Spatial attention-based implicit neural representation for arbitrary reduction of MRI slice spacing.

Magnetic resonance (MR) images collected in 2D clinical protocols typically have large inter-slice spacing, resulting in high in-plane resolution and reduced through-plane resolution. Super-resolution technique can enhance the through-plane resolution of MR images to facilitate downstream visualization and computer-aided diagnosis. However, most existing works train the super-resolution network at a fixed scaling factor, which is not friendly to clinical scenes of varying inter-slice spacing in MR scanning. Inspired by the recent progress in implicit neural representation, we propose a Spatial Attention-based Implicit Neural Representation (SA-INR) network for arbitrary reduction of MR inter-slice spacing. The SA-INR aims to represent an MR image as a continuous implicit function of 3D coordinates. In this way, the SA-INR can reconstruct the MR image with arbitrary inter-slice spacing by continuously sampling the coordinates in 3D space. In particular, a local-aware spatial attention operation is introduced to model nearby voxels and their affinity more accurately in a larger receptive field. Meanwhile, to improve the computational efficiency, a gradient-guided gating mask is proposed for applying the local-aware spatial attention to selected areas only. We evaluate our method on the public HCP-1200 dataset and the clinical knee MR dataset to demonstrate its superiority over other existing methods.

sTBI-GAN: An adversarial learning approach for data synthesis on traumatic brain segmentation.

Automatic brain segmentation of magnetic resonance images (MRIs) from severe traumatic brain injury (sTBI) patients is critical for brain abnormality assessments and brain network analysis. Construction of sTBI brain segmentation model requires manually annotated MR scans of sTBI patients, which becomes a challenging problem as it is quite impractical to implement sufficient annotations for sTBI images with large deformations and lesion erosion. Data augmentation techniques can be applied to alleviate the issue of limited training samples. However, conventional data augmentation strategies such as spatial and intensity transformation are unable to synthesize the deformation and lesions in traumatic brains, which limits the performance of the subsequent segmentation task. To address these issues, we propose a novel medical image inpainting model named sTBI-GAN to synthesize labeled sTBI MR scans by adversarial inpainting. The main strength of our sTBI-GAN method is that it can generate sTBI images and corresponding labels simultaneously, which has not been achieved in previous inpainting methods for medical images. We first generate the inpainted image under the guidance of edge information following a coarse-to-fine manner, and then the synthesized MR image is used as the prior for label inpainting. Furthermore, we introduce a registration-based template augmentation pipeline to increase the diversity of the synthesized image pairs and enhance the capacity of data augmentation. Experimental results show that the proposed sTBI-GAN method can synthesize high-quality labeled sTBI images, which greatly improves the 2D and 3D traumatic brain segmentation performance compared with the alternatives. Code is available at .

Prediction of preoperative microvascular invasion by dynamic radiomic analysis based on contrast-enhanced computed tomography.

PURPOSE: Microvascular invasion (MVI) is a common complication of hepatocellular carcinoma (HCC) surgery, which is an important predictor of reduced surgical prognosis. This study aimed to develop a fully automated diagnostic model to predict pre-surgical MVI based on four-phase dynamic CT images. METHODS: A total of 140 patients with HCC from two centers were retrospectively included (training set, n = 98; testing set, n = 42). All CT phases were aligned to the portal venous phase, and were then used to train a deep-learning model for liver tumor segmentation. Radiomics features were extracted from the tumor areas of original CT phases and pairwise subtraction images, as well as peritumoral features. Lastly, linear discriminant analysis (LDA) models were trained based on clinical features, radiomics features, and hybrid features, respectively. Models were evaluated by area under curve (AUC), accuracy, sensitivity, specificity, positive and negative predictive values (PPV and NPV). RESULTS: Overall, 86 and 54 patients with MVI- (age, 55.92 ± 9.62 years; 68 men) and MVI+ (age, 53.59 ± 11.47 years; 43 men) were included. Average dice coefficients of liver tumor segmentation were 0.89 and 0.82 in training and testing sets, respectively. The model based on radiomics (AUC = 0.865, 95% CI: 0.725-0.951) showed slightly better performance than that based on clinical features (AUC = 0.841, 95% CI: 0.696-0.936). The classification model based on hybrid features achieved better performance in both training (AUC = 0.955, 95% CI: 0.893-0.987) and testing sets (AUC = 0.913, 95% CI: 0.785-0.978), compared with models based on clinical and radiomics features (p-value < 0.05). Moreover, the hybrid model also provided the best accuracy (0.857), sensitivity (0.875), and NPV (0.917). CONCLUSION: The classification model based on multimodal intra- and peri-tumoral radiomics features can well predict HCC patients with MVI.

