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The rapid growth of multi-omics datasets, in addition to the wealth of existing biological prior knowledge, necessitates the development of effective methods for their integration. Such methods are essential for building predictive models and identifying disease-related molecular markers. We propose a framework for supervised integration of multi-omics data with biological priors represented as knowledge graphs. Our framework is based on the use of graph neural networks (GNNs) to model the relationships among features from high-dimensional 'omics data and set transformers to integrate low dimensional representations of 'omics features. Furthermore, our framework incorporates explainability methods to elucidate important biomarkers and extract interaction relationships between biological quantities of interest. We demonstrate the effectiveness of our approach by applying it to Alzheimer's disease (AD) multi-omics data from the ROSMAP cohort, showing that the integration of transcriptomics and proteomics data with AD biological domain network priors improves the prediction accuracy of AD status and highlights robust AD biomarkers.

Targeted therapy and imaging are the most popular techniques for the intervention and diagnosis of cancer. A potential therapeutic target for the treatment of cancer is the epidermal growth factor receptor (EGFR), primarily for glioblastoma, lung, and breast cancer. Over-production of ligand, transcriptional up-regulation due to autocrine/paracrine signalling, or point mutations at the genomic locus may contribute to the malfunction of EGFR in malignancies. This exploit makes use of EGFR, an established biomarker for cancer diagnostics and treatment. Despite considerable development in the last several decades in making EGFR inhibitors, they are still not free from limitations like toxicity and a short serum half-life. Nanobodies and antibodies share similar binding properties, but nanobodies have the additional advantage that they can bind to antigenic epitopes deep inside the target that conventional antibodies are unable to access. For targeted therapy, anti-EGFR nanobodies can be conjugated to various molecules such as drugs, peptides, toxins and photosensitizers. These nanobodies can be designed as novel immunoconjugates using the universal modular antibody-based platform technology (UniCAR). Furthermore, Anti-EGFR nanobodies can be expressed in neural stem cells and visualised by effective fluorescent and radioisotope labelling., Competing Interests: Declaration of competing interest All authors declare that they have no conflict of interest., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background: L-asparaginase (ASNase) has played a key role in the management of acute lymphoblastic leukaemia (ALL). As an amidohydrolase, it catalyzes the hydrolysis of L-asparagine, a crucial step in the treatment of ALL. Various ASNase variants have evolved from diverse sources since it was first used in paediatric patients in the 1960s. This review describes the available ASNase and approaches being used to develop ASNase as a biobetter candidate., Scope of Review: The review discusses the Glycosylation and PEGylation techniques, which are frequently used to develop biobetter versions of the majority of the therapeutic proteins. Further, it explores current ASNase biobetters in therapeutic use and discusses the protein engineering and chemical modification approaches that were employed to reduce immunogenicity, extend protein half-life, and enhance protease stability of ASNase. Emerging strategies like immobilization and encapsulation are also highlighted as potential pathways for improving ASNase properties., Major Conclusions: The purpose of the development of ASNase biobetter is to achieve a novel therapeutic candidate that could improve catalytic efficiency, in vivo stability with minimum glutaminase (GLNase) activity and toxicity. Modification of ASNase by immobilization and encapsulation or by fusion technologies like Albumin fusion, Fc fusion, ELP fusion, XTEN fusion, etc. can be exploited to develop a novel biobetter candidate suitable for therapeutic approaches., General Significance: This review emphasizes the importance of biobetter development for therapeutic proteins like ASNase. Improved ASNase molecules have the potential to significantly advance the treatment of ALL and have broader implications in the pharmaceutical industry., Competing Interests: Declaration of Competing Interest The authors declare that they have no conflict of interest., (Copyright © 2023. Published by Elsevier B.V.)

The elimination of ocular artifacts is critical in analyzing electroencephalography (EEG) data for various brain-computer interface (BCI) applications. Despite numerous promising solutions, electrooculography (EOG) recording or an eye-blink detection algorithm is required for the majority of artifact removal algorithms. This reliance can hinder the model's implementation in real-world applications. This paper proposes EEGANet, a framework based on generative adversarial networks (GANs), to address this issue as a data-driven assistive tool for ocular artifacts removal (source code is available at https://github.com/IoBT-VISTEC/EEGANet). After the model was trained, the removal of ocular artifacts could be applied calibration-free without relying on the EOG channels or the eye blink detection algorithms. First, we tested EEGANet's ability to generate multi-channel EEG signals, artifacts removal performance, and robustness using the EEG eye artifact dataset, which contains a significant degree of data fluctuation. According to the results, EEGANet is comparable to state-of-the-art approaches that utilize EOG channels for artifact removal. Moreover, we demonstrated the effectiveness of EEGANet in BCI applications utilizing two distinct datasets under inter-day and subject-independent schemes. Despite the absence of EOG signals, the classification performance of the signals processed by EEGANet is equivalent to that of traditional baseline methods. This study demonstrates the potential for further use of GANs as a data-driven artifact removal technique for any multivariate time-series bio-signal, which might be a valuable step towards building next-generation healthcare technology.

The Coronavirus disease 2019 (COVID19) pandemic has led to a dramatic loss of human life worldwide and caused a tremendous challenge to public health. Immediate detection and diagnosis of COVID19 have lifesaving importance for both patients and doctors. The availability of COVID19 tests increased significantly in many countries, thereby provisioning a limited availability of laboratory test kits Additionally, the Reverse Transcription-Polymerase Chain Reaction (RT-PCR) test for the diagnosis of COVID 19 is costly and time-consuming. X-ray imaging is widely used for the diagnosis of COVID19. The detection of COVID19 based on the manual investigation of X-ray images is a tedious process. Therefore, computer-aided diagnosis (CAD) systems are needed for the automated detection of COVID19 disease. This paper proposes a novel approach for the automated detection of COVID19 using chest X-ray images. The Fixed Boundary-based Two-Dimensional Empirical Wavelet Transform (FB2DEWT) is used to extract modes from the X-ray images. In our study, a single X-ray image is decomposed into seven modes. The evaluated modes are used as input to the multiscale deep Convolutional Neural Network (CNN) to classify X-ray images into no-finding, pneumonia, and COVID19 classes. The proposed deep learning model is evaluated using the X-ray images from two different publicly available databases, where database A consists of 1225 images and database B consists of 9000 images. The results show that the proposed approach has obtained a maximum accuracy of 96% and 100% for the multiclass and binary classification schemes using X-ray images from dataset A with 5-fold cross-validation (CV) strategy. For dataset B, the accuracy values of 97.17% and 96.06% are achieved using multiscale deep CNN for multiclass and binary classification schemes with 5-fold CV. The proposed multiscale deep learning model has demonstrated a higher classification performance than the existing approaches for detecting COVID19 using X-ray images., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 Elsevier B.V. All rights reserved.)

