Microbiome: A New Lease to Microbiology

Indian Journal of Microbiology -     

Bacosides Encapsulated in Lactoferrin Conjugated PEG-PLA-PCL-OH Based Polymersomes Act as Epigenetic Modulator in Chemically Induced Amnesia

Neurochemical Research - The present study demonstrates the epigenetic mechanisms underlying the effect of Bacoside rich extract of Bacopa monniera—a nootropic herb, on scopolamine treated...     

Whole-Exome Sequencing Identifies Mutated C12orf57 in Recessive Corpus Callosum Hypoplasia

The corpus callosum is the principal cerebral commissure connecting the right and left hemispheres. The development of the corpus callosum is under tight genetic control, as demonstrated by abnormalities in its development in more than 1,000 genetic syndromes. We recruited more than 25 families in which members affected with corpus callosum hypoplasia (CCH) lacked syndromic features and had consanguineous parents, suggesting recessive causes. Exome sequence analysis identified C12orf57 mutations at the initiator methionine codon in four different families. C12orf57 is ubiquitously expressed and encodes a poorly annotated 126 amino acid protein of unknown function. This protein is without significant paralogs but has been tightly conserved across evolution. Our data suggest that this conserved gene is required for development of the human corpus callosum.     

Application of higher order statistics for atrial arrhythmia classification

Atrial fibrillation (AF) and atrial flutter (AFL) are the two common atrial arrhythmia encountered in the clinical practice. In order to diagnose these abnormalities the electrocardiogram (ECG) is widely used. The conventional linear time and frequency domain methods cannot decipher the hidden complexity present in these signals. The ECG is inherently a non-linear, non-stationary and non-Gaussian signal. The non-linear models can provide improved results and capture minute variations present in the time series. Higher order spectra (HOS) is a non-linear dynamical method which is highly rugged to noise. In the present study, the performances of two methods are compared: (i) 3rd order HOS cumulants and (ii) HOS bispectrum. The 3rd order cumulant and bispectrum coefficients are subjected to dimensionality reduction using independent component analysis (ICA) and classified using classification and regression tree (CART), random forest (RF), artificial neural network (ANN) and k-nearest neighbor (KNN) classifiers to select the best classifier. The ICA components of cumulant coefficients have provided the average accuracy, sensitivity, specificity and positive predictive value of 99.50%, 100%, 99.22% and 99.72% respectively using KNN classifier. Similarly, the ICA components of HOS bispectrum coefficients have yielded the average accuracy, sensitivity, specificity and PPV of 97.65%, 98.16%, 98.75% and 99.53% respectively using KNN. So, the ICA performed on the 3rd order HOS cumulants coupled with KNN classifier performed better than the HOS bispectrum method. The proposed methodology is robust and can be used in mass screening of cardiac patients.     

ECG beat classification using PCA,LDA, ICA and Discrete Wavelet Transform

Electrocardiogram (ECG) is the P-QRS-T wave, representing the cardiac function. The information concealed in the ECG signal is useful in detecting the disease afflicting the heart. It is very difficult to identify the subtle changes in the ECG in time and frequency domains. The Discrete Wavelet Transform (DWT) can provide good time and frequency resolutions and is able to decipher the hidden complexities in the ECG. In this study, five types of beat classes of arrhythmia as recommended by Association for Advancement of Medical Instrumentation (AAMI) were analyzed namely: non-ectopic beats, supra-ventricular ectopic beats, ventricular ectopic beats, fusion betas and unclassifiable and paced beats. Three dimensionality reduction algorithms; Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Independent Component Analysis (ICA) were independently applied on DWT sub bands for dimensionality reduction. These dimensionality reduced features were fed to the Support Vector Machine (SVM), neural network (NN) and probabilistic neural network (PNN) classifiers for automated diagnosis. ICA features in combination with PNN with spread value (σ) of 0.03 performed better than the PCA and LDA. It has yielded an average sensitivity, specificity, positive predictive value (PPV) and accuracy of 99.97%, 99.83%, 99.21% and 99.28% respectively using ten-fold cross validation scheme.     

In vivo detection of endotracheal tube biofilms in intubated critical care patients using catheter‐based optical coherence tomography

The formation of biofilms in the endotracheal tubes (ETTs) of intubated patients on mechanical ventilation is associated with a greater risk of ventilator‐associated pneumonia and death. New technologies are needed to detect and monitor ETTs in vivo for the presence of these biofilms. Longitudinal OCT imaging was performed in mechanically ventilated subjects at 24‐hour intervals until extubation to detect the formation and temporal changes of in vivo ETT biofilms. OCT‐derived attenuation coefficient images were used to differentiate between mucus and biofilm. Extubated ETTs were examined with optical and electron microscopy, and all imaging results were correlated with standard‐of‐care clinical test reports. OCT and attenuation coefficient images from four subjects were positive for ETT biofilms and were negative for two subjects. The processed and stained extubated ETTs and clinical reports confirmed the presence/absence of biofilms in all subjects. Our findings confirm that OCT can detect and differentiate between biofilm‐positive and biofilm‐negative groups (P < 10^?5). OCT image‐based features may serve as biomarkers for direct in vivo detection of ETT biofilms and help drive investigation of new management strategies to reduce the incidence of VAP.      

Acute, sublethal and combination effects of azadirachtin and Bacillus thuringiensis toxins on Helicoverpa armigera (Lepidoptera: Noctuidae) larvae

The efficacy of neem (1500 ppm azadirachtin (AI)), Delfin WG, a biological insecticide based on selected strain of Bacillus thuringiensis Berliner (Bt) subspecies kurstaki, and Cry1Ac protein, either individually or in combination, were examined against first to fourth instar Helicoverpa armigera (Hübner) larvae. Using an oral administration method, various growth inhibitory concentrations (EC) and lethal concentrations (LC) were determined for each bioagent. Combinations of sublethal concentrations of Bt spray formulation with azadirachtin at EC50 or EC95 levels not only enhanced the toxicity, but also reduced the duration of action when used in a mixture. The LC20 and LC50 values for Cry1Ac toxin were 0.06 and 0.22 microg ml-1, respectively. Bt-azadirachtin combinations of LC50+EC20 and LC50+EC50 result in 100% mortality. The mortality also was significant in LC20+EC20 and LC20+EC50 mixtures. These studies imply that the combined action is not synergistic but complimentary, with azadirachtin particularly facilitating the action of Bt. The Bt spray-azadirachtin combination is more economical than combinations that involve isolating the toxic protein, as the Bt spray formulations can be combined in a spray mixture with neem. These combinations may be useful for controlling bollworm populations that have acquired resistance to Bt as they may not survive the effect of mixture. Azadirachtin may be useful as a means of reducing the endotoxin concentrations in a mixture, to promote increased economic savings and further reduce the probability of resistance development to either insect control agent.