Multi-modal mechanophores based on cinnamate dimers.
Ontology highlight
ABSTRACT:
SUBMITTER: Zhang H
PROVIDER: S-EPMC5660084 | biostudies-literature | 2017 Oct
REPOSITORIES: biostudies-literature
Publications
Multi-modal mechanophores based on cinnamate dimers.
Zhang Huan H Li Xun X Lin Yangju Y Gao Fei F Tang Zhen Z Su Peifeng P Zhang Wenke W Xu Yuanze Y Weng Wengui W Boulatov Roman R
Nature communications 20171027 1
Mechanochemistry offers exciting opportunities for molecular-level engineering of stress-responsive properties of polymers. Reactive sites, sometimes called mechanophores, have been reported to increase the material toughness, to make the material mechanochromic or optically healable. Here we show that macrocyclic cinnamate dimers combine these productive stress-responsive modes. The highly thermally stable dimers dissociate on the sub-second timescale when subject to a stretching force of 1-2 n ...[more]
Similar Datasets
Project description:As demonstrated by means of DNA nanoconstructs, as well as DNA functionalization of nanoparticles and micrometre-scale colloids, complex self-assembly processes require components to associate with particular partners in a programmable fashion. In many cases the reversibility of the interactions between complementary DNA sequences is an advantage. However, permanently bonding some or all of the complementary pairs may allow for flexibility in design and construction. Here, we show that the substitution of a cinnamate group for a pair of complementary bases provides an efficient, addressable, ultraviolet light-based method to bond complementary DNA covalently. To show the potential of this approach, we wrote micrometre-scale patterns on a surface using ultraviolet light and demonstrated the reversible attachment of conjugated DNA and DNA-coated colloids. Our strategy enables both functional DNA photolithography and multistep, specific binding in self-assembly processes.
| S-EPMC3722280 | biostudies-literature
Project description:Glioblastoma (GBM) is a fatal brain cancer for which new treatment options are sorely needed. Platinum-based drugs have been investigated extensively for GBM treatment but few have shown significant efficacy without major central nervous system (CNS) and systemic toxicities. The relative success of platinum drugs for treatment of non-CNS cancers indicates great therapeutic potential when effectively delivered to the tumor region(s). New insights into the broad anticancer effects of platinum drugs, particularly immunomodulatory effects, and innovative delivery strategies that can maximize these multi-modal effects and minimize toxicities may promote the re-purposing of this chemotherapeutic drug class for GBM treatment.
| S-EPMC5048771 | biostudies-other
Project description:The development of multimodal nano- and micro-structures has become an increasingly popular area of research in recent years. In particular, the combination of two or more desirable properties within a single structure opens multiple opportunities from biomedicine, sensing, and catalysis, to a variety of optical applications. Here, for the first time, we report the synthesis and characterization of multimodal chiroptically active CaCO3 nanocomposites. These composites have been prepared by a modified microemulsion method in the presence of an amino acid (cysteine). Following this, additional modalities have been introduced by loading the composites with luminescent nanoparticles or doping with Eu3+ ions. The luminescent composites have been produced by the incorporation of CuInZnS/ZnS or CdSe@ZnS/ZnS core/shell quantum dots, or via doping with trivalent europium. In this manner, we have produced chiroptically active composites with orange, green, and red luminescence. Overall, this work demonstrates the unique advantage and potential of our approach and new class of chiroptically active CaCO3 nanocomposites, which display tunable functionality to specific requirements via the incorporation of desired ions, nanoparticles, and chirality of the structure.
| S-EPMC10780737 | biostudies-literature
Project description:Mechanically interlocked molecules present opportunities to construct therapeutic drugs and diagnostic imaging agents but harnessing supramolecular chemistry to make biologically active probes in water is a challenge. Here, we describe a rotaxane-based approach to synthesise radiolabelled proteins and peptides for molecular imaging of cancer biomarkers in vivo. Host-guest chemistry using β-cyclodextrin- and cucurbit[6]uril-catalysed cooperative capture synthesis produced gallium-68 or zirconium-89 radiolabelled metallo[4]rotaxanes. Photochemical conjugation to trastuzumab led to a viable positron emission tomography (PET) radiotracer. The rotaxane architecture can be tuned to accommodate different radiometal ion complexes, other protein- or peptide-based drugs, and fluorophores for optical detection. This technology provides a platform to explore how mechanical bonding can improve drug delivery, enhance tumour specificity, control radiotracer pharmacokinetics, and reduce dosimetry.