The oxygen evolution reaction is a kinetically sluggish half-cell reaction which plays an important role in tuning the efficiency of various electrochemical energy conversion systems. However, this process can be facilitated by manipulating the composition and morphology of the electrocatalyst. Here, by tuning the annealing temperature, a series of cobalt borides (CoB@300, CoB@450, CoB@550 and CoB@650) were synthesized from a metal-organic framework Prussian blue analogue (PBA) following boronization. The resulting borides were characterized systematically and we explored their electrocatalytic activity towards the oxygen evolution reaction (OER). In an alkaline electrolyte, the in situ surface transformation of the boride working electrode to the corresponding metaborite and cobalt oxyhydroxide took place which thereafter acted as the active catalytic sites for the OER. Interestingly, the amorphous form of cobalt boride ( i.e. , CoB@300) shows many fold increased catalytic activity compared to those of crystalline CoB and commercial RuO 2 requiring only 290 mV overpotential to reach the benchmarked 10 mA cm -2 current density and the trend follows the order as CoB@300 > CoB@450 > CoB@550 > CoB@650 > PBA. The dominant catalytic activity of the amorphous cobalt boride nanostructure is attributed particularly to its amorphous nature and synergy between the in situ formed catalytically active centres (meta-borites and cobalt oxyhydroxide).

Atrial fibrillation (AF) is the most common type of cardiac arrhythmia and is characterized by the heart's beating in an uncoordinated manner. In clinical studies, patients often do not have visible symptoms during AF, and hence it is harder to detect this cardiac ailment. Therefore, automated detection of AF using the electrocardiogram (ECG) signals can reduce the risk of stroke, coronary artery disease, and other cardiovascular complications. In this paper, a novel time-frequency domain deep learning-based approach is proposed to detect AF and classify terminating and non-terminating AF episodes using ECG signals. This approach involves evaluating the time-frequency representation (TFR) of ECG signals using the chirplet transform. The two-dimensional (2D) deep convolutional bidirectional long short-term memory (BLSTM) neural network model is used to detect and classify AF episodes using the time-frequency images of ECG signals. The proposed TFR based 2D deep learning approach is evaluated using the ECG signals from three public databases. Our developed approach has obtained an accuracy, sensitivity, and specificity of 99.18% (Confidence interval (CI) as [98.86, 99.49]), 99.17% (CI as [98.85 99.49]), and 99.18% (CI as [98.86 99.49]), respectively, with 10-fold cross-validation (CV) technique to detect AF automatically. The proposed approach also classified terminating and non-terminating AF episodes with an average accuracy of 75.86%. The average accuracy value obtained using the proposed approach is higher than the short-time Fourier transform (STFT), discrete-time continuous wavelet transform (DT-CWT), and Stockwell transform (ST) based time-frequency analysis methods with deep convolutional BLSTM models to detect AF. The proposed approach has better AF detection performance than the existing deep learning-based techniques using ECG signals from the MIT-BIH database., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Emotion is interpreted as a psycho-physiological process, and it is associated with personality, behavior, motivation, and character of a person. The objective of affective computing is to recognize different types of emotions for human-computer interaction (HCI) applications. The spatiotemporal brain electrical activity is measured using multi-channel electroencephalogram (EEG) signals. Automated emotion recognition using multi-channel EEG signals is an exciting research topic in cognitive neuroscience and affective computing. This paper proposes the rhythm-specific multi-channel convolutional neural network (CNN) based approach for automated emotion recognition using multi-channel EEG signals. The delta (δ), theta (θ), alpha (α), beta (β), and gamma (γ) rhythms of EEG signal for each channel are evaluated using band-pass filters. The EEG rhythms from the selected channels coupled with deep CNN are used for emotion classification tasks such as low-valence (LV) vs. high valence (HV), low-arousal (LA) vs. high-arousal (HA), and low-dominance (LD) vs. high dominance (HD) respectively. The deep CNN architecture considered in the proposed work has eight convolutions, three average pooling, four batch-normalization, three spatial drop-outs, two drop-outs, one global average pooling and, three dense layers. We have validated our developed model using three publicly available databases: DEAP, DREAMER, and DASPS. The results reveal that the proposed multivariate deep CNN approach coupled with β-rhythm has obtained the accuracy values of 98.91%, 98.45%, and 98.69% for LV vs. HV, LA vs. HA, and LD vs. HD emotion classification strategies, respectively using DEAP database with 10-fold cross-validation (CV) scheme. Similarly, the accuracy values of 98.56%, 98.82%, and 98.99% are obtained for LV vs. HV, LA vs. HA, and LD vs. HD classification schemes, respectively, using deep CNN and θ-rhythm. The proposed multi-channel rhythm-specific deep CNN classification model has obtained the average accuracy value of 57.14% using α-rhythm and trial-specific CV using DASPS database. Moreover, for 8-quadrant based emotion classification strategy, the deep CNN based classifier has obtained an overall accuracy value of 24.37% using γ-rhythms of multi-channel EEG signals. Our developed deep CNN model can be used for real-time automated emotion recognition applications., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

The heart valve ailments (HVAs) are due to the defects in the valves of the heart and if untreated may cause heart failure, clots, and even sudden cardiac death. Automated early detection of HVAs is necessary in the hospitals for proper diagnosis of pathological cases, to provide timely treatment, and to reduce the mortality rate. The heart valve abnormalities will alter the heart sound and murmurs which can be faithfully captured by phonocardiogram (PCG) recordings. In this paper, a time-frequency based deep layer kernel sparse representation network (DLKSRN) is proposed for the detection of various HVAs using PCG signals. Spline kernel-based Chirplet transform (SCT) is used to evaluate the time-frequency representation of PCG recording, and the features like L1-norm (LN), sample entropy (SEN), and permutation entropy (PEN) are extracted from the different frequency components of the time-frequency representation of PCG recording. The DLKSRN formulated using the hidden layers of extreme learning machine- (ELM-) autoencoders and kernel sparse representation (KSR) is used for the classification of PCG recordings as normal, and pathology cases such as mitral valve prolapse (MVP), mitral regurgitation (MR), aortic stenosis (AS), and mitral stenosis (MS). The proposed approach has been evaluated using PCG recordings from both public and private databases, and the results demonstrated that an average sensitivity of 100%, 97.51%, 99.00%, 98.72%, and 99.13% are obtained for normal, MVP, MR, AS, and MS cases using the hold-out cross-validation (CV) method. The proposed approach is applicable for the Internet of Things- (IoT-) driven smart healthcare system for the accurate detection of HVAs., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2020 Samit Kumar Ghosh et al.)

