Caffeinated Soda Intake in Children Is Associated with Neurobehavioral Risk Factors for Substance Misuse.

BACKGROUND AND OBJECTIVES: Use of psychotropic substances in childhood has been associated with both impulsivity and other manifestations of poor executive function as well as escalation over time to use of progressively stronger substances. However, how this relationship may start in earlier childhood has not been well explored. Here, we investigated the neurobehavioral correlates of daily caffeinated soda consumption in preadolescent children and examined whether caffeinated soda intake is associated with a higher risk of subsequent alcohol initiation. METHODS: Using Adolescent Brain Cognitive Development study data (N = 2,092), we first investigated cross-sectional relationships between frequent caffeinated soda intake and well-known risk factors of substance misuse: impaired working memory, high impulsivity, and aberrant reward processing. We then examined whether caffeinated soda intake at baseline predicts more alcohol sipping at 12 months follow-up using a machine learning algorithm. RESULTS: Daily consumption of caffeinated soda was cross-sectionally associated with neurobehavioral risk factors for substance misuse such as higher impulsivity scores and lower working memory performance. Furthermore, caffeinated soda intake predicted a 2.04 times greater likelihood of alcohol sipping after 12 months, even after controlling for rates of baseline alcohol sipping rates. CONCLUSIONS: These findings suggest that previous linkages between caffeine and substance use in adolescence also extend to younger initiation, and may stem from core neurocognitive features thought conducive to substance initiation.

Selective CO2 Capture from CO2/N2 Gas Mixtures Utilizing Tetrabutylammonium Fluoride Hydrates.

Gas hydrates, a type of inclusion compound capable of trapping gas molecules within a lattice structure composed of water molecules, are gaining attention as an environmentally benign gas storage or separation platform. In general, the formation of gas hydrates from water requires high-pressure and low-temperature conditions, resulting in significant energy consumption. In this study, tetrabutylammonium fluoride (TBAF) was utilized as a thermodynamic promoter forming a semi-clathrate-type hydrate, enabling gas capture or separation at room temperature. Those TBAF hydrate systems were explored to check their capability of CO2 separation from flue gas, the mixture of CO2 and N2 gases. The formation rates and gas storage capacities of TBAF hydrates were systematically investigated under various concentrations of CO2, and they presented selective CO2 capture behavior during the hydrate formation process. The maximum gas storage capacities were achieved at 2.36 and 2.38 mmol/mol for TBAF·29.7 H2O and TBAF·32.8 H2O hydrate, respectively, after the complete enclathration of the feed gas of CO2 (80%) + N2 (20%). This study provides sufficient data to support the feasibility of TBAF hydrate systems to be applied to CO2 separation from CO2/N2 gas mixtures based on their CO2 selectivity.

Does the evolution of micromorphology accompany chromosomal changes on dysploid and polyploid levels in the Barnardia japonica complex (Hyacinthaceae)?

BACKGROUND: Chromosome number and genome size changes via dysploidy and polyploidy accompany plant diversification and speciation. Such changes often impact also morphological characters. An excellent system to address the questions of how extensive and structured chromosomal changes within one species complex affect the phenotype is the monocot species complex of Barnardia japonica. This taxon contains two well established and distinct diploid cytotypes differing in base chromosome numbers (AA: x = 8, BB: x = 9) and their allopolyploid derivatives on several ploidy levels (from 3x to 6x). This extensive and structured genomic variation, however, is not mirrored by gross morphological differentiation. RESULTS: The current study aims to analyze the correlations between the changes of chromosome numbers and genome sizes with palynological and leaf micromorphological characters in diploids and selected allopolyploids of the B. japonica complex. The chromosome numbers varied from 2n = 16 and 18 (2n = 25 with the presence of supernumerary B chromosomes), and from 2n = 26 to 51 in polyploids on four different ploidy levels (3x, 4x, 5x, and 6x). Despite additive chromosome numbers compared to diploid parental cytotypes, all polyploid cytotypes have experienced genome downsizing. Analyses of leaf micromorphological characters did not reveal any diagnostic traits that could be specifically assigned to individual cytotypes. The variation of pollen grain sizes correlated positively with ploidy levels. CONCLUSIONS: This study clearly demonstrates that karyotype and genome size differentiation does not have to be correlated with morphological differentiation of cytotypes.

