A Hybrid Framework of Dual-Domain Signal Restoration and Multi-depth Feature Reinforcement for Low-Dose Lung CT Denoising.

Low-dose computer tomography (LDCT) has been widely used in medical diagnosis. Various denoising methods have been presented to remove noise in LDCT scans. However, existing methods cannot achieve satisfactory results due to the difficulties in (1) distinguishing the characteristics of structures, textures, and noise confused in the image domain, and (2) representing local details and global semantics in the hierarchical features. In this paper, we propose a novel denoising method consisting of (1) a 2D dual-domain restoration framework to reconstruct noise-free structure and texture signals separately, and (2) a 3D multi-depth reinforcement U-Net model to further recover image details with enhanced hierarchical features. In the 2D dual-domain restoration framework, the convolutional neural networks are adopted in both the image domain where the image structures are well preserved through the spatial continuity, and the sinogram domain where the textures and noise are separately represented by different wavelet coefficients and processed adaptively. In the 3D multi-depth reinforcement U-Net model, the hierarchical features from the 3D U-Net are enhanced by the cross-resolution attention module (CRAM) and dual-branch graph convolution module (DBGCM). The CRAM preserves local details by integrating adjacent low-level features with different resolutions, while the DBGCM enhances global semantics by building graphs for high-level features in intra-feature and inter-feature dimensions. Experimental results on the LUNA16 dataset and 2016 NIH-AAPM-Mayo Clinic LDCT Grand Challenge dataset illustrate the proposed method outperforms the state-of-the-art methods on removing noise from LDCT images with clear structures and textures, proving its potential in clinical practice.

Ligustilide-loaded liposome ameliorates mitochondrial impairments and improves cognitive function via the PKA/AKAP1 signaling pathway in a mouse model of Alzheimer's disease.

BACKGROUND: Oxidative stress is an early event in the development of Alzheimer's disease (AD) and maybe a pivotal point of interaction governing AD pathogenesis; oxidative stress contributes to metabolism imbalance, protein misfolding, neuroinflammation and apoptosis. Excess reactive oxygen species (ROS) are a major contributor to oxidative stress. As vital sources of ROS, mitochondria are also the primary targets of ROS attack. Seeking effective avenues to reduce oxidative stress has attracted increasing attention for AD intervention. METHODS: We developed liposome-packaged Ligustilide (LIG) and investigated its effects on mitochondrial function and AD-like pathology in the APPswe/PS1dE9 (APP/PS1) mouse model of AD, and analyzed possible mechanisms. RESULTS: We observed that LIG-loaded liposome (LIG-LPs) treatment reduced oxidative stress and ß-amyloid (Aß) deposition and mitigated cognitive impairment in APP/PS1 mice. LIG management alleviated the destruction of the inner structure in the hippocampal mitochondria and ameliorated the imbalance between mitochondrial fission and fusion in the APP/PS1 mouse brain. We showed that the decline in cAMP-dependent protein kinase A (PKA) and A-kinase anchor protein 1 for PKA (AKAP1) was associated with oxidative stress and AD-like pathology. We confirmed that LIG-mediated antioxidant properties and neuroprotection were involved in upregulating the PKA/AKAP1 signaling in APPswe cells in vitro. CONCLUSION: Liposome packaging for LIG is relatively biosafe and can overcome the instability of LIG. LIG alleviates mitochondrial dysfunctions and cognitive impairment via the PKA/AKAP1 signaling pathway. Our results provide experimental evidence that LIG-LPs may be a promising agent for AD therapy.

Water-Driven Synthesis of Deep-Blue Perovskite Colloidal Quantum Wells for Electroluminescent Devices.

Perovskite colloidal quantum wells (QWs) are promising to realize narrow deep-blue emission, but the poor optical performance and stability suppress their practical application. Here, we creatively propose a water-driven synthesis strategy to obtain size-homogenized and strongly confined deep-blue CsPbBr3 QWs, corresponding to three monolayers, which emit at the deep-blue wavelength of 456 nm. The water controls the orientation and distribution of the ligands on the surface of the nanocrystals, thus inducing orientated growth through the Ostwald ripening process by phagocytizing unstable nanocrystals to form well-crystallized QWs. These QWs present remarkable stability and high photoluminescence quantum yield of 94 %. Furthermore, we have prepared light-emitting diodes based on the QWs via the all-solution fabrication strategy, achieving an external quantum efficiency of 1 % and luminance of 2946 cd m-2 , demonstrating state-of-the-art brightness for perovskite QW-based LEDs.

Modeling and probabilistic analysis of civil aircraft operational risk for suborbital disintegration accidents.

