Laser-induced graphene-based electrochemical biosensors for environmental applications: a perspective.

Biosensors are miniaturized devices that provide the advantage of in situ and point-of-care monitoring of analytes of interest. Electrochemical biosensors use the mechanism of oxidation-reduction reactions and measurement of corresponding electron transfer as changes in current, voltage, or other parameters using different electrochemical techniques. The use of electrochemically active materials is critical for the effective functioning of electrochemical biosensors. Laser-induced graphene (LIG) has garnered increasing interest in biosensor development and improvement due to its high electrical conductivity, specific surface area, and simple and scalable fabrication process. The effort of this perspective is to understand the existing classes of analytes and the mechanisms of their detection using LIG-based biosensors. The manuscript has highlighted the potential use of LIG, its modifications, and its use with various receptors for sensing various environmental pollutants. Although the conventional graphene-based sensors effectively detect trace levels for many analytes in different applications, the chemical and energy-intensive fabrication and time-consuming processes make it imperative to explore a low-cost and scalable option such as LIG for biosensors production. The focus of these potential biosensors has been kept on detection analytes of environmental significance such as heavy metals ions, organic and inorganic compounds, fertilizers, pesticides, pathogens, and antibiotics. The use of LIG directly as an electrode, its modifications with nanomaterials and polymers, and its combination with bioreceptors such as aptamers and polymers has been summarized. The strengths, weaknesses, opportunities, and threats analysis has also been done to understand the viability of incorporating LIG-based electrochemical biosensors for environmental applications.

A protein microarray-based serum proteomic investigation reveals distinct autoantibody signature in colorectal cancer.

PURPOSE: Colorectal cancer (CRC) has been reported as the second leading cause of cancer death worldwide. The 5-year annual survival is around 50%, mainly due to late diagnosis, striking necessity for early detection. This study aims to identify autoantibody in patients' sera for early screening of cancer. EXPERIMENTAL DESIGN: The study used a high-density human proteome array with approximately 17,000 recombinant proteins. Screening of sera from healthy individuals, CRC from Indian origin, and CRC from middle-east Asia origin were performed. Bio-statistical analysis was performed to identify significant autoantibodies altered. Pathway analysis was performed to explore the underlying mechanism of the disease. RESULTS: The comprehensive proteomic analysis revealed dysregulation of 15 panels of proteins including CORO7, KCNAB1, WRAP53, NDUFS6, KRT30, and COLGALT2. Further biological pathway analysis for the top dysregulated autoantigenic proteins revealed perturbation in important biological pathways such as ECM degradation and cytoskeletal remodeling etc. CONCLUSIONS AND CLINICAL RELEVANCE: The generation of an autoimmune response against cancer-linked pathways could be linked to the screening of the disease. The process of immune surveillance can be detected at an early stage of cancer. Moreover, AAbs can be easily extracted from blood serum through the least invasive test for disease screening.

Evaluation of autoantibody signatures in pituitary adenoma patients using human proteome arrays.

PURPOSE: To identify the specific diagnostic biomarkers related to pituitary adenomas (PAs), we performed serological antibody profiles for three types of PAs, namely Acromegaly, Cushing's and Nonfunctional Pituitary Adenomas (NFPAs), using the human proteome (HuProt) microarray. This is the first study describing the serum autoantibody profile of PAs. EXPERIMENTAL DESIGN: We performed serological autoantibody profiling of four healthy controls, four Acromegaly, three Cushing's and three NFPAs patient samples to obtain their autoantibody profiles, which were used for studying expression, interaction and altered biological pathways. Further, significant autoantibodies of PAs were compared with data available for glioma, meningioma and AAgAtlas for their specificity. RESULTS: Autoantibody profile of PAs led to the identification of differentially expressed significant proteins such as AKNAD1 (AT-Hook Transcription Factor [AKNA] Domain Containing 1), NINJ1 (Nerve injury-induced protein 1), L3HYPDH (Trans-3-hydroxy-L-proline dehydratase), RHOG (Rho-related GTP-binding protein) and PTP4A1 (Protein Tyrosine Phosphatase Type IVA 1) in Acromegaly. Protein ABR (Active breakpoint cluster region-related protein), ST6GALNAC6 (ST6 N-acetylgalactosaminide alpha-2, 6-sialyltransferase 6), NOL3 (Nucleolar protein 3), ANXA8 (Annexin A8) and POLR2H (RNA polymerase II, I and III subunit H) showed an antigenic response in Cushing's patient's serum samples. Protein dipeptidyl peptidase 3 (DPP3) and reticulon-4 (RTN4) exhibited a very high antigenic response in NFPA patients. These proteins hold promise as potential autoantibody biomarkers in PAs.

