Influential role of 7-Ketocholesterol in the progression of Alzheimer's disease.

Millions of people are affected by neurodegenerative diseases worldwide. They occur due to the loss of brain functions or peripheral nervous system dysfunction. If untreated, prolonged condition ultimately leads to death. Mostly they are associated with stress, altered cholesterol metabolism, inflammation and organelle dysfunction. Endogenous cholesterol and phospholipids in brain undergo auto-oxidation by enzymatic as well as non-enzymatic modes leading to the formation of by-products such as 4-hydroxynonenal and oxysterols. Among various oxysterols, 7-ketocholesterol (7KCh) is one of the major toxic components involved in altering neuronal lipid metabolism, contributing to inflammation and nerve cell damage. More evidently 7KCh is proven to induce oxidative stress and affects membrane permeability. Loss in mitochondrial membrane potential affects metabolism of cell organelles such as lysosomes and peroxisomes which are involved in lipid and protein homeostasis. This in turn could affect amyloidogenesis, tau protein phosphorylation and accumulation in pathological conditions of neurodegenerative diseases. Lipid alterations and the consequent pathogenic protein accumulation, results in the damage of cell organelles and microglial cells. This could be a reason behind disease progression and predominantly reported characteristics of neurodegenerative disorders such as Alzheimer's disease. This review focuses on the role of 7KCh mediated neurodegenerative Alzheimer's disease with emphasis on alterations in the lipid raft microdomain. In addition, current trends in the significant therapies related to 7KCh inhibition are highlighted.

Predictive Analysis on Behavioural Risk Factor Surveillance System through Ensemble Classifiers and Clusters.

INTRODUCTION: The World Health Organization (WHO) has been asserting the importance of health care in today's world. The objective of this research is to find out the type of medication that needs to be provided to the people at early stages to prevent behavioural risk factors. The health department of United States has a great vision to improve the immune system of the people and has taken measures to do the same through a Behavioural Risk Factor Surveillance System (BRFSS). This research aims to prevent behavioural risk factors by predictive analysis using the above mentioned dataset from the Centres for Disease Control and Prevention (CDC). METHODOLOGY: The methods of ensemble classification and clustering is applied on the dataset, pre and post weighted classification, thereby classifying and prescribing the type of healthcare required for people exhibiting behaviours such as obesity, nutrition and physical activity. RESULTS AND DISCUSSION: This analyses help improve the quality of health of the citizens. In an extensive study, it was observed that the result obtained was 92.87% accurate.

On the emergence of critical regions at the onset of thermoacoustic instability in a turbulent combustor.

We use complex network theory to investigate the dynamical transition from stable operation to thermoacoustic instability via intermittency in a turbulent combustor with a bluff body stabilized flame. A spatial network is constructed, representing each of these three dynamical regimes of combustor operation, based on the correlation between time series of local velocity obtained from particle image velocimetry. Network centrality measures enable us to identify critical regions of the flow field during combustion noise, intermittency, and thermoacoustic instability. We find that during combustion noise, the bluff body wake turns out to be the critical region that determines the dynamics of the combustor. As the turbulent combustor transitions to thermoacoustic instability, during intermittency, the wake of the bluff body loses its significance in determining the flow dynamics and the region on top of the bluff body emerges as the most critical region in determining the flow dynamics during thermoacoustic instability. The knowledge about this critical region of the reactive flow field can help us devise optimal control strategies to evade thermoacoustic instability.

Biocompatibility and antibacterial activity of the Adathoda vasica Linn extract mediated silver nanoparticles.

The aim of this study is to investigate the biocompatibility and anti-Vibrio efficacy of green synthesized silver nanoparticles (AgNPs) using an aqueous leaf extract of Adathoda vasica (A. vasica). The green synthesized silver nanoparticles were characterized by UV-vis, Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD), transmission electron microscopy (TEM), scanning electron microscopy (SEM) and energy dispersive X-ray analysis (EDX). A. vasica AgNPs showed significant antibacterial activity against Vibrio parahaemolyticus in agar bioassay and well diffusion method. Further, nanoparticles interactions with bacteria and its antibacterial activity were confirmed by CLSM analysis. In vivo evaluation results confirmed that synthesized A. vasica AgNPs had good antibacterial efficacy and also nontoxic to the Artemia nauplii.