To reduce the collision risk to civil airliners caused by suborbital vehicle disintegration events, this paper uses a covariance propagation algorithm to model the debris landing point of suborbital disintegration accidents and gives a collision probability analysis method for civil airliners encountering debris during the cruise. Collision warning is performed for airborne risk targets to improve the emergency response capability of the ATC surveillance system to hazardous situations. The algorithm models the three-dimensional spatial motion target localization problem as a Gauss-Markov process, quantifying the location of debris landing points in the vicinity of nominal trajectories. By predicting the aircraft trajectory, the calculation of the inter-target collision probability is converted into an integration problem of a two-dimensional normally distributed probability density function in a circular domain. Compared with the traditional Monte Carlo method, the calculation speed of debris drop points is improved, which can meet the requirements of civil aviation for real-time response to unexpected situations.

Kai-Xin-San Inhibits Tau Pathology and Neuronal Apoptosis in Aged SAMP8 Mice.

Alzheimer's disease (AD) is an age-related neurological disorder. Currently, there is no effective cure for AD due to its complexity in pathogenesis. In light of the complex pathogenesis of AD, the traditional Chinese medicine (TCM) formula Kai-Xin-San (KXS), which was used for amnesia treatment, has been proved to improve cognitive function in AD animal models. However, the active ingredients and the mechanism of KXS have not yet been clearly elucidated. In this study, network pharmacology analysis predicts that KXS yields 168 candidate compounds acting on 863 potential targets, 30 of which are associated with AD. Enrichment analysis revealed that the therapeutic mechanisms of KXS for AD are associated with the inhibition of Tau protein hyperphosphorylation, inflammation, and apoptosis. Therefore, we chose 7-month-old senescence-accelerated mouse prone 8 (SAMP8) mice as AD mouse model, which harbors the behavioral and pathological hallmarks of AD. Subsequently, the potential underlying action mechanisms of KXS on AD predicted by the network pharmacology analyses were experimentally validated in SAMP8 mice after intragastric administration of KXS for 3 months. We observed that KXS upregulated AKT phosphorylation, suppressed GSK3ß and CDK5 activation, and inhibited the TLR4/MyD88/NF-κB signaling pathway to attenuate Tau hyperphosphorylation and neuroinflammation, thus suppressing neuronal apoptosis and improving the cognitive impairment of aged SAMP8 mice. Taken together, our findings reveal a multi-component and multi-target therapeutic mechanism of KXS for attenuating the progression of AD, contributing to the future development of TCM modernization, including KXS, and broader clinical application.

Molecular detection of Anaplasma spp., Babesia spp. and Theileria spp. in yaks (Bos grunniens) and Tibetan sheep (Ovis aries) on the Qinghai-Tibetan Plateau, China.

BACKGROUND: Anaplasma, Babesia and Theileria are tick-borne pathogens (TBPs) that affect livestock worldwide. However, information on these pathogens in yaks (Bos grunniens) and Tibetan sheep (Ovis aries) on the Qinghai-Tibet Plateau (QTP), China, is limited. In this study, Anaplasma spp., Babesia spp. and Theileria spp. infections were assessed in yaks and Tibetan sheep from Qinghai Province. METHODS: A total of 734 blood samples were collected from 425 yaks and 309 Tibetan sheep at nine sampling sites. Standard or nested polymerase chain reaction was employed to screen all the blood samples using species- or genus-specific primers. RESULTS: The results showed that 14.1% (60/425) of yaks and 79.9% (247/309) of Tibetan sheep were infected with at least one pathogen. Anaplasma ovis, Anaplasma bovis, Anaplasma capra, Anaplasma phagocytophilum, Babesia bovis and Theileria spp. were detected in this study, with total infection rates for all the assessed animals of 22.1% (162/734), 16.3% (120/734), 23.6% (173/734), 8.2% (60/734), 2.7% (20/734) and 19.3% (142/734), respectively. For yaks, the infection rate of A. bovis was 6.4% (27/425), that of B. bovis was 4.7% (20/425) and that of Theileria spp. was 3.3% (14/425). Moreover, 52.4% (162/309) of the Tibetan sheep samples were infected with A. ovis, 30.1% (93/309) with A. bovis, 56.0% (173/309) with A. capra, 19.4% (60/309) with A. phagocytophilum and 41.4% (128/309) with Theileria spp. CONCLUSIONS: This study revealed the prevalence of Anaplasma spp., Babesia spp. and Theileria spp. in yaks and Tibetan sheep in Qinghai Province, China, and provides new data for a better understanding of the epidemiology of TBPs in these animals in this area of the QTP, China.

Machine Assisted Experimentation of Extrusion-Based Bioprinting Systems.

