Bright Bifacial White-Light Illumination by Highly Deformable Electroluminescent Devices Based on Transparent Ionic-Hydrogel Electrodes and Quantum-Dot Color Conversion.

Deformable alternating-current electroluminescent (ACEL) devices are of increasing interest because of their potential to drive innovation in soft optoelectronics. Despite the research focus on efficient white ACEL devices, achieving deformable devices with high luminance remains difficult. In this study, this challenge is addressed by fabricating white ACEL devices using color-conversion materials, transparent and durable hydrogel electrodes, and high-k nanoparticles. The incorporation of quantum dots enables the highly efficient generation of red and green light through the color conversion of blue electroluminescence. Although the ionic-hydrogel electrode provides high toughness, excellent light transmittance, and superior conductivity, the luminance of the device is remarkably enhanced by the incorporation of a high-k dielectric, BaTiO3. The fabricated ACEL device uniformly emits very bright white light (489 cd m-2) with a high color-rendering index (91) from both the top and bottom. The soft and tough characteristics of the device allow seamless operation in various deformed states, including bending, twisting, and stretching up to 400%, providing a promising platform for applications in a wide array of soft optoelectronics.

CWAS-Plus: Estimating category-wide association of rare noncoding variation from whole-genome sequencing data with cell-type-specific functional data.

Variants in cis-regulatory elements link the noncoding genome to human brain pathology; however, detailed analytic tools for understanding the association between cell-level brain pathology and noncoding variants are lacking. CWAS-Plus, adapted from a Python package for category-wide association testing (CWAS) employs both whole-genome sequencing and user-provided functional data to enhance noncoding variant analysis, with a faster and more efficient execution of the CWAS workflow. Here, we used single-nuclei assay for transposase-accessible chromatin with sequencing to facilitate CWAS-guided noncoding variant analysis at cell-type specific enhancers and promoters. Examining autism spectrum disorder whole-genome sequencing data (n = 7,280), CWAS-Plus identified noncoding de novo variant associations in transcription factor binding sites within conserved loci. Independently, in Alzheimer's disease whole-genome sequencing data (n = 1,087), CWAS-Plus detected rare noncoding variant associations in microglia-specific regulatory elements. These findings highlight CWAS-Plus's utility in genomic disorders and scalability for processing large-scale whole-genome sequencing data and in multiple-testing corrections. CWAS-Plus and its user manual are available at https://github.com/joonan-lab/cwas/ and https://cwas-plus.readthedocs.io/en/latest/, respectively.

SpatialSPM: statistical parametric mapping for the comparison of gene expression pattern images in multiple spatial transcriptomic datasets.

Spatial transcriptomic (ST) techniques help us understand the gene expression levels in specific parts of tissues and organs, providing insights into their biological functions. Even though ST dataset provides information on the gene expression and its location for each sample, it is challenging to compare spatial gene expression patterns across tissue samples with different shapes and coordinates. Here, we propose a method, SpatialSPM, that reconstructs ST data into multi-dimensional image matrices to ensure comparability across different samples through spatial registration process. We demonstrated the applicability of this method by kidney and mouse olfactory bulb datasets as well as mouse brain ST datasets to investigate and directly compare gene expression in a specific anatomical region of interest, pixel by pixel, across various biological statuses. Beyond traditional analyses, SpatialSPM is capable of generating statistical parametric maps, including T-scores and Pearson correlation coefficients. This feature enables the identification of specific regions exhibiting differentially expressed genes across tissue samples, enhancing the depth and specificity of ST studies. Our approach provides an efficient way to analyze ST datasets and may offer detailed insights into various biological conditions.

Development of finely tuned liposome nanoplatform for macrophage depletion.

