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Significance: Assessing the nanostructure of polymer solutions and biofluids is broadly useful for understanding drug delivery and disease progression and for monitoring therapy. Aim: Our objective is to quantify bronchial mucus solids concentration (wt. %) during hypertonic saline (HTS) treatment in vitro via nanostructurally constrained diffusion of gold nanorods (GNRs) monitored by polarization-sensitive optical coherence tomography (PS-OCT). Approach: Using PS-OCT, we quantified GNR translational (DT) and rotational (DR) diffusion coefficients within polyethylene oxide solutions (0 to 3 wt. %) and human bronchial epithelial cell (hBEC) mucus (0 to 6.4 wt. %). Interpolation of DT and DR data is used to develop an assay to quantify mucus concentration. The assay is demonstrated on the mucus layer of an air-liquid interface hBEC culture during HTS treatment. Results: In polymer solutions and mucus, DT and DR monotonically decrease with increasing concentration. DR is more sensitive than DT to changes above 1.5 wt. % of mucus and exhibits less intrasample variability. Mucus on HTS-treated hBEC cultures exhibits dynamic mixing from cilia. A region of hard-packed mucus is revealed by DR measurements. Conclusions: The extended dynamic range afforded by simultaneous measurement of DT and DR of GNRs using PS-OCT enables resolving concentration of the bronchial mucus layer over a range from healthy to disease in depth and time during HTS treatment in vitro.

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Personal electronic health records (PEHRs) enable patients access to their own medical records. Differences in access and use of PEHRs may create health disparities. We conducted a narrative literature review regarding the effects of race, language preference, education, income, and homelessness on PEHR usage as well as PEHRs content, particularly stigmatizing language. Of 3177 citations found, 75 articles were relevant. Patient race, language, income, and education predicted PEHR use, which could potentially exacerbate health disparities.

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In 2012 Australia created a national Personal Controlled Electronic Health Record (PCEHR) known as "My Health Record" (MHR). However, MHR has seen low patient utilization. Debate regarding MHR has centered on utility and moral issues (e.g. data privacy). We conducted a narrative review to assess patient perception and clinical utility of PCEHRs worldwide. Results show patient and clinician support for PCEHRs but little evidence of improved outcomes and patient concerns regarding data providence.

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We analyzed PubMed citations since 1988 to explore the dissemination of medical/health informatics concepts between countries and across medical domains. We extracted countries from the PubMed author affiliation field to identify and analyze the top 10 informatics publishing countries. We found that the informatics publications are becoming more similar over time and that the rate of exchange across countries has increased with the introduction of e-publishing. Nonetheless, with the exception of machine learning, the impact of core informatics concepts on mainstream medicine and radiology publications remains small.

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The Learning Health Systems (LHS) framework demonstrates the potential for iterative interrogation of health data in real time and implementation of insights into practice. Yet, the lack of appropriately skilled workforce results in an inability to leverage existing data to design innovative solutions. We developed a tailored professional development program to foster a skilled workforce. The short course is wholly online, for interdisciplinary professionals working in the digital health arena. To transform healthcare systems, the workforce needs an understanding of LHS principles, data driven approaches, and the need for diversly skilled learning communities that can tackle these complex problems together.

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ABSTRACT: Amniotic fluid embolism (AFE) is a rare, sudden, and catastrophic complication of pregnancy that can result in cardiopulmonary arrest, potentially leading to death. The pathophysiology of an AFE includes an inflammatory and coagulopathic response due to fetal materials entering maternal circulation with the hallmark triad of symptoms: acute respiratory distress, cardiovascular collapse, and coagulopathy. Management of AFE should include high-quality cardiopulmonary resuscitation, immediate delivery of the fetus if applicable, early intubation to provide adequate oxygenation and ventilation, fluid volume resuscitation, and ongoing evaluation of coagulopathy. Priorities include thromoboelastography interpretation if available, control of hemorrhage and coagulopathy with blood component therapy, and cardiovascular support through inotropes and vasopressor administration. More recent approaches include implementing the A-OK (atropine, ondansetron, and ketorolac) protocol for suspected AFE protocol, extracorporeal cardiopulmonary resuscitation (ECPR), and extracorporeal membrane oxygenation (ECMO) therapies to increase survival and decrease complications. Venoarterial ECMO is the highest form of life support that provides support in patients with pulmonary and cardiac failure. ECPR is the application of Venoarterial ECMO during cardiopulmonary resuscitation in cases where the cause of arrest is believed to be reversible. Early implementation of ECPR during the acute phase of AFE can provide support for end-organ perfusion in place of the weakened and recovering heart while optimizing oxygenation, making venoarterial ECMO an ideal adjunctive therapy. Because of the rarity of AFE, many obstetrical teams may have limited prior experience in managing these catastrophic cases; however, with ongoing education and simulation, teams can be better prepared in the recognition and management of these life-threatening events.

