Physiologic and environmental factors can modulate antibiotic activity and thus pose a significant challenge to antibiotic treatment. The quinolone class of antibiotics, which targets bacterial topoisomerases, fails to kill bacteria that have grown to high density; however, the mechanistic basis for this persistence is unclear. Here, we show that exhaustion of the metabolic inputs that couple carbon catabolism to oxidative phosphorylation is a primary cause of growth phase-dependent persistence to quinolone antibiotics. Supplementation of stationary-phase cultures with glucose and a suitable terminal electron acceptor to stimulate respiratory metabolism is sufficient to sensitize cells to quinolone killing. Using this approach, we successfully sensitize high-density populations of Escherichia coli, Staphylococcus aureus, and Mycobacterium smegmatis to quinolone antibiotics. Our findings link growth-dependent quinolone persistence to discrete impairments in respiratory metabolism and identify a strategy to kill non-dividing bacteria.

Understanding and Sensitizing Density-Dependent Persistence to Quinolone Antibiotics

In the current age of technology, various diseases in the body are also on the rise. Tumours that cause more discomfort in the body are set to increase the discomfort of most patients. Patients experience different effects depending on the tumour size and type. Future developments in the medical field are moving towards the development of tools based on IoT devices. These advances will in the future follow special features designed based on multiple machine learning developed by artificial intelligence. In that order, an improved algorithm named Internet of Things-based enhanced machine learning is proposed in this paper. What makes it special is that it involves separate functions to diagnose each type of tumour. It analyzes and calculates things like the size, shape, and location of the tumour. Cure from cancer is determined by the stage at which we find cancer. Early detection of cancer has the potential to cure quickly. At a saturation point, the proposed Internet of Things-based enhanced machine learning model achieved 94.56% of accuracy, 94.12% of precision, 94.98% of recall, 95.12% of F1-score, and 1856 ms of execution time. The simulation is conducted to test the efficacy of the model, and the results of the simulation show that the proposed Internet of Things-based enhanced machine learning obtains a higher rate of intelligence than other methods.

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IOTEML: An Internet of Things (IoT)-Based Enhanced Machine Learning Model for Tumour Investigation

Nowadays, brain tumor detection has turned upas a general causality in the realm of health care. Brain tumor can be denoted as a malformed mass of tissue wherein the cells multiply abruptly and ceaselessly, that is there is no control over the growth of the cells. The process of Image segmentation is adopted for extracting abnormal tumor region within the brain. In the MRI (magnetic resonance image), segmentation of brain tissue holds very significant in order to identify the presence of outlines concerning the brain tumor. There is abundance of hidden information in stored in the Health care sector. With appropriate use of accurate data mining classification techniques, early prediction of any disease can be effectively performed. In the medical field, the techniques of ML (machine learning) and Data mining holds a significant stand. Majority of which is adopted effectively. The research examines list of risk factors that are being traced out in brain tumor surveillance systems. Also the method proposed assures to be highly efficient and precise for brain tumor detection, classification and segmentation. To achieve this precise automatic or semi-automatic methods are needed. The research proposes an automatic segmentation method that relies upon CNN (Convolution Neural Networks), determining small 3 × 3 kernels. By incorporating this single technique, segmentation and classification is accomplished. CNN (a ML technique) from NN (Neural Networks)wherein it has layer based for results classification. Various levels involved in the proposed mechanisms are: 1. Data collection, 2. Pre-processing, 3. Average filtering, 4. segmentation, 5. feature extraction, 6. CNN via classification and identification. By utilizing the DM (data mining) techniques, significant relations and patterns from the data can be extracted. The techniques of ML (machine learning) and Data mining are being effectively employed for brain tumor detection and prevention at an early stage.

Design and Implementing Brain Tumor Detection Using Machine Learning Approach

A tumor cell is a form of cell that develops out of control of the ordinary forces and standardizes growth. Brain tumor is one of the major reasons for human death every year. Around 50% of brain tumor diagnosed patient die with primary brain tumors each year in the United States. Electronic modalities are used to diagnose brain tumors. Among all electronic modalities, Magnetic Resonance Imaging (MRI) is one of the most used and popular for brain tumor diagnosis. In this research study, an automated approach has been proposed where MRI gray-scale images were incorporated for brain tumor detection. This study proposed an automated approach that includes enhancement at the initial stage to minimize gray-scale color variations. Filter operation was used to remove unwanted noises as much as possible to assist better segmentation. As this study test grayscale images therefore; threshold based OTSU segmentation was used instead of color segmentation. Finally, pathology experts provided feature information was used to identify the region of interests (brain tumor region). The experimental results showed that the proposed approach was able to perform better results compared to existing available approaches in terms of accuracy while maintaining the pathology experts' acceptable accuracy rate.

Development of Automated Brain Tumor Identification Using MRI Images

A brain tumor is an accumulation of abnormal cells in human brain. As tumor increases in size, it induces brain damage. Hence it is essential to diagnose the type of brain tumor. The effective modality used for brain tumor diagnose is MRI because of its remarkable image resolution, the speed of acquisition, and high safety profile for patients. The analysis of brain MRI is an important part of patient care and decision. Hence in the proposed Clinical Support System, the brain MRI image is preprocessed using Genetic Optimized Median Filter followed by brain tumor region segmentation using Hierarchical Fuzzy Clustering Algorithm. The features of the tumor region are extracted through GLCM feature extraction method. Lion Optimized Boosting Support Vector machine model is used for further classification of tumor by Brain Tumor Image Segmentation (BraTS) dataset. Hence the proposed clinical support system provides an integrated model for Detection and classification of brain tumor which assists the doctors in appropriate evaluation of tumor.