BACKGROUND: Immunotherapy with clodronate-encapsulated liposomes, which induce macrophage depletion, has been studied extensively. However, previously reported liposomal formulation-based drugs (Clodrosome® and m-Clodrosome®) are limited by their inconsistent size and therapeutic efficacy. Thus, we aimed to achieve consistent therapeutic effects by effectively depleting macrophages with uniform-sized liposomes. RESULTS: We developed four types of click chemistry-based liposome nanoplatforms that were uniformly sized and encapsulated with clodronate, for effective macrophage depletion, followed by conjugation with Man-N3 and radiolabeling. Functionalization with Man-N3 improves the specific targeting of M2 macrophages, and radioisotope labeling enables in vivo imaging of the liposome nanoplatforms. The functionalized liposome nanoplatforms are stable under physiological conditions. The difference in the biodistribution of the four liposome nanoplatforms in vivo were recorded using positron emission tomography imaging. Among the four platforms, the clodronate-encapsulated mannosylated liposome effectively depleted M2 macrophages in the normal liver and tumor microenvironment ex vivo compared to that by Clodrosome® and m-Clodrosome®. CONCLUSION: The newly-developed liposome nanoplatform, with finely tuned size control, high in vivo stability, and excellent ex vivo M2 macrophage targeting and depletion effects, is a promising macrophage-depleting agent.

Low-Molecular Collagen Peptide Supplementation and Body Fat Mass in Adults Aged ≥ 50 Years: A Randomized, Double-Blind, Placebo-Controlled Trial.

A randomized, double-blind, placebo-controlled trial was conducted to confirm whether collagen peptide supplementation for 12 week has a beneficial effect on body fat control in older adults at a daily physical activity level. Participants were assigned to either the collagen group (15 g/day of collagen peptide) or the placebo group (placebo drink). Body composition was measured by bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DEXA). In total, 74 participants (collagen group, n = 37; placebo group, n = 37) were included in the final analysis. The collagen group showed a significant reduction in total body fat mass compared with the placebo group, as evidenced by both BIA (p = 0.021) and DEXA (p = 0.041) measurements. Body fat mass and percent body fat of the whole body and trunk reduced at 12 weeks compared with baseline only in the collagen group (whole body: body fat mass, p = 0.002; percent body fat, p = 0.002; trunk: body fat mass, p = 0.001; percent body fat, p = 0.000). Total fat mass change (%) (collagen group, -0.49 ± 3.39; placebo group, 2.23 ± 4.20) showed a significant difference between the two groups (p = 0.041). Physical activity, dietary intake, and biochemical parameters showed no significant difference between the groups. The results confirmed that collagen peptide supplementation had a beneficial effect on body fat reduction in older adults aged ≥ 50 years with daily physical activity level. Thus, collagen peptide supplementation has a positive effect on age-related changes.

CFD-based design optimization of ducted hydrokinetic turbines.

Hydrokinetic turbines extract kinetic energy from moving water to generate renewable electricity, thus contributing to sustainable energy production and reducing reliance on fossil fuels. It has been hypothesized that a duct can accelerate and condition the fluid flow passing the turbine blades, improving the overall energy extraction efficiency. However, no substantial evidence has been provided so far for hydrokinetic turbines. To investigate this problem, we perform a CFD-based optimization study with a blade-resolved Reynolds-averaged Navier-Stokes (RANS) solver to explore the design of a ducted hydrokinetic turbine that maximizes the efficiency of energy extraction. A gradient-based optimization approach is utilized to effectively deal with the high-dimensional design space of the blade and duct geometry, with gradients being calculated through the adjoint method. The final design is re-evaluated through higher-fidelity unsteady RANS (URANS) simulations. Our optimized ducted turbine achieves an efficiency of about 54% over a range of operating conditions, higher than the typical 46% efficiency of unducted turbines.

Reference-based cell type matching of in situ image-based spatial transcriptomics data on primary visual cortex of mouse brain.

