Time-domain heart rate dynamics in the prognosis of progressive atherosclerosis.

BACKGROUND AND AIM: The regular uptake of a high-fat diet (HFD) with changing lifestyle causes atherosclerosis leading to cardiovascular diseases and autonomic dysfunction. Therefore, the current study aimed to investigate the correlation of autonomic activity to lipid and atherosclerosis markers. Further, the study proposes a support vector machine (SVM) based model in the prediction of atherosclerosis severity. METHODS AND RESULTS: The Lead-II electrocardiogram and blood markers were measured from both the control and the experiment subjects each week for nine consecutive weeks. The time-domain heart rate variability (HRV) parameters were derived, and the significance level was tested using a one-way Analysis of Variance. The correlation analysis was performed to determine the relation between autonomic parameters and lipid and atherosclerosis markers. The statistically significant time-domain values were used as features of the SVM. The observed results demonstrated the reduced time domain HRV parameters with the increase in lipid and atherosclerosis index markers with the progressive atherosclerosis severity. The correlation analysis revealed a negative association between time-domain HRV parameters with lipid and atherosclerosis parameters. The percentage accuracy increases from 86.58% to 98.71% with the increase in atherosclerosis severity with regular consumption of HFD. CONCLUSIONS: Atherosclerosis causes autonomic dysfunction with reduced HRV. The negative correlation between autonomic parameters and lipid profile and atherosclerosis indexes marker revealed the potential role of vagal activity in the prognosis of atherosclerosis progression. The support vector machine presented a respectable accuracy in the prediction of atherosclerosis severity from the control group.

Laparoscopic Management of Median Arcuate Ligament Syndrome: A Case Report.

Median arcuate ligament syndrome (MALS) is an uncommon disorder characterized by postprandial abdominal pain, vomiting, anorexia, and weight loss. The symptoms are due to the MAL compressing the celiac artery and irritating the celiac plexus/ganglion. We report the case of one patient who presented with chronic abdominal pain, bloating, vomiting, and weight loss. Computed tomography angiography led to the diagnosis of MALS. The patient was treated surgically by laparoscopic division of the MLA and excision of the celiac plexus. He was discharged on day two without complications. On one-month follow-up, he was symptom-free, eating well, and had gained weight. He is on regular follow-up.

Andrographolide: A Herbal-Chemosynthetic Approach for Enhancing Immunity, Combating Viral Infections, and Its Implication on Human Health.

Plants consistently synthesize and accumulate medically valuable secondary metabolites which can be isolated and clinically tested under in vitro conditions. An advancement with such important phytochemical production has been recognized and utilized as herbal drugs. Bioactive andrographolide (AGL; C20H30O5) isolated from Andrographis paniculate (AP) (Kalmegh) is a diterpenoid lactones having multifunctional medicinal properties including anti-manic, anti-inflammatory, liver, and lung protective. AGL is known for its immunostimulant activity against a variety of microbial infections thereby, regulating classical and alternative macrophage activation, Ag-specific antibody production during immune disorder therapy. In vitro studies with AGL found it to be effective against multiple tumors, neuronal disorders, diabetes, pneumonia, fibrosis, and other diverse therapeutic misadventures. Generally, virus-based diseases like ZIKA, influenza A virus subtype (H1NI), Ebola (EBOV), Dengue (DENV), and coronavirus (COVID-19) epidemics have greatly increased scientific interest and demands to develop more effective and economical immunomodulating drugs with minimal side effects. Trials and in vitro pharmacological studies with AGL and medicinally beneficial herbs might contribute to benefit the human population without using chemical-based synthetic drugs. In this review, we have discussed the possible role of AGL as a promising herbal-chemo remedy during human diseases, viral infections and as an immunity booster.

Heart rate variability time domain features in automated prediction of diabetes in rat.

Diabetes is a very common occurring disease, diagnosed by hyperglycemia. The established mode of diagnosis is the analysis of blood glucose level with the help of a hand-held glucometer. Nowadays, it is also known for affecting multi-organ functions, particularly the microvasculature of the cardiovascular system. In this work, an alternative diagnostic system based on the heart rate variability (HRV) analysis and artificial neural network (ANN) and support vector machine (SVM) have been proposed. The experiment and data recording has been performed on male Wister rats of 10-12 week of age and 200 ± 20 gm of weight. The digital lead-I electrocardiogram (ECG) data are recorded from control (n = 5) and Streptozotocin-induced diabetic rats (n = 5). Nine time-domain linear HRV parameters are computed from 60 s of ECG data epochs and used for the training and testing of backpropagation ANN and SVM. Total 526 (334 Control and 192 diabetics) such datasets are computed for the testing of ANN for the identification of the diabetic conditions. The ANN has been optimized for architecture 9:5:1 (Input: hidden: output neurons, respectively) with the optimized learning rate parameter at 0.02. With this network, a very good classification accuracy of 96.2% is achieved. While similar accuracy of 95.2% is attained using SVM. Owing to the successful implementation of HRV parameters based automated classifiers for diabetic conditions, a non-invasive, ECG based online prognostic system can be developed for accurate and non-invasive prediction of the diabetic condition.

