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European Journal of Paediatric... Jun 2024Music therapy (MT) is proposed to enrich the acoustic environment of very preterm infants (VPT) on the neonatal intensive care unit during a vulnerable period of brain...
OBJECTIVE
Music therapy (MT) is proposed to enrich the acoustic environment of very preterm infants (VPT) on the neonatal intensive care unit during a vulnerable period of brain development. The objective of this study was to investigate the effect of MT on the white matter (WM) microstructure. It is hypothesized that MT affects WM integrity in VPT.
METHODS
Randomized controlled trial enrolling infants born <32 weeks' gestation. Infants were randomized to MT or standard care. Live MT was provided twice weekly from the second postnatal week onwards by a trained music therapist. At term equivalent age, participants underwent a cranial magnetic resonance imaging scan including sequences for diffusion tensor imaging analysis. Differences in WM microstructure were assessed using tract based spatial statistics with fractional anisotropy.
RESULTS
Of 80 infants enrolled, 42 were eligible for diffusion tensor imaging analysis (MT: n = 22, standard care: n = 20). While primary tract based spatial statistics analysis revealed no significant differences between groups, post hoc analysis with uncorrected p-values and a significance threshold of p < 0.01 revealed significant fractional anisotropy differences in several WM tracts including the bilateral superior longitudinal fasciculus, the left forceps minor and left fasciculus uncinatus, the corpus callosum, the left external capsule, and the right corticospinal tract.
CONCLUSION
Post hoc analysis results suggest an effect of MT on WM integrity in VPT. Larger studies including long-term outcome are necessary to confirm these effects of MT on WM microstructure and to assess its impact on clinical neurodevelopment.
CLINICAL TRIAL REGISTRATION
Clinical trial number DRKS00025753.
PubMed: 38941879
DOI: 10.1016/j.ejpn.2024.06.009 -
Computer Methods and Programs in... Jun 2024In this work, the analysis of the importance of hemodynamic updates on a mechanobiological model of atheroma plaque formation is proposed.
BACKGROUND AND OBJECTIVE
In this work, the analysis of the importance of hemodynamic updates on a mechanobiological model of atheroma plaque formation is proposed.
METHODS
For that, we use an idealized and axisymmetric model of carotid artery. In addition, the behavior of endothelial cells depending on hemodynamical changes is analyzed too. A total of three computational simulations are carried out and their results are compared: an uncoupled model and two models that consider the opposite behavior of endothelial cells caused by hemodynamic changes. The model considers transient blood flow using the Navier-Stokes equation. Plasma flow across the endothelium is determined with Darcy's law and the Kedem-Katchalsky equations, considering the three-pore model, which is also employed for the flow of substances across the endothelium. The behavior of the considered substances in the arterial wall is modeled with convection-diffusion-reaction equations, and the arterial wall is modeled as a hyperelastic Yeoh's material.
RESULTS
Significant variations are noted in both the morphology and stenosis ratio of the plaques when comparing the uncoupled model to the two models incorporating updates for geometry and hemodynamic stimuli. Besides, the phenomenon of double-stenosis is naturally reproduced in the models that consider both geometric and hemodynamical changes due to plaque growth, whereas it cannot be predicted in the uncoupled model.
CONCLUSIONS
The findings indicate that integrating the plaque growth model with geometric and hemodynamic settings is essential in determining the ultimate shape and dimensions of the carotid plaque.
PubMed: 38941860
DOI: 10.1016/j.cmpb.2024.108296 -
Journal of Hazardous Materials Jun 2024Water pollution from industrial or household waste, containing dyes from the textile industry, poses a significant environmental challenge requiring immediate attention....
Water pollution from industrial or household waste, containing dyes from the textile industry, poses a significant environmental challenge requiring immediate attention. In this study, we have developed a crosslinked-smart-polymer film based on 2-(dimethylamino)ethyl methacrylate copolymerized with other hydrophilic and hydrophobic commercial monomers, and its efficacy in removing 21 different textile dyes was assessed. The smart polymer effectively interacts with and adsorbs dyes, inducing a noticeable colour change. UV-Vis spectroscopy analysis confirmed a removal efficiency exceeding 90 % for anionic dyes, with external diffusion identified as the primary influencing factor on process kinetics, consistent with both pseudo-first-order kinetics and the Crank-Dual model. Isothermal studies revealed distinct adsorption behaviors, with indigo carmine adhering to a Freundlich isotherm while others conformed to the Langmuir model. Permeation and fluorescence analyses corroborated isotherm observations, verifying surface adsorption. Significantly, our proof-of-concept demonstrated the resilience of the smart-film to common fabric softeners and detergents without compromising adsorption capacity. Additionally, the material exhibited reusability (for at least 5 cycles), durability, and good thermal and mechanical properties, with T and T values of 265 °C and 342 °C, respectively, a Tg of 168 °C, and a water swelling percentage of 54.3 %, thus confirming its stability and suitability for industrial application. ENVIRONMENTAL IMPLICATION: Dyes released during laundry processes should be classified as "hazardous materials" owing to their significant toxicity towards aquatic organisms, with the potential to disrupt ecosystems and harm aquatic biodiversity. This paper discusses the development of a novel acrylic material in film form, engineered to extract toxic anionic dyes. This study directly contributes to mitigating the environmental impact associated with the fashion industry and the domestic use of textiles. It can be implemented on both an industrial and personal scale, thereby encouraging more sustainable practices and promoting collaborative citizen science efforts towards.