SCAR marker specific to detect Magnaporthe grisea infecting finger millets (Eleusine coracana).

AIMS: To determine the molecular variability and develop specific Sequence Characterized Amplified Region (SCAR) marker for the detection of Magnaporthe grisea causing blast disease in finger millet. METHODS AND RESULTS: Random amplified polymorphic DNA (RAPD) was performed with 14 isolates of M. grisea using 20 random primers. SCAR marker was developed for accurate and specific detection of M. grisea infecting only finger millets. The genetic similarity coefficient within each group and variation between the groups was observed. Among the primers, OPF-08 generated a RAPD polymorphic profile that showed common fragment of 478 bp in all the isolates. This fragment was cloned and sequenced. SCAR primers, Mg-SCAR-FP and Mg-SCAR-RP, were designed using sequence of the cloned product. The specificity of the SCAR primers was evaluated using purified DNA from M. grisea isolates from finger millets and other pathogens viz., Pyricularia oryzae, Colletotrichum gloeosporioides, Colletotrichum falcatum and Colletotrichum capcisi infecting different crops. The SCAR primers amplified only specific 460 bp fragment from DNA of M. grisea isolates and this fragment was not amplified in other pathogens tested. CONCLUSION: SCAR primers distinguish blast disease of finger millet from rice as there is no amplification in the rice blast pathogen. PCR-based SCAR marker is a convenient tool for specific and rapid detection of M. grisea in finger millets. SIGNIFICANCE AND IMPACT OF THE STUDY: Genetic diversity in fungal population helps in developing a suitable SCAR marker to identify the blast pathogen at the early stage of infection.

Development of leaffolder resistant transgenic rice expressing cry2AX1 gene driven by green tissue-specific rbcS promoter.

The insecticidal cry genes of Bacillus thuringiensis (Bt) have been successfully used for development of insect resistant transgenic rice plants. In this study, a novel cry2AX1 gene consisting a sequence of cry2Aa and cry2Ac gene driven by rice rbcS promoter was introduced into a rice cultivar, ASD16. Among 27 putative rice transformants, 20 plants were found to be positive for cry2AX1 gene. The expression of Cry2AX1 protein in transgenic rice plants ranged from 5.95 to 122.40 ng/g of fresh leaf tissue. Stable integration of the transgene was confirmed in putative transformants of rice by Southern blot hybridization analysis. Insect bioassay on T0 transgenic rice plants against rice leaffolder (Cnaphalocrosis medinalis) recorded larval mortality up to 83.33%. Stable inheritance and expression of cry2AX1 gene in T1 progenies was demonstrated using Southern and ELISA. The detached leaf bit bioassay with selected T1 plants showed 83.33-90.00% mortality against C. medinalis. The whole plant bioassay for T1 plants with rice leaffolder showed significant level of resistance even at a lower level of Cry2AX1 expression varying from 131 to 158 ng/g fresh leaf tissue during tillering stage.

Biocatalytic and antibacterial visualization of green synthesized silver nanoparticles using Hemidesmus indicus.

In the present investigation, we described the green synthesis of silver nanoparticles using plant leaf extract of Hemidesmus indicus. The synthesized silver nanoparticles were characterized by UV-visible spectroscopy, fourier transform infra-red spectroscopy (FTIR), X-ray diffraction (XRD), transmission electron microscopy (TEM) and energy dispersive X-ray spectroscopy (EDX). TEM images proved that the synthesized silver nanoparticles were spherical in shape with an average particle size of 25.24 nm. To evaluate antibacterial efficacy, bacteria was isolated from poultry gut and subjected to 16S rRNA characterization and confirmed as Shigella sonnei. The in vitro antibacterial efficacy of synthesized silver nanoparticles was studied by agar bioassay, well diffusion and confocal laser scanning microscopy (CLSM) assay. The H. indicus mediated synthesis of silver nanoparticles shows rapid synthesis and higher inhibitory activity (34 ± 0.2 mm) against isolated bacteria S. sonnei at 40 µg/ml.

