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Selvaraj, Abirami,Jain, Ravindra Kumar,Nagi, Ravleen,Balasubramaniam, Arthi Korean Academy of Oral and Maxillofacial Radiology 2022 Imaging Science in Dentistry Vol.52 No.2
Purpose: The aim of this review was to systematically analyze the available literature on the correlation between the gray values (GVs) of cone-beam computed tomography (CBCT) and the Hounsfield units (HUs) of computed tomography (CT) for assessing bone mineral density. Materials and Methods: A literature search was carried out in PubMed, Cochrane Library, Google Scholar, Scopus, and LILACS for studies published through September 2021. In vitro, in vivo, and animal studies that analyzed the correlations GVs of CBCT and HUs of CT were included in this review. The review was prepared according to the PRISMA checklist for systematic reviews, and the risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool. A quantitative analysis was performed using a fixed-effects model. Results: The literature search identified a total of 5,955 studies, of which 14 studies were included for the qualitative analysis and 2 studies for the quantitative analysis. A positive correlation was observed between the GVs of CBCT and HUs of CT. Out of the 14 studies, 100% had low risks of bias for the domains of patient selection, index test, and reference standards, while 95% of studies had a low risk of bias for the domain of flow and timing. The fixed-effects meta-analysis performed for Pearson correlation coefficients between CBCT and CT showed a moderate positive correlation (r=0.669; 95% CI, 0.388 to 0.836; P<0.05). Conclusion: The available evidence showed a positive correlation between the GVs of CBCT and HUs of CT.
Arasu Abirami,Prabha Nagaram,Devi Durga,Issac Praveen Kumar,Alarjani Khaloud Mohammed,Al Farraj Dunia A.,Aljeidi Reem A.,Tayyeb Jehad Zuhair,Guru Ajay,Arockiaraj Jesu,Mohan Magesh,Hussein Dina S. 한국미생물학회 2023 The journal of microbiology Vol.61 No.11
Listeria monocytogenes is an important food-borne pathogen that causes listeriosis and has a high case fatality rate despite its low incidence. Medicinal plants and their secondary metabolites have been identified as potential antibacterial substances, serving as replacements for synthetic chemical compounds. The present studies emphasize two significant medicinal plants, Allium cepa and Zingiber officinale, and their efficacy against L. monocytogenes. Firstly, a bacterial isolate was obtained from milk and identified through morphology and biochemical reactions. The species of the isolate were further confirmed through 16S rRNA analysis. Furthermore, polar solvents such as methanol and ethanol were used for the extraction of secondary metabolites from A. cepa and Z. officinale. Crude phytochemical components were identified using phytochemical tests, FTIR, and GC–MS. Moreover, the antibacterial activity of the crude extract and its various concentrations were tested against L. monocytogenes. Among all, A. cepa in methanolic extracts showed significant inhibitory activity. Since, the A. cepa for methanolic crude extract was used to perform autography to assess its bactericidal activity. Subsequently, molecular docking was performed to determine the specific compound inhibition. The docking results revealed that four compounds displayed strong binding affinity with the virulence factor Listeriolysin-O of L. monocytogenes. Based on the above results, it can be concluded that the medicinal plant A. cepa has potential antibacterial effects against L. monocytogenes, particularly targeting its virulence.
( Manoj Kushwaha ),( M. S. Abirami ),( Corresponding Author M. S. Abirami ) 한국감성과학회 2021 추계학술대회 Vol.2021 No.0
Accidents occurred usually on roads, which bring enormous losses to society. Road accidents are a universal problem which causes the loss of precious human lives and property. The purpose of this paper is to extract important influence features of road accidents and reduce the dimensionality of datasets for getting better results from machine learning algorithms. Collected datasets from Kaggle and constructed new datasets from existing datasets based on the influence feature of road accidents and perform preprocessing, feature selection and feature extraction. Feature selection is done using heat map and correlation matrix. Feature extraction is done using dimensionality reduction methods such as the Principal Component Analysis (PCA), Linear discriminate analysis (LDA) and t-Distributed Stochastic Neighbor Embedding (t-SNE). The different feature extraction techniques are applied and the results are compared based on the accuracy parameter. It was found that LDA performs better than PCA with accuracy of 85% which uses Random Forest classifier.
Meenakshi Sundaram Sri Abirami Saraswathi,Dipak Rana,Subbiah Alwarappan,Shanmugaraj Gowrishankar,Prabu Vijayakumar,Alagumalai Nagendran 한국공업화학회 2019 Journal of Industrial and Engineering Chemistry Vol.76 No.-
Poly (ether imide) [PEI] ultrafiltration membranes are coated by polydopamine (PD) and immobilizedwith silver nanoparticles (AgNPs) to improve the permeation, contaminant separation and anti-foulingproperties. The tailored membranes displayed enhanced permeability (97.2 Lm 2 h 1), hydraulicresistance (13.8 kPa/Lm 2 h 1), average roughness (43 nm), contaminant rejection (>97%) with a higherflux recovery ratio (>95%). PEI/PD/Ag membranes showed anti-biofouling property against gramnegative and gram positive bacteria and facilitated the separation of toxic contaminants. The outstandingstability of PD coating and the presence of AgNPs offer effective and safe water separation.
