Monitoring of lung edema by microwave reflectometry during lung ischemia-reperfusion injury in vivo

It is still unclear whether lung edema can be monitored by microwave reflectometry and whether the measured changes in lung dry matter content (DMC) are accompanied by changes in PaO(2) and in pro-to anti-inflammatory cytokine expression (IFN-gamma and IL-10). Right rat lung hili were cross-clamped at 37 degrees C for 0, 60, 90 or 120 min ischemia followed by 120 min reperfusion. After 90 min (DMC: 15.9 +/- 1.4%; PaO(2): 76.7 +/- 18 mm Hg) and 120 min ischemia (DMC: 12.8 +/- 0.6%; PaO(2): 43 +/- 7 mm Hg), a significant decrease in DMC and PaO(2) throughout reperfusion compared to 0 min ischemia (DMC: 19.5 +/- 1.11%; PaO(2): 247 +/- 33 mm Hg; p < 0.05) was observed. DMC and PaO(2) decreased after 60 min ischemia but recovered during reperfusion (DMC: 18.5 +/- 2.4%; PaO(2) : 173 +/- 30 mm Hg). DMC values reflected changes on the physiological and molecular level. In conclusion, lung edema monitoring by microwave reflectometry might become a tool for the thoracic surgeon. Copyright (c) 2006 S. Karger AG, Basel

Kinetic Monte Carlo Method for Rule-based Modeling of Biochemical Networks

We present a kinetic Monte Carlo method for simulating chemical transformations specified by reaction rules, which can be viewed as generators of chemical reactions, or equivalently, definitions of reaction classes. A rule identifies the molecular components involved in a transformation, how these components change, conditions that affect whether a transformation occurs, and a rate law. The computational cost of the method, unlike conventional simulation approaches, is independent of the number of possible reactions, which need not be specified in advance or explicitly generated in a simulation. To demonstrate the method, we apply it to study the kinetics of multivalent ligand-receptor interactions. We expect the method will be useful for studying cellular signaling systems and other physical systems involving aggregation phenomena.Comment: 18 pages, 5 figure

Providing High-Quality Care for Limited English Proficient Patients: The Importance of Language Concordance and Interpreter Use

Background: Provider–patient language discordance is related to worse quality care for limited English proficient (LEP) patients who speak Spanish. However, little is known about language barriers among LEP Asian-American patients. Objective: We examined the effects of language discordance on the degree of health education and the quality of interpersonal care that patients received, and examined its effect on patient satisfaction. We also evaluated how the presence/absence of a clinic interpreter affected these outcomes. Design: Cross-sectional survey, response rate 74%. Participants: A total of 2,746 Chinese and Vietnamese patients receiving care at 11 health centers in 8 cities. Measurements: Provider–patient language concordance, health education received, quality of interpersonal care, patient ratings of providers, and the presence/absence of a clinic interpreter. Regression analyses were used to adjust for potential confounding. Results: Patients with language-discordant providers reported receiving less health education (β = 0.17, p &lt; 0.05) compared to those with language-concordant providers. This effect was mitigated with the use of a clinic interpreter. Patients with language-discordant providers also reported worse interpersonal care (β = 0.28, p &lt; 0.05), and were more likely to give low ratings to their providers (odds ratio [OR] = 1.61; CI = 0.97–2.67). Using a clinic interpreter did not mitigate these effects and in fact exacerbated disparities in patients’ perceptions of their providers. Conclusion: Language barriers are associated with less health education, worse interpersonal care, and lower patient satisfaction. Having access to a clinic interpreter can facilitate the transmission of health education. However, in terms of patients’ ratings of their providers and the quality of interpersonal care, having an interpreter present does not serve as a substitute for language concordance between patient and provider

A Detailed Observational Analysis of V1324 Sco, the Most Gamma-Ray Luminous Classical Nova to Date

It has recently been discovered that some, if not all, classical novae emit GeV gamma rays during outburst, but the mechanisms involved in the production of the gamma rays are still not well understood. We present here a comprehensive multi-wavelength dataset---from radio to X-rays---for the most gamma-ray luminous classical nova to-date, V1324 Sco. Using this dataset, we show that V1324 Sco is a canonical dusty Fe-II type nova, with a maximum ejecta velocity of 2600 km s$^{-1}$ and an ejecta mass of few $\times 10^{-5}$ M$_{\odot}$. There is also evidence for complex shock interactions, including a double-peaked radio light curve which shows high brightness temperatures at early times. To explore why V1324~Sco was so gamma-ray luminous, we present a model of the nova ejecta featuring strong internal shocks, and find that higher gamma-ray luminosities result from higher ejecta velocities and/or mass-loss rates. Comparison of V1324~Sco with other gamma-ray detected novae does not show clear signatures of either, and we conclude that a larger sample of similarly well-observed novae is needed to understand the origin and variation of gamma rays in novae.Comment: 26 pages, 13 figure