Scalable Multipartite Entanglement Created by Spin Exchange in an Optical Lattice.

Ultracold atoms in optical lattices form a competitive candidate for quantum computation owing to the excellent coherence properties, the highly parallel operations over spins, and the ultralow entropy achieved in qubit arrays. For this, a massive number of parallel entangled atom pairs have been realized in superlattices. However, the more formidable challenge is to scale up and detect multipartite entanglement, the basic resource for quantum computation, due to the lack of manipulations over local atomic spins in retroreflected bichromatic superlattices. In this Letter, we realize the functional building blocks in quantum-gate-based architecture by developing a cross-angle spin-dependent optical superlattice for implementing layers of quantum gates over moderately separated atoms incorporated with a quantum gas microscope for single-atom manipulation and detection. Bell states with a fidelity of 95.6(5)% and a lifetime of 2.20±0.13 s are prepared in parallel, and then connected to multipartite entangled states of one-dimensional ten-atom chains and two-dimensional plaquettes of 2×4 atoms. The multipartite entanglement is further verified with full bipartite nonseparability criteria. This offers a new platform toward scalable quantum computation and simulation.

Assessment of geothermal resource potential in Changbaishan utilizing high-precision gravity-based man-machine interactive inversion technology.

As one of the clean energy sources, geothermal resources have no negative impact in changing the climate. However, the accurate assessment and precise identification of the potential geothermal resource is still complex and dynamic. In this paper, ~2,000 large-scale high-precision gravity survey points are conducted in the north of the Tianchi caldera, Changbaishan. Advanced data processing technologies can provide straightforward information on deep geothermal resources (Hot source, caprock, geothermal reservoir and geothermal migration pathway). Upwards continuation and the technologies decode two dome shaped low and gentle anomalies (-48 × 10-5 m/s2-65 m/s2) and a positive gravity gradient anomaly (0.4 × 10-7 m/s2-1.6 × 10-5 m/s2) in large-scale high-precision gravity planar. According to two point five dimensional man-machine interactive inversion technology and the research on petrophysical parameters, the density of the shied-forming basalts in the two orthogonal gravity sections is 2.58 g/cm3. The relatively intermediate to high density (2.60-2.75 g/cm3) represents geothermal reservoir, and low density (low to 2.58 g/cm3) is the geothermal migration pathway. In addition, large-scale high-precision gravity planar with a solution of about 1/50,000 indicate that the north of the Tianchi caldera exits the sedimentary basin and uplift mountain geothermal system.

Interrelated Thermalization and Quantum Criticality in a Lattice Gauge Simulator.

Gauge theory and thermalization are both topics of essential importance for modern quantum science and technology. The recently realized atomic quantum simulator for lattice gauge theories provides a unique opportunity for studying thermalization in gauge theory, in which theoretical studies have shown that quantum thermalization can signal the quantum phase transition. Nevertheless, the experimental study remains a challenge to accurately determine the critical point and controllably explore the thermalization dynamics due to the lack of techniques for locally manipulating and detecting matter and gauge fields. We report an experimental investigation of the quantum criticality in the lattice gauge theory from both equilibrium and nonequilibrium thermalization perspectives, with the help of the single-site addressing and atom-number-resolved detection capabilities. We accurately determine the quantum critical point and observe that the Néel state thermalizes only in the critical regime. This result manifests the interplay between quantum many-body scars, quantum criticality, and symmetry breaking.

Population genetics of marmosets in Asian primate research centers and loci associated with epileptic risk revealed by whole-genome sequencing.

