STUDIES ON KINETIC PARAMETERS AND BIOCHEMICAL CHARACTERISTICS OF POLYPHENOL OXIDASE PURIFIED FROM JACKFRUIT (ARTOCARPUS HETEROPHYLLUS) WASTE

Objectives: Polyphenol oxidase activity was extensively studied in jackfruit for its role in enzymatic browning. PPO and the phenolic compound play a vital role in defensive mechanism against pest and diseases. Thus, to facilitate further studies in jack fruit waste, Polyphenol oxidase [PPO] was purified and characterized.Methods: Partial Purification of PPO from waste done through a sequential process of ammonium sulfate precipitation, dialysis and ion-exchange chromatography [DEAE- Cellulose]. Then the partially purified PPO was subjected to check various parameters like molecular weight and kinetic activity, the following characteristics of enzyme are checked: SDS-PAGE, pH, temperature, thermal stability, heat inactivation, metal ions, surfactants and inhibitor.Results: Purified PPO resulted in ~23 folds enriched in the specific activity of 1360 [µkat/mg] and it was found to be the monomer with a molecular weight of 63 kDa revealed by Coomasie Brilliant Blue staining. PPO exhibited optimum activity at pH 7.0 and temperature 20oC. PPO showed the maximum stability between pH 6.4- 7.6 at 10 oC - 40 oC. PPO showed the enzyme activity towards Diphenol> Triphenol> Monophenol, the substrate specificity was especially high towards the catechol at 0.1 M. The PPO activity was activated by Mn2+, Triton X- 100, EDTA, Sorbic acid and Citric acid, but inhibited by L- cysteine, Ascorbic acid, SDS, Cetyl trimethyl ammonium bromide [CTAB], K+, Zn2+, Ca2+ and Mg2+. Kinetic constant for PPO was found to be km= 15.82 mM and Vmax= 2182 U/ml min using catechol as substrate.Conclusion: Partial Purification of PPO from waste done through a sequential process of ammonium sulfate precipitation, dialysis and ion-exchange chromatography [DEAE- Cellulose]. The best substrate for PPO was identified as catechol [diphenol] and best inhibitor was L-cysteine and ascorbic acid. Â

Comparative study of the diagnostic power of full outline of unresponsiveness score and Glasgow coma scale in patients with traumatic brain injury in an emergency department

Background: The Glasgow coma scale (GCS) is the most commonly used scale, and the full outline of unresponsiveness (FOUR) score is new validated coma scale as an alternative to GCS to measure the level of consciousness and evaluate the severity of the injury in traumatic brain injury (TBI) patients. Aims and Objectives: The present study compared the performance of FOUR scores and GCS in outcome predictions of TBI cases. Materials and Methods: This prospective and cross-sectional study was conducted over a year by the Emergency Department of a tertiary care hospital, Kovai Medical Center and Hospital, Coimbatore, India. Of 159, 123 patients were recruited from intensive care unit (ICU), and 36 from the ward were included in this study. Data were collected using an observation checklist to determine the similarities and differences in predicting outcomes using the two assessment scales (GCS and FOUR). Results: Most patients were 51–60 years (38%), with a mean age of 41.57. About 82% were male, 18% were female, and 31% had comorbidity of hypertension. Data analysis showed a significant statistical difference in mean FOUR and GCS scores between ICU and ward admission. A multivariate logistic regression study revealed that the probability of ICU admission in trauma patients from the emergency department was associated with a decline in GCS and FOUR scores. The range of predicted ICU admission was similar in both GCS and FOUR score models. Conclusion: Although both scores are good predictors of TBI patients, we concluded that the FOUR score is a recommended predictive model for patients admitted to the medical ICU

