A comprehensive review of dye-sensitized solar cell optimal fabrication conditions, natural dye selection, and application-based future perspectives.

Dye-sensitized solar cells (DSSC) constructed using natural dyes possess irreplaceable advantages in energy applications. The main reasons are its performance, environmentally benign dyes, impressible performance in low light, ecologically friendly energy production, and versatile solar product integration. Though DSSCs using natural dyes as sensitizers have many advantages, they suffer from poor efficiency compared to conventional silicon solar cells. Moreover, the difficulty in converting them to practical devices for the day-to-day energy needs has to be addressed. This review will outline the optimization of conditions to be followed for better efficiency in DSSCs using natural dyes as sensitizers. This review has taken into account the importance of the first step towards the fabrication of DSSC, i.e. the selection process. The selection of plant parts has a noticeable impact on the overall efficiency of the device. Accordingly, a proper study has been done to analyse the plant's parts that have shown better results in terms of device efficiency. In addition to this, a wide range of techniques and factors such as extraction methods, the solvent used, coating techniques, immersing time, and co-sensitization have been taken into consideration from the studies done over the period of 10 years to examine their influence on the overall performance of the DSSC device. These results have been addressed to stipulate the best suitable condition that will help supplement the efficiency of the device even further. Also, the future perspectives, such as the DSSCs use in wearable devices, incorporating various approaches to enhance the power conversion efficiency of DSSCs using natural dyes, and thermochromism ability for DSSCs have been discussed.

Detection of ADHD From EEG Signals Using Different Entropy Measures and ANN.

Attention deficit hyperactivity disorder (ADHD) is a prevalent behavioral, cognitive, neurodevelopmental pediatric disorder. Clinical evaluations, symptom surveys, and neuropsychological assessments are some of the ADHD assessment methods, which are time-consuming processes and have a certain degree of uncertainty. This research investigates an efficient computer-aided technological solution for detecting ADHD from the acquired electroencephalography (EEG) signals based on different nonlinear entropy estimators and an artificial neural network classifier. Features extracted through fuzzy entropy, log energy entropy, permutation entropy, SURE entropy, and Shannon entropy are analyzed for effective discrimination of ADHD subjects from the control group. The experimented results confirm that the proposed techniques can effectively detect and classify ADHD subjects. The permutation entropy gives the highest classification accuracy of 99.82%, sensitivity of 98.21%, and specificity of 98.82%. Also, the potency of different entropy estimators derived from the t-test reflects that the Shannon entropy has a higher P-value (>.001); therefore, it has a limited scope than other entropy estimators for ADHD diagnosis. Furthermore, the considerable variance found from potential features obtained in the frontal polar (FP) and frontal (F) lobes using different entropy estimators under the eyes-closed condition shows that the signals received in these lobes will have more significance in distinguishing ADHD from normal subjects.

Automated Epileptic Seizure Detection in Pediatric Subjects of CHB-MIT EEG Database-A Survey.

Epilepsy is a neurological disorder of the brain that causes frequent occurrence of seizures. Electroencephalography (EEG) is a tool that assists neurologists in detecting epileptic seizures caused by an unexpected flow of electrical activities in the brain. Automated detection of an epileptic seizure is a crucial task in diagnosing epilepsy which overcomes the drawback of a visual diagnosis. The dataset analyzed in this article, collected from Children's Hospital Boston (CHB) and the Massachusetts Institute of Technology (MIT), contains long-term EEG records from 24 pediatric patients. This review paper focuses on various patient-dependent and patient-independent personalized medicine approaches involved in the computer-aided diagnosis of epileptic seizures in pediatric subjects by analyzing EEG signals, thus summarizing the existing body of knowledge and opening up an enormous research area for biomedical engineers. This review paper focuses on the features of four domains, such as time, frequency, time-frequency, and nonlinear features, extracted from the EEG records, which were fed into several classifiers to classify between seizure and non-seizure EEG signals. Performance metrics such as classification accuracy, sensitivity, and specificity were examined, and challenges in automatic seizure detection using the CHB-MIT database were addressed.

Local Pattern Transformation Based Feature Extraction for Recognition of Parkinson's Disease Based on Gait Signals.