The common marmoset ( Callithrix jacchus) has emerged as a valuable nonhuman primate model in biomedical research with the recent release of high-quality reference genome assemblies. Epileptic marmosets have been independently reported in two Asian primate research centers. Nevertheless, the population genetics within these primate centers and the specific genetic variants associated with epilepsy in marmosets have not yet been elucidated. Here, we characterized the genetic relationships and risk variants for epilepsy in 41 samples from two epileptic marmoset pedigrees using whole-genome sequencing. We identified 14 558 184 single nucleotide polymorphisms (SNPs) from the 41 samples and found higher chimerism levels in blood samples than in fingernail samples. Genetic analysis showed fourth-degree of relatedness among marmosets at the primate centers. In addition, SNP and copy number variation (CNV) analyses suggested that the WW domain-containing oxidoreductase ( WWOX) and Tyrosine-protein phosphatase nonreceptor type 21 ( PTPN21) genes may be associated with epilepsy in marmosets. Notably, KCTD18-like gene deletion was more common in epileptic marmosets than control marmosets. This study provides valuable population genomic resources for marmosets in two Asian primate centers. Genetic analyses identified a reasonable breeding strategy for genetic diversity maintenance in the two centers, while the case-control study revealed potential risk genes/variants associated with epilepsy in marmosets.

Hypobaric Hypoxia Aggravates Renal Injury by Inducing the Formation of Neutrophil Extracellular Traps through the NF-κB Signaling Pathway.

OBJECTIVE: The hypersensitivity of the kidney makes it susceptible to hypoxia injury. The involvement of neutrophil extracellular traps (NETs) in renal injury resulting from hypobaric hypoxia (HH) has not been reported. In this study, we aimed to investigate the expression of NETs in renal injury induced by HH and the possible underlying mechanism. METHODS: A total of 24 SD male rats were divided into three groups (n=8 each): normal control group, hypoxia group and hypoxia+pyrrolidine dithiocarbamate (PDTC) group. Rats in hypoxia group and hypoxia+PDTC group were placed in animal chambers with HH which was caused by simulating the altitude at 7000 meters (oxygen partial pressure about 6.9 kPa) for 7 days. PDTC was administered at a dose of 100 mg/kg intraperitoneally once daily for 7 days. Pathological changes of the rat renal tissues were observed under a light microscope; the levels of serum creatinine (SCr), blood urea nitrogen (BUN), cell-free DNA (cf-DNA) and reactive oxygen species (ROS) were measured; the expression levels of myeloperoxidase (MPO), citrullinated histone H3 (cit-H3), B-cell lymphoma 2 (Bcl-2), Bax, nuclear factor kappa B (NF-κB) p65 and phospho-NF-κB p65 (p-NF-κB p65) in rat renal tissues were detected by qRT-qPCR and Western blotting; the localization of NF-κB p65 expression in rat renal tissues was observed by immunofluorescence staining and the expression changes of NETs in rat renal tissues were detected by multiplex fluorescence immunohistochemical staining. RESULTS: After hypoxia, the expression of NF-κB protein in renal tissues was significantly increased, the levels of SCr, BUN, cf-DNA and ROS in serum were significantly increased, the formation of NETs in renal tissues was significantly increased, and a large number of tubular dilatation and lymphocyte infiltration were observed in renal tissues. When PDTC was used to inhibit NF-κB activation, NETs formation in renal tissue was significantly decreased, the expression level of Bcl-2 in renal tissues was significantly increased, the expression level of Bax was significantly decreased, and renal injury was significantly alleviated. CONCLUSION: HH induces the formation of NETs through the NF-κB signaling pathway, and it promotes apoptosis and aggravates renal injury by decreasing Bcl-2 and increasing Bax expression.

Knee Cartilage Defect Assessment by Graph Representation and Surface Convolution.

Knee osteoarthritis (OA) is the most common osteoarthritis and a leading cause of disability. Cartilage defects are regarded as major manifestations of knee OA, which are visible by magnetic resonance imaging (MRI). Thus early detection and assessment for knee cartilage defects are important for protecting patients from knee OA. In this way, many attempts have been made on knee cartilage defect assessment by applying convolutional neural networks (CNNs) to knee MRI. However, the physiologic characteristics of the cartilage may hinder such efforts: the cartilage is a thin curved layer, implying that only a small portion of voxels in knee MRI can contribute to the cartilage defect assessment; heterogeneous scanning protocols further challenge the feasibility of the CNNs in clinical practice; the CNN-based knee cartilage evaluation results lack interpretability. To address these challenges, we model the cartilages structure and appearance from knee MRI into a graph representation, which is capable of handling highly diverse clinical data. Then, guided by the cartilage graph representation, we design a non-Euclidean deep learning network with the self-attention mechanism, to extract cartilage features in the local and global, and to derive the final assessment with a visualized result. Our comprehensive experiments show that the proposed method yields superior performance in knee cartilage defect assessment, plus its convenient 3D visualization for interpretability.