Optimization of extrusion-based bioprinting (EBB) parameters have been systematically conducted through experimentation. However, the process is time- and resource-intensive and not easily translatable to other laboratories. This study approaches EBB parameter optimization through machine learning (ML) models trained using data collected from the published literature. We investigated regression-based and classification-based ML models and their abilities to predict printing outcomes of cell viability and filament diameter for cell-containing alginate and gelatin composite bioinks. In addition, we interrogated if regression-based models can predict suitable extrusion pressure given the desired cell viability when keeping other experimental parameters constant. We also compared models trained across data from general literature to models trained across data from one literature source that utilized alginate and gelatin bioinks. The results indicate that models trained on large amounts of data can impart physical trends on cell viability, filament diameter, and extrusion pressure seen in past literature. Regression models trained on the larger dataset also predict cell viability closer to experimental values for material concentration combinations not seen in training data of the single-paper-based regression models. While the best performing classification models for cell viability can achieve an average prediction accuracy of 70%, the cell viability predictions remained constant despite altering input parameter combinations. Our trained models on bioprinting literature data show the potential usage of applying ML models to bioprinting experimental design.

Increases of iASPP-Keap1 interaction mediated by syringin enhance synaptic plasticity and rescue cognitive impairments via stabilizing Nrf2 in Alzheimer's models.

Oxidative stress is an important pathogenic manifestation of Alzheimer's disease (AD) that contributes to synaptic dysfunction, which precedes Aß accumulation and neurofibrillary tangle formation. However, the molecular machineries that govern the decline of antioxidative defence in AD remains to be elucidated, and effective candidate for AD treatment is limited. Here, we showed that the decreases in the inhibitor of apoptosis-stimulating protein of p53 (iASPP) was associated with the vulnerability to oxidative stress in the amyloid precursor protein (APP)/presenilin 1 (PS1) mouse brain. Treatment with an antioxidant, syringin, could ameliorate AD-related pathologic and behavioural impairments. Interestingly, syringin treatment resulted in an upregulation of iASPP and the increase in the interaction of iASPP with Kelchlike ECH-associating protein 1 (Keap1). Syringin reduced neuronal apoptosis independently of p53. We confirmed that syringin-induced enhancement of antioxidant defenses involved the stabilization of Nrf2 in overexpressing human Swedish mutant APP (APPswe) cells in vitro. Syringin-mediated Nrf2 nuclear translocation facilitated the activation of the Nrf2 downstream genes via iASPP/Nrf2 axis. Our results demonstrate that syringin-mediated increases of iASPP-Keap1 interaction restore cellular redox balance. Further study on the syringin-iASPP interactions may help in understanding the regulatory mechanism and designing novel potent modulators for AD treatment.

Achieving high thermal stability of different rare-earth ions in a single matrix host via the manipulation of the local structure by a solid solution.

Thermal quenching seriously restricts the practical application of phosphors, particularly under high temperature and long-term working conditions. Here, we demonstrate that the as-obtained series of solid solutions of Ca2-xYxAl2Si1-xAlxO7:Tb3+ (x = 0-1, Ca2Al2SiO7 → CaYAl3O7) phosphors exhibit an adjustable optical performance, where CaYAl3O7:Tb3+ exhibits a greatly improved thermal stability with a shortened bond distance of the related polyhedron compared with Ca2Al2SiO7:Tb3+. The shrunken bond distance strengthens the pressure of the local structure and suppresses the non-radiative transition effectively, contributing to the prevention of the thermal degradation. The formed phosphor with excellent structural stability could be effectively incorporated with various lanthanide ions (Eu3+, Tb3+, Sm3+, Dy3+, and Pr3+) to address a pleochroism output.

Detection of Cell Viability via Fluorescence Labeling of Silicate Phosphor with a Low-Temperature Superlong Persistent Luminescence.

Cell viability detection is usually accompanied by large errors and toxicity. For the accuracy and safety of detection, it is urgent to develop a practical approach to realize the real-time monitoring of cell viability. Here, a low-temperature persistent phosphor was employed to facilitate the detection of cell viability. Stable low-temperature persistent luminescence originating from Ba4Si6O16:Eu2+, Pr3+ (BSEP) was developed, which could be quantified to describe the viability of baby Syrian hamster kidney (BHK-21) cells. The defect state structure guarantees the excellent persistent luminescence at 193 K of more than 200 h of the as-prepared BSEP phosphor, which ensures the long-term biological application. The structural thermal stability and water tolerance of BSEP suggest the feasibility of the practical application. Our results provide the underlying insights of the low-temperature persistent phosphors for real-time biological detection.