The categorization of sleep stages helps to diagnose different sleep-related ailments. In this paper, an entropy-based information-theoretic approach is introduced for the automated categorization of sleep stages using multi-channel electroencephalogram (EEG) signals. This approach comprises of three stages. First, the decomposition of multi-channel EEG signals into sub-band signals or modes is performed using a novel multivariate projection-based fixed boundary empirical wavelet transform (MPFBEWT) filter bank. Second, entropy features such as bubble and dispersion entropies are computed from the modes of multi-channel EEG signals. Third, a hybrid learning classifier based on class-specific residuals using sparse representation and distances from nearest neighbors is used to categorize sleep stages automatically using entropy-based features computed from MPFBEWT domain modes of multi-channel EEG signals. The proposed approach is evaluated using the multi-channel EEG signals obtained from the cyclic alternating pattern (CAP) sleep database. Our results reveal that the proposed sleep staging approach has obtained accuracies of 91.77%, 88.14%, 80.13%, and 73.88% for the automated categorization of wake vs. sleep, wake vs. rapid eye movement (REM) vs. Non-REM, wake vs. light sleep vs. deep sleep vs. REM sleep, and wake vs. S1-sleep vs. S2-sleep vs. S3-sleep vs. REM sleep schemes, respectively. The developed method has obtained the highest overall accuracy compared to the state-of-art approaches and is ready to be tested with more subjects before clinical application.

Atrial fibrillation (AF) is a cardiac arrhythmia which is characterized based on the irregsular beating of atria, resulting in, the abnormal atrial patterns that are observed in the electrocardiogram (ECG) signal. The early detection of this pathology is very helpful for minimizing the chances of stroke, other heart-related disorders, and coronary artery diseases. This paper proposes a novel method for the detection of AF pathology based on the analysis of the ECG signal. The method adopts a multi-rate cosine filter bank architecture for the evaluation of coefficients from the ECG signal at different subbands, in turn, the Fractional norm (FN) feature is evaluated from the extracted coefficients at each subband. Then, the AF detection is carried out using a deep learning approach known as the Hierarchical Extreme Learning Machine (H-ELM) from the FN features. The proposed method is evaluated by considering normal and AF pathological ECG signals from public databases. The experimental results reveal that the proposed multi-rate cosine filter bank based on FN features is effective for the detection of AF pathology with an accuracy, sensitivity and specificity values of 99.40%, 98.77%, and 100%, respectively. The performance of the proposed diagnostic features of the ECG signal is compared with other existing features for the detection of AF. The low-frequency subband FN features found to be more significant with a difference of the mean values as 0.69 between normal and AF classes.

Sleep apnea is a sleep related pathology in which breathing or respiratory activity of an individual is obstructed, resulting in variations in the cardio-pulmonary (CP) activity. The monitoring of both cardiac (heart rate (HR)) and pulmonary (respiration rate (RR)) activities are important for the automated detection of this ailment. In this paper, we propose a novel automated approach for sleep apnea detection using the bivariate CP signal. The bivariate CP signal is formulated using both HR and RR signals extracted from the electrocardiogram (ECG) signal. The approach consists of three stages. First, the bivariate CP signal is decomposed into intrinsic mode functions (IMFs) and residuals for both HR and RR channels using bivariate fast and adaptive empirical mode decomposition (FAEMD). Second, the features are extracted using time-domain analysis, spectral analysis, and time-frequency domain analysis of IMFs from CP signal. The time-frequency domain features are computed from the cross time-frequency matrices of IMFs of CP signal. The cross time-frequency matrix of each IMF is evaluated using the Stockwell (S)-transform. Third, the support vector machine (SVM) and the random forest (RF) classifiers are used for automated detection of sleep apnea with the features from the bivariate CP signal. Our proposed approach has demonstrated an average sensitivity and specificity of 82.27% and 78.67%, respectively for sleep apnea detection using the 10-fold cross-validation method. The approach has yielded an average sensitivity and specificity of 73.19% and 73.13%, respectively for the subject-specific cross-validation. The performance of the approach was compared with other CPC features used for the detection of sleep apnea., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Heart valve diseases (HVDs) are a group of cardiovascular abnormalities, and the causes of HVDs are blood clots, congestive heart failure, stroke, and sudden cardiac death, if not treated timely. Hence, the detection of HVDs at the initial stage is very important in cardiovascular engineering to reduce the mortality rate. In this article, we propose a new approach for the detection of HVDs using phonocardiogram (PCG) signals. The approach uses the Chirplet transform (CT) for the time-frequency (TF) based analysis of the PCG signal. The local energy (LEN) and local entropy (LENT) features are evaluated from the TF matrix of the PCG signal. The multiclass composite classifier formulated based on the sparse representation of the test PCG instance for each class and the distances from the nearest neighbor PCG instances are used for the classification of HVDs such as mitral regurgitation (MR), mitral stenosis (MS), aortic stenosis (AS), and healthy classes (HC). The experimental results show that the proposed approach has sensitivity values of 99.44%, 98.66%, and 96.22% respectively for AS, MS and MR classes. The classification results of the proposed CT based features are compared with existing approaches for the automated classification of HVDs. The proposed approach has obtained the highest overall accuracy as compared to existing methods using the same database. The approach can be considered for the automated detection of HVDs with the Internet of Medical Things (IOMT) applications., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

The oxygen electrocatalysis, i.e. the oxygen reduction and evolution reactions, is traditionally executed using noble metal and metal oxide-based nanostructures. However, they are associated with many disadvantages such as high cost, lower durability/selectivity and detrimental environmental effects; this motivates researchers to develop new electroactive materials. In this study, we presented the synthesis of a Co-containing metal-organic framework (Co-MOF) and explored its electrocatalytic application towards the oxygen electrocatalysis (i.e. the oxygen reduction reaction and oxygen evolution reaction). The Co-MOF efficiently catalyzes the ORR with a lower onset (0.85 V vs. RHE)/reduction potential and higher reduction current density by a four-electron reduction path. Moreover, the MOF shows higher durability with >70% performance retention after 25 hours of reaction and tolerance towards methanol; this demonstrates its potential for application in direct methanol fuel cells (DMFCs); furthermore, due to the availability of more active sites and accessible surface area, the Co-MOF performs well towards the OER with lower onset potential and small Tafel slope as compared to the commercial RuO2 nanoparticles. Moreover, it needs only 280 mV overpotential to deliver the state-of-the-art current density of 10 mA cm-2 and robust stability. It shows the high TOF value of 93.21 s-1 at the overpotential of 350 mV as compared to the reported MOF/nanoparticle-based electrocatalysts and the state-of-the-art RuO2. Therefore, we believe that the as-developed Co-MOF holds the potential to be used as both a cathode and an anode electrocatalyst in the future energy storage and conversion systems.