Impact assessment of mercury accumulation and biochemical and molecular response of Mentha arvensis: a potential hyperaccumulator plant.

The present study was focused on examining the effect of Hg oxidative stress induced physiochemical and genetic changes in M. arvensis seedlings. The growth rate of Hg treated seedlings was decreased to 56.1% and 41.5% in roots and shoots, respectively, compared to the control. Accumulation of Hg level in both roots and shoots was increased with increasing the concentration of Hg. Superoxide dismutase (SOD), catalase (CAT), and ascorbate peroxidase (APX) activities were found to be increased with increasing the Hg concentration up to 20 mg/L; however, it was decreased at 25 mg/L Hg concentration. The POX enzyme activity was positively correlated with Hg dose. The changes occurring in the random amplification of ploymorphic DNA (RAPD) profiles generated from Hg treated seedlings included variations in band intensity, disappearance of bands, and appearance of new bands compared with the control seedlings. It was concluded that DNA polymorphisms observed with RAPD profile could be used as molecular marker for the evaluation of heavy metal induced genotoxic effects in plant species. The present results strongly suggested that Mentha arvensis could be used as a potential phytoremediator plant in mercury polluted environment.

Prevalence of Risk Factors of Diabetes Among Urban Poor South Indian Population.

OBJECTIVE: The objective of the study was to assess the prevalence of risk factors of diabetes among urban poor population in Chennai, Tamil Nadu, South India. STUDY AND METHODS: A cross-sectional study was conducted from Dec-2010 to Feb-2011 in Chennai city. Five hundred twenty-nine subjects aged above 18 years (409 women and 120 men) habitants of three slums were randomly selected. A structured questionnaire was used to collect the socio-demographic and clinical information. Anthropometric measures, blood pressure and random blood glucose (RCBG) estimation were done. Dietary intake was assessed using 24-hour diet recall and duration of physical activity/day in all components was recorded. RESULTS: The mean age and BMI of the study population was 45.4±12.8 years and 25.8±4.9 kg/m2 respectively. About 20.4% were illiterate and 34.8% were either unemployed, retired or housewives. Median family income per month was 3000 INR. Prevalence of obesity (≥25 kg/m2) was 57.3% and central obesity (≥90 cms in male and ≥80 cms in female) was 75.7%. The median duration spent in walking, standing and sitting were 30, 120, and 240 minutes/day respectively. There was a gross inadequacy in vegetable intake. The prevalence of self-reported diabetes and hypertension was 20.8% and 24.2%, respectively. Among the subjects without a previous diagnosis of diabetes, 12.5% had RCBG ≥140 mgs. 10.6% were diagnosed with hypertension. CONCLUSIONS: Sedentarism coupled with unhealthy diet pattern are the major contributing factors for the higher prevalence of diabetes and hypertension in urban poor population.

[Characterization and cloning of the cry2A gene from indigenous isolates of Bacillus thuringiensis].

Discovery of novel cry genes of Bacillus thuringiensis (Bt) with higher toxicity is important for the development of transgenic Bt crops resistant to target pests. Two new indigenous isolates of Bt were characterized for their colony type, crystal inclusion and toxicity with Helicoverpa armigera Hubner and Spodoptera litura Linn. Screening of cry2A genes from the two new isolates of Bt, T30 and T48, by PCR with cry2A family primers showed the presence of cry2A genes only in the isolate T30. Further screening of these isolates of Bt with three different cry2A gene specific primers showed the presence of cry2Aa, cry2Ab and cry2Ac genes in both of the new Bt isolates. The cry2A open reading frame (ORF) of Bt T48 (~1.9 kb) was amplified and cloned in a T/A vector (~2.8 kb). All the E. coli transformants showed only the cry2Aa gene. Comparison of the nucleotide sequence data generated from the cry2Aa (~1.9 kb) gene showed a 99 percent homology and one amino acid variation when compared with the holotype sequence of Cry2Aa1.

Oral Wound Healing: A Scoping Review and Proposal of a New Index for Palatal Mucosa.

