Down regulated lncRNA MEG3 eliminates mycobacteria in macrophages via autophagy

Small non-coding RNA play a major part in host response to bacterial agents. However, the role of long non-coding RNA (lncRNA) in this context remains unknown. LncRNA regulate gene expression by acting e.g. as transcriptional coactivators, RNA decoys or microRNA sponges. They control development, differentiation and cellular processes such as autophagy in disease conditions. Here, we provide an insight into the role of lncRNA in mycobacterial infections. Human macrophages were infected with Mycobacterium bovis BCG and lncRNA expression was studied early post infection. For this purpose, lncRNA with known immune related functions were preselected and a lncRNA specific RT-qPCR protocol was established. In addition to expression-based prediction of lncRNA function, we assessed strategies for thorough normalisation of lncRNA. Arrayed quantification showed infection-dependent repression of several lncRNA including MEG3. Pathway analysis linked MEG3 to mTOR and PI3K-AKT signalling pointing to regulation of autophagy. Accordingly, IFN-γ induced autophagy in infected macrophages resulted in sustained MEG3 down regulation and lack of IFN-γ allowed for counter regulation of MEG3 by viable M. bovis BCG. Knockdown of MEG3 in macrophages resulted in induction of autophagy and enhanced eradication of intracellular M. bovis BCG.

Infection-induced 5'-half molecules of tRNAHisGUG activate Toll-like receptor 7

Toll-like receptors (TLRs) play a crucial role in the innate immune response. Although endosomal TLR7 recognizes single-stranded RNAs, their endogenous RNA ligands have not been fully explored. Here, we report 5'-tRNA half molecules as abundant activators of TLR7. Mycobacterial infection and accompanying surface TLR activation up-regulate the expression of 5'-tRNA half molecules in human monocyte-derived macrophages (HMDMs). The abundant accumulation of 5'-tRNA halves also occur in HMDM-secreted extracellular vehicles (EVs); the abundance of EV-5'-tRNAHisGUG half molecules is >200-fold higher than that of the most abundant EV-microRNA (miRNA). Sequence identification of the 5'-tRNA halves using cP-RNA-seq revealed abundant and selective packaging of specific 5'-tRNA half species into EVs. The EV-5'-tRNAHisGUG half was experimentally demonstrated to be delivered into endosomes in recipient cells and to activate endosomal TLR7. Up-regulation of the 5'-tRNA half molecules was also observed in the plasma of patients infected with Mycobacterium tuberculosis. These results unveil a novel tRNA-engaged pathway in the innate immune response and assign the role of "immune activators" to 5'-tRNA half molecules.

Deep Learning for Image Enhancement and Correction in Magnetic Resonance Imaging-State-of-the-Art and Challenges

Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast for clinical diagnoses and research which underpin many recent breakthroughs in medicine and biology. The post-processing of reconstructed MR images is often automated for incorporation into MRI scanners by the manufacturers and increasingly plays a critical role in the final image quality for clinical reporting and interpretation. For image enhancement and correction, the post-processing steps include noise reduction, image artefact correction, and image resolution improvements. With the recent success of deep learning in many research fields, there is great potential to apply deep learning for MR image enhancement, and recent publications have demonstrated promising results. Motivated by the rapidly growing literature in this area, in this review paper, we provide a comprehensive overview of deep learning-based methods for post-processing MR images to enhance image quality and correct image artefacts. We aim to provide researchers in MRI or other research fields, including computer vision and image processing, a literature survey of deep learning approaches for MR image enhancement. We discuss the current limitations of the application of artificial intelligence in MRI and highlight possible directions for future developments. In the era of deep learning, we highlight the importance of a critical appraisal of the explanatory information provided and the generalizability of deep learning algorithms in medical imaging.