Sleep is a prominent physiological activity in our daily life. Sleep apnea is the category of sleep disorder during which the breathing of the person diminishes causing the alternation in the upper airway resistance. The electrocardiogram derived respiration (EDR) and heart rate (RR-time-series) signals are normally used for the detection of sleep apnea as these two signals capture cardio-pulmonary activity information. Hence, the analysis of these two signals provides vital information about sleep apnea. In this paper, we propose the novel sparse residual entropy (SRE) features for the automated detection of sleep apnea using EDR and heart rate signals. The features required for the automated detection of sleep apnea are extracted in three steps: (i) atomic decomposition based residual estimation from both EDR and heart rate signals using orthogonal matching pursuit (OMP) with different dictionaries, (ii) estimation of probabilities from each sparse residual, and (iii) calculation of the entropy features. The proposed SRE features are fed to the combination of fuzzy K-means clustering and support vector machine (SVM) to pick the best performing classifier. The experimental results demonstrate that the proposed SRE features with radial basis function (RBF) kernel-based SVM classifier yielded higher performance with accuracy, sensitivity and specificity values of 78.07%, 78.01%, and 78.13%, respectively with Fourier dictionary and 10-fold cross-validation. For subject-specific or leave-one-out validation case, the SVM classifier has sensitivity and specificity of 85.43% and 92.60%, respectively using SRE features with Fourier dictionary (FD)., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Background and Objective: The congestive heart failure (CHF) is a life-threatening cardiac disease which arises when the pumping action of the heart is less than that of the normal case. This paper proposes a novel approach to design a classifier-based system for the automated detection of CHF., Methods: The approach is founded on the use of the Stockwell (S)-transform and frequency division to analyze the time-frequency sub-band matrices stemming from electrocardiogram (ECG) signals. Then, the entropy features are evaluated from the sub-band matrices of ECG. A hybrid classification scheme is adopted taking the sparse representation classifier and the average of the distances from the nearest neighbors into account for the detection of CHF. The proposition is validated using ECG signals from CHF subjects and normal sinus rhythm from public databases., Results: The results reveal that the proposed system is successful for the detection of CHF with an accuracy, a sensitivity and a specificity values of 98.78%, 98.48%, and 99.09%, respectively. A comparison with the existing approaches for the detection of CHF is accomplished., Conclusions: The time-frequency entropy features of the ECG signal in the frequency range from 11 Hz to 30 Hz have higher performance for the detection of CHF using a hybrid classifier. The approach can be used for the automated detection of CHF in tele-healthcare monitoring systems., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Accurate detection and classification of life-threatening ventricular arrhythmia episodes such as ventricular fibrillation (VF) and rapid ventricular tachycardia (VT) from electrocardiogram (ECG) is a challenging problem for patient monitoring and defibrillation therapy. This paper introduces a novel method for detection and classification of life-threatening ventricular arrhythmia episodes. The ECG signal is decomposed into various oscillatory modes using digital Taylor-Fourier transform (DTFT). The magnitude feature and a novel phase feature namely the phase difference (PD) are evaluated from the mode Taylor-Fourier coefficients of ECG signal. The least square support vector machine (LS-SVM) classifier with linear and radial basis function (RBF) kernels is employed for detection and classification of VT vs. VF, non-shock vs. shock and VF vs. non-VF arrhythmia episodes. The accuracy, sensitivity, and specificity values obtained using the proposed method are 89.81, 86.38, and 93.97%, respectively for the classification of Non-VF and VF episodes. Comparison with the performance of the state-of-the-art features demonstrate the advantages of the proposition.

In this Letter, a method is proposed to investigate the effect of noise with and without active noise control (ANC) on multichannel electroencephalogram (EEG) signal. The multichannel EEG signal is recorded during different listening conditions such as silent, music, noise, ANC with background noise and ANC with both background noise and music. The multiscale analysis of EEG signal of each channel is performed using the discrete wavelet transform. The multivariate multiscale matrices are formulated based on the sub-band signals of each EEG channel. The singular value decomposition is applied to the multivariate matrices of multichannel EEG at significant scales. The singular value features at significant scales and the extreme learning machine classifier with three different activation functions are used for classification of multichannel EEG signal. The experimental results demonstrate that, for ANC with noise and ANC with noise and music classes, the proposed method has sensitivity values of 75.831% ( p < 0.001 ) and 99.31% ( p < 0.001 ), respectively. The method has an accuracy value of 83.22% for the classification of EEG signal with music and ANC with music as stimuli. The important finding of this study is that by the introduction of ANC, music can be better perceived by the human brain.

Recombinant human interferon-β (rhIFN-β), a therapeutic protein, is produced using both prokaryotic and eukaryotic expression systems. However, instability of recombinant plasmid during cultivation of Escherichia coli results in low yield of the recombinant proteins. In addition, use of antibiotics during the cultivation imposes a major concern. In this study, we have compared the expression yield of rhIFN-β in E. coli BL21 (DE3) and E coli SE1 cells. Gene-encoding rhIFN-β was expressed in E. coli BL21 (DE3) and SE1 cells and the cultivation of recombinant E. coli cells was done in a laboratory scale bioreactor. Our results suggest that, compared to BL21(DE3) cells, the SE1 cells expressing rhIFN-β protein can be cultivated in the medium without antibiotic and provide increased stability of recombinant plasmid and higher expression yield of rhIFN-β protein. This system can be used for the production of rhIFN-β proteins for biomedical applications., Competing Interests: Compliance with ethical standardsNone.

Human paraoxonase 1 (h-PON1) is a ~45-kDa serum enzyme that can hydrolyze a variety of substrates, including organophosphate (OP) compounds. It is a potential candidate for the development of antidote against OP poisoning in humans. However, insufficient OP-hydrolyzing activity of native enzyme affirms the urgent need to develop improved variant(s) having enhanced OP-hydrolyzing activity. The crystal structure of h-PON1 remains unsolved, and the molecular details of how the enzyme catalyses hydrolysis of different types of substrates are also not clear. Understanding the molecular details of the catalytic mechanism of h-PON1 is essential to engineer better variant(s) of enzyme. In this study, we have used a random mutagenesis approach to increase the OP-hydrolyzing activity of recombinant h-PON1. The mutants not only showed a 10-340-fold increased OP-hydrolyzing activity against different OP substrates but also exhibited differential lactonase and arylesterase activities. In order to investigate the mechanistic details of the effect of observed mutations on the hydrolytic activities of enzyme, molecular docking studies were performed with selected mutants. The results suggested that the observed mutations permit differential binding of substrate/inhibitor into the enzyme's active site. This may explain differential hydrolytic activities of the enzyme towards different substrates.