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Neither the thermodynamically determined probability isotherm nor its kinetically manifest rate isotherm can be applied to photo-absorptive reactions such that the participants, including photons, may be treated as if they were chemical reactants. Photons and chemical reactants differ from each other fundamentally: firstly, a photon's energy is absolute and, in all instances of practical relevance to the present paper, independent of its surrounding electrochemical field, while the energy of a chemical reactant is relative and defined by its surrounding field; secondly, while both photons and chemical reactants can and do engage in entropy creation, only chemical reactants can engage in entropy exchange. Clarification of these differences requires identification and abandonment of fundamental historical errors in photochemical thought deriving from inappropriate overreach of analogies drawn between light and ideal gases, and including: treatment of photo-absorption as a reversible chemical reaction; attribution to light of thermal potential, or temperature (as distinct from the idealised abstraction of a 'temperature signature'); attribution to light of exchangeable entropy content. We begin by addressing widespread misapprehensions concerning the perennially misunderstood concept of entropy and the frequently overlooked distinction between entropy creation and entropy exchange. Armed with these clarifications, we arrive at a useful perspective for understanding energy absorption and transfer in photosynthetic processes which, through the chemical 'kidnapping' of metastable excited states within structured metabolic pathways, achieves outcomes which the Second Law denies to thermal chemical reactions.

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BACKGROUND: Neuromodulation therapies, such as deep brain stimulation (DBS), spinal cord stimulation (SCS), responsive neurostimulation (RNS), transcranial magnetic stimulation (TMS), transcranial direct stimulation (tDCS), and vagus nerve stimulation (VNS) are used to treat neurological and psychiatric conditions for patients who have failed to benefit from other treatment approaches. Although generally effective, seemingly similar cases often have very different levels of effectiveness. While there is ongoing interest in developing predictors, it can be difficult to aggregate the necessary data from limited cohorts of patients at individual treatment centers. OBJECTIVE: In order to increase the predictive power in neuromodulation studies, we created an informatics platform called the International Neuromodulation Registry (INR). The INR platform has a data flow process that will allow researchers to pool data across multiple centers to enable population health research. METHODS: This custom informatics platform has a Neo4j graph database and includes a harmonization process that allows data from different studies to be aggregated and compared. Users of the INR can download deidentified patient imaging, patient demographic data, device settings, and medical rating scales. The INR supports complex network analysis and patient timeline visualization. RESULTS: The INR currently houses and allows visualization of deidentified imaging and clinical data from hundreds of patients with a wide range of diagnoses and neuromodulation therapies. CONCLUSION: Ultimately, we believe that widespread adoption of the INR platform will improve population health research in neuromodulation therapy.

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BACKGROUND: Metabolic networks represent all chemical reactions that occur between molecular metabolites in an organism's cells. They offer biological context in which to integrate, analyze, and interpret omic measurements, but their large scale and extensive connectivity present unique challenges. While it is practical to simplify these networks by placing constraints on compartments and hubs, it is unclear how these simplifications alter the structure of metabolic networks and the interpretation of metabolomic experiments. RESULTS: We curated and adapted the latest systemic model of human metabolism and developed customizable tools to define metabolic networks with and without compartmentalization in subcellular organelles and with or without inclusion of prolific metabolite hubs. Compartmentalization made networks larger, less dense, and more modular, whereas hubs made networks larger, more dense, and less modular. When present, these hubs also dominated shortest paths in the network, yet their exclusion exposed the subtler prominence of other metabolites that are typically more relevant to metabolomic experiments. We applied the non-compartmental network without metabolite hubs in a retrospective, exploratory analysis of metabolomic measurements from 5 studies on human tissues. Network clusters identified individual reactions that might experience differential regulation between experimental conditions, several of which were not apparent in the original publications. CONCLUSIONS: Exclusion of specific metabolite hubs exposes modularity in both compartmental and non-compartmental metabolic networks, improving detection of relevant clusters in omic measurements. Better computational detection of metabolic network clusters in large data sets has potential to identify differential regulation of individual genes, transcripts, and proteins.