Toxicity of titanium oxide nanoparticles causes functionality and DNA damage in buffalo (Bubalus bubalis) sperm in vitro

The present study has examined the effect of different concentrations (1 μg/ml, 10 μg/ml and 100 μg/ml) of titanium oxide (TiO2) nanoparticles (NPs) (<100 nm) on viability, membrane integrity, capacitation status and DNA integrity of buffalo spermatozoa. Characterization of NPs was done by the transmission electron microscopy (TEM) and dynamic light scattering (DLS). Sperm chromatin dispersion (SCD) test and acridine orange test (AOT) were employed to detect DNA fragmentation in sperm treated with NPs. There was significant (p < 0.05) decrease in cell viability and membrane integrity (assessed by enzyme leakage) at 6 h of incubation with NPs. However, significant (p < 0.05) increase in sperm capacitation was observed for TiO2 NP albeit at lower concentrations. In DNA fragmentation assay, there was dose-dependent increase in the DNA fragmentation (r = 0.96). Ultrathin cross-sections revealed TiO2 NPs inside head and plasma membrane of the buffalo spermatozoa as assessed by TEM. These studies suggest that TiO2 NPs may have cytotoxic effect on buffalo spermatozoa by affecting sperm functionality and causing high amount of DNA fragmentations.

Mycobacterium bovis BCG Interferes with miR-3619-5p Control of Cathepsin S in the Process of Autophagy

Main survival mechanism of pathogenic mycobacteria is to escape inimical phagolysosomal environment inside the macrophages. Many efforts have been made to unravel the molecular mechanisms behind this process. However, little is known about the involvement of microRNAs (miRNAs) in the regulation of phagolysosomal biosynthesis and maturation. Based on a bottom up approach, we searched for miRNAs that were involved in phagolysosomal processing events in the course of mycobacterial infection of macrophages. After infecting THP-1 derived macrophages with viable and heat killed Mycobacterium bovis BCG (BCG), early time points were identified after co-localization studies of the phagosomal marker protein LAMP1 and BCG. Differences in LAMP1 localization on the phagosomes of both groups were observed at 30 min and 4 h. After in silico based pre-selection of miRNAs, expression analysis at the identified time points revealed down-regulation of three miRNAs: miR-3619-5p, miR-637, and miR-324-3p. Consequently, most likely targets were predicted that were supposed to be mutually regulated by these three studied miRNAs. The lysosomal cysteine protease Cathepsin S (CTSS) and Rab11 family-interacting protein 4 (RAB11FIP4) were up-regulated and were considered to be connected to lysosomal trafficking and autophagy. Interaction studies verified the regulation of CTSS by miR-3619-5p. Down-regulation of CTSS by ectopic miR-3619-5p as well as its specific knockdown by siRNA affected the process of autophagy in THP-1 derived macrophages.

Suppressing motion artefacts in MRI using an Inception-ResNet network with motion simulation augmentation

The suppression of motion artefacts from MR images is a challenging task. The purpose of this paper was to develop a standalone novel technique to suppress motion artefacts in MR images using a data-driven deep learning approach. A simulation framework was developed to generate motion-corrupted images from motion-free images using randomly generated motion profiles. An Inception-ResNet deep learning network architecture was used as the encoder and was augmented with a stack of convolution and upsampling layers to form an encoder-decoder network. The network was trained on simulated motion-corrupted images to identify and suppress those artefacts attributable to motion. The network was validated on unseen simulated datasets and real-world experimental motion-corrupted in vivo brain datasets. The trained network was able to suppress the motion artefacts in the reconstructed images, and the mean structural similarity (SSIM) increased from 0.9058 to 0.9338. The network was also able to suppress the motion artefacts from the real-world experimental dataset, and the mean SSIM increased from 0.8671 to 0.9145. The motion correction of the experimental datasets demonstrated the effectiveness of the motion simulation generation process. The proposed method successfully removed motion artefacts and outperformed an iterative entropy minimization method in terms of the SSIM index and normalized root mean squared error, which were 5-10% better for the proposed method. In conclusion, a novel, data-driven motion correction technique has been developed that can suppress motion artefacts from motion-corrupted MR images. The proposed technique is a standalone, post-processing method that does not interfere with data acquisition or reconstruction parameters, thus making it suitable for routine clinical practice.