The complex wavelet sub-band bi-spectrum (CWSB) features are proposed for detection and classification of myocardial infarction (MI), heart muscle disease (HMD) and bundle branch block (BBB) from 12-lead ECG. The dual tree CW transform of 12-lead ECG produces CW coefficients at different sub-bands. The higher-order CW analysis is used for evaluation of CWSB. The mean of the absolute value of CWSB, and the number of negative phase angle and the number of positive phase angle features from the phase of CWSB of 12-lead ECG are evaluated. Extreme learning machine and support vector machine (SVM) classifiers are used to evaluate the performance of CWSB features. Experimental results show that the proposed CWSB features of 12-lead ECG and the SVM classifier are successful for classification of various heart pathologies. The individual accuracy values for MI, HMD and BBB classes are obtained as 98.37, 97.39 and 96.40%, respectively, using SVM classifier and radial basis function kernel function. A comparison has also been made with existing 12-lead ECG-based cardiac disease detection techniques.

In this letter, the authors propose a new entropy measure for analysis of time series. This measure is termed as the state space correlation entropy (SSCE). The state space reconstruction is used to evaluate the embedding vectors of a time series. The SSCE is computed from the probability of the correlations of the embedding vectors. The performance of SSCE measure is evaluated using both synthetic and real valued signals. The experimental results reveal that, the proposed SSCE measure along with SVM classifier have sensitivity value of 91.60%, which is higher than the performance of both sample entropy and permutation entropy features for detection of shockable ventricular arrhythmia.

Organophosphate (OP) compounds are neurotoxic chemicals, and current treatments available for OP-poisoning are considered as unsatisfactory and inadequate. There is an urgent need for the development of more effective treatment(s) for OP-poisoning. Human paraoxonase 1 (h-PON1) is known to hydrolyze a variety of OP-compounds and is a leading candidate for the development of prophylactic and therapeutic agent against OP-poisoning in humans. Non-availability of effective system(s) for the production of recombinant h-PON1 (rh-PON1) makes it hard to produce improved variant(s) of this enzyme and analyze their in vivo efficacy in animal models. Production of recombinant h-PON1 (rh-PON1) using an Escherichia coli expression system is a key to develop variant(s) of h-PON1. Recently, we have developed a procedure to produce active rh-PON1 enzymes by using E. coli expression system. In this study, we have characterized the OP-hydrolyzing properties of refolded rh-PON1(wt) and rh-PON1(H115W;R192K) variant. Our results show that refolded rh-PON1(H115W;R192K) variant exhibit enhanced OP-hydrolyzing activity in in vitro and ex vivo assays and exhibited prophylactic activity in mouse model of OP-poisoning, suggesting that refolded rh-PON1 can be developed as a therapeutic candidate.

The cardiac activities such as the depolarization and the relaxation of atria and ventricles are observed in electrocardiogram (ECG). The changes in the morphological features of ECG are the symptoms of particular heart pathology. It is a cumbersome task for medical experts to visually identify any subtle changes in the morphological features during 24 hours of ECG recording. Therefore, the automated analysis of ECG signal is a need for accurate detection of cardiac abnormalities. In this paper, a novel method for automated detection of cardiac abnormalities from multilead ECG is proposed. The method uses multiscale phase alternation (PA) features of multilead ECG and two classifiers, k-nearest neighbor (KNN) and fuzzy KNN for classification of bundle branch block (BBB), myocardial infarction (MI), heart muscle defect (HMD) and healthy control (HC). The dual tree complex wavelet transform (DTCWT) is used to decompose the ECG signal of each lead into complex wavelet coefficients at different scales. The phase of the complex wavelet coefficients is computed and the PA values at each wavelet scale are used as features for detection and classification of cardiac abnormalities. A publicly available multilead ECG database (PTB database) is used for testing of the proposed method. The experimental results show that, the proposed multiscale PA features and the fuzzy KNN classifier have better performance for detection of cardiac abnormalities with sensitivity values of 78.12 %, 80.90 % and 94.31 % for BBB, HMD and MI classes. The sensitivity value of proposed method for MI class is compared with the state-of-art techniques from multilead ECG.

Ventricular tachycardia (VT) and ventricular fibrillation (VF) are shockable ventricular cardiac ailments. Detection of VT/VF is one of the important step in both automated external defibrillator (AED) and implantable cardioverter defibrillator (ICD) therapy. In this paper, we propose a new method for detection and classification of shockable ventricular arrhythmia (VT/VF) and non-shockable ventricular arrhythmia (normal sinus rhythm, ventricular bigeminy, ventricular ectopic beats, and ventricular escape rhythm) episodes from Electrocardiogram (ECG) signal. The variational mode decomposition (VMD) is used to decompose the ECG signal into number of modes or sub-signals. The energy, the renyi entropy and the permutation entropy of first three modes are evaluated and these values are used as diagnostic features. The mutual information based feature scoring is employed to select optimal set of diagnostic features. The performance of the diagnostic features is evaluated using random forest (RF) classifier. Experimental results reveal that, the feature subset derived from mutual information based scoring and the RF classifier produces accuracy, sensitivity and specificity values of 97.23 %, 96.54 %, and 97.97 %, respectively. The proposed method is compared with some of the existing techniques for detection of shockable ventricular arrhythmia episodes from ECG.

In this Letter, a novel principal component (PC)-based diagnostic measure (PCDM) is proposed to quantify loss of clinical components in the multi-lead electrocardiogram (MECG) signals. The analysis of MECG shows that, the clinical components are captured in few PCs. The proposed diagnostic measure is defined as the sum of weighted percentage root mean square difference (PRD) between the PCs of original and processed MECG signals. The values of the weight depend on the clinical importance of PCs. The PCDM is tested over MECG enhancement and a novel MECG data reduction scheme. The proposed measure is compared with weighted diagnostic distortion, wavelet energy diagnostic distortion and PRD. The qualitative evaluation is performed using Spearman rank-order correlation coefficient (SROCC) and Pearson linear correlation coefficient. The simulation result demonstrates that the PCDM performs better to quantify loss of clinical components in MECG and shows a SROCC value of 0.9686 with subjective measure.