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Titanium(iv) isopropoxide in ethanol is aged under acidic conditions with a small amount of water. After adding a small amount of N,N-dimethylformamide, TiO2 nanofibers with average diameters of ∼70 nm are prepared by direct electrospinning. During in situ heating of the nanofibers, crystallization into anatase and rutile phases is observed.

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OBJECTIVE: Time-sensitive communication of critical imaging findings like pneumothorax or pulmonary embolism to referring physicians is essential for patient safety. The definitive communication is the radiology free-text report. Quality assurance initiatives require that institutions audit these communications, a time-intensive manual task. We propose using a rule-based natural language processing system to improve the process for auditing critical findings communications. METHODS: We present a pilot assessment of the feasibility of using an automated critical finding identification system to assist quality assurance teams' evaluation of critical findings communication compliance. Our assessment is based on chest imaging reports. Critical findings are identified in radiology reports using pyConTextNLP, an open source Python implementation of the ConText algorithm. RESULTS: In our test set, there were 75 reports with critical findings and 591 reports without critical findings. pyConTextNLP correctly identified 69 of the positive cases with 8 false-positives for a sensitivity of 0.92 and a specificity of 0.99. DISCUSSION: Natural language processing can provide valuable assistance to auditing critical findings communications.

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This paper explores cutting-edge deep learning methods for information extraction from medical imaging free text reports at a multi-institutional scale and compares them to the state-of-the-art domain-specific rule-based system - PEFinder and traditional machine learning methods - SVM and Adaboost. We proposed two distinct deep learning models - (i) CNN Word - Glove, and (ii) Domain phrase attention-based hierarchical recurrent neural network (DPA-HNN), for synthesizing information on pulmonary emboli (PE) from over 7370 clinical thoracic computed tomography (CT) free-text radiology reports collected from four major healthcare centers. Our proposed DPA-HNN model encodes domain-dependent phrases into an attention mechanism and represents a radiology report through a hierarchical RNN structure composed of word-level, sentence-level and document-level representations. Experimental results suggest that the performance of the deep learning models that are trained on a single institutional dataset, are better than rule-based PEFinder on our multi-institutional test sets. The best F1 score for the presence of PE in an adult patient population was 0.99 (DPA-HNN) and for a pediatrics population was 0.99 (HNN) which shows that the deep learning models being trained on adult data, demonstrated generalizability to pediatrics population with comparable accuracy. Our work suggests feasibility of broader usage of neural network models in automated classification of multi-institutional imaging text reports for a variety of applications including evaluation of imaging utilization, imaging yield, clinical decision support tools, and as part of automated classification of large corpus for medical imaging deep learning work.

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A method for quantification of plasmon mode quality factors using a novel collinear single-beam interferometric nonlinear optical (INLO) microscope is described. A collinear sequence of phase-stabilized femtosecond laser pulses generated by a series of birefringent optics is used for the INLO experiments. Our experimental designs allow for the creation of pulse replicas (800 nm carrier wave) that exhibit interpulse phase stability of 33 mrad (approximately 14 attoseonds), which can be incrementally temporally delayed from attosecond to picosecond time scales. This temporal tuning range allows for resonant electronic Fourier spectroscopy of plasmonic gold nanoparticles. The collinear geometry of the pulse pair facilitates integration into an optical microscopy platform capable of single-nanoparticle sensitivity. Analysis of the Fourier spectra in the frequency domain yields the sample plasmon resonant response and homogeneous line width; the latter provided quantification of the plasmon mode quality factor. We have applied this INLO approach to quantitatively determine the influence of encapsulation of gold nanorods with silica shells on plasmon quality factors. We have studied a series of three gold nanorod samples, distinguished by surface passivation. These include cetyltrimethylammonium bromide (CTAB)-passivated nanorods, as well as ones encapsulated by 5 and 20 nanometer-thick silica shells. The Q-factor results show a trend of increasing quality factor, increasing by 46% from 54 ± 8 to 79 ± 9, in going from CTAB- to 20 nm silica-coated AuNRs. The straightforward method of INLO enables analysis of plasmon responses to environmental influences, such as analyte binding and solvent effects, as well as quantification of structure-specific plasmon coherence dynamics.