Multichannel compressive sensing MRI using noiselet encoding

The incoherence between measurement and sparsifying transform matrices and the restricted isometry property (RIP) of measurement matrix are two of the key factors in determining the performance of compressive sensing (CS). In CS-MRI, the randomly under-sampled Fourier matrix is used as the measurement matrix and the wavelet transform is usually used as sparsifying transform matrix. However, the incoherence between the randomly under-sampled Fourier matrix and the wavelet matrix is not optimal, which can deteriorate the performance of CS-MRI. Using the mathematical result that noiselets are maximally incoherent with wavelets, this paper introduces the noiselet unitary bases as the measurement matrix to improve the incoherence and RIP in CS-MRI. Based on an empirical RIP analysis that compares the multichannel noiselet and multichannel Fourier measurement matrices in CS-MRI, we propose a multichannel compressive sensing (MCS) framework to take the advantage of multichannel data acquisition used in MRI scanners. Simulations are presented in the MCS framework to compare the performance of noiselet encoding reconstructions and Fourier encoding reconstructions at different acceleration factors. The comparisons indicate that multichannel noiselet measurement matrix has better RIP than that of its Fourier counterpart, and that noiselet encoded MCS-MRI outperforms Fourier encoded MCS-MRI in preserving image resolution and can achieve higher acceleration factors. To demonstrate the feasibility of the proposed noiselet encoding scheme, a pulse sequences with tailored spatially selective RF excitation pulses was designed and implemented on a 3T scanner to acquire the data in the noiselet domain from a phantom and a human brain. The results indicate that noislet encoding preserves image resolution better than Fouirer encoding.

Exploration of CCA-added RNAs revealed the expression of mitochondrial non-coding RNAs regulated by CCA-adding enzyme

Post-transcriptional non-template additions of nucleotides to 3'-ends of RNAs play important roles in the stability and function of RNA molecules. Although tRNA nucleotidyltransferase (CCA-adding enzyme) is known to add CCA trinucleotides to 3'-ends of tRNAs, whether other RNA species can be endogenous substrates of CCA-adding enzyme has not been widely explored yet. Herein, we used YAMAT-seq to identify non-tRNA substrates of CCA-adding enzyme. YAMAT-seq captures RNA species that form secondary structures with 4-nt protruding 3'-ends of the sequence 5'-NCCA-3', which is the hallmark structure of RNAs that are generated by CCA-adding enzyme. By executing YAMAT-seq for human breast cancer cells and mining the sequence data, we identified novel candidate substrates of CCA-adding enzyme. These included fourteen 'CCA-RNAs' that only contain CCA as non-genomic sequences, and eleven 'NCCA-RNAs' that contain CCA and other nucleotides as non-genomic sequences. All newly-identified (N)CCA-RNAs were derived from the mitochondrial genome and were localized in mitochondria. Knockdown of CCA-adding enzyme severely reduced the expression levels of (N)CCA-RNAs, suggesting that the CCA-adding enzyme-catalyzed CCA additions stabilize the expression of (N)CCA-RNAs. Furthermore, expression levels of (N)CCA-RNAs were severely reduced by various cellular treatments, including UV irradiation, amino acid starvation, inhibition of mitochondrial respiratory complexes, and inhibition of the cell cycle. These results revealed a novel CCA-mediated regulatory pathway for the expression of mitochondrial non-coding RNAs.