Assessing the impacts of historical and future land use and climate change on the streamflow and sediment yield of a tropical mountainous river basin in South India.

In this study, the impacts of land use/land cover (LULC) and climate change on the streamflow and sediment yield were investigated for the Payaswani River Basin, Western Ghats, India. The LULC was determined using Landsat images, and climate data were procured from five general circulation models for representative concentration pathway (RCP) 4.5 (moderate emission) and 8.5 (high emission). The land change modeler was used to derive the future LULC and its changes from 1988 (historical) to 2030 (future) by using the transition matrix method. The SWAT model was used to assess the impacts of LULC and climate change for the streamflow and sediment yield. The results showed that decrease in forests and grasslands and increase in plantation, agricultural, and urban areas from 1988 to 2030 would lead to an increase in the mean streamflow (11.23%) and sediment yield (17.41%). Under RCP 4.5, climate change would decrease the streamflow by 2.38% in 2030. However, under RCP 8.5, climate change would increase the streamflow by 0.12% in 2030. The sediment yield under RCP 4.5 and 8.5 would increase by 1.23% and 3.33%, respectively. In comparison with the baseline condition, by 2030 future changes in the LULC and climate would increase the streamflow by 7.05% and 11.71% under RCP 4.5 and 8.5, respectively. The sediment yield would increase by 7.92% and 27.11% under RCP 4.5 and 8.5, respectively. The streamflow and sediment yield were predicted to increase in the summer and winter but decrease in the monsoon season.

(E)-ß-farnesene gene reduces Lipaphis erysimi colonization in transgenic Brassica juncea lines.

Aphids are the major concern that significantly reduces the yield of crops. (E)-ß-farnesene (Eßf) is the principal component of the alarm pheromone of many aphids. The results of current research support the direct defense response of (E)-ß-farnesene (Eßf) against aphid Lipaphis erysimi (L.) Kaltenbach in Brassica juncea. Eßf gene was isolated from Mentha arvensis and transformed into B. juncea, showed direct repellent against aphid colonization. The seasonal mean population (SMP) recorded under field condition showed significantly higher aphid colonization in wild type in comparison to most of the transgenic lines, and shows positive correlation with the repellency of transgenic plant expressing (E)-ß-farnesene. The current research investigation provides direct evidence for aphid control in B. juncea using Eßf, a non-toxic mode of action.

Classification of Two Class Motor Imagery Tasks Using Hybrid GA-PSO Based K-Means Clustering.

Transferring the brain computer interface (BCI) from laboratory condition to meet the real world application needs BCI to be applied asynchronously without any time constraint. High level of dynamism in the electroencephalogram (EEG) signal reasons us to look toward evolutionary algorithm (EA). Motivated by these two facts, in this work a hybrid GA-PSO based K-means clustering technique has been used to distinguish two class motor imagery (MI) tasks. The proposed hybrid GA-PSO based K-means clustering is found to outperform genetic algorithm (GA) and particle swarm optimization (PSO) based K-means clustering techniques in terms of both accuracy and execution time. The lesser execution time of hybrid GA-PSO technique makes it suitable for real time BCI application. Time frequency representation (TFR) techniques have been used to extract the feature of the signal under investigation. TFRs based features are extracted and relying on the concept of event related synchronization (ERD) and desynchronization (ERD) feature vector is formed.

Support vector machine and fuzzy C-mean clustering-based comparative evaluation of changes in motor cortex electroencephalogram under chronic alcoholism.

In this study, the magnitude and spatial distribution of frequency spectrum in the resting electroencephalogram (EEG) were examined to address the problem of detecting alcoholism in the cerebral motor cortex. The EEG signals were recorded from chronic alcoholic conditions (n = 20) and the control group (n = 20). Data were taken from motor cortex region and divided into five sub-bands (delta, theta, alpha, beta-1 and beta-2). Three methodologies were adopted for feature extraction: (1) absolute power, (2) relative power and (3) peak power frequency. The dimension of the extracted features is reduced by linear discrimination analysis and classified by support vector machine (SVM) and fuzzy C-mean clustering. The maximum classification accuracy (88 %) with SVM clustering was achieved with the EEG spectral features with absolute power frequency on F4 channel. Among the bands, relatively higher classification accuracy was found over theta band and beta-2 band in most of the channels when computed with the EEG features of relative power. Electrodes wise CZ, C3 and P4 were having more alteration. Considering the good classification accuracy obtained by SVM with relative band power features in most of the EEG channels of motor cortex, it can be suggested that the noninvasive automated online diagnostic system for the chronic alcoholic condition can be developed with the help of EEG signals.