Identification and Characterization of Plasmin-Independent Thrombolytic Enzymes.

RATIONALE: Current thrombolytic agents activate plasminogen to plasmin which triggers fibrinolysis to dissolve thrombi. Since plasmin is a nonspecific proteolytic enzyme, all of the current plasmin-dependent thrombolytics lead to serious hemorrhagic complications, demanding a new class of fibrinolytic enzymes independent from plasmin activation and undesirable side effects. We speculated that the mammalian version of bacterial heat-shock proteins could selectively degrade intravascular thrombi, a typical example of a highly aggregated protein mixture. OBJECTIVE: The objective of this study is to identify enzymes that can dissolve intravascular thrombi specifically without affecting fibrinogen and fibronectin so that the wound healing processes remain uninterrupted and tissues are not damaged. In this study, HtrA (high-temperature requirement A) proteins were tested for its specific proteolytic activity on intravascular thrombi independently from plasmin activation. METHODS AND RESULTS: HtrA1 and HtrA2/Omi proteins, collectively called as HtrAs, lysed ex vivo blood thrombi by degrading fibrin polymers. The thrombolysis by HtrAs was plasmin-independent and specific to vascular thrombi without causing the systemic activation of plasminogen and preventing nonspecific proteolysis of other proteins including fibrinogen and fibronectin. As expected, HtrAs did not disturb clotting and wound healing of excised wounds from mouse skin. It was further confirmed in a tail bleeding and a rebleeding assay that HtrAs allowed normal clotting and maintenance of clot stability in wounds, unlike other thrombolytics. Most importantly, HtrAs completely dissolved blood thrombi in tail thrombosis mice, and the intravenous injection of HtrAs to mice with pulmonary embolism completely dissolved intravascular thrombi and thus rescued thromboembolism. CONCLUSIONS: Here, we identified HtrA1 and HtrA2/Omi as plasmin-independent and highly specific thrombolytics that can dissolve intravascular thrombi specifically without bleeding risk. This work is the first report of a plasmin-independent thrombolytic pathway, providing HtrA1 and HtrA2/Omi as ideal therapeutic candidates for various thrombotic diseases without hemorrhagic complications.

Can cartilage intermediate layer protein 1 (CILP1) use as a novel biomarker for canine myxomatous mitral valve degeneration levels or not?

BACKGROUND: Myxomatous mitral valve degeneration (MMVD) is the most common degenerative heart disease in dogs and is associated with irreversible changes in the valve tissue. Although traditional cardiac biomarkers are efficient for diagnosing MMVD, there are limitations, therefore, it is important to find novel biomarkers. Cartilage intermediate layer protein 1 (CILP1), an extracellular matrix-derived protein, acts as a transforming growth factor-ß antagonist and is involved in myocardial fibrosis. This study aimed to evaluate serum CILP1 levels in canines with MMVD. Dogs with MMVD were staged according to the American College of Veterinary Internal Medicine consensus guidelines. Data analysis was performed using the Mann-Whitney U test, Spearman's correlation, and receiver operating characteristic (ROC) curves. RESULTS: CILP1 levels were elevated in dogs with MMVD (n = 27) compared to healthy controls (n = 8). Furthermore, results showed that CILP1 levels were significantly higher in stage C group dogs compared to healthy controls. The ROC curve of CILP1 and NT-proBNP were good predictors of MMVD, although no similarity was observed between the two. Left ventricular end-diastolic diameter normalized to the body weight (LVIDdn) and left atrial to aorta dimension (LA/Ao) showed a strong association with CILP1 levels; however, no correlation was observed between CILP1 levels and vertebral heart size (VHS) and vertebral left atrial score (VLAS). The optimal cut-off value was selected from the ROC curve and dogs were classified according to the cut-off value (1.068 ng/mL, sensitivity 51.9%, specificity 100%). Results showed a significant association of CILP1 with cardiac remodeling indicators, such as VHS, VLAS, LA/Ao, and LVIDdn. CONCLUSIONS: CILP1 can be an indicator of cardiac remodeling in canines with MMVD and therefore, can be used as an MMVD biomarker.