Purpose: The aim of this scoping review was to evaluate the wound healing indices available in literature and propose a new intra-oral wound healing index to assess the healing of palatal mucosa. Materials and Methods: A PubMed database search was conducted to identify relevant studies using the search strategy: ('Oral Wound healing') OR ('Palatal tissue healing') OR ('Healing indices in Oral and Maxillofacial Surgery') OR ('Palatal wound healing') OR ('Complications in wound healing'). A qualitative and quantitative synthesis of the results was done and data was presented following the PRISMA-ScR guidelines. Results: The search resulted in 9 articles published between 2019 and 2022, which were eligible for inclusion in the study. The data revealed that the indices currently available for the assessment of intra-oral healing were limited and primarily concerned with the assessment of gingival and periodontal tissues. Conclusion: The healing indices devised for gingival and periodontal tissues cannot be applied to palatal healing due to the differences in clinical and histological aspects. Therefore, a new index to monitor the healing response specifically for the soft tissues in the palate has been proposed. This maybe particularly useful in cleft palate repair and other procedures performed over the palatal tissues.

Signet cell rectal carcinoma with prostatic involvement detected by FAPI-04 PET-MRI fusion.

A 60-year-old male patient diagnosed with mucinous adenocarcinoma of lower third of rectum underwent abdominoperineal resection and permanent colostomy followed by adjuvant chemotherapy. Response evaluation with F-18 FDG PET-CT showed a complete metabolic response. After 6 months, CEA levels started increasing and clinically a recurrence was suspected. A restaging FDG PET-CT showed no obvious malignant disease. Patient presented again within a month with complaints of urinary retention and haematuria. CEA levels were further elevated, and Ga-68 FAPI-04 (FAPI) PET-CT was performed. FAPI PET-CT revealed prostatic and seminal vesicle disease involvement. Additionally, an MRI of pelvis was done and fused with FAPI PET for confirmation of prostatic involvement.

Computer Vision System for Mango Fruit Defect Detection Using Deep Convolutional Neural Network.

Machine learning techniques play a significant role in agricultural applications for computerized grading and quality evaluation of fruits. In the agricultural domain, automation improves the quality, productivity, and economic growth of a country. The quality grading of fruits is an essential measure in the export market, especially defect detection of a fruit's surface. This is especially pertinent for mangoes, which are highly popular in India. However, the manual grading of mango is a time-consuming, inconsistent, and subjective process. Therefore, a computer-assisted grading system has been developed for defect detection in mangoes. Recently, machine learning techniques, such as the deep learning method, have been used to achieve efficient classification results in digital image classification. Specifically, the convolution neural network (CNN) is a deep learning technique that is employed for automated defect detection in mangoes. This study proposes a computer-vision system, which employs CNN, for the classification of quality mangoes. After training and testing the system using a publicly available mango database, the experimental results show that the proposed method acquired an accuracy of 98%.

Region-Based Segmentation and Classification for Ovarian Cancer Detection Using Convolution Neural Network.

Ovarian cancer is a serious sickness for elderly women. According to data, it is the seventh leading cause of death in women as well as the fifth most frequent disease worldwide. Many researchers classified ovarian cancer using Artificial Neural Networks (ANNs). Doctors consider classification accuracy to be an important aspect of making decisions. Doctors consider improved classification accuracy for providing proper treatment. Early and precise diagnosis lowers mortality rates and saves lives. On basis of ROI (region of interest) segmentation, this research presents a novel annotated ovarian image classification utilizing FaRe-ConvNN (rapid region-based Convolutional neural network). The input photos were divided into three categories: epithelial, germ, and stroma cells. This image is segmented as well as preprocessed. After that, FaRe-ConvNN is used to perform the annotation procedure. For region-based classification, the method compares manually annotated features as well as trained feature in FaRe-ConvNN. This will aid in the analysis of higher accuracy in disease identification, as human annotation has lesser accuracy in previous studies; therefore, this effort will empirically prove that ML classification will provide higher accuracy. Classification is done using a combination of SVC and Gaussian NB classifiers after the region-based training in FaRe-ConvNN. The ensemble technique was employed in feature classification due to better data indexing. To diagnose ovarian cancer, the simulation provides an accurate portion of the input image. FaRe-ConvNN has a precision value of more than 95%, SVC has a precision value of 95.96%, and Gaussian NB has a precision value of 97.7%, with FR-CNN enhancing precision in Gaussian NB. For recall/sensitivity, SVC is 94.31 percent and Gaussian NB is 97.7 percent, while for specificity, SVC is 97.39 percent and Gaussian NB is 98.69 percent using FaRe-ConvNN.

