Mechanistic insight into the dermal delivery from nanoparticle-coated submicron O/W emulsions

The influence of silica nanoparticle coating of negatively and positively charged submicron emulsion oil droplets on the dermal delivery of a lipophilic fluorescent probe, acridine orange 10-nonyl bromide (AONB) using an ex vivo porcine skin model is reported. The skin retention and depth of the penetration of AONB significantly increased (p ≤ 0.05) up to a skin depth of ～265 µm by nanoparticle coating of negative lecithin-stabilised emulsion oil droplets especially when nanoparticles were added from the water phase. The extent and depth of penetration of AONB incorporated into positively charged silica-coated oleylamine-stabilised emulsions significantly increased up to the upper dermis (～290 µm) with more pronounced effect by nanoparticle incorporation from the water phase of the control oleylamine emulsion. The permeation of AONB through full-thickness porcine skin was negligible (<0.12% of the topically applied dose). The skin penetration profile of AONB was well correlated to the more facilitated transport of the electrostatically bond silica–AONB complex compared to free AONB as one of the potential mechanisms of the improved delivery. The skin permeation of silica nanoparticles was negligible (<1 µg mL^?1 after a 6-h exposure time) which demonstrated the potential of nanoparticle-coated emulsions for topical targeting. © 2009 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 99:890–904, 2010     

Consistent linear and non-linear responses to invasive electrical brain stimulation across individuals and primate species with implanted electrodes

BackgroundElectrical neuromodulation via implanted electrodes is used in treating numerous neurological disorders, yet our knowledge of how different brain regions respond to varying stimulation parameters is sparse.Objective/HypothesisWe hypothesized that the neural response to electrical stimulation is both region-specific and non-linearly related to amplitude and frequency.MethodsWe examined evoked neural responses following 400 ms trains of 10–400 Hz electrical stimulation ranging from 0.1 to 10 mA. We stimulated electrodes implanted in cingulate cortex (dorsal anterior cingulate and rostral anterior cingulate) and subcortical regions (nucleus accumbens, amygdala) of non-human primates (NHP, N = 4) and patients with intractable epilepsy (N = 15) being monitored via intracranial electrodes. Recordings were performed in prefrontal, subcortical, and temporal lobe locations.ResultsIn subcortical regions as well as dorsal and rostral anterior cingulate cortex, response waveforms depended non-linearly on frequency (Pearson's linear correlation r < 0.39), but linearly on current (r > 0.58). These relationships between location, and input-output characteristics were similar in homologous brain regions with average Pearson's linear correlation values r > 0.75 between species and linear correlation values between participants r > 0.75 across frequency and current values per brain region. Evoked waveforms could be described by three main principal components (PCs) which allowed us to successfully predict response waveforms across individuals and across frequencies using PC strengths as functions of current and frequency using brain region specific regression models.ConclusionsThese results provide a framework for creation of an atlas of input-output relationships which could be used in the principled selection of stimulation parameters per brain region.     

Generation of large numbers of highly purified dendritic cells from bone marrow progenitor cells after co-culture with syngeneic murine splenocytes

Dendritic cells (DCs) are called the sentinels of the human immune system because of their function as antigen presenting cells (APCs) that elicit a protective immune response. Given that DCs have been used for many years as target cells in a great number of experiments, it became essential to devise a new method for producing DCs in higher quantities and of greater purity. Here we report a novel technique for obtaining more dendritic cells, and with higher purity, from in-vitro co-culture of bone marrow (BM) cells with splenocytes.     

The role of efferocytosis in neuro-degenerative diseases

Neurological Sciences - Efferocytosis has a critical role in maintaining tissues and organs’ homeostasis by removing apoptotic cells. It is essential for human health, and disturbances in...     