Quantitative magnetic resonance imaging of chronic kidney disease: an experimental in vivo study using rat chronic kidney disease models.

BACKGROUND: Recent advances in magnetic resonance imaging (MRI) may allow it to be an alternative emerging tool for the non-invasive evaluation of renal parenchymal disease. PURPOSE: To validate the usefulness of quantitative multiparametric MRI protocols and suggest the suitable quantitative MR sequence protocol to evaluate parenchymal fibrosis using an animal model of chronic kidney disease (CKD) by long-term adenine intake. MATERIAL AND METHODS: In this prospective animal study, 16 male Wistar rats were analyzed and categorized into three groups. Rats in the CKD groups underwent 0.25% adenine administration for three or six weeks. Quantitative MRI protocols, including diffusion-weighted imaging (DWI), T1ρ (T1 rho), and T2* mapping were performed using a 9.4-T animal MR scanner. A semi-quantitative histopathologic analysis for renal fibrosis was conducted. Quantitative MR values measured from anatomic regions of kidneys underwent intergroup comparative analyses. RESULTS: The apparent diffusion coefficient (ADC) and T1 (T1 rho) values were significantly increased in all CKD groups. Values measured from the cortex and outer medulla showed significant intergroup differences. Total ADC values tended to increase according to periods, and T1ρ values increased in three weeks and decreased in six weeks. CONCLUSION: Quantitative MRI protocols could be a non-invasive assessment modality in the diagnosis and evaluation of CKD. Particularly, T1ρ may be a suitable MR sequence to quantitatively assess renal parenchymal fibrosis.

Prediction of Cable Behavior Using Finite Element Analysis Results for Flexible Cables.

In actual industrial sites, verifying the framework for cable manipulation is crucial. Therefore, it is necessary to simulate the deformation of the cable to predict its behavior accurately. By simulating the behavior in advance, it is possible to reduce the time and cost required for work. Although finite element analysis is used in various fields, the results may differ from the actual behavior depending on the method of defining the analysis model and analysis conditions. This paper aims to select appropriate indicators that can effectively cope with finite element analysis and experiments during cable winding work. We perform finite element analysis of the behavior of flexible cables and compare the analysis results with results from experiments. Despite some differences between the experimental and analysis outcomes, an indicator was developed through trial and error to align the two cases. Errors occurred during the experiments depending on the analysis and experimental conditions. To address this, weights were derived through optimization to update the cable analysis results. Additionally, deep learning was utilized to update the errors caused by material properties using the weights. This allowed for finite element analysis even when the exact physical properties of the material were unknown, ultimately improving the analysis performance.

Rhein Induces Oral Cancer Cell Apoptosis and ROS via Suppresse AKT/mTOR Signaling Pathway In Vitro and In Vivo.

Oral cancer remains the leading cause of death worldwide. Rhein is a natural compound extracted from the traditional Chinese herbal medicine rhubarb, which has demonstrated therapeutic effects in various cancers. However, the specific effects of rhein on oral cancer are still unclear. This study aimed to investigate the potential anticancer activity and underlying mechanisms of rhein in oral cancer cells. The antigrowth effect of rhein in oral cancer cells was estimated by cell proliferation, soft agar colony formation, migration, and invasion assay. The cell cycle and apoptosis were detected by flow cytometry. The underlying mechanism of rhein in oral cancer cells was explored by immunoblotting. The in vivo anticancer effect was evaluated by oral cancer xenografts. Rhein significantly inhibited oral cancer cell growth by inducing apoptosis and S-phase cell cycle arrest. Rhein inhibited oral cancer cell migration and invasion through the regulation of epithelial-mesenchymal transition-related proteins. Rhein induced reactive oxygen species (ROS) accumulation in oral cancer cells to inhibit the AKT/mTOR signaling pathway. Rhein exerted anticancer activity in vitro and in vivo by inducing oral cancer cell apoptosis and ROS via the AKT/mTOR signaling pathway in oral cancer. Rhein is a potential therapeutic drug for oral cancer treatment.