Fabrication and investigation of agricultural monitoring system with IoT & AI

Artificial intelligence (AI) can be used in a variety of fields and has the potential to alter how we currently view farming. Due to its emphasis on effectiveness and usability artificial intelligence has the largest impact on agriculture of all industries. We highlight the automation-supporting technologies such as Artificial Intelligence (AI), Machine Learning, and Long-Range (LoRa) technology which provides data integrity and protection. We also offer a structure for smart farming that depends on the location of data processing after a comprehensive investigation of numerous designs. As part of our future study we have divided the unresolved difficulties in smart agriculture into two categories such as networking issues and technology issues. Artificial Intelligence and Machine Learning are examples of technologies whereas the Moderate Resolution Imaging Spectroradiometer satellite and LoRa are used for all network-related jobs. The goal of the research is to deploy a network of sensors throughout agricultural fields to gather real-time information on a variety of environmental factors including temperature, humidity, soil moisture and nutrient levels. The seamless data transmission and communication made possible by these sensors’ integration with Internet of Things technologies. With the use of AI techniques and algorithms the gathered data is examined. The technology may offer practical insights and suggestions for improving agricultural practices because the AI models are trained to spot patterns, correlations, and anomalies in the data. We are also focusing on indoor farming by supplying Ultra Violet radiation and artificial lighting in accordance with plant growth. When a pest assault is detected using AI and LoRa even in poor or no network coverage area and notifies the farmer’s mobile in any part of the world. The irrigation system is put to the test with various plants at various humidity and temperature levels in both dry and typical situations. To keep the water content in those specific regions soil moisture sensors are used

An improved GBSO-TAENN-based EEG signal classification model for epileptic seizure detection.

Detection and classification of epileptic seizures from the EEG signals have gained significant attention in recent decades. Among other signals, EEG signals are extensively used by medical experts for diagnosing purposes. So, most of the existing research works developed automated mechanisms for designing an EEG-based epileptic seizure detection system. Machine learning techniques are highly used for reduced time consumption, high accuracy, and optimal performance. Still, it limits by the issues of high complexity in algorithm design, increased error value, and reduced detection efficacy. Thus, the proposed work intends to develop an automated epileptic seizure detection system with an improved performance rate. Here, the Finite Linear Haar wavelet-based Filtering (FLHF) technique is used to filter the input signals and the relevant set of features are extracted from the normalized output with the help of Fractal Dimension (FD) analysis. Then, the Grasshopper Bio-Inspired Swarm Optimization (GBSO) technique is employed to select the optimal features by computing the best fitness value and the Temporal Activation Expansive Neural Network (TAENN) mechanism is used for classifying the EEG signals to determine whether normal or seizure affected. Numerous intelligence algorithms, such as preprocessing, optimization, and classification, are used in the literature to identify epileptic seizures based on EEG signals. The primary issues facing the majority of optimization approaches are reduced convergence rates and higher computational complexity. Furthermore, the problems with machine learning approaches include a significant method complexity, intricate mathematical calculations, and a decreased training speed. Therefore, the goal of the proposed work is to put into practice efficient algorithms for the recognition and categorization of epileptic seizures based on EEG signals. The combined effect of the proposed FLHF, FD, GBSO, and TAENN models might dramatically improve disease detection accuracy while decreasing complexity of system along with time consumption as compared to the prior techniques. By using the proposed methodology, the overall average epileptic seizure detection performance is increased to 99.6% with f-measure of 99% and G-mean of 98.9% values

Inhibition of COX-2 in Colon Cancer Modulates Tumor Growth and MDR-1 Expression to Enhance Tumor Regression in Therapy-Refractory Cancers In Vivo

AbstractHigher cyclooxygenase 2 (COX-2) expression is often observed in aggressive colorectal cancers (CRCs). Here, we attempt to examine the association between COX-2 expression in therapy-refractory CRC, how it affects chemosensitivity, and whether, in primary tumors, it is predictive of clinical outcomes. Our results revealed higher COX-2 expression in chemoresistant CRC cells and tumor xenografts. In vitro, the combination of either aspirin or celecoxib with 5-fluorouracil (5-FU) was capable of improving chemosensitivity in chemorefractory CRC cells, but a synergistic effect with 5-FU could only be demonstrated with celecoxib. To examine the potential clinical significance of these observations, in vivo studies were undertaken, which also showed that the greatest tumor regression was achieved in chemoresistant xenografts after chemotherapy in combination with celecoxib, but not aspirin. We also noted that these chemoresistant tumors with higher COX-2 expression had a more aggressive growth rate. Given the dramatic response to a combination of celecoxib + 5-FU, the possibility that celecoxib may modulate chemosensitivity as a result of its ability to inhibit MDR-1 was examined. In addition, assessment of a tissue microarray consisting of 130 cases of CRCs revealed that, in humans, higher COX-2 expression was associated with poorer survival with a 68% increased risk of mortality, indicating that COX-2 expression is a marker of poor clinical outcome. The findings of this study point to a potential benefit of combining COX-2 inhibitors with current regimens to achieve better response in the treatment of therapy-refractory CRC and in using COX-2 expression as a prognostic marker to help identify individuals who would benefit the greatest from closer follow-up and more aggressive therapy