Measuring the signal-to-noise ratio of a neuron

The signal-to-noise ratio (SNR), a commonly used measure of fidelity in physical systems, is defined as the ratio of the squared amplitude or variance of a signal relative to the variance of the noise. This definition is not appropriate for neural systems in which spiking activity is more accurately represented as point processes. We show that the SNR estimates a ratio of expected prediction errors and extend the standard definition to one appropriate for single neurons by representing neural spiking activity using point process generalized linear models (PP-GLM). We estimate the prediction errors using the residual deviances from the PP-GLM fits. Because the deviance is an approximate χ² random variable, we compute a bias-corrected SNR estimate appropriate for single-neuron analysis and use the bootstrap to assess its uncertainty. In the analyses of four systems neuroscience experiments, we show that the SNRs are −10 dB to −3 dB for guinea pig auditory cortex neurons, −18 dB to −7 dB for rat thalamic neurons, −28 dB to −14 dB for monkey hippocampal neurons, and −29 dB to −20 dB for human subthalamic neurons. The new SNR definition makes explicit in the measure commonly used for physical systems the often-quoted observation that single neurons have low SNRs. The neuron’s spiking history is frequently a more informative covariate for predicting spiking propensity than the applied stimulus. Our new SNR definition extends to any GLM system in which the factors modulating the response can be expressed as separate components of a likelihood function.The signal-to-noise ratio (SNR), defined as the amplitude squared of a signal or the signal variance divided by the variance of the system noise, is a widely applied measure for quantifying system fidelity and for comparing performance among different systems (1–4). Commonly expressed in decibels as 10log₁₀(SNR), the higher the SNR, the stronger the signal or information in the signal relative to the noise or distortion. Use of the SNR is most appropriate for systems defined as deterministic or stochastic signals plus Gaussian noise (2, 4). For the latter, the SNR can be computed in the time or frequency domain.Use of the SNR to characterize the fidelity of neural systems is appealing because information transmission by neurons is a noisy stochastic process. However, the standard concept of SNR cannot be applied in neuronal analyses because neurons transmit both signal and noise primarily in their action potentials, which are binary electrical discharges also known as spikes (5–8). Defining what is the signal and what is the noise in neural spiking activity is a challenge because the putative signals or stimuli for neurons differ appreciably among brain regions and experiments. For example, neurons in the visual cortex and in the auditory cortex respond respectively to features of light (9) and sound stimuli (10) while neurons in the somatosensory thalamus respond to tactile stimuli (11). In contrast, neurons in the rodent hippocampus respond robustly to the animal’s position in its environment (11, 12), whereas monkey hippocampal neurons respond to the process of task learning (13). As part of responding to a putative stimulus, a neuron’s spiking activity is also modulated by biophysical factors such as its absolute and relative refractory periods, its bursting propensity, and local network and rhythm dynamics (14, 15). Hence, the definition of SNR must account for the extent to which a neuron’s spiking responses are due to the applied stimulus or signal and to these intrinsic biophysical properties.Formulations of the SNR for neural systems have been studied. Rieke et al. (16) adapted information theory measures to define Gaussian upper bounds on the SNR for individual neurons. Coefficients of variation and Fano factors based on spike counts (17–19) have been used as measures of SNR. Similarly, Gaussian approximations have been used to derive upper bounds on neuronal SNR (16). These approaches do not consider the point process nature of neural spiking activity. Moreover, these measures and the Gaussian approximations are less accurate for neurons with low spike rates or when information is contained in precise spike times.Lyamzin et al. (20) developed an SNR measure for neural systems using time-dependent Bernoulli processes to model the neural spiking activity. Their SNR estimates, based on variance formulae, do not consider the biophysical properties of the neuron and are more appropriate for Gaussian systems (16, 21, 22). The Poisson regression model used widely in statistics to relate count observations to covariates provides a framework for studying the SNR for non-Gaussian systems because it provides an analog of the square of the multiple correlation coefficient (R²) used to measure goodness of fit in linear regression analyses (23). The SNR can be expressed in terms of the R² for linear and Poisson regression models. However, this relationship has not been exploited to construct an SNR estimate for neural systems or point process models. Finally, the SNR is a commonly computed statistic in science and engineering. Extending this concept to non-Gaussian systems would be greatly aided by a precise statement of the theoretical quantity that this statistic estimates (24, 25).We show that the SNR estimates a ratio of expected prediction errors (EPEs). Using point process generalized linear models (PP-GLM), we extend the standard definition to one appropriate for single neurons recorded in stimulus−response experiments. In analyses of four neural systems, we show that single-neuron SNRs range from −29 dB to −3 dB and that spiking history is often a more informative predictor of spiking propensity than the signal being represented. Our new SNR definition generalizes to any problem in which the modulatory components of a system’s output can be expressed as separate components of a GLM.     

