J P Vijayashree

Bio: J P Vijayashree is an academic researcher. The author has contributed to research in topics: Computer science & Artificial intelligence. The author has an hindex of 1, co-authored 1 publications receiving 35 citations.

Molecular characterization of plasmid-encoded blaTEM, blaSHV and blaCTX-M among extended spectrum β-lactamases [ESBLs] producing Acinetobacter baumannii.

Abstract: Acinetobacter baumannii associated with nosocomial infections are among the top six drug resistant microbes. Extensive use of the β-lactam group of antibiotics has resulted in the emergence of drug...

Image Enhancement using ESRGAN for CNN based X-Ray Classification

Abstract: There is tremendous amount of computational power in artificial intelligence models like computing variety of complex mathematical calculations and recognizing objects. In the past six to seven years, the amount of computing power used by record-breaking AI models doubled frequently in the time span of months. An interesting way in which these models learn and progress is through deep learning. Deep learning is an intelligent machine’s way in which machines learn without being supervised by us and grants them the power to recognize speech, translate, and even make or take data-driven decisions. Machines consider this as a studying method, inspired by the architecture of the human brain and how we learn. An important deep learning method where we train the machines on information that is unlabeled is called unsupervised learning. A strong part of neural networks that are utilized for unsupervised learning is Generative Adversarial Networks. When it comes to applications on images quality improvement, Super Resolution GAN (SRGAN) have a key role to play in it. It was proposed by researchers at Twitter. The motive of this GAN is to super resolute the fine textures of the image by upscaling it. In order to enhance the images further, ESRGAN is used. As the name suggests, ESRGAN is an implementation of SRGAN and uses some added components of SRGAN.

Image Classification of Lung X-ray Images using Deep learning

Abstract: X-rays have been the best support for medical research to make better diagnoses that help in predicting the type of disease. Several machines capture X-ray images of different body parts like the Lungs, Teeth, hands, legs, etc. The role of X-ray images came up in medical research and became very important in diagnosing the health condition of a lung X-ray. In this paper, we propose a new pooling layer before sending the image into the dense neural network by considering the lung X-rays dataset where normal and pneumonia images are taken and using the convolutional neural network (CNN) we determine the condition of the X-ray and classify them into a Normal or Pneumonia. We evaluated our model using a confusion matrix, noted the metrics of precision and recall scores, and compared them with existing models. This paper explains the CNN algorithm deeply and tries to confirm that: (I) X-ray pictures of diseased lungs can be classified using deep learning techniques if the training data is substantial. (II) Adding the average pool layer at the end of the architecture can get better results than many standard existing models. (III) Hyperparameter tuning can improve the deep learning model accuracies and helps the model to perform better. (IV) With a proper amount of training, hyperparameter tweaking, and using data augmentation we can be able to get better accuracy than many existing CNN models with the lowest number of trainable parameters. This makes it possible to accurately automate the process of interpreting X-ray images that could avoid deep MRI and CT scans which may affect patients with high radioactive waves.

Cloud-based intelligent monitoring system to implement mask violation detection and alert simulation

Abstract: The importance of wearing a mask in public places came to light when the COVID-19 pandemic has started due to the coronavirus. To strictly control the spread of the virus, wearing a mask is mandatory to avoid getting the virus through others or spreading the virus to others if we are carrying it. Since it’s not possible to check each individual in public places whether he/she is wearing a mask, this paper proposed a face mask detection using Deep Learning (DL) and Convolutional Neural Network (CNN) techniques. A cloud-based approach that adopted DL is used to identify the persons violating the rules. The dataset used in the work is collected from various studies, such as Prajnasb/observations and Kaggle’s Face Mask Detection Dataset that contains images of people wearing and not wearing masks. The faces in the images will be detected and cropped with the help of a trained face detector which will be used for checking whether the face in the image is wearing a mask or not. Face mask detection is done with the help of CNN. The input image is fed into the CNN and the output is binary format, whether person wearing or not wearing a mask. The work uses Max Pooling and Average Pooling layers of CNN. The outcome of the work shows that the proposed method achieves 98 % of accuracy using Max Pooling which is better than the currently available works. © Komal Venugopal V., Lalith M., Arun Kumar T., Jayashree J., Vijayashre J., 2022.