McSTRA: A multi-branch cascaded swin transformer for point spread function-guided robust MRI reconstruction

Deep learning MRI reconstruction methods are often based on Convolutional neural network (CNN) models; however, they are limited in capturing global correlations among image features due to the intrinsic locality of the convolution operation. Conversely, the recent vision transformer models (ViT) are capable of capturing global correlations by applying self-attention operations on image patches. Nevertheless, the existing transformer models for MRI reconstruction rarely leverage the physics of MRI. In this paper, we propose a novel physics-based transformer model titled, the Multi-branch Cascaded Swin Transformers (McSTRA) for robust MRI reconstruction. McSTRA combines several interconnected MRI physics-related concepts with the Swin transformers: it exploits global MRI features via the shifted window self-attention mechanism; it extracts MRI features belonging to different spectral components via a multi-branch setup; it iterates between intermediate de-aliasing and data consistency via a cascaded network with intermediate loss computations; furthermore, we propose a point spread function-guided positional embedding generation mechanism for the Swin transformers which exploit the spread of the aliasing artifacts for effective reconstruction. With the combination of all these components, McSTRA outperforms the state-of-the-art methods while demonstrating robustness in adversarial conditions such as higher accelerations, noisy data, different undersampling protocols, out-of-distribution data, and abnormalities in anatomy.

Accelerating k-t sparse using k-space aliasing for dynamic MRI imaging

Dynamic imaging is challenging in MRI and acceleration techniques are usually needed to acquire dynamic scene. K-t sparse is an acceleration technique based on compressed sensing, it acquires fewer amounts of data in k-t space by pseudo random ordering of phase encodes and reconstructs dynamic scene by exploiting sparsity of k-t space in transform domain. Another recently introduced technique accelerates dynamic MRI scans by acquiring k-space data in aliased form. K-space aliasing technique uses multiple RF excitation pulses to deliberately acquire aliased k-space data. During reconstruction a simple Fourier transformation along time frames can unaliase the acquired aliased data. This paper presents a novel method to combine k-t sparse and k-space aliasing to achieve higher acceleration than each of the individual technique alone. In this particular combination, a very critical factor of compressed sensing, the ratio of the number of acquired phase encodes to the number of total phase encode (n/N) increases therefore compressed sensing component of reconstruction performs exceptionally well. Comparison of k-t sparse and the proposed technique for acceleration factors of 4, 6 and 8 is demonstrated in simulation on cardiac data.

High-pressure microfluidisation positively impacts structural properties and improves functional characteristics of almond proteins obtained from almond meal

Almond protein isolate (API) obtained from almond meal was processed using dynamic high-pressure microfluidisation (0, 40, 80, 120, and 160 MPa pressure; single pass). Microfluidisation caused significant reductions in the particle size and increased absolute zeta potential. SDS-PAGE analysis indicated reduction in band intensity and the complete disappearance of bands beyond 80 MPa. Structural analysis (by circular dichroism, UV-Vis, and intrinsic-fluorescence spectra) of the API revealed disaggregation (up to 80 MPa) and then re-aggregation beyond 80 MPa. Significant increments in protein digestibility (1.16-fold) and the protein digestibility corrected amino acid score (PDCAAS; 1.15-fold) were observed for the API (80 MPa) than control. Furthermore, significant improvements (P < 0.05) in the functional properties were observed, viz., the antioxidant activity, protein solubility, and emulsifying properties. Overall, the results revealed that moderate microfluidisation treatment (80 MPa) is an effective and sustainable technique for enhancing physico-chemical and functional attributes of API, thus potentially enabling its functional food/nutraceuticals application.

The tRNAVal half: A strong endogenous Toll-like receptor 7 ligand with a 5'-terminal universal sequence signature