With the advent of multiplex fluorescence in situ hybridization (FISH) and in situ RNA sequencing technologies, spatial transcriptomics analysis is advancing rapidly, providing spatial location and gene expression information about cells in tissue sections at single cell resolution. Cell type classification of these spatially-resolved cells can be inferred by matching the spatial transcriptomics data to reference atlases derived from single cell RNA-sequencing (scRNA-seq) in which cell types are defined by differences in their gene expression profiles. However, robust cell type matching of the spatially-resolved cells to reference scRNA-seq atlases is challenging due to the intrinsic differences in resolution between the spatial and scRNA-seq data. In this study, we systematically evaluated six computational algorithms for cell type matching across four image-based spatial transcriptomics experimental protocols (MERFISH, smFISH, BaristaSeq, and ExSeq) conducted on the same mouse primary visual cortex (VISp) brain region. We find that many cells are assigned as the same type by multiple cell type matching algorithms and are present in spatial patterns previously reported from scRNA-seq studies in VISp. Furthermore, by combining the results of individual matching strategies into consensus cell type assignments, we see even greater alignment with biological expectations. We present two ensemble meta-analysis strategies used in this study and share the consensus cell type matching results in the Cytosplore Viewer ( https://viewer.cytosplore.org ) for interactive visualization and data exploration. The consensus matching can also guide spatial data analysis using SSAM, allowing segmentation-free cell type assignment.

Development of a Gait Feature-Based Model for Classifying Cognitive Disorders Using a Single Wearable Inertial Sensor.

BACKGROUND AND OBJECTIVES: Gait changes are potential markers of cognitive disorders (CDs). We developed a model for classifying older adults with CD from those with normal cognition using gait speed and variability captured from a wearable inertial sensor and compared its diagnostic performance for CD with that of the model using the Mini-Mental State Examination (MMSE). METHODS: We enrolled community-dwelling older adults with normal gait from the Korean Longitudinal Study on Cognitive Aging and Dementia and measured their gait features using a wearable inertial sensor placed at the center of body mass while they walked on a 14-m long walkway thrice at comfortable paces. We randomly split our entire dataset into the development (80%) and validation (20%) datasets. We developed a model for classifying CD using logistic regression analysis from the development dataset and validated it in the validation dataset. In both datasets, we compared the diagnostic performance of the model with that using the MMSE. We estimated optimal cutoff score of our model using receiver operator characteristic analysis. RESULTS: In total, 595 participants were enrolled, of which 101 of them experienced CD. Our model included both gait speed and temporal gait variability and exhibited good diagnostic performance for classifying CD from normal cognition in both the development (area under the receiver operator characteristic curve [AUC] = 0.788, 95% CI 0.748-0.823, p < 0.001) and validation datasets (AUC = 0.811, 95% CI 0.729-0.877, p < 0.001). Our model showed comparable diagnostic performance for CD with that of the model using the MMSE in both the development (difference in AUC = 0.026, standard error [SE] = 0.043, z statistic = 0.610, p = 0.542) and validation datasets (difference in AUC = 0.070, SE = 0.073, z statistic = 0.956, p = 0.330). The optimal cutoff score of the gait-based model was >-1.56. DISCUSSION: Our gait-based model using a wearable inertial sensor may be a promising diagnostic marker of CD in older adults. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that gait analysis can accurately distinguish older adults with CDs from healthy controls.

PyOncoPrint: a python package for plotting OncoPrints.

OncoPrint, the plot to visualize an overview of genetic variants in sequencing data, has been widely used in the field of cancer genomics. However, still, there have been no Python libraries capable to generate OncoPrint yet, a big hassle to plot OncoPrints within Python-based genetic variants analysis pipelines. This paper introduces a new Python package PyOncoPrint, which can be easily used to plot OncoPrints in Python. The package is based on the existing widely used scientific plotting library Matplotlib, the resulting plots are easy to be adjusted for various needs.

Development of Spleen Targeting H2S Donor Loaded Liposome for the Effective Systemic Immunomodulation and Treatment of Inflammatory Bowel Disease.