PubMed: 38941828
DOI: 10.1016/j.jhazmat.2024.135006 -
European Journal of Radiology Jun 2024To assess T1 mapping performance in distinguishing between benign and malignant breast lesions and to explore its correlation with histopathologic features in breast...
PURPOSE
To assess T1 mapping performance in distinguishing between benign and malignant breast lesions and to explore its correlation with histopathologic features in breast cancer.
METHODS
This study prospectively enrolled 103 participants with a total of 108 lesions, including 25 benign and 83 malignant lesions. T1 mapping, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) were performed. Two radiologists independently outlined the ROIs and analyzed T1 and apparent diffusion coefficient (ADC) values for each lesion, assessing interobserver reliability with the intraclass correlation coefficient (ICC). T1 and ADC values were compared between benign and malignant lesions, across different histopathological characteristics (histological grades, estrogen, progesterone and HER2 receptors expression, Ki67, N status). Receiver operating characteristic (ROC) analysis and Pearson correlation coefficient (ρ) were performed.
RESULTS
T1 values showed statistically significant differences between benign and malignant groups (P < 0.001), with higher values in the malignant (1817.08 ms ± 126.64) compared to the benign group (1429.31 ms ± 167.66). In addition, T1 values significantly increased in the ER (-) group (P = 0.001). No significant differences were found in T1 values among HER2, Ki67, N status, and histological grades groups. Furthermore, T1 values exhibited a significant correlation (ρ) with ER (P < 0.01) and PR (P = 0.03). The AUC for T1 value in distinguishing benign from malignant lesions was 0.69 (95 % CI: 0.55 - 0.82, P = 0.005), and for evaluating ER status, it was 0.75 (95 % CI: 0.62 - 0.87, P = 0.002).
CONCLUSIONS
T1 mapping holds the potential as an imaging biomarker to assist in the discrimination of benign and malignant breast lesions and assessing the ER expression status in breast cancer.
PubMed: 38941821
DOI: 10.1016/j.ejrad.2024.111589 -
PloS One 2024Drawing on the diffusion of innovation theory, we argue that the development of digital economy has a positive effect on urban economic resilience. Using panel data from...
Drawing on the diffusion of innovation theory, we argue that the development of digital economy has a positive effect on urban economic resilience. Using panel data from 284 cities in China from 2011 to 2018, we empirically examine the relationship between digital economy and urban economic resilience. We find a positive and significant link between them, mediated by technological innovation and entrepreneurial vitality. Moreover, the heterogeneity analysis shows that the impact of digital economy is most pronounced in smaller cities, with its effects diminishing in larger cities and megacities. Our results underscore the importance and the direction of fostering digital economy development.
Topics: Cities; Humans; Inventions; Entrepreneurship; China; Economic Development
PubMed: 38941292
DOI: 10.1371/journal.pone.0303782 -
Insights Into Imaging Jun 2024We aimed to develop MRI-based radiomic models (RMs) to improve the diagnostic accuracy of radiologists in characterizing intestinal fibrosis in patients with Crohn's...
OBJECTIVES
We aimed to develop MRI-based radiomic models (RMs) to improve the diagnostic accuracy of radiologists in characterizing intestinal fibrosis in patients with Crohn's disease (CD).
METHODS
This retrospective study included patients with refractory CD who underwent MR before surgery from November 2013 to September 2021. Resected bowel segments were histologically classified as none-mild or moderate-severe fibrosis. RMs based on different MR sequence combinations (RM1: T2WI and enhanced-T1WI; RM2: T2WI, enhanced-T1WI, diffusion-weighted imaging [DWI], and apparent diffusion coefficient [ADC]); RM3: T2WI, enhanced-T1WI, DWI, ADC, and magnetization transfer MRI [MTI]), were developed and validated in an independent test cohort. The RMs' diagnostic performance was compared to that of visual interpretation using identical sequences and a clinical model.