Jasmonate-induced biosynthesis of andrographolide in Andrographis paniculata.

Andrographolide is a prominent secondary metabolite found in Andrographis paniculata that exhibits enormous pharmacological effects. In spite of immense value, the normal biosynthesis of andrographolide results in low amount of the metabolite. To induce the biosynthesis of andrographolide, we attempted elicitor-induced activation of andrographolide biosynthesis in cell cultures of A. paniculata. This was carried out by using methyl jasmonate (MeJA) as an elicitor. Among the various concentrations of MeJA tested at different time periods, 5 µM MeJA yielded 5.25 times more andrographolide content after 24 h of treatment. The accumulation of andrographolide was correlated with the expression level of known regulatory genes (hmgs, hmgr, dxs, dxr, isph and ggps) of mevalonic acid (MVA) and 2-C-methyl-d-erythritol-4-phosphate (MEP) pathways. These results established the involvement of MeJA in andrographolide biosynthesis by inducing the transcription of its biosynthetic pathways genes. The coordination of isph, ggps and hmgs expression highly influenced the andrographolide biosynthesis.

Establishment of in vitro adventitious root cultures and analysis of andrographolide in Andrographis paniculata.

Andrographolide is the principal bioactive component of the medicinal plant Andrographis paniculata, to which various diverse pharmacological properties are attributed. Traditionally, andrographolide was extracted from the leaves, stems and other parts of the plant. Leaves have the highest andrographolide content (2-3%) in comparison with the other plant parts. Adventitious root culture of leaf explants of A. paniculata was studied using different strength MS medium supplemented by different concentrations of auxins and a combination of NAA + kinetin for growth and andrographolide production. Among the different auxin treatments in adventitious root culture, only NAA was able to induce adventitious roots. Adventitious roots grown in modified strength MS medium showed the highest root growth (26.7 +/- 1.52), as well as the highest amount of andrographolide (133.3 +/- 1.5 mg/g DW) as compared with roots grown in half- and full-strength MS medium. Growth kinetics showed maximum biomass production after five weeks of culture in different strength MS liquid medium. The produced andrographolide content was 3.5 - 5.5 folds higher than that of the natural plant, depending on the medium strength.

Small CAB-like proteins prevent formation of singlet oxygen in the damaged photosystem II complex of the cyanobacterium Synechocystis sp. PCC 6803.

The cyanobacterial small CAB-like proteins (SCPs) are single-helix membrane proteins mostly associated with the photosystem II (PSII) complex that accumulate under stress conditions. Their function is still ambiguous although they are assumed to regulate chlorophyll (Chl) biosynthesis and/or to protect PSII against oxidative damage. In this study, the effect of SCPs on the PSII-specific light-induced damage and generation of singlet oxygen ((1)O(2)) was assessed in the strains of the cyanobacterium Synechocystis sp. PCC 6803 lacking PSI (PSI-less strain) or lacking PSI together with all SCPs (PSI-less/scpABCDE(-) strain). The light-induced oxidative modifications of the PSII D1 protein reflected by a mobility shift of the D1 protein and by generation of a D1-cytochrome b-559 adduct were more pronounced in the PSI-less/scpABCDE(-) strain. This increased protein oxidation correlated with a faster formation of (1)O(2) as detected by the green fluorescence of Singlet Oxygen Sensor Green assessed by a laser confocal scanning microscopy and by electron paramagnetic resonance spin-trapping technique using 2, 2, 6, 6-tetramethyl-4-piperidone (TEMPD) as a spin trap. In contrast, the formation of hydroxyl radicals was similar in both strains. Our results show that SCPs prevent (1)O(2) formation during PSII damage, most probably by the binding of free Chl released from the damaged PSII complexes.

DFAspike: a new computational proposition for efficient recognition of epileptic spike in EEG.

An automated method has been presented for the detection of epileptic spikes in the electroencephalogram (EEG) using a deterministic finite automata (DFA) and has been named as DFAspike. EEG data (sampled, 256 Hz) files are the inputs to the DFAspike. The DFAspike was tested with different data files containing epileptic spikes. The obtained recognition rate of epileptic spike was 99.13% on an average. This system does not require any kind of prior training or human intrusion. The result shows that the designed system can be very effectively used for the detection of spikes present in the recorded EEG signals.

