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Pharmaceutics May 2024Huntington's disease (HD) is a monogenic neurodegenerative disorder caused by a cytosine-adenine-guanine (CAG) trinucleotide repeat expansion in the gene. There are no...
Huntington's disease (HD) is a monogenic neurodegenerative disorder caused by a cytosine-adenine-guanine (CAG) trinucleotide repeat expansion in the gene. There are no cures for HD, but the genetic basis of this disorder makes gene therapy a viable approach. Adeno-associated virus (AAV)-miRNA-based therapies have been demonstrated to be effective in lowering HTT mRNA; however, the blood-brain barrier (BBB) poses a significant challenge for gene delivery to the brain. Delivery strategies include direct injections into the central nervous system, which are invasive and can result in poor diffusion of viral particles through the brain parenchyma. Focused ultrasound (FUS) is an alternative approach that can be used to non-invasively deliver AAVs by temporarily disrupting the BBB. Here, we investigate FUS-mediated delivery of a single-stranded AAV9 bearing a cDNA for GFP in 2-month-old wild-type mice and the zQ175 HD mouse model at 2-, 6-, and 12-months. FUS treatment improved AAV9 delivery for all mouse groups. The delivery efficacy was similar for all WT and HD groups, with the exception of the zQ175 12-month cohort, where we observed decreased GFP expression. Astrocytosis did not increase after FUS treatment, even within the zQ175 12-month group exhibiting higher baseline levels of GFAP expression. These findings demonstrate that FUS can be used to non-invasively deliver an AAV9-based gene therapy to targeted brain regions in a mouse model of Huntington's disease.
PubMed: 38931834
DOI: 10.3390/pharmaceutics16060710 -
Pharmaceutics May 2024Cannabidiol (CBD) is a safe and non-psychotropic phytocannabinoid with a wide range of potential therapeutic anti-inflamatory and antioxidant activities. Due to its...
Cannabidiol (CBD) is a safe and non-psychotropic phytocannabinoid with a wide range of potential therapeutic anti-inflamatory and antioxidant activities. Due to its lipophilicity, it is normally available dissolved in oily phases. The main aim of this work was to develop and characterize a new formulation of a microemulsion with potential anti-inflammatory and antioxidant activity for the topical treatment of inflammatory skin disorders. The microemulsion system was composed of a 20% CBD oil, which served as the hydrophobic phase; Labrasol/Plurol Oleique (1:1), which served as surfactant and cosurfactant (S/CoS), respectively; and an aqueous vegetal extract obtained from L. () ripe fruits, which has potential anti-oxidant and anti-inflammatory activity and which served as the aqueous phase. A pseudo-ternary phase diagram was generated, leading to the selection of an optimal proportion of 62% (S/CoS), 27% CBD oil and 11% water and, after its reproducibility was tested, the aqueous phases were replaced by the vegetal hydrophilic extract. The defined systems were characterized in terms of conductivity, droplet size (by laser scattering), compatibility of components (by differential scanning calorimetry) and rheological properties (using a rotational rheometer). The designed microemulsion showed good stability and slight pseudo-plastic behavior. The release properties of CBD from the oil phase and caffeic acid from the aqueous phase of the microemulsion were studied via in vitro diffusion experiments using flow-through diffusion cells and were compared to those of a CBD oil and a microemulsion containing only CBD as an active substance. It was found that the inclusion of the original oil in microemulsions did not result in a significant modification of the release of CBD, suggesting the possibility of including hydrophilic active compounds in the formulation and establishing an interesting strategy for the development of future formulations.
PubMed: 38931831
DOI: 10.3390/pharmaceutics16060705 -
Sensors (Basel, Switzerland) Jun 2024The monitoring of body temperature is a recent addition to the plethora of parameters provided by wellness and fitness wearable devices. Current wearable temperature...
The monitoring of body temperature is a recent addition to the plethora of parameters provided by wellness and fitness wearable devices. Current wearable temperature measurements are made at the skin surface, a measurement that is impacted by the ambient environment of the individual. The use of near-infrared spectroscopy provides the potential for a measurement below the epidermal layer of skin, thereby having the potential advantage of being more reflective of physiological conditions. The feasibility of noninvasive temperature measurements is demonstrated by using an in vitro model designed to mimic the near-infrared spectra of skin. A miniaturizable solid-state laser-diode-based near-infrared spectrometer was used to collect diffuse reflectance spectra for a set of seven tissue phantoms composed of different amounts of water, gelatin, and Intralipid. Temperatures were varied between 20-24 °C while collecting these spectra. Two types of partial least squares (PLS) calibration models were developed to evaluate the analytical utility of this approach. In both cases, the collected spectra were used without pre-processing and the number of latent variables was the only optimized parameter. The first approach involved splitting the whole dataset into separate calibration and prediction subsets for which a single optimized PLS model was developed. For this first case, the coefficient of determination (R) is 0.95 and the standard error of prediction (SEP) is 0.22 °C for temperature predictions. The second strategy used a leave-one-phantom-out methodology that resulted in seven PLS models, each predicting the temperatures for all spectra in the held-out phantom. For this set of phantom-specific predicted temperatures, R and SEP values range from 0.67-0.99 and 0.19-0.65 °C, respectively. The stability and reproducibility of the sample-to-spectrometer interface are identified as major sources of spectral variance within and between phantoms. Overall, results from this in vitro study justify the development of future in vivo measurement technologies for applications as wearables for continuous, real-time monitoring of body temperature for both healthy and ill individuals.