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Purpose To evaluate the performance of a deep learning convolutional neural network (CNN) model compared with a traditional natural language processing (NLP) model in extracting pulmonary embolism (PE) findings from thoracic computed tomography (CT) reports from two institutions. Materials and Methods Contrast material-enhanced CT examinations of the chest performed between January 1, 1998, and January 1, 2016, were selected. Annotations by two human radiologists were made for three categories: the presence, chronicity, and location of PE. Classification of performance of a CNN model with an unsupervised learning algorithm for obtaining vector representations of words was compared with the open-source application PeFinder. Sensitivity, specificity, accuracy, and F1 scores for both the CNN model and PeFinder in the internal and external validation sets were determined. Results The CNN model demonstrated an accuracy of 99% and an area under the curve value of 0.97. For internal validation report data, the CNN model had a statistically significant larger F1 score (0.938) than did PeFinder (0.867) when classifying findings as either PE positive or PE negative, but no significant difference in sensitivity, specificity, or accuracy was found. For external validation report data, no statistical difference between the performance of the CNN model and PeFinder was found. Conclusion A deep learning CNN model can classify radiology free-text reports with accuracy equivalent to or beyond that of an existing traditional NLP model. © RSNA, 2017 Online supplemental material is available for this article.

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This paper seeks to develop a more thermodynamically sound pedagogy for students of biological transport than is currently available from either of the competing schools of linear non-equilibrium thermodynamics (LNET) or Michaelis-Menten kinetics (MMK). To this end, a minimal model of facilitated diffusion was constructed comprising four reversible steps: cis-substrate binding, cis→trans bound enzyme shuttling, trans-substrate dissociation and trans→cis free enzyme shuttling. All model parameters were subject to the second law constraint of the probability isotherm, which determined the unidirectional and net rates for each step and for the overall reaction through the law of mass action. Rapid equilibration scenarios require sensitive 'tuning' of the thermodynamic binding parameters to the equilibrium substrate concentration. All non-equilibrium scenarios show sigmoidal force-flux relations, with only a minority of cases having their quasi-linear portions close to equilibrium. Few cases fulfil the expectations of MMK relating reaction rates to enzyme saturation. This new approach illuminates and extends the concept of rate-limiting steps by focusing on the free energy dissipation associated with each reaction step and thereby deducing its respective relative chemical impedance. The crucial importance of an enzyme's being thermodynamically 'tuned' to its particular task, dependent on the cis- and trans-substrate concentrations with which it deals, is consistent with the occurrence of numerous isoforms for enzymes that transport a given substrate in physiologically different circumstances. This approach to kinetic modelling, being aligned with neither MMK nor LNET, is best described as intuitive non-equilibrium thermodynamics, and is recommended as a useful adjunct to the design and interpretation of experiments in biotransport.

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Mucus hydration (wt%) has become an increasingly useful metric in real-time assessment of respiratory health in diseases like cystic fibrosis and COPD, with higher wt% indicative of diseased states. However, available in vivo rheological techniques are lacking. Gold nanorods (GNRs) are attractive biological probes whose diffusion through tissue is sensitive to the correlation length of comprising biopolymers. Through employment of dynamic light scattering theory on OCT signals from GNRs, we find that weakly-constrained GNR diffusion predictably decreases with increasing wt% (more disease-like) mucus. Previously, we determined this method is robust against mucus transport on human bronchial epithelial (hBE) air-liquid interface cultures (R2=0.976). Here we introduce diffusion-sensitive OCT (DS-OCT), where we collect M-mode image ensembles, from which we derive depth- and temporally-resolved GNR diffusion rates. DS-OCT allows for real-time monitoring of changing GNR diffusion as a result of topically applied mucus-thinning agents, enabling monitoring of the dynamics of mucus hydration never before seen. Cultured human airway epithelial cells (Calu-3) with a layer of endogenous mucus were doped with topically deposited GNRs (80×22nm), and subsequently treated with hypertonic saline (HS) or isotonic saline (IS). DS-OCT provided imaging of the mucus thinning response up to a depth of 600µm with 4.65µm resolution, over a total of 8 minutes in increments of ≥3 seconds. For both IS and HS conditions, DS-OCT captured changes in the pattern of mucus hydration over time. DS-OCT opens a new window into understanding mechanisms of mucus thinning during treatment, enabling real-time efficacy feedback needed to optimize and tailor treatments for individual patients.