The sexual brain, genes, and cognition: A machine-predicted brain sex score explains individual differences in cognitive intelligence and genetic influence in young children.

Sex impacts the development of the brain and cognition differently across individuals. However, the literature on brain sex dimorphism in humans is mixed. We aim to investigate the biological underpinnings of the individual variability of sexual dimorphism in the brain and its impact on cognitive performance. To this end, we tested whether the individual difference in brain sex would be linked to that in cognitive performance that is influenced by genetic factors in prepubertal children (N = 9,658, ages 9-10 years old; the Adolescent Brain Cognitive Development study). To capture the interindividual variability of the brain, we estimated the probability of being male or female based on the brain morphometry and connectivity features using machine learning (herein called a brain sex score). The models accurately classified the biological sex with a test ROC-AUC of 93.32%. As a result, a greater brain sex score correlated significantly with greater intelligence (pfdr < .001, ηp2  = .011-.034; adjusted for covariates) and higher cognitive genome-wide polygenic scores (GPSs) (pfdr < .001, ηp2 < .005). Structural equation models revealed that the GPS-intelligence association was significantly modulated by the brain sex score, such that a brain with a higher maleness score (or a lower femaleness score) mediated a positive GPS effect on intelligence (indirect effects = .006-.009; p = .002-.022; sex-stratified analysis). The finding of the sex modulatory effect on the gene-brain-cognition relationship presents a likely biological pathway to the individual and sex differences in the brain and cognitive performance in preadolescence.

Bayesian deep learning-based 1 H-MRS of the brain: Metabolite quantification with uncertainty estimation using Monte Carlo dropout.

PURPOSE: To develop a Bayesian convolutional neural network (BCNN) with Monte Carlo dropout sampling for metabolite quantification with simultaneous uncertainty estimation in deep learning-based proton MRS of the brain. METHODS: Human brain spectra were simulated using basis spectra for 17 metabolites and macromolecules (N = 100 000) at 3.0 Tesla. In addition, actual in vivo spectra (N = 5) were modified by adjusting SNR and linewidth with increasing severity of spectral degradation (N = 50). A BCNN was trained on the simulated spectra to generate a noise-free, line-narrowed, macromolecule signal-removed, metabolite-only spectrum from a typical human brain spectrum. At inference, each input spectrum was Monte Carlo dropout sampled (50 times), and the resulting mean spectrum and variance spectrum were used for metabolite quantification and uncertainty estimation, respectively. RESULTS: Using the simulated spectra, the mean absolute percent errors of the BCNN-predicted metabolite content were < 10% for Cr, Glu, Gln, mI, NAA, and Tau (< 5% for Glu, NAA, and mI). For all metabolites, the correlations (r's) between the ground-truth error and BCNN-predicted uncertainty ranged 0.72-0.94 (0.83 ± 0.06; p < 0.001). Using the modified in vivo spectra, the extent of variation in the estimated metabolite content against the increasing severity of spectral degradation tended to be smaller with BCNN than with linear combination of model spectra (LCModel). Overall, the variation in metabolite content tended to be more highly correlated with the uncertainty from BCNN than with the Cramér-Rao lower-bounds from LCModel (0.938 ± 0.019 vs. 0.881 ± 0.057 [p = 0.115]). CONCLUSION: The BCNN with Monte Carlo dropout sampling may be used in deep learning-based MRS for the estimation of uncertainty in the machine-predicted metabolite content, which is important in the clinical application of deep learning-based MRS.

Single-cell RNA-sequencing of Nicotiana attenuata corolla cells reveals the biosynthetic pathway of a floral scent.