Nanoparticles are primarily taken up by immune cells after systemic administration. Thus, they are considered an ideal drug delivery vehicle for immunomodulation. Because the spleen is the largest lymphatic organ and regulates the systemic immune system, there have been studies to develop spleen targeting nanoparticles for immunomodulation of cancer and immunological disorders. Inflammatory bowel disease (IBD) includes disorders involving chronic inflammation in the gastrointestinal tract and is considered incurable despite a variety of treatment options. Hydrogen sulfide (H2S) is one of the gasotransmitters that carries out anti-inflammatory functions and has shown promising immunomodulatory effects in various inflammatory diseases including IBD. Herein, we developed a delicately tuned H2S donor delivering liposome for spleen targeting (ST-H2S lipo) and studied its therapeutic effects in a dextran sulfate sodium (DSS) induced colitis model. We identified the ideal PEG type and ratio of liposome for a high stability, loading efficiency, and spleen targeting effect. In the treatment of the DSS-induced colitis model, we found that ST-H2S lipo and conventional long-circulating liposomes loaded with H2S donors (LC-H2S lipo) reduced the severity of colitis, whereas unloaded H2S donors did not. Furthermore, the therapeutic effect of ST-H2S lipo was superior to that of LC-H2S lipo due to its better systemic immunomodulatory effect than that of LC-H2S lipo. Our findings demonstrate that spleen targeting H2S lipo may have therapeutic potential for IBD.

Spatial Transcriptomics-Based Identification of Molecular Markers for Nanomedicine Distribution in Tumor Tissue.

The intratumoral accumulation of nanomedicine has been considered a passive process, referred to as the enhanced permeability and retention effect. Recent studies have suggested that the tumor uptake of nanomedicines follows an energy-dependent pathway rather than being a passive process. Herein, to explore the factor candidates that are associated with nanomedicine tumor uptake, a molecular marker identification platform is developed by integrating microscopic fluorescence images of a nanomedicine distribution with spatial transcriptomics information. When this approach is applied to PEGylated liposomes, molecular markers related to hypoxia, glycolysis, and apoptosis can be identified as being related to the intratumoral distribution of the nanomedicine. It is expected that the method can be applied to explain the distribution of a wide range of nanomedicines and that the data obtained from this analysis can enhance the precise utilization of nanomedicines.

Long-Term Outcomes of Bilateral Subthalamic Nucleus Deep Brain Stimulation for Patients With Parkinson's Disease: 10 Years and Beyond.

BACKGROUND: Deep brain stimulation (DBS) of the subthalamic nucleus (STN) represents an effective treatment for severe Parkinson's disease (PD), but little is known about the long-term benefit. OBJECTIVE: To investigate the survival rate and long-term outcome of DBS. METHODS: We investigated all 81 patients including 37 males and 44 females who underwent bilateral STN DBS from March 2005 to March 2008 at a single institution. The current survival status of the patients was investigated. Preoperative and postoperative follow-up assessments were analyzed. RESULTS: The mean age at the time of surgery was 62 (range 27-82) years, and the median clinical follow-up duration was 145 months. Thirty-five patients (43%) died during the follow-up period. The mean duration from DBS surgery to death was 110.46 ± 40.8 (range 0-155) months. The cumulative survival rate is as follows: 98.8 ± 1.2% (1 year), 95.1 ± 2.4% (5 years), and 79.0 ± 4.5% (10 years). Of the 81 patients, 33 (40%) were ambulatory up to more than 11 years. The Unified Parkinson's Disease Rating Scale (UPDRS) score was significantly improved until 5 years after surgery although it showed a tendency to increase again after 10 years. The patient group with both electrodes located within the STN showed a higher rate of survival and maintained ambulation. CONCLUSION: STN DBS is a safe and effective treatment for patients with advanced PD. This study based on the long-term follow-up of large patient populations can be used to elucidate the long-term fate of patients who underwent bilateral STN DBS for PD.

Discovery of potential imaging and therapeutic targets for severe inflammation in COVID-19 patients.