Human paraoxonase 1 (h-PON1) is a serum enzyme that can hydrolyze a variety of substrates. The enzyme exhibits anti-inflammatory, anti-oxidative, anti-atherogenic, anti-diabetic, anti-microbial and organophosphate-hydrolyzing activities. Thus, h-PON1 is a strong candidate for the development of therapeutic intervention against a variety conditions in human. However, the crystal structure of h-PON1 is not solved and the molecular details of how the enzyme hydrolyzes different substrates are not clear yet. Understanding the catalytic mechanism(s) of h-PON1 is important in developing the enzyme for therapeutic use. Literature suggests that R/Q polymorphism at position 192 in h-PON1 dramatically modulates the substrate specificity of the enzyme. In order to understand the role of the amino acid residue at position 192 of h-PON1 in its various hydrolytic activities, site-specific mutagenesis at position 192 was done in this study. The mutant enzymes were produced using Escherichia coli expression system and their hydrolytic activities were compared against a panel of substrates. Molecular dynamics simulation studies were employed on selected recombinant h-PON1 (rh-PON1) mutants to understand the effect of amino acid substitutions at position 192 on the structural features of the active site of the enzyme. Our results suggest that, depending on the type of substrate, presence of a particular amino acid residue at position 192 differentially alters the micro-environment of the active site of the enzyme resulting in the engagement of different subsets of amino acid residues in the binding and the processing of substrates. The result advances our understanding of the catalytic mechanism of h-PON1.

Human PON1 (h-PON1) is a Ca(2+)-dependent serum enzyme and can hydrolyze (and inactivate) a wide range of substrates. It is a multifaceted enzyme and exhibit anti-inflammatory, anti-oxidative, anti-atherogenic, anti-diabetic, anti-microbial, and organophosphate (OP)-detoxifying properties. Thus, h-PON1 is a strong candidate for the development of therapeutic intervention against these conditions in humans. Insufficient hydrolyzing activity of native h-PON1 against desirable substrate affirms the urgent need to develop improved variant(s) of h-PON1 having enhanced activity. Production of recombinant h-PON1 (rh-PON1) using an Escherichia coli expression system is a key to develop such variant(s). However, generation of rh-PON1 using E. coli expression system has been elusive until now because of the aggregation of over-expressed rh-PON1 protein in inactive form as inclusion bodies (IBs) in the bacterial cells. In this study, we have over-expressed rh-PON1(wt) and rh-PON1(H115W;R192K) proteins as IBs in E. coli, and refolded the inactive enzymes present in the IBs to their active form using in vitro refolding. The active enzymes were isolated from the refolding mixture by ion-exchange chromatography. The catalytic properties of the refolded enzymes were similar to their soluble counterparts. Our results show that the pure and the active variant of rh-PON1 enzyme having enhanced hydrolyzing activity can be produced in large quantities using E. coli expression system. This method can be used for the industrial scale production of rh-PON1 enzymes and will aid in developing h-PON1 as a therapeutic candidate., (Copyright © 2015 Elsevier Inc. All rights reserved.)

AiiA is a "28-kDa lactonase" from Gram-positive Bacillus sp. 240B1. The enzyme can hydrolyze and inactivate a variety of acyl homoserine lactones (AHLs), quorum sensor molecules involve in bacterial quorum sensing (QS). AiiA is a strong candidate for the development of bio-decontaminating agent that can disrupt QS in industrial and environmental samples. However, commercial application of AiiA suffer from several limitations including high cost of production of enzyme and lack of efficient recovery mean(s) of enzyme from the application environment for its reuse. In this study we have cloned, expressed and purified recombinant AiiA (r-AiiA) enzyme. The purified enzyme was covalently immobilized onto magnetic nanoparticles (MNPs) and the quorum quenching ability of r-AiiA-MNP nanobiocatalyst was evaluated in aqueous buffer. Our results show that r-AiiA-MNPs (a) can hydrolyze 3O-C10AHL and inhibit QS in aqueous buffer, (b) can be recovered from the reaction mixture using external magnetic field, and (c) can be reused multiple times to hydrolyze 3O-C10AHL in aqueous buffer. Results of this study can be used to develop a formulation of AiiA enzyme for industrial applications., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Organophosphorus hydrolase (OPH) is a ∼38kDa enzyme encoded by opd gene of Flavobacterium sp. The enzyme can hydrolyze and inactivate variety of organophosphate (OP)-compounds, including chemical warfare nerve agents. Thus, OPH is a strong candidate for the development of therapeutic intervention against OP-poisoning in humans and other animals. It is also a promising bio-decontaminating agent for clean-up of OP-contaminated objects and areas. For successful commercial application, long-term storage stability of purified OPH enzyme is important. In this study we have cloned and expressed recombinant OPH (r-OPH) in Escherichia coli and the effect of different excipients on the long-term storage stability of purified enzyme was analyzed. The enzyme was stored in either aqueous solution or in lyophilized form at 25°C for 60days in the presence or absence of different excipients and the stability of the enzyme was determined by monitoring the paraoxon-hydrolyzing activity. Our results suggest that, (a) maltose, trehalose, arginine and proline were most effective in stabilizing the enzyme when stored in aqueous buffer at 25°C, and (b) maltose, trehalose, and mannose exerted maximum stabilization effect when the enzyme was stored in lyophilized form at 25°C for 60days. The study shows that common excipients can be used to stabilize purified OPH enzyme in order to store it for long period of time under different storage conditions. The results of this study can be used to develop formulation(s) of OPH enzyme for commercial use., (Copyright © 2015 Elsevier Inc. All rights reserved.)

In this paper, a novel technique on a multiscale energy and eigenspace (MEES) approach is proposed for the detection and localization of myocardial infarction (MI) from multilead electrocardiogram (ECG). Wavelet decomposition of multilead ECG signals grossly segments the clinical components at different subbands. In MI, pathological characteristics such as hypercute T-wave, inversion of T-wave, changes in ST elevation, or pathological Q-wave are seen in ECG signals. This pathological information alters the covariance structures of multiscale multivariate matrices at different scales and the corresponding eigenvalues. The clinically relevant components can be captured by eigenvalues. In this study, multiscale wavelet energies and eigenvalues of multiscale covariance matrices are used as diagnostic features. Support vector machines (SVMs) with both linear and radial basis function (RBF) kernel and K-nearest neighbor are used as classifiers. Datasets, which include healthy control, and various types of MI, such as anterior, anteriolateral, anterioseptal, inferior, inferiolateral, and inferioposterio-lateral, from the PTB diagnostic ECG database are used for evaluation. The results show that the proposed technique can successfully detect the MI pathologies. The MEES approach also helps localize different types of MIs. For MI detection, the accuracy, the sensitivity, and the specificity values are 96%, 93%, and 99% respectively. The localization accuracy is 99.58%, using a multiclass SVM classifier with RBF kernel.