Precise prediction of multiple anticancer drug efficacy using multi target regression and support vector regression analysis.

BACKGROUND AND OBJECTIVES: The prediction of multiple drug efficacies using machine learning prediction techniques based on clinical and molecular attributes of tumors is a new approach in the field of precision medicine of oncology. The selection of suitable and effective therapeutic drugs among the potential drugs is performed computationally considering the tumor features. In this study, we developed and validated machine learning models to predict the efficacy of five anti-cancer drugs according to the clinical and molecular attributes of 30 oral squamous cell carcinoma (OSCC) cohorts. This sounds a bit odd - consider: Ranking of the drugs was achieved using their apoptotic priming. METHODS: We developed multiple drug efficacy prediction models based on three types of tumor characteristics by applying machine learning methods, including multi-target regression (MTR) and support vector regression (SVR). The prediction accuracy of existing machine learning methods was enhanced by introducing novel pre-processing techniques to develop Enhanced MTR (E_MTR), Enhanced Log-based MTR (EL_MTR), Enhanced Multi-target SVR (EM_SVR), and Enhanced Log-based Multi-target SVR (ELM_SVR). As a unique capability, ELM_SVR and EL_MTR rank the drugs based on their predicted efficacy. All the drug efficacy prediction models were built using OSCC real samples and theoretical samples. The best model was selected was based on dataset size and evaluation metrics, such as error terms, residuals and parameter tuning, and cross-validated (CV) using 30 real samples and 340 theoretical samples. RESULTS: When 30 real tumor samples were used for the train-test and CV methods, MTR models predicted the efficacy with less error than SVR models. Comparatively, using 340 theoretical samples for the train-test and CV methods, though MTR improved the performance, SVR predicted the efficacy with zero error. We found that, for small samples, the proposed MTR provided a 0.01 difference between actual apoptotic priming and predicted priming of five drugs. For large samples, the predicted values by the proposed SVR had a difference of 0.00001. The error terms (Actual vs. Predicted) also reveal that the enhanced log model is suitable when MTR is applied. Meanwhile, the enhanced model is suitable for SVR learning for multiple drug efficacy prediction. It was found that the predicted ranks of the drugs based on the multi-targeted efficacy prediction exactly match the actual rankings. CONCLUSION: We developed efficient statistical and machine learning models using MTR and SVR analysis for anticancer drug efficacy, which will be useful in the field of precision medicine to choose the most suitable drugs in personalized manner. The performance results of the proposed enhanced ranking techniques are described as follows: i) EL_MTR is the best to predict multiple anticancer drug efficacies and improve the accuracy of ranking drugs, irrespective of sample size; and ii) ELM_SVR performs better than other MTR models with a large sample size and precise ranking process.

Computer-aided diagnosis of COVID-19 from chest X-ray images using histogram-oriented gradient features and Random Forest classifier.

The decision-making process is very crucial in healthcare, which includes quick diagnostic methods to monitor and prevent the COVID-19 pandemic disease from spreading. Computed tomography (CT) is a diagnostic tool used by radiologists to treat COVID patients. COVID x-ray images have inherent texture variations and similarity to other diseases like pneumonia. Manually diagnosing COVID X-ray images is a tedious and challenging process. Extracting the discriminant features and fine-tuning the classifiers using low-resolution images with a limited COVID x-ray dataset is a major challenge in computer aided diagnosis. The present work addresses this issue by proposing and implementing Histogram Oriented Gradient (HOG) features trained with an optimized Random Forest (RF) classifier. The proposed HOG feature extraction method is evaluated with Gray-Level Co-Occurrence Matrix (GLCM) and Hu moments. Results confirm that HOG is found to reflect the local description of edges effectively and provide excellent structural features to discriminate COVID and non-COVID when compared to the other feature extraction techniques. The performance of the RF is compared with other classifiers such as Linear Regression (LR), Linear Discriminant Analysis (LDA), K-nearest neighbor (kNN), Classification and Regression Trees (CART), Random Forest (RF), Support Vector Machine (SVM), and Multi-layer perceptron neural network (MLP). Experimental results show that the highest classification accuracy (99. 73%) is achieved using HOG trained by using the Random Forest (RF) classifier. The proposed work has provided promising results to assist radiologists/physicians in automatic COVID diagnosis using X-ray images.