Parkinson's disease (PD) is a neuro-degenerative disorder primarily triggered due to the deterioration of dopamine-producing neurons in the substantia nigra of the human brain. The early detection of Parkinson's disease can assist in preventing deteriorating health. This paper analyzes human gait signals using Local Binary Pattern (LBP) techniques during feature extraction before classification. Supplementary to the LBP techniques, Local Gradient Pattern (LGP), Local Neighbour Descriptive Pattern (LNDP), and Local Neighbour Gradient Pattern (LNGP) were utilized to extract features from gait signals. The statistical features were derived and analyzed, and the statistical Kruskal-Wallis test was carried out for the selection of an optimal feature set. The classification was then carried out by an Artificial Neural Network (ANN) for the identified feature set. The proposed Symmetrically Weighted Local Neighbour Gradient Pattern (SWLNGP) method achieves a better performance, with 96.28% accuracy, 96.57% sensitivity, and 95.94% specificity. This study suggests that SWLNGP could be an effective feature extraction technique for the recognition of Parkinsonian gait.

Detection of Focal and Non-Focal Electroencephalogram Signals Using Fast Walsh-Hadamard Transform and Artificial Neural Network.

The discrimination of non-focal class (NFC) and focal class (FC), is vital in localizing the epileptogenic zone (EZ) during neurosurgery. In the conventional diagnosis method, the neurologist has to visually examine the long hour electroencephalogram (EEG) signals, which consumes time and is prone to error. Hence, in this present work, automated diagnosis of FC EEG signals from NFC EEG signals is developed using the Fast Walsh-Hadamard Transform (FWHT) method, entropies, and artificial neural network (ANN). The FWHT analyzes the EEG signals in the frequency domain and decomposes it into the Hadamard coefficients. Five different nonlinear features, namely approximate entropy (ApEn), log-energy entropy (LogEn), fuzzy entropy (FuzzyEn), sample entropy (SampEn), and permutation entropy (PermEn) are extracted from the decomposed Hadamard coefficients. The extracted features detail the nonlinearity in the NFC and the FC EEG signals. The judicious entropy features are supplied to the ANN classifier, with a 10-fold cross-validation method to classify the NFC and FC classes. Two publicly available datasets such as the University of Bonn and Bern-Barcelona dataset are used to evaluate the proposed approach. A maximum sensitivity of 99.70%, the accuracy of 99.50%, and specificity of 99.30% with the 3750 pairs of NFC and FC signal are achieved using the Bern-Barcelona dataset, while the accuracy of 92.80%, the sensitivity of 91%, and specificity of 94.60% is achieved using University of Bonn dataset. Compared to the existing technique, the proposed approach attained a maximum classification performance in both the dataset.

Robust Classification of Intramuscular EMG Signals to Aid the Diagnosis of Neuromuscular Disorders.

Goal: This article presents the design and validation of an accurate automatic diagnostic system to classify intramuscular EMG (iEMG) signals into healthy, myopathy, or neuropathy categories to aid the diagnosis of neuromuscular diseases. Methods: First, an iEMG signal is decimated to produce a set of "disjoint" downsampled signals, which are decomposed by the lifting wavelet transform (LWT). The Higuchi's fractal dimensions (FDs) of LWT coefficients in the subbands are computed. The FDs of LWT subband coefficients are fused with one-dimensional local binary pattern derived from each downsampled signal. Next, a multilayer perceptron neural network (MLPNN) determines the class labels of downsampled signals. Finally, the sequence of class labels is fed to the Boyer-Moore majority vote (BMMV) algorithm, which assigns a class to every iEMG signal. Results: The MLPNN-BMMV classifier was experimented with 250 iEMG signals belonging to three categories. The performance of the classifier was validated in comparison with state-of-the-art approaches. The MLPNN-BMMV has resulted in impressive performance measures (%) using a 10-fold cross-validation-accuracy = [Formula: see text], sensitivity (normal) = [Formula: see text], sensitivity (myopathy) = [Formula: see text], sensitivity (neuropathy) = [Formula: see text], specificity (normal) = [Formula: see text], specificity (myopathy) = [Formula: see text], and specificity (neuropathy) = [Formula: see text]-surpassing the existing approaches. Conclusions: A future research direction is to validate the classifier performance with diverse iEMG datasets, which would lead to the design of an affordable real-time expert system for neuromuscular disorder diagnosis.

An effective approach to classify epileptic EEG signal using local neighbor gradient pattern transformation methods.