Dynamic Prime Chunking Algorithm for Data Deduplication in Cloud Storage
( Manogar Ellappan ),( Abirami S ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.4
The data deduplication technique identifies the duplicates and minimizes the redundant storage data in the backup server. The chunk level deduplication plays a significant role in detecting the appropriate chunk boundaries, which solves the challenges such as minimum throughput and maximum chunk size variance in the data stream. To provide the solution, we propose a new chunking algorithm called Dynamic Prime Chunking (DPC). The main goal of DPC is to dynamically change the window size within the prime value based on the minimum and maximum chunk size. According to the result, DPC provides high throughput and avoid significant chunk variance in the deduplication system. The implementation and experimental evaluation have been performed on the multimedia and operating system datasets. DPC has been compared with existing algorithms such as Rabin, TTTD, MAXP, and AE. Chunk Count, Chunking time, throughput, processing time, Bytes Saved per Second (BSPS) and Deduplication Elimination Ratio (DER) are the performance metrics analyzed in our work. Based on the analysis of the results, it is found that throughput and BSPS have improved. Firstly, DPC quantitatively improves throughput performance by more than 21% than AE. Secondly, BSPS increases a maximum of 11% than the existing AE algorithm. Due to the above reason, our algorithm minimizes the total processing time and achieves higher deduplication efficiency compared with the existing Content Defined Chunking (CDC) algorithms.
Hemavathi Dhandapani,Abirami Seetharaman,Hascitha Jayakumar,Selvaluxmy Ganeshrajah,Shirley Sunder Singh,Rajkumar Thangarajan,Priya Ramanathan 대한부인종양학회 2021 Journal of Gynecologic Oncology Vol.32 No.4
Objective: Dendritic cells (DCs) are administered as immunotherapeutic adjuvants after the completion of standard treatment in most settings. However, our Phase I trial indicated that one patient out of four, who received autologous tumor lysate-pulsed dendritic cell (TLDC) also received cisplatin chemotherapy and experienced complete regression of her lung lesion, continuing to be disease free till date. Hence, the objective of our current study is to evaluate the sustenance or augmentation of immune responses when autologous human papillomavirus positive cervical tumor lysate pulsed DC- are combined with cisplatin, using co-culture assays in vitro. Methods: Before treatment, peripheral blood and punch biopsy samples were collected from 23 cervical cancer patients after obtaining an informed consent. DC functionality was confirmed through phenotypic and functional assays using autologous peripheral blood mononuclear cells as responders. For cisplatin experiments, the drug was added at 150, 200 (clinical dose equivalent), and 400 µM concentrations to DCs alone or DC-T cell co- cultures. Phenotypic assessment and functional characterization of DCs was done using flow cytometry. Cytokine enzyme-linked immunosorbent assay and interferon (IFN)-γ enzyme- linked immune absorbent spot assays were also performed. Results: The functionality of TLDCs was not compromised upon cisplatin treatment in vitro even at the highest (400 μM) concentration. Even though cisplatin treatment reduced the secretion of IFN-γ and interleukin (IL)-12p40 in co-cultures stimulated with TLDCs, this effect was not significant (p>0.05). A doubling of IFN-γ secretion following cisplatin treatment was observed in at least one of three independent experiments. Additional experiments showed a reduction in both FOXP3+ regulatory T cells and IL-10 levels. Conclusion: Our results provide evidence that cisplatin treatment may be given after autologous TLDC administration to maintain or improve a productive anti-tumor response in vaccinated patients.
( G. Keerthi ),( M. S. Abirami ) 한국감성과학회 2021 한국감성과학회 국제학술대회(ICES) Vol.2021 No.-
Diabetes is a leading reason of death, disability, and economic loss around the world. Type 2 diabetes is the maximum shared kind of diabetes in women (80-90 percent worldwide).It can be avoided or postponed by receiving the appropriate maintenance and interventions, including an initial diagnosis. There has remained a lot of progress in the area of medical diagnosis using many machine learning algorithms. However, due to incomplete medical data sets, accuracy suffers, resulting in a higher frequency of misclassifications, which might lead to dangerous complications. Many researchers find that accurately predicting and diagnosing a disease is a difficult scientific topic. As a result, the goal was to improve the diagnostic. The first technique is to collect the dataset, which comprises of 769 pregnant women's records. On the foundation of accuracy, machine learning approaches are utilized to forecast diabetes and non-diabetes women. We used seven machine learning algorithms to calculate diabetes using the dataset. We discovered that a diabetes prediction model that combines Linear Regression and Support Vector Machine performs well, with an accuracy of 77 percent -78 percent.