Sensors incipient fault detection and isolation of nuclear power plant using extended Kalman filter and Kullback-Leibler divergence.

Sensor real-time monitoring is an indispensable to achieve reliable plant operation along with stricter safety and environmental measures. This paper presents a statistical algorithm for sensors time-varying incipient fault detection and isolation. The proposed approach formulates the fault detection index and fault signature using the extended Kalman filter. Algorithm relaxes assumption on a monitored system stability and a priori knowledge of the fault profile. Further, fault decision statistics has been devised using Kullback-Leibler Divergence (KLD) and mixed with an Exponential Weighted Moving Average (EWMA) control chart. Pressurized water reactor nuclear power plant temperature and neutron flux sensors incipient fault detection and isolation have been demonstrated to illustrate the effectiveness of proposed methodology.

Study of THz-Plasmon hybridization of a loop Yagi-Uda absorber.

In this article we present a three-dimensional loop Yagi-Uda array for efficient, polarization independent and directional absorption of THz radiation over a narrow frequency range (f0 = 0.657 THz & Q factor = 7.5). Unit cell of the array consists of three vertically stacked gold micro rings separated from each other by 30 µm thick SU-8 layers. The proposed array also exhibits a filtering response in its transmittance spectrum. The characteristics are explained by plasmon hybridization method. The transmission, reflection and absorption spectra of the structure are measured and they show a good agreement with corresponding simulated results.

TCAD simulation for alpha-particle spectroscopy using SIC Schottky diode.

There is a growing requirement of alpha spectroscopy in the fields context of environmental radioactive contamination, nuclear waste management, site decommissioning and decontamination. Although silicon-based alpha-particle detection technology is mature, high leakage current, low displacement threshold and radiation hardness limits the operation of the detector in harsh environments. Silicon carbide (SiC) is considered to be excellent material for radiation detection application due to its high band gap, high displacement threshold and high thermal conductivity. In this report, an alpha-particle-induced electron-hole pair generation model for a reverse-biased n-type SiC Schottky diode has been proposed and verified using technology computer aided design (TCAD) simulations. First, the forward-biased I-V characteristics were studied to determine the diode ideality factor and compared with published experimental data. The ideality factor was found to be in the range of 1.4-1.7 for a corresponding temperature range of 300-500 K. Next, the energy-dependent, alpha-particle-induced EHP generation model parameters were optimised using transport of ions in matter (TRIM) simulation. Finally, the transient pulses generated due to alpha-particle bombardment were analysed for (1) different diode temperatures (300-500 K), (2) different incident alpha-particle energies (1-5 MeV), (3) different reverse bias voltages of the 4H-SiC-based Schottky diode (-50 to -250 V) and (4) different angles of incidence of the alpha particle (0°-70°).The above model can be extended to other (wide band-gap semiconductor) device technologies useful for radiation-sensing application.

Impact of ambulation in wearable-ECG.

Ambulatory ElectroCardioGram (ECG) analysis is adversely affected by motion artifacts induced due to body movements. Knowledge of the extent of motion artifacts could facilitate better ECG analysis. In this paper, our purpose is to determine the impact of body movement kinematics on the extent of ECG motion artifact by defining a notion called impact signal. Two approaches have been adopted in this paper to validate our experiments. One of them involves measuring local acceleration using motion sensors at appropriate body positions, in conjunction with the ECG, while performing routine activities at different intensity levels. The other method consists of ECG acquisition during Treadmill testing at controlled speeds and fixed duration. Data has been acquired from both healthy subjects as well as patients with suspected cardio-vascular disorders. In case of patients, the treadmill tests were carried out under the supervision of a cardiologist. We demonstrate that the impact signal shows a proportional increase with the increasing activity levels. The measured accelerations obtained are also found to be well correlated with the impact signal. The impact analysis thus indicates the suitability of the proposed method for quantification of body movement kinematics from the ECG signal itself, even in the absence of any accelerometer sensors. Such quantification would also help in automatic documentation of patient activity levels, which could aid in better interpretation of ambulatory ECG.

Strain specificity in antimicrobial activity of silver and copper nanoparticles.