Principal component analysis on face recognition using artificial firefirefly swarm optimization algorithm

Abstract: In the modern environment, the innovations emerging in information technology has driven us to focus on strengthening the security process. This led to the recent advancement in face recognition technology and special attention is given to the recognition process by applying a biometric system for personal identification. Face recognition is renowned as one of the efficacious applications of picture study, popularly applied for reliable biometric where security is the important quality attribute to be achieved. In this paper, a highly effective face recognition system has been proposed by incorporating genetic algorithms for better search strategy. The proposed model works in two-step processes: face feature extraction and face pattern matching. The Haralick features and features extracted from face databases using PCA are used for face recognition. The most eminent artificial firefirefly swarm optimization algorithm is employed for better searching and matching of facial features. From the simulation experiments performed on the faces warehoused in the OUR database, the result has shown that the model is highly efficient, the PCA method has achieved 80.6% of recognition rate and the AFSA has acquired 88.9% accuracy in correct recognition rate.

Acinetobacter baumannii Antibiotic Resistance Mechanisms.

Abstract: Acinetobacter baumannii is a Gram-negative ESKAPE microorganism that poses a threat to public health by causing severe and invasive (mostly nosocomial) infections linked with high mortality rates. During the last years, this pathogen displayed multidrug resistance (MDR), mainly due to extensive antibiotic abuse and poor stewardship. MDR isolates are associated with medical history of long hospitalization stays, presence of catheters, and mechanical ventilation, while immunocompromised and severely ill hosts predispose to invasive infections. Next-generation sequencing techniques have revolutionized diagnosis of severe A. baumannii infections, contributing to timely diagnosis and personalized therapeutic regimens according to the identification of the respective resistance genes. The aim of this review is to describe in detail all current knowledge on the genetic background of A. baumannii resistance mechanisms in humans as regards beta-lactams (penicillins, cephalosporins, carbapenems, monobactams, and beta-lactamase inhibitors), aminoglycosides, tetracyclines, fluoroquinolones, macrolides, lincosamides, streptogramin antibiotics, polymyxins, and others (amphenicols, oxazolidinones, rifamycins, fosfomycin, diaminopyrimidines, sulfonamides, glycopeptide, and lipopeptide antibiotics). Mechanisms of antimicrobial resistance refer mainly to regulation of antibiotic transportation through bacterial membranes, alteration of the antibiotic target site, and enzymatic modifications resulting in antibiotic neutralization. Virulence factors that may affect antibiotic susceptibility profiles and confer drug resistance are also being discussed. Reports from cases of A. baumannii coinfection with SARS-CoV-2 during the COVID-19 pandemic in terms of resistance profiles and MDR genes have been investigated.

Acinetobacter baumannii Resistance: A Real Challenge for Clinicians.

Abstract: Acinetobacter baumannii (named in honor of the American bacteriologists Paul and Linda Baumann) is a Gram-negative, multidrug-resistant (MDR) pathogen that causes nosocomial infections, especially in intensive care units (ICUs) and immunocompromised patients with central venous catheters. A. baumannii has developed a broad spectrum of antimicrobial resistance, associated with a higher mortality rate among infected patients compared with other non-baumannii species. In terms of clinical impact, resistant strains are associated with increases in both in-hospital length of stay and mortality. A. baumannii can cause a variety of infections; most involve the respiratory tract, especially ventilator-associated pneumonia, but bacteremia and skin wound infections have also been reported, the latter of which has been prominently observed in the context of war-related trauma. Cases of meningitis associated with A. baumannii have been documented. The most common risk factor for the acquisition of MDR A baumannii is previous antibiotic use, following by mechanical ventilation, length of ICU/hospital stay, severity of illness, and use of medical devices. Current efforts focus on addressing all the antimicrobial resistance mechanisms described in A. baumannii, with the objective of identifying the most promising therapeutic scheme. Bacteriophage- and artilysin-based therapeutic approaches have been described as effective, but further research into their clinical use is required

Aging and Immune System: An Overview

Abstract: The world is seeing a quick segment move towards a more established populace, a pattern with significant clinical, social, monetary and political ramifications. Maturing is a multifaceted procedure, including various sub-atomic and cell components with regards to various organ frameworks. A urgent part of maturing is a lot of useful and auxiliary adjustments in the invulnerable framework that can show as a diminished capacity to battle contamination, lessened reaction to inoculation, increased incidence of cancer, higher prevalence of autoimmunity and constitutive lowgrade inflammation, among others. In addition to cell-intrinsic changes in both innate and adaptive immune cells, alteration in the stromal microenvironment in primary and secondary lymphoid organs plays an important role in age-associated immune dysfunction. This review will provide a broad overview of these phenomena and point out some of their clinical and therapeutic implications. This review study setting, discussing the gradual aging immune system. Data for this study is collected from different search engines like PubMed, Google Scholar, MeSH, Semantic scholar, Cochrane, NCBI, Medline, core science. A total of 53 articles were selected. The aging of the immune system is associated with dramatic changes in the distribution and competence of immune cells. Anti-Aging therapy should aim at prolonging T cells’ survival while weakening inflammation prone to innate immunity.

