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RISS 인기검색어
- 검색키워드
- 저자 : Mohanty Monika (검색결과 2 건)
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Mohanty Monika,Govind Shashirekha,Rath Shakti 대한치과보존학회 2024 Restorative Dentistry & Endodontics Vol.49 No.1
Objectives This study aims to correlate caries-causing microorganism load, lactic acid estimation, and blood groups to high caries risk in diabetic and non-diabetic individuals and low caries risk in healthy individuals. Materials and Methods This study includes 30 participants divided into 3 groups: Group A, High-risk caries diabetic individuals; Group B, High-risk caries non-diabetic individuals; and Group C, Low-risk caries individuals. The medical condition, oral hygiene, and caries risk assessment (American Dental Association classification and International Caries Detection and Assessment System scoring) were documented. Each individual’s 3 mL of saliva was analyzed for microbial load and lactic acid as follows: Part I: 2 mL for microbial quantity estimation using nutrient agar and blood agar medium, biochemical investigation, and carbohydrate fermentation tests; Part II: 0.5 mL for lactic acid estimation using spectrophotometric analysis. Among the selected individuals, blood group correlation was assessed. The χ2 test, Kruskal-Wallis test, and post hoc analysis were done using Dunn’s test (p < 0.05). Results Group A had the highest microbial load and lactic acid concentration, followed by Groups B and C. The predominant bacteria were Lactobacilli (63.00 ± 15.49) and Streptococcus mutans (76.00 ± 13.90) in saliva. Blood Group B is prevalent in diabetic and non-diabetic high-risk caries patients but statistically insignificant. Conclusions Diabetic individuals are more susceptible to dental caries due to high microbial loads and increased lactic acid production. These factors also lower the executing tendency of neutrophils, which accelerates microbial accumulation and increases the risk of caries in diabetic individuals.
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Predicting mortality rate and associated risks in COVID-19 patients
Satpathy Suneeta,Mangla Monika,Sharma Nonita,Deshmukh Hardik,Mohanty Sachinandan 대한공간정보학회 2021 Spatial Information Research Vol.29 No.4
The genesis of novel coronavirus (COVID-19) was from Wuhan city, China in December 2019, which was later declared as a global pandemic in view of its exponential rise and spread around the world. Resultantly, the scientific and medical research communities around the globe geared up to curb its spread. In this manuscript, authors claim competence of AI-mediated methods to predict mortality rate. Efficient prediction model enables healthcare professionals to be well prepared to handle this unpredictable situation. The prime focus of the study is to investigate efficient prediction model. In order to determine the most effective prediction model, authors perform comparative analysis of numerous models. The performance of various prediction models is compared using various error metrics viz. Root mean square error, mean absolute error, mean square error and R2. During comparative analysis, Auto seasonal auto regressive integrated moving average model proves its competence over comparative models.
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