Electroencephalographic (EEG) signal records the neuronal activity in the brain and it is used in the diagnosis of epileptic seizure activities. Human inspection of non-stationary EEG signal for diagnosing epilepsy is cumbersome, time-consuming and inaccurate. In this paper an effective automatic approach to detect epilepsy using two feature extraction techniques namely local neighbor gradient pattern (LNGP) and symmetrically weighted local neighbor gradient pattern (SWLNGP) are proposed. Extracted features are fed into machine learning algorithms like k-nearest neighbor (k-NN), quadratic linear discriminant analysis, support vector machine, ensemble classifier and artificial neural network (ANN) to classify the EEG signals. In this study, the classification performance for 17 different cases using 10-fold cross validation with the following classification problems are executed (i) healthy-ictal, (ii) interictal-ictal, (iii) healthy-interictal, (iv) seizure free-ictal and (v) healthy-interictal-ictal. The experimental result shows that in all the cases LNGP and SWLNGP attained higher classification accuracy using ANN. Further, the computational performance and the classification accuracy of the proposed methods are compared with the recently proposed techniques for epileptic detection. It shows that the performance of LNGP and SWLNGP method with ANN classifier are superior over other recently proposed techniques for the aforesaid problems. Hence, the proposed methods are simple, fast, reliable and easily implementable for real-time epileptic detection.

Leptospira surface adhesin (Lsa21) induces Toll like receptor 2 and 4 mediated inflammatory responses in macrophages.

Leptospirosis is zoonotic and emerging infectious disease of global importance. Little is understood about Leptospira pathogenesis and host immune response. In the present work we have investigated how Leptospira modulates the host innate immune response mediated by Toll-like receptors (TLRs) via surface exposed proteins. We screened Leptospira outer membrane/surface proteins for their ability to activate/inhibit TLR2/4 signaling in HEK293 cell lines. Of these the 21 kDa Leptospira surface adhesin, Lsa21 had strong TLR2 and TLR4 activity leading to production of proinflammatory cytokines and expression of costimulatory molecules in mouse macrophages. This activity of Lsa21 on innate response was dependent on activation of mitogen activated protein kinases (MAPKs) via stimulating the rapid phosphorylation of p38, JNK and activation of transcription factor NF-κB. Additionally, neutralizing antibodies against TLR2 and TLR4 significantly inhibited cytokine secretion and attenuated Lsa21 induced phosphorylation of p38 and JNK. Furthermore, Lsa21 induced cytokine levels were significantly lower in TLR2-/- and TLR4-/- than in wild type mouse macrophage cell lines. Confocal microscopy and molecular docking confirmed that Lsa21 interacted with both TLR2 and TLR4. These results indicate that Lsa21 is a potent TLR2 and TLR4 agonist that induces strong innate response and may play important role in Leptospira pathogenesis.

Expression of avian influenza virus (H5N1) hemagglutinin and matrix protein 1 in Pichia pastoris and evaluation of their immunogenicity in mice.

The conventional avian influenza vaccines rely on development of neutralizing antibodies against the HA and NA antigens. However, these antigens are highly variable, and hence there is a need for better vaccine candidates which would offer broader protection in animals. The M1 of avian influenza is another major structural protein that has conserved epitopes that are reported to induce CD8+ T cells and can contribute to protection against morbidity and mortality from influenza. Hence in an effort to study the immune response of rM1 either alone or in combination with rHA, the hemagglutinin (HA) and matrix protein (M1) of A/Hatay/2004/H5N1 strain of avian influenza were expressed in Pichia pastoris as his-tagged proteins and purified through Ni-NTA chromatography. The His-tag was removed using TEV protease cleavage site and the immunogenicity of purified rHA and rM1 either alone or in combination was determined in mice. One group of mice was immunized with 5 µg of purified rHA, the other group was immunized with rM1, and a third group of mice were immunized with 5 µg of rHA and rM1. All the animals were boosted twice, once on 28 days postimmunization (dpi) and the second on 42 dpi. The immune response was evaluated by enzyme-linked immunosorbent assay (ELISA) and hemagglutination inhibition (HI) assay. The group of mice immunized with rHA and rM1 together showed significantly higher immune response against rHA and rM1 than mice immunized with either HA or M1 antigens. The addition of rM1 with rHA resulted in increased HI titer in animals immunized with both the antigens. These results suggest that the HA and M1 expressed in P. pastoris can be utilized in combination for the development of faster and cost-effective vaccines for circulating and newer strains of avian influenza and would aid in combating the disease in a pandemic situation, in which production time matters greatly.