A Clinical Support System for Brain Tumor Classification Using Soft Computing Techniques

Magnetic resonance imaging (MRI) is a technique which is used for the evaluation of the brain tumor in medical science. In this paper, a methodology to study and classify the image de-noising filters such as Median filter, Adaptive filter, Averaging filter, Un-sharp masking filter and Gaussian filter is used to remove the additive noises present in the MRI images i.e. Gaussian, Salt & pepper noise and speckle noise. The de-noising performance of all the considered strategies is compared using PSNR and MSE. A novel idea is proposed for successful identification of the brain tumor using normalized histogram and segmentation using K-means clustering algorithm. Efficient classification of the MRIs is done using Naive Bayes Classifier and Support Vector Machine (SVM) so as to provide accurate prediction and classification.

Efficient detection of brain tumor from MRIs using K-means segmentation and normalized histogram

Toxin-antitoxin (TA) modules are ubiquitous gene loci among bacteria and are comprised of a toxin part and its cognate antitoxin part. Under normal physiological conditions, antitoxin counteracts the toxicity of the toxin whereas, during stress conditions, TA modules play a crucial role in bacterial physiology through involvement in the post-segregational killing, abortive infection, biofilms, and persister cell formation. Most of the toxins are proteinaceous that affect translation or DNA replication, although some other intracellular molecular targets have also been described. While antitoxins may be a protein or RNA, that generally neutralizes its cognate toxin by direct interaction or with the help of other signaling elements and thus helps in the TA module regulation. In this review, we have discussed the current state of the multifaceted TA (type I–VIII) modules by highlighting their classification and specific targets. We have also discussed the presence of TA modules in the various pathogens and their role in antibiotic persistence development as well as biofilm formation, by influencing the different cellular processes. In the end, assembling knowledge about ubiquitous TA systems from pathogenic bacteria facilitated us to propose multiple novel antibacterial strategies involving artificial activation of TA modules.

Bacterial toxin-antitoxin modules: classification, functions, and association with persistence.

Mechanofluorochromic (MFC) materials are a class of luminescent materials that have ability to show changes in their emission when subjected to various mechanical stimuli. A wide range of MFC materials...

Mechanochromic luminogens with hypsochromically shifted emission switching property: Recent advances and perspectives

Coded cooperation is a cooperative relaying technique which is bandwidth efficient and can provide better connectivity when used with cognitive radio. Earlier cognitive radio is implemented with cooperative schemes like decode and forward, amplify and forward, compress and forward, etc., which gives moderate spectrum efficiency and diversity with reduced fading problem of wireless communication. But these techniques are not bandwidth efficient so need huge bandwidth for transmission. In this paper, we have developed a mathematical model of new coded-cooperation based multi-relay system for cognitive radio and its outage probability is analytically derived for underlay mode. The performance analysis of proposed system is carried out in terms of interference temperature constraint, channel gain, number of relays through outage probability for single and multilink relays at the primary node for Rayleigh channel. The proposed system shows that the cognitive radio with coded cooperation outperforms the already existing techniques in terms of diversity, bandwidth, spectrum utilization efficiency and improve the communication quality. In addition, the theoretical analysis of the outage probability is validated by asymptotic analysis.

Development of coded-cooperation based multi-relay system for cognitive radio using mathematical modeling and its performance analysis

Receptor for advanced glycation end products (RAGE), a member of the immunoglobulin family, interactions with its ligands trigger downstream signaling and induce an inflammatory response linked to diabetes, inflammation, carcinogenesis, cardiovascular disease, and a variety of other human disorders. The interaction of RAGE and S100A6 has been associated with a variety of malignancies. For the control of RAGE‐related illnesses, there is a great demand for more specialized drug options. To identify the most effective target for combating human malignancies associated with RAGE‐S100A6 complex, we conducted single and differential gene expression analyses of S100A6 and RAGE, comparing normal and malignant tissues. Further, a structure‐based virtual screening was conducted using the ZINC15 database. The chosen compounds were then subjected to a molecular docking investigation on the RAGE active site region, recognized by the various cancer‐related RAGE ligands. An optimized RAGE structure was screened against a library of drug‐like molecules. The screening results suggested that three promising compounds were presented as the top acceptable drug‐like molecules with a high binding affinity at the RAGE V‐domain catalytic region. We depicted that these compounds may be potential RAGE inhibitors and could be used to produce a successful medication against human cancer and other RAGE‐related diseases based on their various assorted parameters, binding energy, hydrogen bonding, ADMET characteristics, etc. MD simulation on a time scale of 50 ns was used to test the stability of the RAGE‐inhibitor complexes. Therefore, targeting RAGE and its ligands using these drug‐like molecules may be an effective therapeutic approach.

Garima Singh

Papers

Efficient detection of brain tumor from MRIs using K-means segmentation and normalized histogram

Bacterial toxin-antitoxin modules: classification, functions, and association with persistence.

Mechanochromic luminogens with hypsochromically shifted emission switching property: Recent advances and perspectives

Development of coded-cooperation based multi-relay system for cognitive radio using mathematical modeling and its performance analysis

Differential gene expression analysis of RAGE‐S100A6 complex for target selection and the design of novel inhibitors for anticancer drug discovery