Parallel algorithm to analyze the brain signals: application on epileptic spikes.

In the current work, we have proposed a parallel algorithm for the recognition of Epileptic Spikes (ES) in EEG. The automated systems are used in biomedical field to help the doctors and pathologist by producing the result of an inspection in real time. Generally, the biomedical signal data to be processed are very large in size. A uniprocessor computer is having its own limitation regarding its speed. So the fastest available computer with latest configuration also may not produce results in real time for the immense computation. Parallel computing can be proved as a useful tool for processing the huge data with higher speed. In the proposed algorithm 'Data Parallelism' has been applied where multiple processors perform the same operation on different part of the data to produce fast result. All the processors are interconnected with each other by an interconnection network. The complexity of the algorithm was analyzed as Θ((n + δn) / N) where, 'n' is the length of the input data, 'N' is the number of processor used in the algorithm and 'δn' is the amount of overlapped data between two consecutive intermediate processors (IPs). This algorithm is scalable as the level of parallelism increase linearly with the increase in number of processors. The algorithm has been implemented in Message Passing Interface (MPI). It was tested with 60 min recorded EEG signal data files. The recognition rate of ES on an average was 95.68%.

Singlet oxygen scavenging activity of plastoquinol in photosystem II of higher plants: electron paramagnetic resonance spin-trapping study.

Singlet oxygen (¹O2) scavenging activity of plastoquinol in photosystem II (PSII) of higher plants was studied by electron paramagnetic resonance (EPR) spin-trapping technique. It is demonstrated here that illumination of spinach PSII membranes deprived of intrinsic plastoquinone results in ¹O2 formation, as monitored by TEMPONE EPR signal. Interestingly, the addition of exogenous plastoquinol (PQH2-1) to PQ-depleted PSII membranes significantly suppressed TEMPONE EPR signal. The presence of exogenous plastoquinols with a different side-chain length (PQH2-n, n isoprenoid units in the side chain) caused a similar extent of ¹O2 scavenging activity. These observations reveal that plastoquinol exogenously added to PQ-depleted PSII membranes serves as efficient scavenger of ¹O2.

Water-splitting manganese complex controls light-induced redox changes of cytochrome b559 in photosystem II.

The effect of water-splitting Mn complex on light-induced redox changes of cytochrome b(559) (cyt b(559)) was studied in spinach photosystem II (PSII) membranes. Photoreduction of the heme iron in the intact PSII membranes was completely suppressed by DCMU, whereas photoreduction and photooxidation of the heme iron in the Mn-depleted PSII membranes were unaffected by DCMU. Interestingly, photoreduction and photooxidation of the heme iron in the Mn-depleted PSII membranes were completely diminished by exogenous superoxide dismutase (SOD), whereas no effect of SOD on photoreduction of the heme iron was observed in the intact PSII membranes. The current work shows that the light-induced redox changes of cyt b(559) proceed via a different mechanism in the both types of PSII membranes. In the intact PSII membranes, photoreduction of the heme iron is mediated by plastoquinol. However, in the Mn-depleted PSII membranes, photoreduction and photooxidation of the heme iron are mediated by superoxide anion radical formed in PSII.

Mechanistic electronic model to simulate and predict the effect of heat stress on the functional genomics of HO-1 system: Vasodilation.

The present work is concerned to model the molecular signalling pathway for vasodilation and to predict the resting young human forearm blood flow under heat stress. The mechanistic electronic modelling technique has been designed and implemented using MULTISIM 8.0 and an assumption of 1V/ degrees C for prediction of forearm blood flow and the digital logic has been used to design the molecular signalling pathway for vasodilation. The minimum forearm blood flow has been observed at 35 degrees C (0 ml 100 ml(-1)min(-1)) and the maximum at 42 degrees C (18.7 ml 100 ml(-1)min(-1)) environmental temperature with respect to the base value of 2 ml 100 ml(-1)min(-1). This model may also enable to identify many therapeutic targets that can be used in the treatment of inflammations and disorders due to heat-related illnesses.

Analysis of age dependent effects of heat stress on EEG frequency components in rats.