Recombinant OmpL1 protein as a diagnostic antigen for the detection of canine leptospirosis.

Microscopic agglutination test (MAT) is the standard method for the diagnosis of leptospirosis, which is laborious and the interpretation of the results is subjective. The present work describes the use of recombinant-based IgG ELISA for the serodiagnosis of leptospirosis. We used recombinant outer membrane protein OmpL1 as an antigen for conducting IgG enzyme-linked immunosorbent assay (ELISA). A total of 475 canine serum samples were subjected to IgG ELISA; 294 sera were positive to ELISA, while 283 were positive to MAT. All samples that were positive to MAT were positive to ELISA also, however, few samples which were negative to MAT were positive to ELISA, which suggested that recombinant-based IgG ELISA showed 100 % sensitivity when compared to MAT. Thus, this present study showed that recombinant OmpL1-based IgG ELISA appears to be a better alternative to MAT for the diagnosis of leptospirosis and rOmpL1 protein could be used as a potential diagnostic antigen in different assay formats for leptospirosis.

Development of rapid flow-through-based dot-immunoassay for serodiagnosis of leptospirosis in dogs.

An IgG-ELISA used recombinant antigen and a rapid flow-through enzyme immunoassay were developed for rapid screening of leptospiral antibodies in dogs using recombinant LipL41, which is one of the conserved outer membrane proteins in pathogenic leptospires as the coating antigen. Results from this study were compared with the standard microscopic agglutination test and found that the sensitivity and specificity of the enzyme-linked immunosorbent assay were 75.46% and 93.29% and whereas that of flow-through-based dot-immunobinding assay were 87.73% and 89.63%, respectively. Relative merits of these tests were also assessed. The flow-through-based dot-immunobinding assay was thus proved to be a valid screening test for canine leptospirosis.

Serodiagnosis of bovine leptospirosis by IgG-enzyme-linked immunosorbent assay and latex agglutination test.

The efficacy of a recombinant leptospiral outer membrane protein LipL41 as an antigen for conducting IgG-Enzyme linked immunosorbent assay (ELISA) and latex agglutination test (LAT) for serodiagnosis of bovine leptospirosis was evaluated. The recombinant LipL41 antigen developed and used for detecting the antibodies was specific in detection of the pathogenic serovars of Leptospira, as the expression of the LipL41 antigen is restricted only to pathogenic leptospires. A total of 430 bovine serum samples were subjected to IgG-ELISA and LAT, and the sensitivity and specificity were assessed in comparison with microscopic agglutination test (MAT). The sensitivity and specificity of IgG-ELISA and LAT were 86.84% and 93.16%, and 95.42% and 98.33% respectively. Both the tests are found to be sensitive, specific and concurred with the standard MAT. The study concluded that the rLipL41 protein could be used as a potential diagnostic antigen in different assay formats for bovine leptospirosis.

Rapid serodiagnosis of leptospirosis by latex agglutination test and flow-through assay.

PURPOSE: Diagnosis of leptospirosis facilitates patient management and initiation of therapy. The microscopic agglutination test (MAT) is the serological test used in reference laboratories because of its high degree of sensitivity and specificity. But the results are not available quickly for patient management. In the present study, in order to develop a simple, rapid immunodiagnostic assay, one of the outer membrane proteins (OMPs), recombinant LipL41 (rLipL41) has been utilised in latex agglutination test (LAT) and flow-through assay. METHODS: Part of LipL41 gene was expressed in Escherichia coli system and purified. The rLipL41 antigen of pathogenic Leptospira interrogans serovar Icterohaemorrhagiae, which is conserved in all pathogenic Leptospira spp. was used as capture antigen in the LAT and flow-through test. Both tests are very rapid and could be completed within 5 minutes. The sensitivity and specificity of rLipL41 was assessed and evaluated in LAT and flow-through assay in comparison with standard MAT. RESULTS: The sensitivity and specificity of the LAT were 89.70 and 90.45% and flow-through assay were 89.09 and 77.70%, respectively. CONCLUSIONS: The developed LAT and flow-through assays were simple, rapid and economical for the detection of leptospira infection and suitable for large-scale screening of samples in endemic areas without any sophisticated equipment.

Anti-inflammatory activity of four solvent fractions of ethanol extract of Mentha spicata L. investigated on acute and chronic inflammation induced rats.