The antimicrobial properties of silver and copper nanoparticles were investigated using Escherichia coli (four strains), Bacillus subtilis and Staphylococcus aureus (three strains). The average sizes of the silver and copper nanoparticles were 3 nm and 9 nm, respectively, as determined through transmission electron microscopy. Energy-dispersive X-ray spectra of silver and copper nanoparticles revealed that while silver was in its pure form, an oxide layer existed on the copper nanoparticles. The bactericidal effect of silver and copper nanoparticles were compared based on diameter of inhibition zone in disk diffusion tests and minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of nanoparticles dispersed in batch cultures. Bacterial sensitivity to nanoparticles was found to vary depending on the microbial species. Disk diffusion studies with E. coli and S. aureus revealed greater effectiveness of the silver nanoparticles compared to the copper nanoparticles. B. subtilis depicted the highest sensitivity to nanoparticles compared to the other strains and was more adversely affected by the copper nanoparticles. Good correlation was observed between MIC and MBC (r2=0.98) measured in liquid cultures. For copper nanoparticles a good negative correlation was observed between the inhibition zone observed in disk diffusion test and MIC/MBC determined based on liquid cultures with the various strains (r2=-0.75). Although strain-specific variation in MIC/MBC was negligible for S. aureus, some strain-specific variation was observed for E. coli.

Impact analysis of body movement in ambulatory ECG.

Ambulatory ECG analysis is adversely affected by motion artifacts induced due to body movements. Knowledge of the extent of motion artifacts facilitates better ECG analysis. In [1], an unsupervised method using recursive principal component analysis (RPCA) was used to detect transitions between body movements. In this paper, we endeavour to quantify the impact of various types of body movements on the extent of ECG motion artifact using the RPCA error signal. For this purpose, acceleration data from different body parts i.e. arm(s), leg and waist, have been obtained using commercially available motion sensors, in conjunction with ECG signal, while carrying out routine body movement activities like climbing stairs, walking, twisting, and arm movements, at three different intensity levels: slow, medium and fast. The acceleration magnitudes and the RPCA error sequence are found to be well correlated, thus validating the body movement impact analysis, and also indicating the suitability of the method for quantification of body movement kinematics from the ECG signal itself in the absence of any accelerometer sensors.

Transition detection in body movement activities for wearable ECG.

It has been shown by Pawar et al. (2007) that the motion artifacts induced by body movement activity (BMA) in a single-lead wearable electrocardiogram (ECG) signal recorder, while monitoring an ambulatory patient, can be detected and removed by using a principal component analysis (PCA)-based classification technique. However, this requires the ECG signal to be temporally segmented so that each segment comprises of artifacts due to a single type of body movement activity. In this paper, we propose a simple, recursively updated PCA-based technique to detect transitions wherever the type of body movement is changed.

Body movement activity recognition for ambulatory cardiac monitoring.

Wearable electrocardiogram (W-ECG) recorders are increasingly in use by people suffering from cardiac abnormalities who also choose to lead an active lifestyle. The challenge presently is that the ECG signal is influenced by motion artifacts induced by body movement activity (BMA) of the wearer. The usual practice is to develop effective filtering algorithms which will eliminate artifacts. Instead, our goal is to detect the motion artifacts and classify the type of BMA from the ECG signal itself. We have recorded the ECG signals during specified BMAs, e.g., sitting still, walking, movements of arms and climbing stairs, etc. with a single-lead system. The collected ECG signal during BMA is presumed to be an additive mix of signals due to cardiac activities, motion artifacts and sensor noise. A particular class of BMA is characterized by applying eigen decomposition on the corresponding ECG data. The classification accuracies range from 70% to 98% for various class combinations of BMAs depending on their uniqueness based on this technique. The above classification is also useful for analysis of P and T waves in the presence of BMA.

Analysis of ambulatory ECG signal.

Ambulatory electrocardiogram (ECG) recorders are increasingly in use by people suffering from cardiac abnormalities. However, the ECG signal acquired by the ambulatory recorder is influenced by motion artifacts induced by any body movement activity (BMA). The goal of the paper is to demonstrate that it is possible to determine the BMA from the motion artifacts in the ECG signal itself. The ECG signal during a specific BMA is presumed to be an additive mix of signals due to cardiac activities, motion artifacts induced due to the BMA and sensor noise. We propose to characterize and determine the BMA from the corresponding motion artifact data in the ECG signal itself. The proposed technique is useful for removal of motion artifacts from the ECG signals for ambulatory cardiac monitoring.