Topics: Phantoms, Imaging; Spectroscopy, Near-Infrared; Humans; Least-Squares Analysis; Calibration; Skin; Gelatin; Temperature; Water; Wearable Electronic Devices; Emulsions; Soybean Oil; Phospholipids
PubMed: 38931768
DOI: 10.3390/s24123985 -
Sensors (Basel, Switzerland) Jun 2024A multichannel speech enhancement system usually consists of spatial filters such as adaptive beamformers followed by postfilters, which suppress remaining noise....
A multichannel speech enhancement system usually consists of spatial filters such as adaptive beamformers followed by postfilters, which suppress remaining noise. Accurate estimation of the power spectral density (PSD) of the residual noise is crucial for successful noise reduction in the postfilters. In this paper, we propose a postfilter utilizing proposed speech presence probability (SPP) and noise PSD estimators, which are based on both the coherence and the statistical models. We model the coherence-based SPP as a simple function of the magnitude of coherence between two microphone signals and combine it with a single-channel SPP based on statistical models. The coherence-based estimator for the PSD of the noise remaining in the beamformer output in the presence of speech is derived using the pseudo-coherence considering the effect of the beamformers, which is used to construct the coherence-based noise PSD estimator. Then, the final noise PSD estimator is obtained by combining the coherence-based and statistical model-based noise PSD estimators with the proposed SPP. The spectral gain function is also modified, incorporating the proposed SPP. Experimental results demonstrate that the proposed method led to more accurate noise PSD estimation and perceptual evaluation of speech quality scores in various diffuse noise environments, and did not degrade the speech quality under the presence of directional interference, although the proposed method utilizes the coherence information.
PubMed: 38931762
DOI: 10.3390/s24123979 -
Sensors (Basel, Switzerland) Jun 2024We present the design, fabrication, and testing of a low-cost, miniaturized detection system that utilizes chemiluminescence to measure the presence of adenosine...
We present the design, fabrication, and testing of a low-cost, miniaturized detection system that utilizes chemiluminescence to measure the presence of adenosine triphosphate (ATP), the energy unit in biological systems, in water samples. The ATP-luciferin chemiluminescent solution was faced to a silicon photomultiplier (SiPM) for highly sensitive real-time detection. This system can detect ATP concentrations as low as 0.2 nM, with a sensitivity of 79.5 A/M. Additionally, it offers rapid response times and can measure the characteristic time required for reactant diffusion and mixing within the reaction volume, determined to be 0.3 ± 0.1 s. This corresponds to a diffusion velocity of approximately 44 ± 14 mm/s.
Topics: Adenosine Triphosphate; Water; Luminescent Measurements; Luminescence; Biosensing Techniques
PubMed: 38931704
DOI: 10.3390/s24123921 -
Sensors (Basel, Switzerland) Jun 2024Universal image restoration (UIR) aims to accurately restore images with a variety of unknown degradation types and levels. Existing methods, including both...
Universal image restoration (UIR) aims to accurately restore images with a variety of unknown degradation types and levels. Existing methods, including both learning-based and prior-based approaches, heavily rely on low-quality image features. However, it is challenging to extract degradation information from diverse low-quality images, which limits model performance. Furthermore, UIR necessitates the recovery of images with diverse and complex types of degradation. Inaccurate estimations further decrease restoration performance, resulting in suboptimal recovery outcomes. To enhance UIR performance, a viable approach is to introduce additional priors. The current UIR methods have problems such as poor enhancement effect and low universality. To address this issue, we propose an effective framework based on a diffusion model (DM) for universal image restoration, dubbed ETDiffIR. Inspired by the remarkable performance of text prompts in the field of image generation, we employ text prompts to improve the restoration of degraded images. This framework utilizes a text prompt corresponding to the low-quality image to assist the diffusion model in restoring the image. Specifically, a novel text-image fusion block is proposed by combining the CLIP text encoder and the DA-CLIP image controller, which integrates text prompt encoding and degradation type encoding into time step encoding. Moreover, to reduce the computational cost of the denoising UNet in the diffusion model, we develop an efficient restoration U-shaped network (ERUNet) to achieve favorable noise prediction performance via depthwise convolution and pointwise convolution. We evaluate the proposed method on image dehazing, deraining, and denoising tasks. The experimental results indicate the superiority of our proposed algorithm.
PubMed: 38931703
DOI: 10.3390/s24123917 -
Sensors (Basel, Switzerland) Jun 2024The transition to a low-carbon economy is one of the main challenges of our time. In this context, solar energy, along with many other technologies, has been developed...