Epigenetic aspects of multiple sclerosis and future therapeutic options

Abstract

Multiple sclerosis (MS) is a chronic inflammatory and neurodegenerative disease accompanied by demyelination of neurons in the central nervous system that mostly affects young adults, especially women. This disease has two phases including relapsing-remitting form (RR-MS) by episodes of relapse and periods of clinical remission and secondary-progressive form (SP-MS), which causes more disability. The inheritance pattern of MS is not exactly identified and there is an agreement that it has a complex pattern with an interplay among environmental, genetic and epigenetic alternations. Epigenetic mechanisms that are identified for MS pathogenesis are DNA methylation, histone modification and some microRNAs’ alternations. Several cellular processes including apoptosis, differentiation and evolution can be modified along with epigenetic changes. Some alternations are associated with epigenetic mechanisms in MS patients and these changes can become key points for MS therapy. Therefore, the aim of this review was to discuss epigenetic mechanisms that are associated with MS pathogenesis and future therapeutic approaches.     

Strangulated small bowel through vaginal vault rupture: late complication of abdominal sacrocolpopexy

Sacrocolpopexy is currently a favourable procedure for management of apical defect and vaginal vault prolapse. Recently, it has been extensively evaluated in terms of its efficacy, durability and potential short- and long-term complications. These complications have been investigated by many authors, including urinary retention, urge incontinence, urinary tract infections, wound infection, haematomas, bowel symptoms and gastrointestinal complications. However, we report the first case of strangulated small bowel due to herniation through vaginal vault rupture as a late complication of sacrocolpopexy. This report reviews the risk factors and precipitating causes of bowel evisceration particularly after sacrocolpopexy, and peri- and intraoperative preventive measures are discussed, as well as various management modalities.     

Factors affecting visualization rates of internal mammary sentinel nodes during lymphoscintigraphy.

There is great variation in the reported frequency of internal mammary (IM) sentinel node (SN) visualization. We observed a marked increase in our IM SN detection rate after 2 factors were changed simultaneously: depth of perilesional injection and dose. METHODS: A retrospective review of 82 consecutive patients (group 1) was compared with 61 consecutive patients (group 2) after changing the depth of perilesional injections and dose. Both groups had perilesional injections of (99m)Tc-sulfur colloid followed by intradermal injections at the areolar cutaneous junction. For group 2, activity was increased in all patients scheduled for next-day surgery. Group 2 had perilesional injections on top of, beside, and just below the estimated level of the tumor in an infiltrative manner, versus injections just on top of and beside the tumor as performed for group 1. RESULTS: The rates of IM SN visualization were 4.9% (4/82) for group 1 and 23.0% (14/61) for group 2 (P < 0.003). IM SNs were hotter in group 2 than in group 1. The total number of IM SNs detected per patient was also higher for group 2 than for group 1: 2.1 and 1.2, respectively. In group 2, patients with small breasts had an IM SN visualization rate of 46.2%; those with medium breasts, 21.1%; and those with large breasts, 0% (P < 0.017). In group 2, primary lesions located medially had a higher rate of IM SN visualization than did lesions located laterally: 38.9% (7/18) and 16.2% (6/37), respectively (P = 0.066). Dose was not a statistically significant factor within group 2 or group 1 when comparing IM SN visualization rate for doses above or below the mean or median. CONCLUSION: Modification of just these 2 factors resulted in a striking change in our IM SN detection rates. The injection depth was the most important factor. Breast size had a marked effect on the probability of detecting IM SNs. This suggests that the variation in detection rates reported in the literature could be at least partly dependent on variations in these factors, among others. Many surgeons do not routinely harvest IM SNs, but information about their presence can potentially alter treatment decisions.