RESULTS
The final population included 123 patients (81 men, 42 women; mean age: 30.26 ± 7.98 years; training cohort, n = 93; test cohort, n = 30). The area under the receiver operating characteristic curve (AUC) of RM1, RM2, and RM3 was 0.86 (p = 0.001), 0.88 (p = 0.001), and 0.93 (p = 0.02), respectively. The decision curve analysis confirmed a progressive improvement in the diagnostic performance of three RMs with the addition of more specific sequences. All RMs performance surpassed the visual interpretation based on the same MR sequences (visual model 1, AUC = 0.65, p = 0.56; visual model 2, AUC = 0.63, p = 0.04; visual model 3, AUC = 0.77, p = 0.002), as well as the clinical model composed of C-reactive protein and erythrocyte sedimentation rate (AUC = 0.60, p = 0.13).
CONCLUSIONS
The RMs, utilizing various combinations of conventional, DWI and MTI sequences, significantly enhance radiologists' ability to accurately characterize intestinal fibrosis in patients with CD.
CRITICAL RELEVANCE STATEMENT
The utilization of MRI-based RMs significantly enhances the diagnostic accuracy of radiologists in characterizing intestinal fibrosis.
KEY POINTS
MRI-based RMs can characterize CD intestinal fibrosis using conventional, diffusion, and MTI sequences. The RMs achieved AUCs of 0.86-0.93 for assessing fibrosis grade. MRI-radiomics outperformed visual interpretation for grading CD intestinal fibrosis.
PubMed: 38940988
DOI: 10.1186/s13244-024-01740-6 -
Medeniyet Medical Journal Jun 2024While the coronavirus disease-2019 (COVID-19) pandemic has generally resulted in milder illness among children than adults, persistent respiratory symptoms have been...
OBJECTIVE
While the coronavirus disease-2019 (COVID-19) pandemic has generally resulted in milder illness among children than adults, persistent respiratory symptoms have been increasingly reported in this population.
METHODS
We conducted a prospective, single-center cohort study focusing on children experiencing prolonged respiratory symptoms after contracting COVID-19. Spirometry, 6- minute walk tests (6MWTs), and tests of lung volume, the diffusing capacity of the lungs for carbon monoxide (DLCO), and fractional exhaled nitric oxide (FeNO) were performed on COVID-19 survivors at least 4 weeks after infection and a group of healthy control subjects.
RESULTS
Fifty-five children with long-term COVID and 55 healthy control subjects were recruited. The weight, height, and body mass index Z-scores were similar in the groups. Within a median duration of 85 days (minimummaximum: 35-194) following COVID-19 infection, a restrictive pattern was observed to be more common in the study group (p=0.021). In children with long COVID, 6MWT distances, DLCO Z-scores, and the predicted values of spirometry and lung volume tests were found to be significantly lower but in the normal range. The average predicted values for DLCO, FeNO, and 6MWT were similar in the two groups.
CONCLUSIONS
Prolonged respiratory symptoms often persist long after COVID-19 infection, necessitating comprehensive evaluation of affected children. Close monitoring, including spirometry and lung volume assessments, is crucial for children with abnormalities in lung imaging. However, FeNO measurements were found to be ineffective in monitoring long COVID.
PubMed: 38940402
DOI: 10.4274/MMJ.galenos.2024.15853 -
Bioinformatics (Oxford, England) Jun 2024RNA design shows growing applications in synthetic biology and therapeutics, driven by the crucial role of RNA in various biological processes. A fundamental challenge...
MOTIVATION
RNA design shows growing applications in synthetic biology and therapeutics, driven by the crucial role of RNA in various biological processes. A fundamental challenge is to find functional RNA sequences that satisfy given structural constraints, known as the inverse folding problem. Computational approaches have emerged to address this problem based on secondary structures. However, designing RNA sequences directly from 3D structures is still challenging, due to the scarcity of data, the nonunique structure-sequence mapping, and the flexibility of RNA conformation.
RESULTS
In this study, we propose RiboDiffusion, a generative diffusion model for RNA inverse folding that can learn the conditional distribution of RNA sequences given 3D backbone structures. Our model consists of a graph neural network-based structure module and a Transformer-based sequence module, which iteratively transforms random sequences into desired sequences. By tuning the sampling weight, our model allows for a trade-off between sequence recovery and diversity to explore more candidates. We split test sets based on RNA clustering with different cut-offs for sequence or structure similarity. Our model outperforms baselines in sequence recovery, with an average relative improvement of 11% for sequence similarity splits and 16% for structure similarity splits. Moreover, RiboDiffusion performs consistently well across various RNA length categories and RNA types. We also apply in silico folding to validate whether the generated sequences can fold into the given 3D RNA backbones. Our method could be a powerful tool for RNA design that explores the vast sequence space and finds novel solutions to 3D structural constraints.