High-throughput single-cell RNA sequencing (scRNA-Seq) identifies distinct cell populations based on cell-to-cell heterogeneity in gene expression. By examining the distribution of the density of gene expression profiles, we can observe the metabolic features of each cell population. Here, we employ the scRNA-Seq technique to reveal the entire biosynthetic pathway of a flower volatile. The corolla of the wild tobacco Nicotiana attenuata emits a bouquet of scents that are composed mainly of benzylacetone (BA). Protoplasts from the N. attenuata corolla limbs and throat cups were isolated at three different time points, and the transcript levels of > 16 000 genes were analyzed in 3756 single cells. We performed unsupervised clustering analysis to determine which cell clusters were involved in BA biosynthesis. The biosynthetic pathway of BA was uncovered by analyzing gene co-expression in scRNA-Seq datasets and by silencing candidate genes in the corolla. In conclusion, the high-resolution spatiotemporal atlas of gene expression provided by scRNA-Seq reveals the molecular features underlying cell-type-specific metabolism in a plant.

Synthetic MRI: Technologies and Applications in Neuroradiology.

Synthetic MRI is a technique that synthesizes contrast-weighted images from multicontrast MRI data. There have been advances in synthetic MRI since the technique was introduced. Although a number of synthetic MRI methods have been developed for quantifying one or more relaxometric parameters and for generating multiple contrast-weighted images, this review focuses on several methods that quantify all three relaxometric parameters (T1 , T2 , and proton density) and produce multiple contrast-weighted images. Acquisition, quantification, and image synthesis techniques are discussed for each method. We discuss the image quality and diagnostic accuracy of synthetic MRI methods and their clinical applications in neuroradiology. Based on this analysis, we highlight areas that need to be addressed for synthetic MRI to be widely implemented in the clinic. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 1.

Dysfunctional energy metabolisms in fibromyalgia compared with healthy subjects.

This study aimed to investigate the levels of creatine (Cr) metabolites in the anterior cingulate cortex (ACC), thalamus, and insula of patients with fibromyalgia (FM) using proton magnetic resonance spectroscopy (MRS). The levels of Cr and phosphocreatine (PCr) relative to total Cr (tCr), which includes Cr and PCr, in the ACC, thalamus, and insula were determined using MRS in 12 patients with FM and in 13 healthy controls. The FM group had lower levels of PCr/tCr in the ACC and right insula compared to healthy controls. There was a negative correlation between Cr/tCr in the ACC and total pain levels (McGill Pain Questionnaire-Total; r = -0.579, p = 0.049) and between Cr/tCr in the left insula and affective pain levels (McGill Pain Questionnaire-Affective; r = -0.638, p = 0.047) in patients with FM. In addition, there were negative correlations between stress levels (Stress Response Inventory) and Cr/tCr in the right (r = -0.780, p = 0.005) and left thalamus (r = -0.740, p = 0.006), as well as in the right insula (r = -0.631, p = 0.028) in patients with FM. There were negative correlations between symptom levels of post-traumatic stress disorder (PTSD; PTSD checklist) and Cr/tCr in the right (r = -0.783, p = 0.004) and left thalamus (r = -0.642, p = 0.024) of patients with FM. These findings are paramount to understanding the decisive pathologies related to brain energy metabolism in patients with FM.

Abnormal neurometabolites in fibromyalgia patients: Magnetic resonance spectroscopy study.

This study aimed to investigate distinct neurometabolites in the anterior cingulate cortex (ACC), right and left thalamus, and insula of patients with fibromyalgia (FM) compared with healthy controls using proton magnetic resonance spectroscopy (MRS). Levels of N-acetylaspartate (NAA), N-acetylaspartylglutamate (NAAG), total NAA (tNAA = NAA + NAAG), myo-inositol (ml), glutamine (Gln), glutamate (Glu), Glx (Glu + Gln), glycerophosphocholine (GPC), total choline (tCho = GPC + phosphocholine) and glutathione (GSH) levels relative to total creatine (tCr) levels including creatine (Cr) and phosphocreatine (PCr) and relative to Cr levels were determined in the ACC, right and left thalamus, and insula in 12 patients with FM and 13 healthy controls using MRS. In the ACC, NAA/tCr (P = 0.028) and tCho/tCr (P = 0.047) were higher in patients with FM. In the right and left insula, tNAA/tCr (P = 0.019, P = 0.007, respectively) was lower in patients with FM. Patients with FM showed lower levels of ml/Cr (P = 0.037) in the right insula than healthy controls. These findings are paramount to understand decisive pathophysiological mechanisms related to abnormal features in the brain and parasympathetic nervous systems in FM. We suggest that the results presented herein may be essential to understand hidden pathological mechanisms and also life system potential as protective and recovering metabolic strategies in patients with FM.