Rational Design of Pathogen-Mimicking Amphiphilic Materials as Nanoadjuvants

An opportunity exists today for cross-cutting research utilizing advances in materials science, immunology, microbial pathogenesis, and computational analysis to effectively design the next generation of adjuvants and vaccines. This study integrates these advances into a bottom-up approach for the molecular design of nanoadjuvants capable of mimicking the immune response induced by a natural infection but without the toxic side effects. Biodegradable amphiphilic polyanhydrides possess the unique ability to mimic pathogens and pathogen associated molecular patterns with respect to persisting within and activating immune cells, respectively. The molecular properties responsible for the pathogen-mimicking abilities of these materials have been identified. The value of using polyanhydride nanovaccines was demonstrated by the induction of long-lived protection against a lethal challenge of Yersinia pestis following a single administration ten months earlier. This approach has the tantalizing potential to catalyze the development of next generation vaccines against diseases caused by emerging and re-emerging pathogens

Leaching Hazardous Substances out of Photovoltaic Modules

Photovoltaic modules contain hazardous substances such as lead and cadmium. Under normal operation conditions, these materials will not be released into the environment. This study identifies conditions resulting in release. Our worst case study uses milled module pieces of 0.2 mm size. Depending on the pH value of water based solutions, more or less amounts of hazardous substances are leached out. Solutions with low pH values (acidic solutions) yield substantial leaching. Three different solutions simulate different environmental conditions: i) “low mineralized water” conditions, via water containing sodium hydroxide, ii) “sea water” conditions, via water containing sodium hydroxide and sodium chloride, and iii) “rainwater” conditions, via water containing acetic acid. In “rain water”-like solutions with low pH, already after a few days, around 30 % of the cadmium is leached out from milled cadmium telluride module pieces, increasing to 50 % after 56 days! In the same time, more than 15 % of lead is leached out from c-Si module pieces. Tellurium elutes in the range of 30 to 40 % with a weak dependence on the pH value of the solution indicating an instability of the compound cadmiumtelluride out of the cadmiumtelluride modules. Most of the extractions increase during several weeks of measurement. Therefore, the usual one-day-elution test does not give enough information. Meaningful leaching experiments should last for at least ten days

Resting-state EEG reveals four subphenotypes of amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis is a devastating disease characterized primarily by motor system degeneration, with clinical evidence of cognitive and behavioural change in up to 50% of cases. Amyotrophic lateral sclerosis is both clinically and biologically heterogeneous. Subgrouping is currently undertaken using clinical parameters, such as site of symptom onset (bulbar or spinal), burden of disease (based on the modified El Escorial Research Criteria) and genomics in those with familial disease. However, with the exception of genomics, these subcategories do not take into account underlying disease pathobiology, and are not fully predictive of disease course or prognosis. Recently, we have shown that resting-state EEG can reliably and quantitatively capture abnormal patterns of motor and cognitive network disruption in amyotrophic lateral sclerosis. These network disruptions have been identified across multiple frequency bands, and using measures of neural activity (spectral power) and connectivity (comodulation of activity by amplitude envelope correlation and synchrony by imaginary coherence) on source-localized brain oscillations from high-density EEG. Using data-driven methods (similarity network fusion and spectral clustering), we have now undertaken a clustering analysis to identify disease subphenotypes and to determine whether different patterns of disruption are predictive of disease outcome. We show that amyotrophic lateral sclerosis patients (n = 95) can be subgrouped into four phenotypes with distinct neurophysiological profiles. These clusters are characterized by varying degrees of disruption in the somatomotor (α-band synchrony), frontotemporal (β-band neural activity and γl-band synchrony) and frontoparietal (γl-band comodulation) networks, which reliably correlate with distinct clinical profiles and different disease trajectories. Using an in-depth stability analysis, we show that these clusters are statistically reproducible and robust, remain stable after reassessment using a follow-up EEG session, and continue to predict the clinical trajectory and disease outcome. Our data demonstrate that novel phenotyping using neuroelectric signal analysis can distinguish disease subtypes based exclusively on different patterns of network disturbances. These patterns may reflect underlying disease neurobiology. The identification of amyotrophic lateral sclerosis subtypes based on profiles of differential impairment in neuronal networks has clear potential in future stratification for clinical trials. Advanced network profiling in amyotrophic lateral sclerosis can also underpin new therapeutic strategies that are based on principles of neurobiology and designed to modulate network disruption