AVAILABILITY AND IMPLEMENTATION
The source code is available at https://github.com/ml4bio/RiboDiffusion.
Topics: RNA; Nucleic Acid Conformation; RNA Folding; Computational Biology; Algorithms; Software; Neural Networks, Computer; Sequence Analysis, RNA
PubMed: 38940178
DOI: 10.1093/bioinformatics/btae259 -
Bioinformatics (Oxford, England) Jun 2024Recently developed spatial lineage tracing technologies induce somatic mutations at specific genomic loci in a population of growing cells and then measure these...
MOTIVATION
Recently developed spatial lineage tracing technologies induce somatic mutations at specific genomic loci in a population of growing cells and then measure these mutations in the sampled cells along with the physical locations of the cells. These technologies enable high-throughput studies of developmental processes over space and time. However, these applications rely on accurate reconstruction of a spatial cell lineage tree describing both past cell divisions and cell locations. Spatial lineage trees are related to phylogeographic models that have been well-studied in the phylogenetics literature. We demonstrate that standard phylogeographic models based on Brownian motion are inadequate to describe the spatial symmetric displacement (SD) of cells during cell division.
RESULTS
We introduce a new model-the SD model for cell motility that includes symmetric displacements of daughter cells from the parental cell followed by independent diffusion of daughter cells. We show that this model more accurately describes the locations of cells in a real spatial lineage tracing of mouse embryonic stem cells. Combining the spatial SD model with an evolutionary model of DNA mutations, we obtain a phylogeographic model for spatial lineage tracing. Using this model, we devise a maximum likelihood framework-MOLLUSC (Maximum Likelihood Estimation Of Lineage and Location Using Single-Cell Spatial Lineage tracing Data)-to co-estimate time-resolved branch lengths, spatial diffusion rate, and mutation rate. On both simulated and real data, we show that MOLLUSC accurately estimates all parameters. In contrast, the Brownian motion model overestimates spatial diffusion rate in all test cases. In addition, the inclusion of spatial information improves accuracy of branch length estimation compared to sequence data alone. On real data, we show that spatial information has more signal than sequence data for branch length estimation, suggesting augmenting lineage tracing technologies with spatial information is useful to overcome the limitations of genome-editing in developmental systems.
AVAILABILITY AND IMPLEMENTATION
The python implementation of MOLLUSC is available at https://github.com/raphael-group/MOLLUSC.
Topics: Animals; Mice; Cell Movement; Cell Division; Cell Lineage; Likelihood Functions; Phylogeography; Mutation; Phylogeny
PubMed: 38940146
DOI: 10.1093/bioinformatics/btae221 -
Bioinformatics (Oxford, England) Jun 2024High-resolution Hi-C contact matrices reveal the detailed three-dimensional architecture of the genome, but high-coverage experimental Hi-C data are expensive to...
MOTIVATION
High-resolution Hi-C contact matrices reveal the detailed three-dimensional architecture of the genome, but high-coverage experimental Hi-C data are expensive to generate. Simultaneously, chromatin structure analyses struggle with extremely sparse contact matrices. To address this problem, computational methods to enhance low-coverage contact matrices have been developed, but existing methods are largely based on resolution enhancement methods for natural images and hence often employ models that do not distinguish between biologically meaningful contacts, such as loops and other stochastic contacts.
RESULTS
We present Capricorn, a machine learning model for Hi-C resolution enhancement that incorporates small-scale chromatin features as additional views of the input Hi-C contact matrix and leverages a diffusion probability model backbone to generate a high-coverage matrix. We show that Capricorn outperforms the state of the art in a cross-cell-line setting, improving on existing methods by 17% in mean squared error and 26% in F1 score for chromatin loop identification from the generated high-coverage data. We also demonstrate that Capricorn performs well in the cross-chromosome setting and cross-chromosome, cross-cell-line setting, improving the downstream loop F1 score by 14% relative to existing methods. We further show that our multiview idea can also be used to improve several existing methods, HiCARN and HiCNN, indicating the wide applicability of this approach. Finally, we use DNA sequence to validate discovered loops and find that the fraction of CTCF-supported loops from Capricorn is similar to those identified from the high-coverage data. Capricorn is a powerful Hi-C resolution enhancement method that enables scientists to find chromatin features that cannot be identified in the low-coverage contact matrix.
AVAILABILITY AND IMPLEMENTATION
Implementation of Capricorn and source code for reproducing all figures in this paper are available at https://github.com/CHNFTQ/Capricorn.
Topics: Chromatin; Machine Learning; Humans; Computational Biology; Algorithms; Software
PubMed: 38940142
DOI: 10.1093/bioinformatics/btae211