| S-EPMC9400884 | biostudies-literature
Project description:MotivationOur ability to identify causative genetic factors for mouse genetic models of human diseases and biomedical traits has been limited by the difficulties associated with identifying true causative factors, which are often obscured by the many false positive genetic associations produced by a GWAS.ResultsTo accelerate the pace of genetic discovery, we developed a graph neural network (GNN)-based automated pipeline (GNNHap) that could rapidly analyze mouse genetic model data and identify high probability causal genetic factors for analyzed traits. After assessing the strength of allelic associations with the strain response pattern; this pipeline analyzes 29M published papers to assess candidate gene-phenotype relationships; and incorporates the information obtained from a protein-protein interaction network and protein sequence features into the analysis. The GNN model produces markedly improved results relative to that of a simple linear neural network. We demonstrate that GNNHap can identify novel causative genetic factors for murine models of diabetes/obesity and for cataract formation, which were validated by the phenotypes appearing in previously analyzed gene knockout mice. The diabetes/obesity results indicate how characterization of the underlying genetic architecture enables new therapies to be discovered and tested by applying 'precision medicine' principles to murine models.Availability and implementationThe GNNHap source code is freely available at https://github.com/zqfang/gnnhap, and the new version of the HBCGM program is available at https://github.com/zqfang/haplomap.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC9992076 | biostudies-literature
Project description:BackgroundClinical phenotypic heterogeneity represents a major barrier to trials in motor neuron disease (MND) and objective surrogate outcome measures are required, especially for slowly progressive patients. We assessed responsiveness of clinical, electrophysiological and radiological muscle-based assessments to detect MND-related progression.Materials and methodsA prospective, longitudinal cohort study of 29 MND patients and 22 healthy controls was performed. Clinical measures, electrophysiological motor unit number index/size (MUNIX/MUSIX) and relative T2- and diffusion-weighted whole-body muscle magnetic resonance (MR) were assessed three times over 12 months. Multi-variable regression models assessed between-group differences, clinico-electrophysiological associations, and longitudinal changes. Standardized response means (SRMs) assessed sensitivity to change over 12 months.ResultsMND patients exhibited 18% higher whole-body mean muscle relative T2-signal than controls (95% CI 7-29%, p < 0.01), maximal in leg muscles (left tibialis anterior 71% (95% CI 33-122%, p < 0.01). Clinical and electrophysiological associations were evident. By 12 months, 16 patients had died or could not continue. In the remainder, relative T2-signal increased over 12 months by 14-29% in right tibialis anterior, right quadriceps, bilateral hamstrings and gastrocnemius/soleus (p < 0.01), independent of onset-site, and paralleled progressive weakness and electrophysiological loss of motor units. Highest clinical, electrophysiological and radiological SRMs were found for revised ALS-functional rating scale scores (1.22), tibialis anterior MUNIX (1.59), and relative T2-weighted leg muscle MR (right hamstrings: 0.98), respectively. Diffusion MR detected minimal changes.ConclusionMUNIX and relative T2-weighted MR represent objective surrogate markers of progressive denervation in MND. Radiological changes were maximal in leg muscles, irrespective of clinical onset-site.
| S-EPMC6954906 | biostudies-literature
Project description:Brain network dysfunction has been characterized by resting-state electroencephalography (EEG) and magnetic resonance imaging (MRI) in the prodromal stage. This study aimed to identify multi-modal electrophysiological and neuroimaging biomarkers for differential diagnosis in synucleinopathies and phenoconversion in isolated rapid eye movement sleep behavior disorder (iRBD). We enrolled 35 patients with multiple system atrophy (MSA), 32 with Parkinson's disease (PD), 30 with iRBD and 30 matched healthy controls (HC). Power spectral density (PSD) was calculated in different frequency bands. EEG functional connectivity (FC) was calculated using the weighted Phase Lag Index (wPLI) after source localization. Significant network disruptions were further confirmed by MRI FC analysis. Quantitative EEG analysis demonstrated that delta and theta power spectral density significantly differed among MSA, PD and HC. The increased PSD was correlated with cognitive decline and olfactory dysfunction in PD. Band-specific FC profiles were observed in theta, alpha, and gamma bands. The hypoconnected alpha network significantly correlated with motor dysfunction, while the gamma FC distinguished PD from MSA. By integrating EEG and MRI network analyses, we found that FC between the olfactory cortex and dorsolateral prefrontal cortex was significantly different between MSA and PD. A multimodal discriminative model for MSA and PD, integrating spectral and FC attributes of EEG and MRI, yielded an area under the receiver operating characteristic curve of 0.900. Simultaneously, we found the FC abnormalities were more prominent than spectral features in iRBD indicating prodromal dysfunction. The decreased FC between the angular gyrus and striatum was identified in α-synucleinopathies. This hypoconnectivity was associated with dopaminergic degeneration in iRBD examined by dopamine transporter imaging. Our study demonstrated EEG spectral and functional profiles in prodromal and clinical-defined synucleinopathies. Multimodal EEG and MRI provided a novel approach to discriminate MSA and PD, and monitor neurodegenerative progression in the preclinical phase.