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Studies in the literature describe the ability of dietary supplementation by omega-3 fish oil to increase the pumping efficiency of the left ventricle. Here we attempt to reconcile such studies with our own null results. We undertake a quantitative analysis of the improvement that could be expected theoretically, subject to physiological constraints, by posing the following question: By how much could efficiency be expected to increase if inefficiencies could be eliminated? Our approach utilizes thermodynamic analyses to investigate the contributions, both singly and collectively, of the major components of cardiac energetics to total cardiac efficiency. We conclude that it is unlikely that fish oils could achieve the required diminution of inefficiencies without greatly compromising cardiac performance.

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BACKGROUND: In the United States, 795,000 people suffer strokes each year; 10-15 % of these strokes can be attributed to stenosis caused by plaque in the carotid artery, a major stroke phenotype risk factor. Studies comparing treatments for the management of asymptomatic carotid stenosis are challenging for at least two reasons: 1) administrative billing codes (i.e., Current Procedural Terminology (CPT) codes) that identify carotid images do not denote which neurovascular arteries are affected and 2) the majority of the image reports are negative for carotid stenosis. Studies that rely on manual chart abstraction can be labor-intensive, expensive, and time-consuming. Natural Language Processing (NLP) can expedite the process of manual chart abstraction by automatically filtering reports with no/insignificant carotid stenosis findings and flagging reports with significant carotid stenosis findings; thus, potentially reducing effort, costs, and time. METHODS: In this pilot study, we conducted an information content analysis of carotid stenosis mentions in terms of their report location (Sections), report formats (structures) and linguistic descriptions (expressions) from Veteran Health Administration free-text reports. We assessed an NLP algorithm, pyConText's, ability to discern reports with significant carotid stenosis findings from reports with no/insignificant carotid stenosis findings given these three document composition factors for two report types: radiology (RAD) and text integration utility (TIU) notes. RESULTS: We observed that most carotid mentions are recorded in prose using categorical expressions, within the Findings and Impression sections for RAD reports and within neither of these designated sections for TIU notes. For RAD reports, pyConText performed with high sensitivity (88 %), specificity (84 %), and negative predictive value (95 %) and reasonable positive predictive value (70 %). For TIU notes, pyConText performed with high specificity (87 %) and negative predictive value (92 %), reasonable sensitivity (73 %), and moderate positive predictive value (58 %). pyConText performed with the highest sensitivity processing the full report rather than the Findings or Impressions independently. CONCLUSION: We conclude that pyConText can reduce chart review efforts by filtering reports with no/insignificant carotid stenosis findings and flagging reports with significant carotid stenosis findings from the Veteran Health Administration electronic health record, and hence has utility for expediting a comparative effectiveness study of treatment strategies for stroke prevention.

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The mammary gland extracellular matrix (ECM) is comprised of biopolymers, primarily collagen I, that are created and maintained by stromal fibroblasts. ECM remodeling by fibroblasts results in changes in ECM fiber spacing (pores) that have been shown to play a critical role in the aggressiveness of breast cancer. However, minimally invasive methods to measure the spatial distribution of ECM pore areas within tissues and in vitro 3D culture models are currently lacking. We introduce diffusion-sensitive optical coherence tomography (DS-OCT) to image the nanoscale porosity of ECM by sensing weakly constrained diffusion of gold nanorods (GNRs). DS-OCT combines the principles of low-coherence interferometry and heterodyne dynamic light scattering. By collecting co- and cross-polarized light backscattered from GNRs within tissue culture, the ensemble-averaged translational self-diffusion rate, DT, of GNRs is resolved within ∼3 coherence volumes (10 × 5 µm, x × z). As GNRs are slowed by intermittent collisions with ECM fibers, DT is sensitive to ECM porosity on the size scale of their hydrodynamic diameter (∼46 nm). Here, we validate the utility of DS-OCT using pure collagen I gels and 3D mammary fibroblast cultures seeded in collagen/Matrigel, and associate differences in artificial ECM pore areas with gel concentration and cell seed density. Across all samples, DT was highly correlated with pore area obtained by scanning electron microscopy (R(2) = 0.968). We also demonstrate that DS-OCT can accurately map the spatial heterogeneity of layered samples. Importantly, DS-OCT of 3D mammary fibroblast cultures revealed the impact of fibroblast remodeling, where the spatial heterogeneity of matrix porosity was found to increase with cell density. This provides an unprecedented view into nanoscale changes in artificial ECM porosity over effective pore diameters ranging from ∼43 to 360 nm using a micron-scale optical imaging technique. In combination with the topical deposition of GNRs, the minimally invasive nature of DS-OCT makes this a promising technology for studying tissue remodeling processes.

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