Low-noise and high-power second harmonic generation of 532 nm laser for trapping ultracold atoms.

Optical lattices for coherently manipulating ultracold atoms demand high-power, low-noise, narrow-line-width, and continuous-wave lasers. Here, we report the implementation of a 30 W 532 nm low-noise laser by second harmonic generation from a 1064 nm fiber laser, which is capable to generate optical lattices for a quantum gas microscope of Rb87 atoms. The overall conversion efficiency is 59% at an input power of 51 W with a lithium triborate crystal coupled to a ring cavity. The relative intensity noise of the output laser is suppressed to -120 dBc/Hz in the range of 10 Hz-100 kHz with a high dynamic range of over 50 dB, which is suitable for long-term trapping and coherent manipulation of the quantum gases.

Rosiglitazone attenuates hypoxia-induced renal cell apoptosis by inhibiting NF-κB signaling pathway in a PPARγ-dependent manner.

BACKGROUND: In recent years, peroxisome proliferator-activated receptor γ (PPARγ) has been found to be closely associated with hypoxia renal disease. The aim of this study was to investigate the relationship between rosiglitazone and mitochondrial apoptosis in renal tissue and its associated mechanisms. METHODS: Twenty-four male Sprague-Dawley rats were randomly divided into three groups (n = 8 in each): normal control group, hypoxia injury group (equal volume of 0.9% saline), and PPARγ agonist group (Rosiglitazone, 10 mg/kg · d, intraperitoneally). The hypoxia injury group and PPARγ agonist group were placed in a hypoxia chamber and the simulated altitude was set at 7,000 m for 7 days. Blood and kidney samples were collected after 7 days. The quantitative real-time polymerase chain reaction and Western blot methods were used to determine the expression of PPARγ, nuclear factor kappa-B (NF-κB), B-cell lymphoma-2 (Bcl-2), and Bax. RESULTS: The results showed that compared with the normal control group, the renal tissue of rats after hypoxia was severely damaged, as shown by massive renal tubular epithelial cell degeneration and detachment, and renal tubular dilation. The NF-κB protein expression significantly increased, the Bcl-2 protein and mRNA expression significantly decreased, and Bax protein and mRNA expression significantly increased (p < .05 for all). Renal injury was much less severe in the PPARγ agonist group compared to the hypoxia injury group. CONCLUSIONS: Rosiglitazone can alleviate hypoxia renal injury, with the possible mechanism involving attenuation of apoptosis by inhibiting the activation of the NF-κB signaling pathway in a PPARγ-dependent manner and increasing Bcl-2 and decreasing Bax expression.

Lelliottia steviae sp. nov. isolated from Stevia rebaudiana Bertoni.

A Gram-negative, aerobic, chemoheterotrophic, rod-shaped, and motile bacterium, designated as LST-1T, was isolated from wild Stevia rebaudiana Bertoni and subjected to a polyphasic taxonomic analysis. The LST-1 strain grew optimally at 37 °C and pH 6.0-7.0 in the presence of 0.5% (w/v) NaCl. Phylogenetic analysis based on the 16S rDNA sequence indicated that LST-1 is closely related to Lelliottia jeotgali PFL01T (99.85%), Lelliottia nimipressuralis LMG10245T (98.82%), and Lelliottia amnigena LMG2784T (98.54%). Multi-locus sequence typing of concatenated partial atpD, infB, gyrB, and rpoB genes was performed to improve the resolution, and clear distinctions between the closest related type strains were observed. The results of average nucleotide identify analyses and DNA-DNA hybridization with four species (16S rDNA similarity > 98.65%) were less than 90 and 40%, respectively, verifying the distinct characteristics from other species of Lelliottia. The cellular fatty acid profile of the strain consisted of C16:0, Summed Feature3, and Summed Feature8 (possibly 16:1 w6c/16:1 w7c and 18:1 w6c) as major components. The major polar lipids included phosphatidylethanolamine, phosphatidylglycerol, an aminophospholipid, three non-characteristic phospholipids, and a non-characteristic lipid. The genome of LST-1T was 4,611,055 bp in size, with a G + C content of 55.02%. The unique combination of several phenotypic, chemotaxonomic, and genomic characteristics proved that strain LST-1T belongs to a novel species, for which the name Lelliottia steviae sp. nov. is proposed. The type strain is LST-1T (= CGMCC 1.19175T = JCM 34938T).Repositories: The genbank accession numbers for the 16S rRNA gene and genome sequences of strain LST-1T are MZ497264 and CP063663, respectively.

