The role of the NAD+ network in host responses to infection
Abstract
Nicotinamide adenine dinucleotide (NAD+) is both a crucial coenzyme and a cosubstrate for various metabolic reactions in all ...living cells. Maintenance of NAD+ levels is essential for cell energy homeostasis, survival, proliferation and function. Mounting evidence points to NAD+ as one of the major modulators of immuno-metabolic circuits, thus regulating immune responses and functions. Recent studies delineate impaired host NAD+ metabolism during chronic infections and inflammation, suggesting NAD+ replenishment as an avenue to ameliorate deleterious inflammatory responses. Here, we discuss aspects of NAD+ biosynthesis and consumption, NAD+ biology during infections and how NAD+ metabolism can be intervened with pharmacologically to enhance the host’s immunological fitness against pathogens.
The electroencephalogram (EEG) based motor imagery (MI) signal classification, also known as motion recognition, is a highly popular area of research due to its applications in robotics, gaming, and ...medical fields. However, the problem is ill-posed as these signals are non-stationary and noisy. Recently, a lot of efforts have been made to improve MI signal classification using a combination of signal decomposition and machine learning techniques but they fail to perform adequately on large multi-class datasets. Previously, researchers have implemented long short-term memory (LSTM), which is capable of learning the time-series information, on the MI-EEG dataset for motion recognition. However, it can not model very long-term dependencies present in the motion recognition data. With the advent of transformer networks in natural language processing (NLP), the long-term dependency issue has been widely addressed. Motivated by the success of transformer algorithms, in this article, we propose a transformer-based deep learning neural network architecture that performs motion recognition on the raw BCI competition III IVa and IV 2a datasets. The validation results show that the proposed method achieves superior performance than the existing state-of-the-art methods. The proposed method produces classification accuracy of 99.7% and 84% on the binary class and the multi-class datasets, respectively. Further, the performance of the proposed transformer-based model is also compared with LSTM.
Content based image retrieval (CBIR) systems enable a quick retrieval of similar images from a large digital repository. However, the performance of the system is heavily reliant on the feature ...definition of images. The challenge lies in extracting suitable features that can work across a variety of datasets. In this paper, Haar-like local ternary co-occurrence pattern (HLTCoP) is designed as a feature for image retrieval applications. In HLTCoP, four different Haar-like filters are deployed to capture directional information of the image and two different local neighborhoods are considered to obtain the local patterns around every pixel. Thereafter, co-occurrence between two filtered images is computed to construct the HLTCoP feature. Additionally, color information is extracted using histograms of hue and saturation planes. Image retrieval performance is verified on diversified benchmark datasets, Corel 10k, CMU-PIE and MIT VisTex. Significant improvement is achieved in comparison to the existing techniques.
In this paper, we investigate modulation techniques for end-to-end communication between two nanomachines placed in a fluid medium. The information is encoded as the number of molecules transmitted ...leading to such schemes being aptly named as amplitude modulation schemes. The propagation of molecules obeys the laws of Brownian motion with a positive drift from the transmitter to the receiver nanomachine. The channel is characterized by two parameters of the fluid medium: the drift velocity and the diffusion coefficient. Assuming the molecules degrade over time, the life expectancy of the molecules also plays a significant role in such communication scenarios. We consider an M -ary modulation scheme and also propose an extended scheme, which is a slight variation of a binary modulation scheme. The received symbol is corrupted by interference from the previous symbols as well as other noise sources present in the medium. Considering maximum likelihood detection at the receiver, we derive analytical expressions for the end-to-end symbol error probability and the capacity for these modulation schemes. Numerical results bring out the impact of various parameters on the performance of the system. Our results show that these schemes offer a promising approach to set up molecular communication over diffusion-based channels.
The global burden of tuberculosis (TB) morbidity and mortality remains immense. A potential new approach to TB therapy is to augment protective host immune responses. We report that the antidiabetic ...drug metformin (MET) reduces the intracellular growth of Mycobacterium tuberculosis (Mtb) in an AMPK (adenosine monophosphate-activated protein kinase)-dependent manner. MET controls the growth of drug-resistant Mtb strains, increases production of mitochondrial reactive oxygen species, and facilitates phagosome-lysosome fusion. In Mtb-infected mice, use of MET ameliorated lung pathology, reduced chronic inflammation, and enhanced the specific immune response and the efficacy of conventional TB drugs. Moreover, in two separate human cohorts, MET treatment was associated with improved control of Mtb infection and decreased disease severity. Collectively, these data indicate that MET is a promising candidate host-adjunctive therapy for improving the effective treatment of TB.
