarding bone and tissue perfusion should lead to more effective patient-specific management of common orthopaedic conditions, requiring deeper penetrance commonly seen with indocyanine green imaging. This will lower morbidity and result in decreased variability associated with how these conditions are managed.

The mechanosensitivity of the optic nerve head (ONH) plays a pivotal role in the pathogenesis of glaucoma. Characterizing elasticity of the ONH over changing physiological pressure may provide a better understanding of how changes in intraocular pressure (IOP) lead to changes in the mechanical environment of the ONH. Optical coherence elastography (OCE) is an emerging technique that can detect tissue biomechanics noninvasively with both high temporal and spatial resolution compared with conventional ultrasonic elastography. We describe a confocal OCE system in measuring ONH elasticity in vitro, utilizing a pressure inflation setup in which IOP is controlled precisely. We further utilize the Lamb wave model to fit the phase dispersion curve during data postprocessing. We present a reconstruction of Young’s modulus of the ONH by combining our OCE system with a Lamb wave model for the first time. This approach enables the quantification of Young’s modulus of the ONH, which can be fit using a piecewise polynomial to the corresponding IOP.

Oral cancer is a growing health issue in low- and middle-income countries due to betel quid, tobacco, and alcohol use and in younger populations of middle- and high-income communities due to the prevalence of human papillomavirus. The described point-of-care, smartphone-based intraoral probe enables autofluorescence imaging and polarized white light imaging in a compact geometry through the use of a USB-connected camera module. The small size and flexible imaging head improves on previous intraoral probe designs and allows imaging the cheek pockets, tonsils, and base of tongue, the areas of greatest risk for both causes of oral cancer. Cloud-based remote specialist and convolutional neural network clinical diagnosis allow for both remote community and home use. The device is characterized and preliminary field-testing data are shared.

Glioma is one of the most refractory types of brain tumor. Accurate tumor boundary identification and complete resection of the tumor are essential for glioma removal during brain surgery. We present a method based on visible resonance Raman (VRR) spectroscopy to identify glioma margins and grades. A set of diagnostic spectral biomarkers features are presented based on tissue composition changes revealed by VRR. The Raman spectra include molecular vibrational fingerprints of carotenoids, tryptophan, amide I/II/III, proteins, and lipids. These basic in situ spectral biomarkers are used to identify the tissue from the interface between brain cancer and normal tissue and to evaluate glioma grades. The VRR spectra are also analyzed using principal component analysis for dimension reduction and feature detection and support vector machine for classification. The cross-validated sensitivity, specificity, and accuracy are found to be 100%, 96.3%, and 99.6% to distinguish glioma tissues from normal brain tissues, respectively. The area under the receiver operating characteristic curve for the classification is about 1.0. The accuracies to distinguish normal, low grade (grades I and II), and high grade (grades III and IV) gliomas are found to be 96.3%, 53.7%, and 84.1% for the three groups, respectively, along with a total accuracy of 75.1%. A set of criteria for differentiating normal human brain tissues from normal control tissues is proposed and used to identify brain cancer margins, yielding a diagnostic sensitivity of 100% and specificity of 71%. Our study demonstrates the potential of VRR as a label-free optical molecular histopathology method used for in situ boundary line judgment for brain surgery in the margins.

Author(s): Escalera, Cristian; Santoyo-Olsson, Jasmine; Stewart, Anita L; Ortiz, Carmen; Napoles, Anna Maria | Abstract: ObjectiveIntrusive thoughts, defined as unwanted and recurrent thoughts about a stressful experience, are associated with psychological distress in women with breast cancer. This study assessed moderating effects of various social support dimensions on associations between intrusive thoughts and psychological distress among Latina breast cancer survivors.MethodsWe used baseline data from a randomized controlled trial of a stress management intervention delivered to 151 Spanish-speaking Latinas with nonmetastatic breast cancer within 1 year of diagnosis. Intrusive thoughts, four dimensions of social support (emotional/informational, tangible, affectionate, and positive social interaction), and symptoms of anxiety and depression were assessed through in-person interviews. Information on age, time since diagnosis, breast cancer variables, history of depression, and marital status served as covariates. Generalized linear models were used to investigate bivariate and multivariate associations and to explore moderation effects of the four dimensions of social support.ResultsIn bivariate models, intrusive thoughts were associated positively with depression (β = .024, .001) and anxiety (β = .047, P l .001) symptoms. Adjusting for other factors, intrusive thoughts remained associated with depression symptoms (β = .022, .008), regardless of level of social support (for all support dimensions). For anxiety, there were significant interactions of tangible (β = -.013, .034) and affectionate (β = -.022, .005) support with intrusive thoughts. Intrusive thoughts were associated more strongly with anxiety symptoms among women reporting less tangible and affectionate support than those with higher levels of these types of support.ConclusionsTangible and affectionate support have protective effects on anxiety symptoms among Spanish-speaking Latina breast cancer survivors experiencing intrusive thoughts, but not depression symptoms.