Automatic Grading Assessments for Knee MRI Cartilage Defects via Self-ensembling Semi-supervised Learning with Dual-Consistency.

Knee cartilage defects caused by osteoarthritis are major musculoskeletal disorders, leading to joint necrosis or even disability if not intervened at early stage. Deep learning has demonstrated its effectiveness in computer-aided diagnosis, but it is time-consuming to prepare a large set of well-annotated data by experienced radiologists for model training. In this paper, we propose a semi-supervised framework to effectively use unlabeled data for better evaluation of knee cartilage defect grading. Our framework is developed based on the widely-used mean-teacher classification model, by designing a novel dual-consistency strategy to boost the consistency between the teacher and student models. The main contributions are three-fold: (1) We define an attention loss function to make the network focus on the cartilage regions, which can both achieve accurate attention masks and boost classification performance simultaneously; (2) Besides enforcing the consistency of classification results, we further design a novel attention consistency mechanism to ensure the focusing of the student and teacher networks on the same defect regions; (3) We introduce an aggregation approach to ensemble the slice-level classification outcomes for deriving the final subject-level diagnosis. Experimental results show that our proposed method can significantly improve both classification and localization performances of knee cartilage defects. Our code is available on https://github.com/King-HAW/DC-MT.

Multimodal MRI Reconstruction Assisted With Spatial Alignment Network.

In clinical practice, multi-modal magnetic resonance imaging (MRI) with different contrasts is usually acquired in a single study to assess different properties of the same region of interest in the human body. The whole acquisition process can be accelerated by having one or more modalities under-sampled in the k -space. Recent research has shown that, considering the redundancy between different modalities, a target MRI modality under-sampled in the k -space can be more efficiently reconstructed with a fully-sampled reference MRI modality. However, we find that the performance of the aforementioned multi-modal reconstruction can be negatively affected by subtle spatial misalignment between different modalities, which is actually common in clinical practice. In this paper, we improve the quality of multi-modal reconstruction by compensating for such spatial misalignment with a spatial alignment network. First, our spatial alignment network estimates the displacement between the fully-sampled reference and the under-sampled target images, and warps the reference image accordingly. Then, the aligned fully-sampled reference image joins the multi-modal reconstruction of the under-sampled target image. Also, considering the contrast difference between the target and reference images, we have designed a cross-modality-synthesis-based registration loss in combination with the reconstruction loss, to jointly train the spatial alignment network and the reconstruction network. The experiments on both clinical MRI and multi-coil k -space raw data demonstrate the superiority and robustness of the multi-modal MRI reconstruction empowered with our spatial alignment network. Our code is publicly available at https://github.com/woxuankai/SpatialAlignmentNetwork.

Deep learning in knee imaging: a systematic review utilizing a Checklist for Artificial Intelligence in Medical Imaging (CLAIM).

