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Current and future development of Acrocomia aculeata focused on biofuel potential and climate change challenges
Brain and intracranial volumes are both enlarged and serve as potential risk factors in normal pressure hydrocephalus
Reduced removal of waste products from energy metabolism takes center stage in human brain aging
A concept for fully automated segmentation of bone in ultrasound imaging
Pharmacologic and endotoxic reprogramming of renal vasodilatory, inflammatory, and apoptotic blemishes in weaning preeclamptic rats
Abstract Preeclampsia (PE) and peripartum sepsis are two complications of pregnancy and are often associated with disturbed renal function due possibly to dysregulated renin angiotensin system. Here we evaluated hemodynamic and renal consequences of separate and combined PE and sepsis insults in weaning mothers and tested whether this interaction is influenced by prenatally-administered losartan (AT1-receptor blocker) or pioglitazone (PPARγ agonist). The PE-rises in blood pressure and proteinuria induced by gestational nitric oxide synthase inhibition (L-NAME, 50 mg/kg/day for 7 days) were attenuated after simultaneous treatment with losartan or pioglitazone. These drugs further improved glomerular and tubular structural defects and impaired vasodilatory responses evoked by adenosinergic (N-ethylcarboxamidoadenosine) or cholinergic (acetylcholine) receptor activation in perfused kidneys of weaning dams. Likewise, treatment of weaning PE dams with a single 4-h dosing of lipopolysaccharides (LPS, 5 mg/kg) weakened renal structural damage, enhanced renal vasodilations and accentuated the upregulated vasodilatory response set off by losartan or pioglitazone. Molecularly, the favorable effect of pharmacologic or endotoxic intervention was coupled with dampened tubular and glomerular expressions of inflammatory (toll-like receptor 4) and apoptotic signals (caspase-3). Our data unveil beneficial and possibly intensified conditioning effect for endotoxemia when combined with losartan or pioglitazone against preeclamptic renovascular dysfunction and inflammation.
A randomised, double-blind clinical study into the effect of zinc citrate trihydrate toothpaste on oral plaque microbiome ecology and function
Abstract The oral microbiome is a diverse community of microbes residing on all oral surfaces. A balanced oral microbiome is associated with good oral health, and disruption can result in imbalance associated with diseases including gingivitis and dental caries. It is important, therefore, to understand how daily use of oral hygiene products impacts the microbiome. Composition and activity of dental plaque microbiome from 115 participants was analysed after brushing with one of two toothpastes, one containing zinc citrate trihydrate and the other a control toothpaste, in a parallel design. Each participant brushed twice daily for 6-weeks, with samples collected at baseline, 2 and 6-weeks. Metataxonomic analysis demonstrated changes in bacterial communities with use of the zinc toothpaste compared to the control product at community and species level. Increases at the species level were observed for taxa from the genus Veillonella with decrease in a taxon from the genus Fusobacterium for the zinc toothpaste. Analysis of microbiome function based on predicted metagenomic and metatranscriptomic analysis show that use of the zinc toothpaste is associated with an in-vivo reduction in glycolysis, consistent with the mode of action of zinc and, increases in processes linked to gum-health (lysine biosynthesis), and to whole-body health (nitrate reduction). Our findings provide the first understanding of the beneficial modulation of microbiome composition and function by zinc-containing toothpaste in-vivo for oral care benefits.
Targeted imaging of pulmonary fibrosis by a cyclic peptide LyP-1
Effect of pyrolytic carbon addition on the structural and optical properties of TiO2 composite thin films
Abstract The article deals with the preparation and characterization of titanium dioxide thin films containing pyrolytic carbon as potential UV protection films for photovoltaic devices. The carbon used as an additive was obtained by pyrolysis of methane, the main product of which is turquoise hydrogen, and the carbon is a by-product of the process. The resulting carbon material was characterized by Raman spectroscopy, scanning electron microscopy and energy dispersive spectroscopy. Titanium dioxide/pyrolytic carbon composite thin films were prepared by sol-gel method, followed by dip-coating technique. The sols were examined using the dynamic light scattering method. The optical properties of the composite films, including transmittance, reflectance, energy band gap, Urbach energy, porosity, along with their surface morphology and resistance to UV degradation, were evaluated. The results indicate that incorporation of pyrolytic carbon improves the optical properties of composite thin films compared to the samples without carbon, leading to an increase of about 5% in transmittance in the visible range of spectrum. Microscopic observations confirm the presence of pyrolytic carbon in the films, and surface smoothing is noticeable at higher carbon concentrations. These findings suggest the potential use of composite films as UV-blocking films.
Effect of combined and intensive rehabilitation on cognitive function in patients with Alzheimer’s disease evaluated through a randomized controlled trial
Abstract This study investigates the impact of combined special education and occupational therapy intervention on cognitive functions in Alzheimer’s patients. Specifically, it evaluates changes measured by the Addenbrooke’s Cognitive Examination (ACE-R) after six months compared to a control group receiving standard care. A longitudinal, controlled experiment was conducted with random assignment to experimental and control groups. The experimental group underwent three weekly interventions of 45–50 min over eight months in 2021. Cognitive functions were periodically assessed using ACE-R. Power analysis determined a sample size of 128 participants for adequate statistical power; the study included 60 participants (30 per group). Data were analyzed using non-parametric methods due to non-normal data distribution. The experimental group showed significant improvement in ACE-R scores compared to the control group. The mean difference in scores was 10.27 points (SD = 2.83) for the experimental group, indicating improved cognitive function, while the control group showed a mean decrease of 5.67 points (SD = 2.06). Statistical analysis confirmed significant differences between groups at both interim and final assessments (p < 0.001). The combined special education and occupational therapy intervention led to significant cognitive improvements in Alzheimer’s patients compared to standard care. The study supports the efficacy of such interventions in enhancing cognitive functions, as evidenced by the substantial score increases in the experimental group.