Capturing cell type-specific chromatin compartment patterns by applying topic modeling to single-cell Hi-C data.

Single-cell Hi-C (scHi-C) interrogates genome-wide chromatin interaction in individual cells, allowing us to gain insights into 3D genome organization. However, the extremely sparse nature of scHi-C data poses a significant barrier to analysis, limiting our ability to tease out hidden biological information. In this work, we approach this problem by applying topic modeling to scHi-C data. Topic modeling is well-suited for discovering latent topics in a collection of discrete data. For our analysis, we generate nine different single-cell combinatorial indexed Hi-C (sci-Hi-C) libraries from five human cell lines (GM12878, H1Esc, HFF, IMR90, and HAP1), consisting over 19,000 cells. We demonstrate that topic modeling is able to successfully capture cell type differences from sci-Hi-C data in the form of "chromatin topics." We further show enrichment of particular compartment structures associated with locus pairs in these topics.

Deep learning-based target metabolite isolation and big data-driven measurement uncertainty estimation in proton magnetic resonance spectroscopy of the brain.

PURPOSE: The aim of this study was to develop a method for metabolite quantification with simultaneous measurement uncertainty estimation in deep learning-based proton magnetic resonance spectroscopy (1 H-MRS). METHODS: The reliability of metabolite quantification depends on signal-to-noise ratio (SNR), linewidth, and degree of spectral overlap (DSO), and therefore knowledge about these factors may be utilized in measurement uncertainty estimation in deep learning-based 1 H-MRS. While SNR and linewidth are typically estimated from a representative singlet, DSO needs to be estimated metabolite-specifically. We developed convolutional neural networks (CNNs) capable of isolating target metabolite signal on simulated rat brain spectra at 9.4T, such that, in addition to metabolite content, the signal-to-background ratio (SBR) as a quantitative metric of DSO can be estimated directly from CNN-output for each metabolite. The CNN-predicted SBR was adjusted according to its pre-defined relationship to the ground-truth SBR by exploiting the big spectral data (N = 80 000), and used for measurement uncertainty estimation together with the SNR and linewidth from the CNN-input spectrum. The proposed method was tested first on the simulated spectra in comparison with LCModel and jMRUI and further on in vivo spectra. RESULTS: The proposed method outperformed LCModel and jMRUI in both quantitative accuracy and measurement uncertainty estimation. Using in vivo data, the metabolite concentrations from the proposed method were close to the reported ranges with the measurement uncertainty of glutamine, glutamate, myo-inositol, N-acetylaspartate, and Tau less than 10%. CONCLUSION: The proposed method may be used for metabolite quantification with measurement uncertainty estimation in rat brain at 9.4T by exploiting the spectral isolation capability of the CNNs and the availability of big spectral data.

Reconstruction of spectra from truncated free induction decays by deep learning in proton magnetic resonance spectroscopy.

PURPOSE: To explore the applicability of convolutional neural networks (CNNs) in the reconstruction of spectra from truncated FIDs (tFIDs) in 1 H-MRS, which can be valuable in situations in which data sampling is highly limited, such as spectroscopic magnetic resonance fingerprinting. METHODS: Rat brain FIDs were simulated at 9.4 T based on in vivo data (N = 11) and randomly truncated by retaining 8, 16, 32, 64, 128, 256, 512, and 1024 (null truncation) points (denoted as tFID8 , tFID16 , … tFID1024 ). Using a U-net, 3 CNNs were individually trained (N = 40 000) in time domain only (FID to FID [FID CNNFID ]), in frequency domain only (spectrum to spectrum [spec CNNspec ]), and across the domains (FID to spectrum [FID CNNspec ]) to map the truncated data to their fully sampled versions. The CNNs were tested on the simulated data (N = 5000), and the CNN with the best performance was further tested on the in vivo data, for which the CNN-predicted fully sampled data were analyzed using the LCModel and the results were compared with those from the original, fully sampled data. RESULTS: The best result on the simulated data was obtained with spec CNNspec , which effectively recovered the spectral details even for those input spectra that appear as a hump due to substantial FID truncation (spectra from tFID16 and tFID32 ). Overall, its performance was significantly degraded on the in vivo data. Nonetheless, using spec CNNspec , several coupled spins in addition to the major singlets can be quantified from tFID128 with the error no larger than 10%. CONCLUSION: Upon the availability of more realistically simulated training data, CNNs can also be used in the reconstruction of spectra from truncated FIDs.