PURPOSE: Our purposes were (1) to explore the methodologic quality of the studies on the deep learning in knee imaging with CLAIM criterion and (2) to offer our vision for the development of CLAIM to assure high-quality reports about the application of AI to medical imaging in knee joint. MATERIALS AND METHODS: A Checklist for Artificial Intelligence in Medical Imaging systematic review was conducted from January 1, 2015, to June 1, 2020, using PubMed, EMBASE, and Web of Science databases. A total of 36 articles discussing deep learning applications in knee joint imaging were identified, divided by imaging modality, and characterized by imaging task, data source, algorithm type, and outcome metrics. RESULTS: A total of 36 studies were identified and divided into: X-ray (44.44%) and MRI (55.56%). The mean CLAIM score of the 36 studies was 27.94 (standard deviation, 4.26), which was 66.53% of the ideal score of 42.00. The CLAIM items achieved an average good inter-rater agreement (ICC 0.815, 95% CI 0.660-0.902). In total, 32 studies performed internal cross-validation on the data set, while only 4 studies conducted external validation of the data set. CONCLUSIONS: The overall scientific quality of deep learning in knee imaging is insufficient; however, deep learning remains a promising technology for diagnostic or predictive purpose. Improvements in study design, validation, and open science need to be made to demonstrate the generalizability of findings and to achieve clinical applications. Widespread application, pre-trained scoring procedure, and modification of CLAIM in response to clinical needs are necessary in the future. KEY POINTS: • Limited deep learning studies were established in knee imaging with mean score of 27.94, which was 66.53% of the ideal score of 42.00, commonly due to invalidated results, retrospective study design, and absence of a clear definition of the CLAIM items in detail. • A previous trained data extraction instrument allowed reaching moderate inter-rater agreement in the application of the CLAIM, while CLAIM still needs improvement in scoring items and result reporting to become a wide adaptive tool in reviews of deep learning studies.

A high-power and low-noise 532-nm continuous-wave laser for quantum gas microscopy.

Low-noise, high-power 532-nm lasers are of great interest in many scientific research studies, such as gravitational wave detection and ultracold atom experiments. In particular, in the experiments of quantum gas microscopy, a large power of laser is necessary during the imaging process, while low noise is important for preventing the atoms from being heated up. In this work, we report on the generation of such a 532-nm continuous-wave laser by coherently combining two laser beams produced by single-pass second-harmonic generation. The power of the combined laser is up to 17 W. With the help of intensity stabilization, we are able to suppress the relative intensity noise to below -120 dBc/Hz. The generated laser satisfies the experimental requirements for integrating optical superlattices with a quantum gas microscope.

Quercetin protects islet ß-cells from oxidation-induced apoptosis via Sirt3 in T2DM.

OBJECTIVES: Sirt3 may regulate ROS production and might be involved in ß-cell apoptosis, which plays an important role in the progression of type 2 diabetes mellitus (T2DM). Quercetin is a potent anti-oxidative bioflavonoid, but its effects on T2DM remain to be explored. This study aimed to investigate the effects of quercetin on ß-cell apoptosis and explore its mechanisms. MATERIALS AND METHODS: The effects of quercetin were conducted on db/db mice and INS1 cells. Fasting blood glucose was determined by the colorimetric method, serum insulin was measured by enzyme-linked immunosorbent assay (ELISA). Meanwhile, Sirt3 in INS1 cells was knocked down by plasmid transfection. The antioxidant proteins (SOD2 and CAT), apoptosis proteins (cleaved Caspase-3, Bax, and BCL-2), and Sirt3 protein in pancreases and INS1 cells were determined by western blotting. RESULTS: When INS1 cells and diabetic mice were treated with quercetin, the levels of SOD2, CAT, and Sirt3 proteins were increased, the levels of cleaved Caspase-3 and the ratio of Bax to BCL-2 were decreased at different degrees, along with reduced blood glucose levels and elevated insulin levels in diabetic mice. When Sirt3 was knocked down in INS1 cells, increase of two antioxidants and decrease of cell apoptosis generated by quercetin could not occur. CONCLUSION: Quercetin protected islet ß-cells from oxidation-induced apoptosis via Sirt3 in T2DM, which would be beneficial to develop new strategies for preventing ß-cell failure in T2DM.

A daytime nap restores hippocampal function and improves declarative learning.

