Molecular and functional insight into anti-EGFR nanobody: Theranostic implications for malignancies

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.

Towards development of biobetter: L-asparaginase a case study

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.

AFCNNet: Automated detection of AF using chirplet transform and deep convolutional bidirectional long short term memory network with ECG signals

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.

Automated accurate emotion recognition system using rhythm-specific deep convolutional neural network technique with multi-channel EEG signals

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.

Detection of Atrial Fibrillation from Single Lead ECG Signal Using Multirate Cosine Filter Bank and Deep Neural Network

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.

Automated detection of congestive heart failure from electrocardiogram signal using Stockwell transform and hybrid classification scheme

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.

Understanding perception of active noise control system through multichannel EEG analysis

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% (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$p \lt 0.001$\end{document}p<0.001) and 99.31% (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$p \lt 0.001$\end{document}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.

Antibiotic-free expression system for the production of human interferon-beta protein

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.

Detection of Cardiac Abnormalities from Multilead ECG using Multiscale Phase Alternation Features

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.

Toward Understanding the Catalytic Mechanism of Human Paraoxonase 1: Site-Specific Mutagenesis at Position 192

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.

A new way of quantifying diagnostic information from multilead electrocardiogram for cardiac disease classification

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.

Characterization of human paraoxonase 1 variants suggest that His residues at 115 and 134 positions are not always needed for the lactonase/arylesterase activities of the enzyme

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.

Oxidized phospholipid content destabilizes the structure of reconstituted high density lipoprotein particles and changes their function

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.