Tumor-specific delivery of a paclitaxel-loading HSA-haemin nanoparticle for cancer treatment.

A cancer-targeted chemotherapy could potentially eradicate cancers if anticancer drugs are delivered precisely to the cancers. Although various types of nanoparticles have been developed for cancer-specific delivery of anticancer drugs, the drug delivery capabilities of these nanoparticles were not specific enough to eradicate cancer. Here, we developed a targeting-enhancing nanoparticle of paclitaxel, in which paclitaxel was encapsulated with a human serum albumin-haemin complex through non-covalent bonding. The average diameter of TENPA was approximately 140 nm with a zeta potential of +29 mV. TENPA maintained its structural integrity and stability without forming protein coronas in the blood for optimal passive targeting. These characteristics of TENPA resulted in paclitaxel accumulation that was 4.1 times greater than that of Abraxane, an albumin-bound paclitaxel, in cancer tissue. The dramatic improvement in cancer targeting of TENPA led to reduced systemic toxicity of paclitaxel and eradication of end-stage cancer in a xenografted mouse experiment.

Expansion microscopy with conventional antibodies and fluorescent proteins.

Expansion microscopy is a technique in which fluorophores on fixed specimens are linked to a swellable polymer that is physically expanded to enable super-resolution microscopy with ordinary microscopes. We have developed and characterized new methods for linking fluorophores to the polymer that now enable expansion microscopy with conventional fluorescently labeled antibodies and fluorescent proteins. Our methods simplify the procedure and expand the palette of compatible labels, allowing rapid dissemination of the technique.

Intact metabolite spectrum mining by deep learning in proton magnetic resonance spectroscopy of the brain.

PURPOSE: To develop a robust method for brain metabolite quantification in proton magnetic resonance spectroscopy (1 H-MRS) using a convolutional neural network (CNN) that maps in vivo brain spectra that are typically degraded by low SNR, line broadening, and spectral baseline into noise-free, line-narrowed, baseline-removed intact metabolite spectra. METHODS: A CNN was trained (n = 40 000) and tested (n = 5000) on simulated brain spectra with wide ranges of SNR (6.90-20.74) and linewidth (10-20 Hz). The CNN was further tested on in vivo spectra (n = 40) from five healthy volunteers with substantially different SNR, and the results were compared with those from the LCModel analysis. A Student t test was performed for the comparison. RESULTS: Using the proposed method the mean-absolute-percent-errors (MAPEs) in the estimated metabolite concentrations were 12.49% ± 4.35% for aspartate, creatine (Cr), γ-aminobutyric acid (GABA), glucose, glutamine, glutamate, glutathione (GSH), myo-Inositol (mI), N-acetylaspartate, phosphocreatine (PCr), phosphorylethanolamine, and taurine over the whole simulated spectra in the test set. The metabolite concentrations estimated from in vivo spectra were close to the reported ranges for the proposed method and the LCModel analysis except mI, GSH, and especially Cr/PCr for the LCModel analysis, and phosphorylcholine to glycerophosphorylcholine ratio (PC/GPC) for both methods. The metabolite concentrations estimated across the in vivo spectra with different SNR were less variable with the proposed method (~10% or less) than with the LCModel analysis. CONCLUSION: The robust performance of the proposed method against low SNR may allow a subminute 1 H-MRS of human brain, which is an important technical development for clinical studies.