STUDY OBJECTIVES: Daytime naps can confer benefits on subsequent declarative learning, but the physiological correlates of this improvement are less well studied. We examined learning following a daytime nap compared with an equivalent waking period using fMRI and polysomnography. METHODS: Forty healthy young adults who slept normally the previous night encoded word pair lists in an MRI scanner at 13:00 and 16:30. Between sessions, participants either stayed awake and watched a documentary (Wake Group; N = 20) or had a 90-minute nap opportunity (Nap Group; N = 20) monitored by polysomnography. Approximately 40 minutes after completing each encoding session, memory for learned words was assessed using cued-recall. RESULTS: A significant Session × Group interaction effect (p < 0.001) was observed in which memory was significantly improved in the Nap but not in the Wake group (p < 0.001). There was also a Session × Run × Group interaction effect in the left hippocampus (p = 0.001), whereby activation during word pair encoding increased only following the nap. Both performance improvement (rs = 0.46, p = 0.04) and nap-related increase in hippocampal activation (rs = 0.46, p = 0.04) were correlated with nap spindle count (12-15 Hz) but not with slow oscillation power (p's ≥ 0.18). CONCLUSIONS: After a habitual nocturnal sleep, participants who had a 90-minute afternoon nap encoded word pairs better than a comparable group who stayed awake. Increases in hippocampal activation following the nap suggest restored hippocampal function. Naptime spindles may contribute to improved memory.

Safety Evaluation of Human Cord-Lining Epithelial Stem Cells Transplantation for Liver Regeneration in a Porcine Model.

We investigated the safety of using umbilical cord-lining stem cells for liver regeneration and tested a novel method for stem cell delivery. Stem cells are known by their ability to repair damaged tissues and have the potential to be used as regenerative therapies. The umbilical cord's outer lining membrane is known to be a promising source of multipotent stem cells and can be cultivated in an epithelial cell growth medium to produce cell populations which possess the properties of both epithelial cells and embryonic stem cells-termed cord-lining epithelial cells (CLEC). Hepatocytes are epithelial cells of the liver and their proliferation upon injury is the main mechanism in restoring the liver. Earlier studies conducted showed CLEC can be differentiated into functioning hepatocyte-like cells (HLC) and can survive in immunologically competent specimens. In this study, we chose a porcine model to investigate CLEC as a treatment modality for liver failure. We selected 16 immune competent Yorkshire-Dutch Landrace pigs, with a mean weight of 40.5 kg, for this study. We performed a 50% hepatectomy to simulate the liver insufficient disease model. After the surgery, four pigs were transplanted with a saline scaffold while seven pigs were transplanted with a HLC scaffold. Five pigs died on the surgical table and were omitted from the study analysis. This study addressed the safety of transplanting human CLEC in a large animal model. The transplant interfaces were evaluated and no signs of cellular rejection were observed in both groups.

Knee Cartilage Thickness Differs Alongside Ages: A 3-T Magnetic Resonance Research Upon 2,481 Subjects via Deep Learning.

Background: It was difficult to distinguish the cartilage thinning of an entire knee joint and to track the evolution of cartilage morphology alongside ages in the general population, which was of great significance for studying osteoarthritis until big imaging data and artificial intelligence are fused. The purposes of our study are (1) to explore the cartilage thickness in anatomical regions of the knee joint among a large collection of healthy knees, and (2) to investigate the relationship between the thinning pattern of the cartilages and the increasing ages. Methods: In this retrospective study, 2,481 healthy knees (subjects ranging from 15 to 64 years old, mean age: 35 ± 10 years) were recruited. With magnetic resonance images of knees acquired on a 3-T superconducting scanner, we automatically and precisely segmented the cartilage via deep learning and calculated the cartilage thickness in 14 anatomical regions. The thickness readings were compared using ANOVA by considering the factors of age, sex, and side. We further tracked the relationship between the thinning pattern of the cartilage thickness and the increasing ages by regression analysis. Results: The cartilage thickness was always thicker in the femur than corresponding regions in the tibia (p < 0.05). Regression analysis suggested cartilage thinning alongside ages in all regions (p < 0.05) except for medial and lateral anterior tibia in both females and males (p > 0.05). The thinning speed of men was faster than women in medial anterior and lateral anterior femur, yet slower in the medial patella (p < 0.05). Conclusion: We established the calculation method of cartilage thickness using big data and deep learning. We demonstrated that cartilage thickness differed across individual regions in the knee joint. Cartilage thinning alongside ages was identified, and the thinning pattern was consistent in the tibia while inconsistent in patellar and femoral between sexes. These findings provide a potential reference to detect cartilage anomaly.