SsoPox, a ~35 kDa enzyme from Sulfolobus solfataricus, can hydrolyze and inactivate a variety of organophosphate (OP)-compounds. The enzyme is a potential candidate for the development of prophylactic and therapeutic agent against OP-poisoning in humans. However, the therapeutic use of recombinant SsoPox suffers from certain limitations associated with the use of recombinant protein pharmaceuticals. Some of these limitations could be overcome by conjugating SsoPox enzyme with polyethylene glycol (PEG). In this study, we report generation and in vitro characterization of N-terminal mono-PEGylated rSsoPox(2p) (a variant of rSsoPox(wt) having enhanced OP-hydrolyzing activity). The enzyme was PEGylated with mPEG-propionaldehyde and the PEGylated protein was isolated using ion-exchange chromatography. Compared with the unmodified enzyme, mono-PEGylation of rSsoPox results in improvement in the thermostability and protease resistance of the enzyme. PEGylated rSsoPox(2p) can be developed as a candidate for the prevention / treatment of OP-poisoning.

A new measure for quantifying diagnostic information from a multilead electrocardiogram (MECG) is proposed. This diagnostic measure is based on principal component (PC) multivariate multiscale sample entropy (PMMSE). The PC analysis is used to reduce the dimension of the MECG data matrix. The multivariate multiscale sample entropy is evaluated over the PC matrix. The PMMSE values along each scale are used as a diagnostic feature vector. The performance of the proposed measure is evaluated using a least square support vector machine classifier for detection and classification of normal (healthy control) and different cardiovascular diseases such as cardiomyopathy, cardiac dysrhythmia, hypertrophy and myocardial infarction. The results show that the cardiac diseases are successfully detected and classified with an average accuracy of 90.34%. Comparison with some of the recently published methods shows improved performance of the proposed measure of cardiac disease classification.

Human paraoxonase 1 (h-PON1) is a Ca(2+)-dependent serum enzyme that catalyzes the hydrolysis of different types of substrates. The crystal structure of h-PON1 is not solved yet and the molecular details of how the enzyme catalyzes different types of reactions are not clear. Literature suggests that the amino acid residues at positions 192 and 115 are important for various hydrolytic activities of h-PON1. It is proposed that catalytic residue H115 (and H134) mediates the lactonase and the arylesterase activities of the enzyme while the amino acid residue at position 192 modulates various other hydrolytic activities of the enzyme. However, the relationship between these two residues in the hydrolytic activities of h-PON1 is not studied in detail. In this study, we have expressed and purified the wild-type recombinant h-PON1 (rh-PON1(wt)) and its point mutants differing in the amino acid residues at positions 192 and/or 115 using an Escherichia coli expression system. The hydrolytic activities of the purified enzymes were compared using enzymatic assays. Our results, for the first time, show that (a) the presence of a particular amino acid residue at position 192 differentially alters the effect of the H115W substitution, and (b) H115 residue is not always needed for the lactonase and arylesterase activities of the enzyme. The results also suggest that the amino acid residues at position 192 and 115 act in conjunction in modulating the hydrolytic activities of the enzyme., (Copyright © 2014 Elsevier Masson SAS. All rights reserved.)

Human PON1 (h-PON1) is a multifaceted enzyme and can hydrolyze (and inactivate) a wide range of substrates. The enzyme shows anti-inflammatory, antioxidative, antiatherogenic, ant-diabetic, antimicrobial, and organophosphate (OP)-detoxifying properties. However, there are certain limitations regarding large-scale production and use of h-PON1 as a therapeutic candidate. These include difficulties in producing recombinant h-PON1 (rh-PON1) using microbial expression system, low hydrolytic activity of wild-type h-PON1 towards certain substrates, and low storage stability of the purified enzyme. This review summarizes the work done in our laboratory to address these limitations. Our results show that (a) optimized polynucleotide sequence encoding rh-PON1 can express the protein in an active form in E. coli and can be used to generate variant of the enzyme having enhanced hydrolytic activity, (b) in vitro refolding of rh-PON1 enzyme can dramatically increase the yield of an active enzyme, (c) common excipients can be used to stabilize purified rh-PON1 enzyme when stored under different storage conditions, and (d) variants of rh-PON1 enzyme impart significant protection against OP-poisoning in human blood (ex vivo) and mouse (in vivo) model of OP-poisoning. The rh-PON1 variants and their process of production discussed here will help to develop h-PON1 as a therapeutic candidate.

Paraoxonase 1 (PON1) is an HDL-associated enzyme and exhibits anti-inflammatory, anti-diabetic, and anti-atherogenic properties. Association of PON1 to HDL particles increases the stability and activity of PON1 and is important for the normal functioning of the enzyme. HDL particles are made up of lipid and protein constituents and apolipoprotein A-I (apoA-I) is a principal protein constituent of HDL that facilitates various biological activities of HDL. In many disease conditions the oxidized phospholipid (Ox-PL) content of HDL is found to be increased and an inverse correlation between the activity of PON1 and oxidation of the HDL is observed. However, the molecular details of the inhibitory action of the Ox-PL-containing HDL on the function of PON1 are not clear yet. In this study we have assembled reconstituted HDL (rHDL) particles with and without Ox-PL and compared their effect on the structure and function of (13)C-labeled recombinant PON1 ((13)C-rPON1) by employing attenuated total reflectance Fourier transformed infrared (ATR-FTIR) spectroscopy and enzymatic assay. Our results show that the presence of the Ox-PL in the rHDL particles alters the structure of rPON1 and decreases its lactonase activity., (© 2013.)

Human paraoxonase 1 (h-PON1) hydrolyzes variety of substrates and the hydrolytic activities of enzyme can be broadly grouped into three categories; arylesterase, phosphotriesterase, and lactonase. Current models of the catalytic mechanism of h-PON1 suggest that catalytic residues H115 and H134 mediate the lactonase and arylesterase activities of the enzyme. H-PON1 is a strong candidate for the development of catalytic bioscavenger for organophosphate poisoning in humans. Recently, Gupta et al. (Nat. Chem. Biol. 2011. 7, 120) identified amino acid substitutions that significantly increased the activity of chimeric-PON1 variant (4E9) against some organophosphate nerve agents. In this study we have examined the effect of these (L69G/S111T/H115W/H134R/R192K/F222S/T332S) and other substitutions (H115W/H134R and H115W/H134R/R192K) on the hydrolytic activities of recombinant h-PON1 (rh-PON1) variants. Our results show that the substitutions resulted in a significant increase in the organophosphatase activity of all the three variants of rh-PON1 enzyme while had a variable effect on the lactonase/arylesterase activities. The results suggest that H residues at positions 115 and 134 are not always needed for the lactonase/arylesterase activities of h-PON1 and force a reconsideration of the current model(s) of the catalytic mechanism of h-PON1., (© 2013 The Protein Society.)