Cancer cells impact the microrheology of endothelial cells during physical contact or through paracrine signalling
A computational framework for extracting biological insights from SRA cancer data
Validation of the Chinese version of the self-injurious thoughts and behaviors interview for adolescent outpatients in Taiwan: a cross-sectional study
Correlation of visual acuity changes and optical coherence tomography imaging in patients with central retinal artery occlusion post-arterial thrombolysis
Bayesian prior uncertainty and surprisal elicit distinct neural patterns during sound localization in dynamic environments
Abstract Estimating the location of a stimulus is a key function in sensory processing, and widely considered to result from the integration of prior information and sensory input according to Bayesian principles. A deviation of sensory input from the prior elicits surprisal, depending on the uncertainty of the prior. While this mechanism is increasingly understood in the visual domain, much less is known about its implementation in audition, especially regarding spatial localization. Here, we combined human EEG with computational modeling to study auditory spatial inference in a noisy, volatile environment and analyzed behavioral and neural patterns associated with prior uncertainty and surprisal. First, our results demonstrate that participants indeed used prior information during periods of stable environmental statistics, but showed evidence of surprisal and discarded prior information following environmental changes. Second, we observed distinct EEG activity patterns associated with prior uncertainty and surprisal in both the time- and time–frequency domain, which are in line with previous studies using visual tasks. Third, these EEG activity patterns were predictive of our participants’ sound localization error, response uncertainty, and prior bias on a trial-by-trial basis. In summary, our work provides novel behavioral and neural evidence for Bayesian inference during dynamic auditory localization.
Skin lesion segmentation with a multiscale input fusion U-Net incorporating Res2-SE and pyramid dilated convolution
Abstract Skin lesion segmentation is crucial for identifying and diagnosing skin diseases. Accurate segmentation aids in identifying and localizing diseases, monitoring morphological changes, and extracting features for further diagnosis, especially in the early detection of skin cancer. This task is challenging due to the irregularity of skin lesions in dermatoscopic images, significant color variations, boundary blurring, and other complexities. Artifacts like hairs, blood vessels, and air bubbles further complicate automatic segmentation. Inspired by U-Net and its variants, this paper proposes a Multiscale Input Fusion Residual Attention Pyramid Convolution Network (MRP-UNet) for dermoscopic image segmentation. MRP-UNet includes three modules: the Multiscale Input Fusion Module (MIF), Res2-SE Module, and Pyramid Dilated Convolution Module (PDC). The MIF module processes lesions of different sizes and morphologies by fusing input information from various scales. The Res2-SE module integrates Res2Net and SE mechanisms to enhance multi-scale feature extraction. The PDC module captures image information at different receptive fields through pyramid dilated convolution, improving segmentation accuracy. Experiments on ISIC 2016, ISIC 2017, ISIC 2018, PH2, and HAM10000 datasets show that MRP-UNet outperforms other methods. Ablation studies confirm the effectiveness of its main modules. Both quantitative and qualitative analyses demonstrate MRP-UNet’s superiority over state-of-the-art methods. MRP-UNet enhances skin lesion segmentation by combining multiscale fusion, residual attention, and pyramid dilated convolution. It achieves higher accuracy across multiple datasets, showing promise for early skin disease diagnosis and improved patient outcomes.
Preparation, characterization and application of chitosan/thyme essential oil composite film
Hyperspectral estimation of chlorophyll content in grapevine based on feature selection and GA-BP
Abstract Leaf chlorophyll content (LCC) is a key indicator for assessing the growth of grapes. Hyperspectral techniques have been applied to LCC research. However, quantitative prediction of grape LCC using this technique remains challenging due to baseline drift, spectral peak overlap, and ambiguity in the sensitive spectral range. To address these issues, two typical crop leaf hyperspectral data were collected to reveal the spectral response characteristics of grape LCC using standardization by variables (SNV) and multiple far scattering correction (MSC) preprocessing variations. The sensitive spectral range is determined by Pearson’s algorithm, and sensitive features are further extracted within that range using Extreme Gradient Boosting (XGBoost), Recursive Feature Elimination (RFE), and Principal components analysis (PCA). Comparison of the prediction ability of Random Forest Regression (RFR) algorithm, Support Vector Machine Regression (SVR) model, and Genetic Algorithm-Based Neural Network (GA-BP) on grape LCC based on sensitive features. A SNV-RFE-GA-BP framework for predicting hyperspectral LCC in grapes is proposed, where $$\:{R}^{2}$$ =0.835 and NRMSE = 0.091. The analysis results show that SNV and MSC treatments improve the correlation between spectral reflectance and LCC, and different feature screening methods have a greater impact on the model prediction accuracy. It was shown that SNV-based processed hyperspectral data combined with GA-BP has great potential for efficient chlorophyll monitoring in grapevine. This method provides a new framework theory for constructing a hyperspectral analytical model of grapevine key growth indicators.