The Coronavirus disease 2019 (COVID-19) has been spreading worldwide with rapidly increased number of deaths. Hyperinflammation mediated by dysregulated monocyte/macrophage function is considered to be the key factor that triggers severe illness in COVID-19. However, no specific targeting molecule has been identified for detecting or treating hyperinflammation related to dysregulated macrophages in severe COVID-19. In this study, previously published single-cell RNA-sequencing data of bronchoalveolar lavage fluid cells from thirteen COVID-19 patients were analyzed with publicly available databases for surface and imageable targets. Immune cell composition according to the severity was estimated with the clustering of gene expression data. Expression levels of imaging target molecules for inflammation were evaluated in macrophage clusters from single-cell RNA-sequencing data. In addition, candidate targetable molecules enriched in severe COVID-19 associated with hyperinflammation were filtered. We found that expression of SLC2A3, which can be imaged by [18F]fluorodeoxyglucose, was higher in macrophages from severe COVID-19 patients. Furthermore, by integrating the surface target and drug-target binding databases with RNA-sequencing data of severe COVID-19, we identified candidate surface and druggable targets including CCR1 and FPR1 for drug delivery as well as molecular imaging. Our results provide a resource in the development of specific imaging and therapy for COVID-19-related hyperinflammation.

Cell segmentation-free inference of cell types from in situ transcriptomics data.

Multiplexed fluorescence in situ hybridization techniques have enabled cell-type identification, linking transcriptional heterogeneity with spatial heterogeneity of cells. However, inaccurate cell segmentation reduces the efficacy of cell-type identification and tissue characterization. Here, we present a method called Spot-based Spatial cell-type Analysis by Multidimensional mRNA density estimation (SSAM), a robust cell segmentation-free computational framework for identifying cell-types and tissue domains in 2D and 3D. SSAM is applicable to a variety of in situ transcriptomics techniques and capable of integrating prior knowledge of cell types. We apply SSAM to three mouse brain tissue images: the somatosensory cortex imaged by osmFISH, the hypothalamic preoptic region by MERFISH, and the visual cortex by multiplexed smFISH. Here, we show that SSAM detects regions occupied by known cell types that were previously missed and discovers new cell types.

Functional States in Tumor-Initiating Cell Differentiation in Human Colorectal Cancer.

Intra-tumor heterogeneity of tumor-initiating cell (TIC) activity drives colorectal cancer (CRC) progression and therapy resistance. Here, we used single-cell RNA-sequencing of patient-derived CRC models to decipher distinct cell subpopulations based on their transcriptional profiles. Cell type-specific expression modules of stem-like, transit amplifying-like, and differentiated CRC cells resemble differentiation states of normal intestinal epithelial cells. Strikingly, identified subpopulations differ in proliferative activity and metabolic state. In summary, we here show at single-cell resolution that transcriptional heterogeneity identifies functional states during TIC differentiation. Furthermore, identified expression signatures are linked to patient prognosis. Targeting transcriptional states associated to cancer cell differentiation might unravel novel vulnerabilities in human CRC.

ShinyButchR: Interactive NMF-based decomposition workflow of genome-scale datasets.

Non-negative matrix factorization (NMF) has been widely used for the analysis of genomic data to perform feature extraction and signature identification due to the interpretability of the decomposed signatures. However, running a basic NMF analysis requires the installation of multiple tools and dependencies, along with a steep learning curve and computing time. To mitigate such obstacles, we developed ShinyButchR, a novel R/Shiny application that provides a complete NMF-based analysis workflow, allowing the user to perform matrix decomposition using NMF, feature extraction, interactive visualization, relevant signature identification, and association to biological and clinical variables. ShinyButchR builds upon the also novel R package ButchR, which provides new TensorFlow solvers for algorithms of the NMF family, functions for downstream analysis, a rational method to determine the optimal factorization rank and a novel feature selection strategy.

Smaller pineal gland is associated with rapid eye movement sleep behavior disorder in Alzheimer's disease.