Oxidative modification of phospholipids generates a variety of oxidized phospholipid (Ox-PL) species which differ considerably in their chemical compositions and molecular structures. Recent results suggest that even closely related Ox-PL species can have considerably different biological effects. However, the molecular mechanism for this is not yet clear. In truncated Ox-PLs (tOx-PLs) the fatty acyl chain is shorter in length than the parent nonoxidized phospholipid molecules and contains a polar functional group(s). In a previous study we showed that two closely related tOx-PL species having a similar polar functional group and differing only in the length of the oxidized fatty acyl chain exerts significantly different effects on the physicochemical properties of the nonoxidized phospholipid particles containing these lipids (Kar et al., Chem Phys Lipids 164:54-61, 2011). In this study we have characterized the effect of polar functional groups of oxidized fatty acyl chain on the physicochemical properties of the nonoxidized phospholipid particles containing these lipids. Our results show that Ox-PL species differing only in the chemical nature of polar functional groups in their oxidized fatty acyl chain modify the properties of nonoxidized phospholipid particles containing them in a distinctive way. These results indicate that different species of Ox-PLs induce unique changes in the physicochemical properties of lipid particles/membranes containing them and that this may lead to their different biological effects.

High density lipoprotein (HDL) particles are made up of lipid and protein constituents and apolipoprotein A-I (apoA-I) is a principal protein component that facilitates various biological activities of HDL particles. Increase in Ox-PL content of HDL particles makes them 'dysfunctional' and such modified HDL particles not only lose their athero-protective properties but also acquire pro-atherogenic and pro-inflammatory functions. The details of Ox-PL-induced alteration in the molecular properties of HDL particles are not clear. Paraoxonase 1 (PON1) is an HDL-associated enzyme that possesses anti-inflammatory and anti-atherogenic properties; and many of the athero-protective functions of HDL are attributed to the associated PON1. In this study we have characterized the physicochemical properties of reconstituted HDL (rHDL) particles containing varying amounts of Ox-PL and have compared their PON1 stimulation capacity. Our results show that increased Ox-PL content (a) modifies the physicochemical properties of the lipid domain of the rHDL particles, (b) decreases the stability and alters the conformation as well as orientation of apoA-I molecules on the rHDL particles, and (c) decreases the PON1 stimulation capacity of the rHDL particles. Our data indicate that the presence of Ox-PLs destabilizes the structure of the HDL particles and modifies their function., (Copyright © 2012 Elsevier B.V. All rights reserved.)

In vivo oxidation of glycerophospholipid generates a variety of products including truncated oxidized phospholipids (tOx-PLs). The fatty acyl chains at the sn-2 position of tOx-PLs are shorter in length than the parent non-oxidized phospholipids and contain a polar functional group(s) at the end. The effect of oxidatively modified sn-2 fatty acyl chain on the physicochemical properties of tOx-PLs aggregates has not been addressed in detail, although there are few reports that modified fatty acyl chain primarily determines the biological activities of tOx-PLs. In this study we have compared the properties of four closely related tOx-PLs which differ only in the type of modified fatty acyl chain present at the sn-2 position: 1-palmitoyl-2-azelaoyl-sn-glycero-3-phosphocholine (PazePC), 1-palmitoyl-2-(9'-oxo-nonanoyl)-sn-glycero-3-phosphocholine (PoxnoPC), 1-palmitoyl-2-glutaroyl-sn-glycero-3-phosphocholine (PGPC), and 1-palmitoyl-2-(5'-oxo-valeroyl)-sn-glycero-3-phosphocholine (POVPC). Aggregates of individual tOx-PL in aqueous solution were characterized by fluorescence spectroscopy, size exclusion chromatography, native polyacrylamide and agarose gel electrophoresis. The data suggest that aggregates of four closely related tOx-PLs form micelle-like particles of considerably different properties. Our result provides first direct evidence that because of the specific chemical composition of the sn-2 fatty acyl chain aggregates of particular tOx-PL possess a distinctive set of physicochemical properties., (Copyright 2010 Elsevier B.V. All rights reserved.)

Apolipoprotein E (apoE) plays a major role in the transport and metabolism of lipid by acting as a ligand for low density lipoprotein-receptors. The amphipathic helical regions of its C-terminal domain are necessary for the lipoprotein binding and assembly of nascent lipoprotein particles. Lipoproteins in the plasma are known to possess a net negative charge, determined by both its protein and lipid components, which regulates the metabolism of lipoproteins. The role of membrane surface charge on the interaction of apoE has not been studied previously. Also the importance of individual amphipathic helical regions of its C-terminal domain in binding to negatively charged lipid membrane is not addressed. In this study we have compared the interaction of four peptide segments of apoE C-terminal domain (apoE((202-223)), apoE((223-244)), apoE((245-266)), and apoE((268-289))) with zwitterionic and negatively charged model membranes by employing UV-visible and fluorescence spectroscopy, circular dichroism, and native PAGE analysis. Our results show that the peptide sequence 202-223, 245-266 and 268-289 of apoE has higher affinity towards negatively charged lipid membrane and are independently capable of forming lipoprotein particles of 17+/-2 nm Stokes diameter. The results suggest that surface charge of lipoprotein regulates its metabolism possibly by modulating the recruitment of apoE on its surface.

The physiological function of apolipoprotein E (apoE) includes transport and metabolism of lipids and its C-terminal domain harbors high affinity lipid-binding sites. Although the binding of apoE with non-oxidized phospholipid containing membranes has been characterized earlier, the interaction of apoE or its fragments with oxidized phospholipid containing membrane has never been studied. In this study we have compared the interaction of amphipathic helical peptide sequences derived from the C-terminal domain of apoE with membrane vesicles containing oxidized phospholipid, 1-palmitoyl-2-azelaoyl-sn-glycero-3-phosphocholine (PazePC), with membrane vesicles without PazePC. The interaction was studied by monitoring (a) fluorescence emission maxima of the peptides, (b) acrylamide quenching of the peptides tryptophan residues and (c) by measuring the equilibrium binding constants by resonance energy transfer (RET) analysis. Our result shows that peptide sequence 202-223, 245-266 and 268-289 of apoE has higher affinity towards membrane containing PazePC, compared to membrane without PazePC. Presence of 1mM divalent cation or 50 mM NaCl in the buffer decreased the binding of peptides to PazePC containing membrane vesicles suggesting possible involvement of the electrostatic interaction in the binding. These observations suggest that the preferential binding of apoE to oxidized phospholipid containing membrane may play a role in the anti-oxidative properties of apoE.

In view of the potential anti-anxiety and anti-depressant activity of the Rasayana Vajikarna drugs, a combined Rasayana Vajikarna schedule has been clinically tried in a series of patients of Depression Neurosis. The tratment consisted of a dose of Asvagandha Curna at bed time and a dose of Kapikacchu Curna at morning continued for 8 weeks. The treatment showed significant clinical recovery in these cases in the form of a notable reduction in the level of clinical anxiety and depression.