| S-EPMC11374649 | biostudies-literature
Project description:Different environmental parameters, such as temperature and humidity, aggravate food spoilage, and different volatile organic compounds (VOCs) are released based on the extent of spoilage. In addition, a lack of efficient monitoring of the dosage of pesticides leads to crop failure. This could lead to the loss of food resources and food production with harmful contaminants and a short lifetime. For this reason, precise monitoring of different environmental parameters and contaminations during food processing and storage is a key factor for maintaining its safety and nutritional value. Thus, developing reliable, efficient, cost-effective sensor devices for these purposes is of utmost importance. This paper shows that Poly-(diallyl-dimethyl ammonium chloride)/reduced Graphene oxide (PDAC/rGO) films produced by a simple Layer-by-Layer deposition can be effectively used to monitor temperature, relative humidity, and the presence of volatile organic compounds as indicators for spoilage odors. At the same time, they show potential for electrochemical detection of organophosphate pesticide dimethoate. By monitoring the resistance/impedance changes during temperature and relative humidity variations or upon the exposure of PDAC/rGO films to methanol, good linear responses were obtained in the temperature range of 10-100 °C, 15-95% relative humidity, and 35 ppm-55 ppm of methanol. Moreover, linearity in the electrochemical detection of dimethoate is shown for the concentrations in the order of 102 µmol dm-3. The analytical response to different external stimuli and analytes depends on the number of layers deposited, affecting sensors' sensitivity, response and recovery time, and long-term stability. The presented results could serve as a starting point for developing advanced multi-modal sensors and sensor arrays with high potential for analytical applications in food safety and quality monitoring.
| S-EPMC9857774 | biostudies-literature
Project description:BackgroundThe information between multimodal magnetic resonance imaging (MRI) is complementary. Combining multiple modalities for brain tumor image segmentation can improve segmentation accuracy, which has great significance for disease diagnosis and treatment. However, different degrees of missing modality data often occur in clinical practice, which may lead to serious performance degradation or even failure of brain tumor segmentation methods relying on full-modality sequences to complete the segmentation task. To solve the above problems, this study aimed to design a new deep learning network for incomplete multimodal brain tumor segmentation.MethodsWe propose a novel cross-modal attention fusion-based deep neural network (CMAF-Net) for incomplete multimodal brain tumor segmentation, which is based on a three-dimensional (3D) U-Net architecture with encoding and decoding structure, a 3D Swin block, and a cross-modal attention fusion (CMAF) block. A convolutional encoder is initially used to extract the specific features from different modalities, and an effective 3D Swin block is constructed to model the long-range dependencies to obtain richer information for brain tumor segmentation. Then, a cross-attention based CMAF module is proposed that can deal with different missing modality situations by fusing features between different modalities to learn the shared representations of the tumor regions. Finally, the fused latent representation is decoded to obtain the final segmentation result. Additionally, channel attention module (CAM) and spatial attention module (SAM) are incorporated into the network to further improve the robustness of the model; the CAM to help focus on important feature channels, and the SAM to learn the importance of different spatial regions.ResultsEvaluation experiments on the widely-used BraTS 2018 and BraTS 2020 datasets demonstrated the effectiveness of the proposed CMAF-Net which achieved average Dice scores of 87.9%, 81.8%, and 64.3%, as well as Hausdorff distances of 4.21, 5.35, and 4.02 for whole tumor, tumor core, and enhancing tumor on the BraTS 2020 dataset, respectively, outperforming several state-of-the-art segmentation methods in missing modalities situations.ConclusionsThe experimental results show that the proposed CMAF-Net can achieve accurate brain tumor segmentation in the case of missing modalities with promising application potential.
| S-EPMC11250309 | biostudies-literature
Project description:BACKGROUND:Recent years have witnessed an increasing interest in multi-omics data, because these data allow for better understanding complex diseases such as cancer on a molecular system level. In addition, multi-omics data increase the chance to robustly identify molecular patient sub-groups and hence open the door towards a better personalized treatment of diseases. Several methods have been proposed for unsupervised clustering of multi-omics data. However, a number of challenges remain, such as the magnitude of features and the large difference in dimensionality across different omics data sources. RESULTS:We propose a multi-modal sparse denoising autoencoder framework coupled with sparse non-negative matrix factorization to robustly cluster patients based on multi-omics data. The proposed model specifically leverages pathway information to effectively reduce the dimensionality of omics data into a pathway and patient specific score profile. In consequence, our method allows us to understand, which pathway is a feature of which particular patient cluster. Moreover, recently proposed machine learning techniques allow us to disentangle the specific impact of each individual omics feature on a pathway score. We applied our method to cluster patients in several cancer datasets using gene expression, miRNA expression, DNA methylation and CNVs, demonstrating the possibility to obtain biologically plausible disease subtypes characterized by specific molecular features. Comparison against several competing methods showed a competitive clustering performance. In addition, post-hoc analysis of somatic mutations and clinical data provided supporting evidence and interpretation of the identified clusters. CONCLUSIONS:Our suggested multi-modal sparse denoising autoencoder approach allows for an effective and interpretable integration of multi-omics data on pathway level while addressing the high dimensional character of omics data. Patient specific pathway score profiles derived from our model allow for a robust identification of disease subgroups.
| S-EPMC7161108 | biostudies-literature