Relatively few imaging and sensing technologies are employed to study human lactation physiology. In particular, human mammary development during pregnancy as well as mammary involution after lactation have been poorly described, despite their importance for breast cancer diagnosis and treatment during these phases. Our case study shows the potential of diffuse optical spectroscopic imaging (DOSI) to uniquely study the spatiotemporal changes in mammary tissue composition during the involution of the lactating breast toward its pre-pregnant state. At nine time intervals over a period of eight months after the cessation of breastfeeding, we reconstructed 2-D maps of mammary water content, lipid content, total hemoglobin (THb) concentration, oxygen saturation (StO2), and tissue optical scattering. Mammary lipid content in the nonareolar region showed a significant relative increase of 59%, whereas water content and THb concentration showed a significant relative decrease of 50% and 48%, respectively. Significant changes were also found in StO2 and tissue optical scattering. Our findings are consistent with the gradual replacement of fibroglandular tissue by adipose tissue and vascular regression during mammary involution. Moreover, our data provide unique insight into the dynamics of breast tissue composition and demonstrate the effectiveness of DOSI as a technique to study human lactation physiology.

International audience; Quantitative diffuse optical imaging has the potential to provide valuable functional information about tissue status, such as oxygen saturation or blood content to healthcare practitioners in real time. However, significant technical challenges have so far prevented such tools from being deployed in the clinic. Toward achieving this goal, prior research introduced methods based on spatial frequency domain imaging (SFDI) that allow real-time (within milliseconds) wide-field imaging of optical properties but at a single wavelength. However, for this technology to be useful to clinicians, images must be displayed in terms of metrics related to the physiological state of the tissue, hence interpretable to guide decision-making. For this purpose, recent developments introduced multispectral SFDI methods for rapid imaging of oxygenation parameters up to 16 frames per seconds (fps). We introduce real-time, wide-field, and quantitative blood parameters imaging using spatiotemporal modulation of light. Using this method, we are able to quantitatively obtain optical properties at 100 fps at two wavelengths (665 and 860 nm), and therefore oxygenation, oxyhemoglobin, and deoxyhemoglobin, using a single camera with, at most, 4.2% error in comparison with standard SFDI acquisitions.

Most new caries lesions are found in the pits and fissures of the occlusal surface. Radiographs have extremely low sensitivity for early occlusal decay, and by the time the lesion is severe enough to appear on a radiograph, it typically has penetrated well into the dentin and surgical intervention is required. The occlusal surfaces are often heavily stained, and visual and tactile detection have poor sensitivity and specificity. Previous near-infrared imaging studies at wavelengths beyond 1300nm have demonstrated that stains are not visible and demineralization on the occlusal surfaces can be viewed without interference from stains. The objective of our study is to determine how the contrast between sound and lesion areas on occlusal surfaces varies with wavelength from the visible to 2350nm and determine to what degree stains interfere with that contrast. The lesion contrast for reflectance is measured in 55 extracted teeth with suspected occlusal lesions from 400 to 2350nm employing silicon and indium gallium arsenide imaging arrays. In addition, the lesion contrast is measured on 25 extracted teeth with suspected occlusal lesions from 400 to 1600nm in reflectance and from 830 to 1400nm in transillumination before and after stains are removed using a ultrasonic scaler. The highest lesion contrast in reflectance is measured at wavelengths >1700 nm. Stains interfere significantly at wavelengths 1700 nm for reflectance (1700 to 2350nm) and near 1300nm (1250 to 1350nm) for transillumination.