Improving 3D convolutional neural network comprehensibility via interactive visualization of relevance maps: Evaluation in Alzheimer's disease

Background: Although convolutional neural networks (CNN) achieve high diagnostic accuracy for detecting Alzheimer's disease (AD) dementia based on magnetic resonance imaging (MRI) scans, they are not yet applied in clinical routine. One important reason for this is a lack of model comprehensibility. Recently developed visualization methods for deriving CNN relevance maps may help to fill this gap. We investigated whether models with higher accuracy also rely more on discriminative brain regions predefined by prior knowledge. Methods: We trained a CNN for the detection of AD in N=663 T1-weighted MRI scans of patients with dementia and amnestic mild cognitive impairment (MCI) and verified the accuracy of the models via cross-validation and in three independent samples including N=1655 cases. We evaluated the association of relevance scores and hippocampus volume to validate the clinical utility of this approach. To improve model comprehensibility, we implemented an interactive visualization of 3D CNN relevance maps. Results: Across three independent datasets, group separation showed high accuracy for AD dementia vs. controls (AUC$\geq$0.92) and moderate accuracy for MCI vs. controls (AUC$\approx$0.75). Relevance maps indicated that hippocampal atrophy was considered as the most informative factor for AD detection, with additional contributions from atrophy in other cortical and subcortical regions. Relevance scores within the hippocampus were highly correlated with hippocampal volumes (Pearson's r$\approx$-0.86, p<0.001). Conclusion: The relevance maps highlighted atrophy in regions that we had hypothesized a priori. This strengthens the comprehensibility of the CNN models, which were trained in a purely data-driven manner based on the scans and diagnosis labels.Comment: 24 pages, 9 figures/tables, supplementary material, source code available on GitHu

Amyloid pathology but not APOE ε4 status is permissive for tau-related hippocampal dysfunction

We investigated whether the impact of tau-pathology on memory performance and on hippocampal/medial temporal memory function in non-demented individuals depends on the presence of amyloid pathology, irrespective of diagnostic clinical stage. We conducted a cross-sectional analysis of the observational, multicentric DZNE-Longitudinal Cognitive Impairment and Dementia Study (DELCODE). Two hundred and thirty-five participants completed task functional MRI and provided CSF (92 cognitively unimpaired, 100 experiencing subjective cognitive decline and 43 with mild cognitive impairment). Presence (A+) and absence (A-) of amyloid pathology was defined by CSF amyloid-β42 (Aβ42) levels. Free recall performance in the Free and Cued Selective Reminding Test, scene recognition memory accuracy and hippocampal/medial temporal functional MRI novelty responses to scene images were related to CSF total-tau and phospho-tau levels separately for A+ and A- individuals. We found that total-tau and phospho-tau levels were negatively associated with memory performance in both tasks and with novelty responses in the hippocampus and amygdala, in interaction with Aβ42 levels. Subgroup analyses showed that these relationships were only present in A+ and remained stable when very high levels of tau (>700 pg/ml) and phospho-tau (>100 pg/ml) were excluded. These relationships were significant with diagnosis, age, education, sex, assessment site and Aβ42 levels as covariates. They also remained significant after propensity score based matching of phospho-tau levels across A+ and A- groups. After classifying this matched sample for phospho-tau pathology (T-/T+), individuals with A+/T+ were significantly more memory-impaired than A-/T+ despite the fact that both groups had the same amount of phospho-tau pathology. ApoE status (presence of the E4 allele), a known genetic risk factor for Alzheimer's disease, did not mediate the relationship between tau pathology and hippocampal function and memory performance. Thus, our data show that the presence of amyloid pathology is associated with a linear relationship between tau pathology, hippocampal dysfunction and memory impairment, although the actual severity of amyloid pathology is uncorrelated. Our data therefore indicate that the presence of amyloid pathology provides a permissive state for tau-related hippocampal dysfunction and hippocampus-dependent recognition and recall impairment. This raises the possibility that in the predementia stage of Alzheimer's disease, removing the negative impact of amyloid pathology could improve memory and hippocampal function even if the amount of tau-pathology in CSF is not changed, whereas reducing increased CSF tau-pathology in amyloid-negative individuals may not proportionally improve memory function