CovMnet-Deep Learning Model for classifying Coronavirus (COVID-19).

Diagnosing COVID-19, current pandemic disease using Chest X-ray images is widely used to evaluate the lung disorders. As the spread of the disease is enormous many medical camps are being conducted to screen the patients and Chest X-ray is a simple imaging modality to detect presence of lung disorders. Manual lung disorder detection using Chest X-ray by radiologist is a tedious process and may lead to inter and intra-rate errors. Various deep convolution neural network techniques were tested for detecting COVID-19 abnormalities in lungs using Chest X-ray images. This paper proposes deep learning model to classify COVID-19 and normal chest X-ray images. Experiments are carried out for deep feature extraction, fine-tuning of convolutional neural networks (CNN) hyper parameters, and end-to-end training of four variants of the CNN model. The proposed CovMnet provide better classification accuracy of 97.4% for COVID-19 /normal than those reported in the previous studies. The proposed CovMnet model has potential to aid radiologist to monitor COVID-19 disease and proves to be an efficient non-invasive COVID-19 diagnostic tool for lung disorders.

Analytical and Experimental Study on Cold-Formed Steel Built-Up Sections for Bending.

In the construction of steel structures, the two most common types of structural members are hot-formed and cold-formed members. This paper mainly describes the analytical and experimental research on the strength and characteristics of CFS bolted built-up sigma sections having different structural arrangements under bending. The cross-sectional dimensions for the parametric study were selected by the sizes available in the market. In this paper, ANSYS workbench software was used to perform FE modeling and observe the local, flexural, and interaction of these buckling. Then, experimental study was performed by varying the arrangement of open section beams between face-to-face and back-to-back, connected using bolts or fasteners different spacings. Further, we conducted bending tests on cold-formed steel built-up members having simple edge stiffeners in the middle. Comparing both analytical and experimental studies, the results indicate that the back-to-back connected built-up beam section provides a flexural capacity higher than the face-to-face built-up section. Moreover, increasing the bolt spacing enhanced the load-carrying capacity of back-to-back sigma section built-up beams. It has also been discovered that the flexural strength of beams is primarily determined by bolt spacing or itsposition.

Effect of curcumin on the modulation of αA- and αB-crystallin and heat shock protein 70 in selenium-induced cataractogenesis in Wistar rat pups.

PURPOSE: To investigate the expression of αA- and αB-crystallin and heat shock protein 70 (Hsp 70) during curcumin treatment of selenium-induced cataractogenesis in Wistar rat pups. METHODS: Group I Wistar rat pups received only saline and served as the control. Group II Wistar rat pups were intraperitoneally injected with selenium (15 µM/kg bodyweight) to induce cataract. Group III Wistar rat pups also underwent selenium-induced cataract but were cotreated with 75 mg/kg body weight of curcumin (single oral dose). Group IV Wistar rat pups with selenium-induced cataract were post-treated with curcumin at the group III dosage 24 h after selenium administration. Group V Wistar rat pups with selenium-induced cataract were pretreated with curcumin at the group III dosage 24 h before selenium administration. RESULTS: This study found higher levels of αA- and αB-crystallin and Hsp 70 in lenses injected with selenium alone (group II) than in control lenses (group I). Similar results were observed in the group III and IV lenses. In contrast, in group V, the presence of curcumin 24 h before selenium injection decreased the αA- and αB-crystallin and Hsp 70 levels to almost the same as those found in group I lenses. CONCLUSIONS: Curcumin suppressed the expression of selenite-induced αA- and αB-crystallin and Hsp 70, and may therefore suppress cataract formation in rat pups.