Toll-like receptors (TLRs) are crucial components of the innate immune system. Endosomal TLR7 recognizes single-stranded RNAs, yet its endogenous ssRNA ligands are not fully understood. We previously showed that extracellular (ex-) 5'-half molecules of tRNAHisGUG (the 5'-tRNAHisGUG half) in extracellular vesicles (EVs) of human macrophages activate TLR7 when delivered into endosomes of recipient macrophages. Here, we fully explored immunostimulatory ex-5'-tRNA half molecules and identified the 5'-tRNAValCAC/AAC half, the most abundant tRNA-derived RNA in macrophage EVs, as another 5'-tRNA half molecule with strong TLR7 activation capacity. Levels of the ex-5'-tRNAValCAC/AAC half were highly up-regulated in macrophage EVs upon exposure to lipopolysaccharide and in the plasma of patients infected with Mycobacterium tuberculosis. The 5'-tRNAValCAC/AAC half-mediated activation of TLR7 effectively eradicated bacteria infected in macrophages. Mutation analyses of the 5'-tRNAValCAC/AAC half identified the terminal GUUU sequence as a determinant for TLR7 activation. We confirmed that GUUU is the optimal ratio of guanosine and uridine for TLR7 activation; microRNAs or other RNAs with the terminal GUUU motif can indeed stimulate TLR7, establishing the motif as a universal signature for TLR7 activation. These results advance our understanding of endogenous ssRNA ligands of TLR7 and offer insights into diverse TLR7-involved pathologies and their therapeutic strategies.

Lead monitoring in air, soil and foliar deposits at Indore city with special reference to automobile pollution

Ambient lead levels in air, soil and dust deposits on selected plant species at ten distinctly located sampling stations of Indore city are presented. The maximum lead level in air was recorded at Palasia, where the traffic density was found to be the highest. Out of the plant species studied, the maximum lead was recorded on Dalbergia sissoo leaves. A possible relationship between leaf morphology and dust accumulation tendency is also discussed.

Correlations Between MR Apparent Diffusion Coefficients and PET Standard Uptake Values in Simultaneous MR-PET Imaging of Prostate Cancer

This study evaluated the hypothesis that 68Ga-PSMA-11 PET SUV, obtained via an advanced DL approach, correlates better with MR ADC maps than values from conventional PET-MR. Additionally, we aimed to identify the optimal SUV threshold for maximum correlation with ADC values. A cohort of 32 prostate cancer patients underwent CT and corresponding PET-MR imaging. The dataset underwent K-fold cross-validation, dividing it into four folds. In each fold, 24 patients were used for training, and 8 for validation to create DL models. ADC maps from 27 out of 32 patients were successfully aligned with T2 images for detailed analysis, revealing an inverse correlation (ρ = -0.20 to -0.51) between ADC and SUV values in prostate cancer zones. Statistically significant differences in mean SUV values were observed between PETMRI and PETDL. DL-based SUV values show a stronger correlation with ADC than conventional PET-MR values in our investigation.

PixCUE: Joint Uncertainty Estimation and Image Reconstruction in MRI using Deep Pixel Classification

Deep learning (DL) models are effective in leveraging latent representations from MR data, emerging as state-of-the-art solutions for accelerated MRI reconstruction. However, challenges arise due to the inherent uncertainties associated with undersampling in k-space, coupled with the over- or under-parameterized and opaque nature of DL models. Addressing uncertainty has thus become a critical issue in DL MRI reconstruction. Monte Carlo (MC) inference techniques are commonly employed to estimate uncertainty, involving multiple reconstructions of the same scan to compute variance as a measure of uncertainty. Nevertheless, these methods entail significant computational expenses, requiring multiple inferences through the DL model. In this context, we propose a novel approach to uncertainty estimation during MRI reconstruction using a pixel classification framework. Our method, PixCUE (Pixel Classification Uncertainty Estimation), generates both the reconstructed image and an uncertainty map in a single forward pass through the DL model. We validate the efficacy of this approach by demonstrating that PixCUE-generated uncertainty maps exhibit a strong correlation with reconstruction errors across various MR imaging sequences and under diverse adversarial conditions. We present an empirical relationship between uncertainty estimations using PixCUE and established reconstruction metrics such as NMSE, PSNR, and SSIM. Furthermore, we establish a correlation between the estimated uncertainties from PixCUE and the conventional MC method. Our findings affirm that PixCUE reliably estimates uncertainty in MRI reconstruction with minimal additional computational cost.