Anti-inflammatory effects of four solvent fractions of ethanol extract of Mentha spicata were evaluated in acute and chronic inflammation induced in Wistar albino rats. Lipid peroxidation (LPO) and some antioxidants produced during chronic inflammation were quantitated. Hexane (320mg/kg of body weight in 25% DMSO), chloroform (320mg/kg body weight in 25% DMSO), ethyl acetate (160mg/kg body weight in 25% DMSO), aqueous (320mg/kg of body weight in ddH(2)O) fractions, two negative control groups (25% DMSO and ddH(2)O) and two anti-inflammatory drugs (Diclofenac: 25mg/kg of body weight; Indomethacin: 10mg/kg of body weight both in ddH(2)O) were administered by oral intubations to the eight groups of rats consisting six animals, each. In acute study, 1% carrageenan was injected subcutaneously in the sub-plantar region of the right hind paw after 1h of administration of test doses. The increased paw edema was measured at 0.5, 1, 2, 4, 8, 16 and 24h intervals. In the chronic study, the oral administration was carried out for seven consecutive days. On eighth day, four sterile cotton pellets (50mg each) were implanted subcutaneously in the dorsal region of the rats. On the sixteenth day, the rats were sacrificed and the cotton pellets with granulomatous tissue were dissected out and weighed (fresh and dry). Both in chronic and acute inflammation, ethyl acetate (EAF) and aqueous fraction (AF) were effective. EAF is comparable with the positive standards in chronic inflammation. The results indicate that EAF's anti-inflammatory activity is largely due to its ability to modulate in vivo antioxidants.

Brain regional responses in antioxidant system to alpha-lipoic acid in arsenic intoxicated rat.

Impaired antioxidant defense mechanisms and oxidative stress are implicated in the pathogenesis of arsenic toxicity. Our study was designed to determine whether alpha-lipoic acid, which has been shown to have substantial antioxidant properties, when administered (70 mg/kg body weight) once daily for 60 days along with arsenic (100 ppm sodium arsenite mixed in drinking water) would prevent arsenic-induced changes in antioxidant defense system, superoxide dismutase (SOD-total SOD, Mn SOD, Cu/Zn SOD), catalase (CAT) and glutathione peroxidase (GSH-PX) in rat brain regions such as cortex, hypothalamus, striatum, cerebellum and hippocampus. The present study also examined the effect of alpha-lipoic acid over arsenic-induced oxidant production and lipid peroxidation level (LPO) in discrete brain regions of rats. The cortex, striatum and hippocampus showed greater decreases in GSH-Px enzyme activity than cerebellum and hypothalamus with arsenic exposure. Striatum had the greatest percentage of decreased activities of total SOD and Mn SOD, whereas cortex had the greatest percentage decrease in the activity of Cu/Zn SOD in arsenic-alone treated rats. Hypothalamus and cerebellum exhibited the lowest catalase activity among all tested regions in arsenic-only treated rats. Rate of dichlorofluorescin oxidation, an indication of reactive oxygen species and other intracellular oxidants production was increased with arsenic exposure in all brain regions studied. Cortex, hippocampus and striatum exhibited greater increase of LPO levels than cerebellum and hypothalamus. SOD, CAT, GSH-Px activities were upregulated in arsenic plus lipoic acid treated versus arsenic-only treated rats. Also, simultaneous lipoic acid treatment along with arsenic proved to be sufficient in reducing oxidant production and LPO level in all rat brain regions. Our results demonstrate that arsenic-induced deficits in antioxidant enzyme activities and increase in oxidant production and lipid peroxidation level in brain regions can be overcome through simultaneous treatment with lipoic acid.

Supplementation of L-carnitine improves mitochondrial enzymes in heart and skeletal muscle of aged rats.

Aging is characterized by a general decline in physiological functions that affects many tissues and increases the risk of death. Deterioration of mitochondria, the major source and target of reactive oxygen species (ROS), is implicated in aging and a variety of age-related diseases. In the present study, the activities of citric acid cycle enzymes, such as isocitrate dehydrogenase, alpha-ketoglutarate dehydrogenase, succinate dehydrogenase, and malate dehydrogenase, were found to be decreased in aged rats as well as that of electron-transferring enzymes such as NADH dehydrogenase and cytochrome c oxidase. After supplementation of carnitine to aged rats, the activities of these enzymes reverted nearer to that of young control rats. These findings suggest that L-carnitine improves the activities of mitochondrial enzymes, increases the electron flow through the electron transport chain, and improves reducing equivalence, thereby improves energy status in aged rats.