CLSI based antibiogram profile and the detection of MDR and XDR strains of Acinetobacter baumannii isolated from urine samples.

Abstract: Background: Acinetobacter baumannii is an emerging nosocomial pathogen causing serious complications due to the propensity of its multi-drug resistant property. Due to the indiscriminate and wide-spread use of antibiotics, A. baumannii strains emerge as MDR-Ab, XDR-Ab and in recent years pan-DR-Ab strains. Routine therapy incorporates the application of fewer antibiotics and antibiotic surveillance data is not monitored frequently. This study is thus an attempt to screen for the frequency of antibiotic resistance profile against different classes of antibiotics as per CLSI guidelines. Methods: Phenotypically and genotypically characterized 73 A. baumannii strains were utilized for the antibiogram profile using Group A, B, and U antibiotics as per CLSI recommendations using standard Kirby Bauer disc diffusion method. Interpretations of susceptible, intermediate and resistance were recorded by measuring zone diameter criteria. Results: Group A antibiogram profile showed highest non-susceptibility (n=73) (100%) to ampicillin-sulbactam, ceftazidime and imipenem followed by 82.19%, 79.45%, 67.12%, 56.16% and 49.31% non-susceptible isolates against ciprofloxacin, gentamicin, meropenem, tobramycin, and levofloxacin respectively. Group B antibiogram profile showed 100% non-susceptibility piperacillin-tazobactam and to amikacin, 91.78% (n=67) resistance against ceftriaxone. Among the cyclines, 19.71% and 6.84% of isolates were resistant to doxycycline and minocycline respectively. Under Group U, 76.71% showed resistance against tetracycline. The frequency of MDR (71.23%) and XDR (39.72%) A. baumannii isolates were detected. Conclusion: Periodical antibiotic surveillance is essential to curb the menace of the emergence of MDR and XDR A. baumannii in the hospital environment thus improving the patient care by the administration of alternate drug of choice or by combination therapy.

Molecular characterisation of csgA gene among ESBL strains of A. baumannii and targeting with essential oil compounds from Azadirachta indica

Abstract: Objectives A. baumannii is considered as a “red alert” nosocomial human pathogen and exhibits an extensive antibiotic resistance spectrum. The biofilm formation mediated by the csgA is a potent virulence factor in A. baumannii and targeting the same would be of a novel strategy to control A. baumannii infections. The aim of the present study is thus to evaluate the anti-biofilm activity of essential bio-compounds from Azadirachta indica against the ESBL producing strains of A. baumannii by in-vitro and in-silico studies. Methods Biofilm formation by Semi-quantitative adherence assay was performed for the 73 strains of ESBL producing A. baumannii. Genomic DNA was extracted and molecular characterization of csgA gene was done by PCR amplification with further sequencing. In-vitro anti biofilm assay from crude extract of A.indica was performed which was then followed by the in-silico docking involving retrieval of csgA protein and ligand optimisation, molinspiration assessment on drug likeliness, docking simulations and visualisations. Results Biofilm assay showed 58.9%, 31.5% and 0.09% as high grade, low grade and non-biofilm formers respectively. 20.54% (15/73) of the screened genomes showed positive amplicons for the csgA gene associated with biofilm formation among the ESBL producing strains of A. baumannii. All the ceftazidime, cefipime and cefotaxime resistant strains showed the presence of csgA gene (100%; 15/15), followed by 46.6% (7/15) resistant isolates for ceftriaxone. In-vitro crystal violet viability assay showed MBEC50 and MBEC90 at a concentration of 20 µl and 40 µl respectively. In-silico assessments on the essential oil compounds from neem showed imidazole to exhibit the highest interaction with least docking energy and high number of hydrogen bonds. Conclusion The current study emphasises that imidazole from A.indica to be a promising candidate for targeting the csgA mediated biofilm formation in ESBL strains of A. baumannii. However, further in-vivo studies have to be implemented for the experimental validation of the same.