Pulsed dye laser irradiation in the wavelength range of 585 to 600nm is currently the gold standard for treatment of port-wine stains (PWSs). However, this treatment method is often ineffective for deeply seated blood vessels and in individuals with moderate to heavy pigmentation. Use of optical particles doped with the FDA-approved near-infrared (NIR) absorber, indocyanine green (ICG), can potentially provide an effective method to overcome these limitations. Herein, we theoretically investigate the effectiveness of particles derived from erythrocytes, which contain ICG, in mediating photothermal destruction of PWS blood vessels. We refer to these particles as NIR erythrocyte-derived transducers (NETs). Our theoretical model consists of a Monte Carlo algorithm to estimate the volumetric energy deposition, a finite elements approach to solve the heat diffusion equation, and a damage integral based on an Arrhenius relationship to quantify tissue damage. The model geometries include simulated PWS blood vessels as well as actual human PWS blood vessels plexus obtained by the optical coherence tomography. Our simulation results indicate that blood vessels containing micron- or nano-sized NETs and irradiated at 755nm have higher levels of photothermal damage as compared to blood vessels without NETs irradiated at 585nm. Blood vessels containing micron-sized NETs also showed higher photothermal damage than blood vessels containing nano-sized NETs. The theoretical model presented can be used in guiding the fabrication of NETs with patient-specific optical properties to allow for personalized treatment based on the depth and size of blood vessels as well as the pigmentation of the individual's skin.

Several studies have reported on wide-spread contamination of U.S. paper currency with cocaine and to a lesser extent other illicit drugs. Canines are trained and employed to search for and alert to drugs. Canine alert to currency has been used as evidence that currency has been directly involved in illicit drug trafficking to justify currency seizure and forfeiture. This assertion, particularly when the only evidence is based upon canine alert, has been challenged in the courts considering that most currency in circulation is contaminated with cocaine. Comprehensive review of the scientific literature establishes that (i) 67-100% of circulated U.S. currency is contaminated with cocaine ranging from a few nanograms to over one milligram/bill (ii) various biological and environmental parameters impact canine alert to drugs. It is concluded that canine alert to U.S. currency is not sufficiently reliable to determine that currency was directly used in an illicit drug transaction.

PURPOSE:Overactive bladder syndrome is defined as urinary urgency, usually accompanied by frequency and nocturia, with or without urgency urinary incontinence in the absence of urinary tract infection or another obvious pathological condition. Electronic questionnaires have been used in a few specialties with the hope of improving treatment outcomes and patient satisfaction. However, they have not been widely used in the urological field. When treating overactive bladder, the main outcome is to improve patient quality of life. The primary objective of this study was to evaluate whether electronic questionnaires would be equally accepted as or preferred to paper questionnaires. The secondary objective was to look at the preference in relation to patient age, education and iPad® tablet familiarity. MATERIALS AND METHODS:We prospectively evaluated the iList® electronic questionnaire application using a friendly iPad tablet in patients with overactive bladder who presented to the urology clinic at our institution. Each of the 80patients who were recruited randomly completed the validated OABSS (Overactive Bladder Symptom Score) and the PPBC (Patient Perception of Bladder Condition) questionnaires in paper and electronic format on the tablet. Variables potentially associated with the outcomes of interest included demographic data, questionnaire method preference, patient response rate and iPad familiarity. We used the 2-sided Z-test to determine whether the proportion of patients who considered the tablet to be the same, better or much better than paper was significantly greater than 50%. The 2-sided chi-square test was applied to assess whether the intervention effect significantly differed among the demographic subgroups. RESULTS:A total of 80 patients 21 to 87 years old were enrolled in the study from November 2015 to August 2016. Of the patients 53% were female and 49% were 65 years or younger. The incidence of those who considered the tablet to be the same or better than paper was 82.5% (95% CI 74.2-90.8, p

No industry has been affected by environmental social movements as much, in so short a time, and on such a wide geographical scale, as pulp and paper manufacturing. Environmental social movements have had a profound influence on the industry since the mid- to late 1980s. In just a few years, “state of the art” pulping and bleaching has become more environmentally friendly. Manufacturers around the world have spent billions of US dollars adopting new technologies, modifying old ones, and developing local innovations to meet rising environmental demands, expectations, and regulations. Additional millions of US dollars have been spent by public and private institutions on research, development, and testing of new nvironmental processes and technologies for the pulp and paper industry (cf. API 1992; Porter and Linde 1995). The transformation is incomplete -- many environmental problems remain -- but nonetheless profound. All of this can be attributed at least initially to the efforts and influence of environmental social movements.