Simulation of grid/standalone solar energy supplied reduced switch converter with optimal fuzzy logic controller using golden BallAlgorithm

This article presents the utilization of a shunt active power filter (SHAPF) in combination with an Energy Storage System (ESS) and a Solar Energy System (SES). Voltage source converters (VSC) are connected in parallel to a direct current (DC) bus. The membership function (MSF) of fuzzy logic controller (FLC) for the shunt control system is optimally adjusted using the golden balloptimization algorithm (GBOA). The present effort aims to achieve the following primary objectives: 1) Quick implementation to stabilize the voltage of the DC Link capacitor (DCLCV); 2) Mitigation of harmonics and improvement of power factor (PF); 3) Satisfactory performance under load as well as solar power varying conditions. The effectiveness of the optimally designed controller is evaluated by studying four test scenarios with grid and standalone conditions. The results are then compared to the existing sliding mode (SMC) and fuzzy logic controllers (FLC)

Regulation of mammary gland branching morphogenesis by the extracellular matrix and its remodeling enzymes.

A considerable body of research indicates that mammary gland branching morphogenesis is dependent, in part, on the extracellular matrix (ECM), ECM-receptors, such as integrins and other ECM receptors, and ECM-degrading enzymes, including matrix metalloproteinases (MMPs) and their inhibitors, tissue inhibitors of metalloproteinases (TIMPs). There is some evidence that these ECM cues affect one or more of the following processes: cell survival, polarity, proliferation, differentiation, adhesion, and migration. Both three-dimensional culture models and genetic manipulations of the mouse mammary gland have been used to study the signaling pathways that affect these processes. However, the precise mechanisms of ECM-directed mammary morphogenesis are not well understood. Mammary morphogenesis involves epithelial 'invasion' of adipose tissue, a process akin to invasion by breast cancer cells, although the former is a highly regulated developmental process. How these morphogenic pathways are integrated in the normal gland and how they become dysregulated and subverted in the progression of breast cancer also remain largely unanswered questions

Key stages in mammary gland development - Involution: apoptosis and tissue remodelling that convert the mammary gland from milk factory to a quiescent organ

Involution of the mammary gland is an essential process that removes the milk-producing epithelial cells when they become redundant at weaning. It is a two-step process that involves the death of the secretory epithelium and its replacement by adipo-cytes. During the first phase, remodelling is inhibited and apoptotic cells can be seen in the lumena of the alveoli. In the second phase, apoptosis is accompanied by remodelling of the surrounding stroma and re-differentiation of the adipocytes. Considerable effort has been directed towards understanding the molecular mechanisms of the involution process and this has resulted in the identification of the principal signalling pathways involved

MGEx-Udb: A Mammalian Uterus Database for Expression-Based Cataloguing of Genes across Conditions, Including Endometriosis and Cervical Cancer

Gene expression profiling of uterus tissue has been performed in various contexts, but a significant amount of the data remains underutilized as it is not covered by the existing general resources.). The database can be queried with gene names/IDs, sub-tissue locations, as well as various conditions such as the cervical cancer, endometrial cycles and disorders, and experimental treatments. Accordingly, the output would be a) transcribed and dormant genes listed for the queried condition/location, or b) expression profile of the gene of interest in various uterine conditions. The results also include the reliability score for the expression status of each gene. MGEx-Udb also provides information related to Gene Ontology annotations, protein-protein interactions, transcripts, promoters, and expression status by other sequencing techniques, and facilitates various other types of analysis of the individual genes or co-expressed gene clusters.In brief, MGEx-Udb enables easy cataloguing of co-expressed genes and also facilitates bio-marker discovery for various uterine conditions