Regulatory KIR+RA+ T cells accumulate with age and are highly activated during viral respiratory disease

Severe respiratory viral infectious diseases such as influenza and COVID‐19 especially affect the older population. This is partly ascribed to diminished CD8⁺ T‐cell responses a result of aging. The phenotypical diversity of the CD8⁺ T‐cell population has made it difficult to identify the impact of aging on CD8⁺ T‐cell subsets associated with diminished CD8⁺ T‐cell responses. Here we identify a novel human CD8⁺ T‐cell subset characterized by expression of Killer‐cell Immunoglobulin‐like Receptors (KIR⁺) and CD45RA (RA⁺). These KIR⁺RA⁺ T cells accumulated with age in the blood of healthy individuals (20–82 years of age, n = 50), expressed high levels of aging‐related markers of T‐cell regulation, and were functionally capable of suppressing proliferation of other CD8⁺ T cells. Moreover, KIR⁺RA⁺ T cells were a major T‐cell subset becoming activated in older adults suffering from an acute respiratory viral infection (n = 36), including coronavirus and influenza virus infection. In addition, older adults with influenza A infection showed that higher activation status of their KIR⁺RA⁺ T cells associated with longer duration of respiratory symptoms. Together, our data indicate that KIR⁺RA⁺ T cells are a unique human T‐cell subset with regulatory properties that may explain susceptibility to viral respiratory disease at old age.     

MAPK signaling in H9c2 cardiomyoblasts exposed to cholesterol secoaldehyde – Role of hydrogen peroxide

3β-Hydroxy-5,6-secocholestan-6-al (cholesterol secoaldehyde or ChSeco), an oxysterol known to be formed in ozone- and singlet oxygen-mediated oxidations of cholesterol, has been detected in the atherosclerotic plaque and in the brain of patients suffering from Alzheimer’s disease and Lewy body dementia. Previously, we have shown that, in H9c2 cardiomyoblasts, ChSeco induces oxidative stress followed by apoptosis involving both intrinsic and extrinsic signaling pathways. In the present study, we investigated the nature of reactive oxygen species (ROS) and its associated redox signaling in H9c2 cells upon treatment with ChSeco. Both catalase and deferoxamine, which lowered intracellular ROS, were found to alleviate the ChSeco-induced cytotoxicity. ChSeco-treated H9c2 cells showed a significant decrease in the intracellular catalase activity, suggesting the involvement of H₂O₂ in the associated cytotoxicity. Additionally, in ChSeco-exposed cells, there was a marked increase in lipid peroxidation and pre-treatment with SB 203580 (p38 MAPK inhibitor) and MEK1/2 inhibitor (ERK1/2 and JNK inhibitor) rendered protection against the cytotoxicity. An early increase in the expression of p-SAPK/JNK or delayed p38 MAPK did not alter ATF-2 but decreased c-Jun expression in these cells. Overall, these findings are consistent with MAPK signaling resulting from increased cellular H₂O₂ in ChSeco-induced cytotoxicity in cardiomyoblasts.     

The stem cell code in oral epithelial tumorigenesis: ‘The cancer stem cell shift hypothesis’

Tumors of the oral cavity provide an ideal model to study various stages of epithelial tumor progression. A group of cancer cells termed cancer stem cells (CSCs) eludes therapy, persists and initiates recurrence augmenting malignant spread of the disease. Hitherto, accurate identification and separation of such minimal residual cells have proven futile due to lack of identifiable traits to single out these cells from the heterogeneous tumor bulk. In this review we have compiled comprehensive evidence from comparative phenotypic and genotypic studies on normal oral mucosa as well as tumors of different grades to elucidate that differential expression patterns of putative stem cells markers may identify ‘minimal residual disease’ in oral squamous cell carcinoma. We propose the “cancer stem cell shift hypothesis” to explain the exact identity and switch-over, tumor-promoting mechanisms adapted by putative CSCs with correlation to tumor staging.     

Comparison of walking parameters obtained from the young,elderly and adults with support

Data mining techniques are highly useful in the study of various medical signals and images in order to obtain useful information to better predict the diagnosis or prognosis or treatment options for the patient. Study of the human walking pattern helps us understand the variability of motion during activities such as high performance walking and normal walking. A comparison of the parameters quantifying this variability in motion in normal young and elderly subjects and the subjects who need support will aid in better understanding of the relationship among walking patterns, age and disabilities. In this study, we measured the tri-axial acceleration along three directions: anteroposterior, lateral and vertical. We also measured gyrational pitch, roll and yaw. These parameters were obtained using sensors attached to the back, left thigh and right thigh of the three classes of subjects (normal, elderly and adults with support) during the three types of exercises: 10-m normal walk, 10-m high performance walk and stepping. These recorded signals were then subjected to wavelet packet decomposition, and three entropies, namely approximate entropy and two bispectral entropies, were obtained from the resultant wavelet coefficients. On analysing these entropies, we could observe the following: (1) the entropy steadily decreases with the increase in age and with the presence of impairments, and (2) the entropy decreases among all the three types of exercises, namely normal walking and high performance walking. We feel that the results of this work can help in the design of supporting devices for elderly subjects.     

Application of recurrence quantification analysis for the automated identification of epileptic EEG signals

Epilepsy is a common neurological disorder that is characterized by the recurrence of seizures. Electroencephalogram (EEG) signals are widely used to diagnose seizures. Because of the non-linear and dynamic nature of the EEG signals, it is difficult to effectively decipher the subtle changes in these signals by visual inspection and by using linear techniques. Therefore, non-linear methods are being researched to analyze the EEG signals. In this work, we use the recorded EEG signals in Recurrence Plots (RP), and extract Recurrence Quantification Analysis (RQA) parameters from the RP in order to classify the EEG signals into normal, ictal, and interictal classes. Recurrence Plot (RP) is a graph that shows all the times at which a state of the dynamical system recurs. Studies have reported significantly different RQA parameters for the three classes. However, more studies are needed to develop classifiers that use these promising features and present good classification accuracy in differentiating the three types of EEG segments. Therefore, in this work, we have used ten RQA parameters to quantify the important features in the EEG signals.These features were fed to seven different classifiers: Support vector machine (SVM), Gaussian Mixture Model (GMM), Fuzzy Sugeno Classifier, K-Nearest Neighbor (KNN), Naive Bayes Classifier (NBC), Decision Tree (DT), and Radial Basis Probabilistic Neural Network (RBPNN). Our results show that the SVM classifier was able to identify the EEG class with an average efficiency of 95.6%, sensitivity and specificity of 98.9% and 97.8%, respectively.     

Design,Synthesis, and Molecular Docking Studies of Some New Quinoxaline Derivatives as EGFR Targeting Agents

Russian Journal of Bioorganic Chemistry - The synthesis of some new quinoxaline derivatives (IVa–n) and their structure determination using 1H NMR, 13C NMR and mass spectral analysis...