BACKGROUND: To investigate the association between pineal gland volume and symptoms of rapid eye movement (REM) sleep behavior disorder (RBD) in Alzheimer's disease (AD) patients without any feature of dementia with Lewy bodies. METHODS: We enrolled 296 community-dwelling probable AD patients who did not meet the diagnostic criteria for possible or probable dementia with Lewy bodies. Among them, 93 were amyloid beta (Aß) positive on 18F-florbetaben amyloid brain positron emission tomography. We measured RBD symptoms using the REM Sleep Behavior Disorder Screening Questionnaire (RBDSQ) and defined probable RBD (pRBD) as the RBDSQ of 5 or higher. We manually segmented pineal gland on 3T structural T1-weighted brain magnetic resonance imaging. RESULTS: The participants with pRBD had smaller pineal parenchyma volume (VPP) than those without pRBD (p <  0.001). The smaller the VPP, the more severe the RBD symptoms (p <  0.001). VPP was inversely associated with risk of prevalent pRBD (odds ratio = 0.909, 95% confidence interval [CI] = 0.878-0.942, p <  0.001). Area under the receiver operator characteristic curve for pRBD of VPP was 0.80 (95% CI = 0.750-0.844, p <  0.0001). These results were not changed when we analyzed the 93 participants with Aß-positive AD separately. CONCLUSIONS: In AD patients, reduced pineal gland volume may be associated with RBD.

Modeling glioblastoma invasion using human brain organoids and single-cell transcriptomics.

BACKGROUND: Glioblastoma (GBM) consists of devastating neoplasms with high invasive capacity, which have been difficult to study in vitro in a human-derived model system. Therapeutic progress is also limited by cellular heterogeneity within and between tumors, among other factors such as therapy resistance. To address these challenges, we present an experimental model using human cerebral organoids as a scaffold for patient-derived GBM cell invasion. METHODS: This study combined tissue clearing and confocal microscopy with single-cell RNA sequencing of GBM cells before and after co-culture with organoid cells. RESULTS: We show that tumor cells within organoids extend a network of long microtubes, recapitulating the in vivo behavior of GBM. Transcriptional changes implicated in the invasion process are coherent across patient samples, indicating that GBM cells reactively upregulate genes required for their dispersion. Potential interactions between GBM and organoid cells identified by an in silico receptor-ligand pairing screen suggest functional therapeutic targets. CONCLUSIONS: Taken together, our model has proven useful for studying GBM invasion and transcriptional heterogeneity in vitro, with applications for both pharmacological screens and patient-specific treatment selection on a time scale amenable to clinical practice.

Pineal gland volume is associated with prevalent and incident isolated rapid eye movement sleep behavior disorder.

We aimed to investigate the association of pineal gland volume with the risk of isolated rapid eye movement (REM) sleep behavior disorder (RBD). We enrolled 245 community-dwelling cognitively normal elderly individuals without major psychiatric or neurological disorders at the baseline evaluation, of whom 146 completed the 2-year follow-up evaluation. We assessed RBD symptoms using the REM Sleep Behavior Disorder Screening Questionnaire (RBDSQ) and defined probable RBD (pRBD) as an RBDSQ score of ≥ 5. We manually segmented the pineal gland on 3T T1-weighted brain magnetic resonance imaging and estimated its volume. The smaller the baseline pineal gland volume, the more severe the RBD symptoms at baseline. The individuals with isolated pRBD showed smaller pineal gland volumes than those without isolated pRBD. The larger the baseline pineal gland volume, the lower the risks of prevalent isolated pRBD at the baseline evaluation and incident isolated pRBD at the 2-year follow-up evaluation. Pineal gland volume showed good diagnostic accuracy for prevalent isolated pRBD and predictive accuracy for incident isolated pRBD in the receiver operator characteristic analysis. Our findings suggest that pineal gland volume may be associated with the severity of RBD symptoms and the risk of isolated RBD in cognitively normal elderly individuals.