OBJECTIVE: To demonstrate changes in different frequencies of cerebral electrical activity or electroencephalogram (EEG) following exposure to high environmental heat in three different age groups of freely moving' rats. METHODS: Rats were divided into three groups (i) acute heat stress--subjected to a single exposure for four hours at 38 degrees C; (ii) chronic heat stress--exposed for 21 days daily for one hour at 38 degrees C, and (iii) handling control groups. The digital polygraphic sleep-EEG recordings were performed just after the heat exposure from acute stressed rats and on 22nd day from chronic stressed rats by simultaneous recording of cortical EEG, EOG (electrooculogram), and EMG (electromyogram). Further, power spectrum analyses were performed to analyze the effects of heat stress. RESULTS: The frequency analysis of EEG signals following exposure to high environmental heat revealed that in all three age groups of rats, changes in higher frequency components (beta 2) were significant in all sleep-wake states following both acute and chronic heat stress conditions. After exposure to acute heat, significant changes in EEG frequencies with respect to their control groups were observed, which were reversed partly or fully in four hours of EEG recording. On the other hand, due to repetitive chronic exposure to hot environment, adaptive and long-term changes in EEG frequency patterns were observed. CONCLUSION: The present study has exhibited that the cortical EEG is sensitive to environmental heat and alterations in EEG frequencies in different sleep-wake states due to heat stress can be differentiated efficiently by EEG power spectrum analysis.

Epileptic spike recognition in electroencephalogram using deterministic finite automata.

This Paper presents an automated method of Epileptic Spike detection in Electroencephalogram (EEG) using Deterministic Finite Automata (DFA). It takes prerecorded single channel EEG data file as input and finds the occurrences of Epileptic Spikes data in it. The EEG signal was recorded at 256 Hz in two minutes separate data files using the Visual Lab-M software (ADLink Technology Inc., Taiwan). It was preprocessed for removal of baseline shift and band pass filtered using an infinite impulse response (IIR) Butterworth filter. A system, whose functionality was modeled with DFA, was designed. The system was tested with 10 EEG signal data files. The recognition rate of Epileptic Spike as on average was 95.68%. This system does not require any human intrusion. Also it does not need any short of training. The result shows that the application of DFA can be useful in detection of different characteristics present in EEG signals. This approach could be extended to a continuous data processing system.

Neural network detects the effects of p-CPA pre-treatment on brain electrophysiology in a rat model of focal brain injury.

OBJECTIVE: To examine the performance of Artificial Neural Network (ANN) in evaluation of the effects of pretreatment of para-Chlorophenylalanine (p-CPA), a serotonin blocker, in experimental brain injury. METHODS: Continuous 4 h digital electroencephalogram (EEG) recordings from male Charles Foster rats and its power spectrum analysis by using fast Fourier transform (FFT) were performed in two experimental (i) drug untreated injury group; (ii) p-CPA pretreated injury group as well as a control group. The EEG power spectrum data were tested by ANN containing 60 nodes in input layer, weighted from the digital values of power spectrum from 0 to 30 Hz, 18 nodes in hidden layer and an output node. The effects of injury and of the drug pretreatment were confirmed with the help of calculation of edematous swelling in the brain. RESULTS: The changes in EEG spectral patterns were compared with the ANN and the accuracy was determined in terms of percent (%). Overall performance of the network was found the best in control group (97.9%) in comparison to p-CPA untreated injury group (96.3%) and p-CPA pretreated injury group (71.9%). The decrease in accuracy in p-CPA pretreated injury group of subjects have occurred due to increase in misclassified patterns due to faster recovery in brain cortical potentials. CONCLUSION: EEG spectrum analysis with ANN was found successful in identifying the changes due to brain swelling as well as the effect of pretreatment of p-CPA in focal brain injury condition. Thus, the training and testing of ANN with EEG power spectra can be used as an effective diagnostic tool for early prediction and monitoring of brain injury as well as the effects of drugs in this condition.

An unsupervised neural network to predict the level of heat stress.

Heat stress is known to induce high mortality rate due to multi-system illness, which demands urgent attention to reduce the fatality rate in such patients. Further, for the diagnosis and supportive therapy, one needs to define the severity of heat stress that can be distinguished as mild, intermediate and severe. The objective of this work is to develop an automated unsupervised artificial system to analyze the clinical outcomes of different levels of heat related illnesses. The Kohonen neural network program written in C++, which has seven normalized values of different clinical symptoms between 0-1 fed to the input layer of the network with 50 Kohonen output neurons, has been presented. The optimized initializing parameters such as neighborhood size and learning rate was set to 50 and 0.7, respectively, to simulate the network for 10 million iterations. The network was found smartly distinguishing all 51 patterns to three different states of heat illnesses. With the advent of these findings, it can be concluded that the Kohonen neural network can be used for automated classification of the severity of heat stress and other related psycho-patho-physiological disorders. However, to replace the expert clinicians with such type of smart diagnostic tool, extensive work is required to optimize the system with variety of known and hidden clinical and pathological parameters.