Epilepsy is a disease recognized as the chronic neurological dysfunction of the human brain which is described by the sudden and excessive electrical discharges of the brain cells. ...Electroencephalogram (EEG) is a prime tool applied for the diagnosis of epilepsy. In this study, a novel and effective approach is introduced to decompose the non-stationary EEG signals using the Fourier decomposition method. The concept of position, velocity, and acceleration has been employed on the EEG signals for feature extraction using Lp norms computed from Fourier intrinsic band functions (FIBFs). The proposed scheme comprises three main sections. In the first section, the EEG signal is decomposed into a finite number of FIBFs. In the second stage, the features are extracted from FIBFs and relevant features are selected by using the Kruskal–Wallis test. In the last stage, the significant features are passed on to the support vector machine (SVM) classifier. By applying 10-fold cross-validation, the proposed method provides better results in comparison to the state-of-the-art methods discussed in the literature, with an average classification accuracy of 99.96% and 99.94% for classification of EEG signals from the BONN dataset and the CHB-MIT dataset, respectively. It can be implemented using the computationally efficient fast Fourier transform (FFT) algorithm.
In this study, we detail a novel approach that combines bacterial fitness fluorescent reporter strains with scRNA-seq to simultaneously acquire the host transcriptome, surface marker expression, and ...bacterial phenotype for each infected cell. This approach facilitates the dissection of the functional heterogeneity of M. tuberculosis-infected alveolar (AMs) and interstitial macrophages (IMs) in vivo. We identify clusters of pro-inflammatory AMs associated with stressed bacteria, in addition to three different populations of IMs with heterogeneous bacterial phenotypes. Finally, we show that the main macrophage populations in the lung are epigenetically constrained in their response to infection, while inter-species comparison reveals that most AMs subsets are conserved between mice and humans. This conceptual approach is readily transferable to other infectious disease agents with the potential for an increased understanding of the roles that different host cell populations play during the course of an infection.
Tuberculosis (TB) remains one of the most devastating infectious diseases and its eradication is still unattainable given the limitations of current technologies for diagnosis, treatment and ...prevention. The World Health Organization's goal to eliminate TB globally by 2050 remains an ongoing challenge as delayed diagnosis and misdiagnosis of TB continue to fuel the worldwide epidemic. Despite considerable improvements in diagnostics for the last few decades, a simple and effective point-of-care TB diagnostic test is yet not available. Here, we review the current assays used for TB diagnosis, and highlight the recent advances in nanotechnology and microfluidics that potentially enable new approaches for TB diagnosis in resource-constrained settings.
Content based image retrieval (CBIR) systems provide a faster way to retrieve images by representing them in terms of their visual contents. In this paper, a novel texture feature, directional local ...ternary co-occurrence pattern (DLTCoP) is proposed for CBIR. First and second order derivatives of the image are extracted through directional filter masks to capture coarse and fine details of the image in four directions. Thereafter, changes in first and second order filter responses are analyzed simultaneously and co-occurrence is computed based on their inter-relations. The information captured by DLTCoP is further enriched by computing histograms for the gray-scale image and the color information is represented as color histograms. The proposed scheme provides a consolidated feature capable of distinguishing between different images. Experiments are conducted on five benchmark data sets, Corel 1000, Corel 5k, Corel 10k, INRIA Holidays and Salsburg Texture. Significant improvement in average precision and recall is obtained with respect to the existing state-of-the-art features.
Patients with type 2 diabetes (T2D) have a lower risk of Mycobacterium tuberculosis infection, progression from infection to tuberculosis (TB) disease, TB morality and TB recurrence, when being ...treated with metformin. However, a detailed mechanistic understanding of these protective effects is lacking. Here, we use mass cytometry to show that metformin treatment expands a population of memory-like antigen-inexperienced CD8
CXCR3
T cells in naive mice, and in healthy individuals and patients with T2D. Metformin-educated CD8
T cells have increased (i) mitochondrial mass, oxidative phosphorylation, and fatty acid oxidation; (ii) survival capacity; and (iii) anti-mycobacterial properties. CD8
T cells from Cxcr3
mice do not exhibit this metformin-mediated metabolic programming. In BCG-vaccinated mice and guinea pigs, metformin enhances immunogenicity and protective efficacy against M. tuberculosis challenge. Collectively, these results demonstrate an important function of CD8
T cells in metformin-derived host metabolic-fitness towards M. tuberculosis infection.
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