The transition to a low-carbon economy is one of the main challenges of our time. In this context, solar energy, along with many other technologies, has been developed to optimize performance. For example, solar trackers follow the sun's path to increase the generation capacity of photovoltaic plants. However, several factors need consideration to further optimize this process. Important variables include the distance between panels, surface reflectivity, bifacial panels, and climate variations throughout the day. Thus, this paper proposes an artificial intelligence-based algorithm for solar trackers that takes all these factors into account-mainly weather variations and the distance between solar panels. The methodology can be replicated anywhere in the world, and its effectiveness has been validated in a real solar plant with bifacial panels located in northeastern Brazil. The algorithm achieved gains of up to 7.83% on a cloudy day and obtained an average energy gain of approximately 1.2% when compared to a commercial solar tracker algorithm.
PubMed: 38931674
DOI: 10.3390/s24123890 -
Sensors (Basel, Switzerland) Jun 2024Metal-organic frameworks (MOFs) stand out as remarkable materials renowned for their exceptionally high surface area and large number of pores, making them invaluable...
Metal-organic frameworks (MOFs) stand out as remarkable materials renowned for their exceptionally high surface area and large number of pores, making them invaluable for diverse sensing applications including gas, biomedical, chemical, and optical sensing. Traditional methods of molecule infusion and release often involve a large number of crystals with varying shapes and sizes, leading to averaged outcomes across a heterogeneous crystal population. In this study, we present continuous monitoring of the infusion and release dynamics of model drug molecules, specifically vitamin B, within individual Tb-mesoMOF crystals. Our findings underscore the critical influence of crystal size and shape on the infusion and diffusion processes and corresponding color change, underscoring the necessity to account for these factors in the design of large-scale systems. Leveraging optical microscopy, we employed a histogram-based algorithm for image processing, enabling automated tracking of diffusion phenomena. This investigation offers crucial insights into the dynamics of these processes, laying the groundwork for optimizing parameters in future sensing systems.
PubMed: 38931625
DOI: 10.3390/s24123842 -
Sensors (Basel, Switzerland) Jun 2024This study describes a novel method for grading pathological sections of gliomas. Our own integrated hyperspectral imaging system was employed to characterize 270 bands...
Study on an Automatic Classification Method for Determining the Malignancy Grade of Glioma Pathological Sections Based on Hyperspectral Multi-Scale Spatial-Spectral Fusion Features.
This study describes a novel method for grading pathological sections of gliomas. Our own integrated hyperspectral imaging system was employed to characterize 270 bands of cancerous tissue samples from microarray slides of gliomas. These samples were then classified according to the guidelines developed by the World Health Organization, which define the subtypes and grades of diffuse gliomas. We explored a hyperspectral feature extraction model called SMLMER-ResNet using microscopic hyperspectral images of brain gliomas of different malignancy grades. The model combines the channel attention mechanism and multi-scale image features to automatically learn the pathological organization of gliomas and obtain hierarchical feature representations, effectively removing the interference of redundant information. It also completes multi-modal, multi-scale spatial-spectral feature extraction to improve the automatic classification of glioma subtypes. The proposed classification method demonstrated high average classification accuracy (>97.3%) and a Kappa coefficient (0.954), indicating its effectiveness in improving the automatic classification of hyperspectral gliomas. The method is readily applicable in a wide range of clinical settings, offering valuable assistance in alleviating the workload of clinical pathologists. Furthermore, the study contributes to the development of more personalized and refined treatment plans, as well as subsequent follow-up and treatment adjustment, by providing physicians with insights into the underlying pathological organization of gliomas.
Topics: Glioma; Humans; Brain Neoplasms; Neoplasm Grading; Hyperspectral Imaging; Algorithms; Image Processing, Computer-Assisted
PubMed: 38931588
DOI: 10.3390/s24123803 -
Sensors (Basel, Switzerland) Jun 2024In recent years, with the increasing demand for high-quality images in various fields, more and more attention has been focused on noise removal techniques for image...
In recent years, with the increasing demand for high-quality images in various fields, more and more attention has been focused on noise removal techniques for image processing. The effective elimination of unwanted noise plays a crucial role in improving image quality. To meet this challenge, many noise removal methods have been proposed, among which the diffusion model has become one of the focuses of many researchers. In order to make the restored image closer to the real image and retain more features of the image, this paper proposes a DIR-SDE method with reference to the diffusion models of IR-SDE and IDM, which improve the feature retention of the image in the de-raining process, and then improve the realism of the image for the image de-raining task. In this study, IR-SDE was used as the base structure of the diffusion model, IR-SDE was improved, and DINO-ViT was combined to enhance the image features. During the diffusion process, the image features were extracted using DINO-ViT, and these features were fused with the original images to enhance the learning effect of the model. The model was also trained and validated with the Rain100H dataset. Compared with the IR-SDE method, it improved 0.003 in the SSIM, 0.003 in the LPIPS, and 1.23 in the FID. The experimental results show that the diffusion model proposed in this study can effectively improve the image restoration performance.
PubMed: 38931498
DOI: 10.3390/s24123715