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- 저자 : Zoraida, B.S.E. (검색결과 4 건)
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( Zoraida Dwi Wahyuni ),( Gatoet Ismanoe ) 대한내과학회 2014 대한내과학회 추계학술대회 Vol.2014 No.1
Introduction: Elizabethkingia meningoseptica, previously known as Chryseobacterium meningosepticum, is a non-fermenting, oxidase-positive, non-motile, Gram-negative aerobic bacillus, has been reported as a cause of neonatal and adult meningitis, bacteraemia/ sepsis, pneumonia, nosocomial outbreaks, and other infection, especially in immunocompromised hosts. This organism is resistant to many antimicrobial agents, frequently used to target Gram-negative bacterial infections. Case: A 65-year-old male, medical doctor, with background diabetes mellitus and hypertension presented with fever since 7 days before admission, accompanied with productive cough. Initial history, physical examination and laboratory tests were consistent with the diagnosis of systemic infi ammatory response syndrome and severe acute respiratory distress syndrome; sputum and blood culture from peripheral were sent. Patient was started on empiric antibiotics and aggressive hydration. Blood cultures from peripheral access grew Elisabethkingia meningoseptica on the fi fth day. Patient was transferred to the intensive care unit for severe ARDS and septic shock where patient needed ventilator and vasopressors. Antibiotics were switched to intravenous levofi oxacin, and given intravenous corticosteroid for management of severe ARDS. Patient showed excellent treatment response to intravenous levofi oxacin and was weaned off ventilator on day 10 and vasopressors on day 4. Discussion: Healthcare associated E meningoseptica infection has been reported to have higher mortality compared to community acquired infection. E meningoseptica is a multidrug-resistant organism, as seen in our case, it is resistant to ß-lactam antibiotics and carbapenem. By given appropriate antibiotic and corticosteroid for management of severe ARDS, patient had a good outcome Lessons Learnt: We have to consider possibility of healthcare associated E meningoseptica infection, especially in an immunocompromised host, because failure to select appropriate antibiotics could lead to fatal consequences.
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Magdalene, J. Jasmine Christina,Zoraida, B.S.E. International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.10
Technology is progressing with every passing day and the enormous usage of electricity is becoming a necessity. One of the techniques to enjoy the assistances in a smart home is the efficiency to manage the electric energy. When electric energy is managed in an appropriate way, it drastically saves sufficient power even to be spent during hard time as when hit by natural calamities. To accomplish this, prediction of energy consumption plays a very important role. This proposed prediction model Coherent Weighted K-Means Clustering ARIMA (CWKMCA) enhances the weighted k-means clustering technique by adding weights to the cluster points. Forecasting is done using the ARIMA model based on the centroid of the clusters produced. The dataset for this proposed work is taken from the Pecan Project in Texas, USA. The level of accuracy of this model is compared with the traditional ARIMA model and the Weighted K-Means Clustering ARIMA Model. When predicting,errors such as RMSE, MAPE, AIC and AICC are analysed, the results of this suggested work reveal lower values than the ARIMA and Weighted K-Means Clustering ARIMA models. This model also has a greater loglikelihood, demonstrating that this model outperforms the ARIMA model for time series forecasting.
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A Deep Learning Model for Predicting User Personality Using Social Media Profile Images
Kanchana, T.S.,Zoraida, B.S.E. International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.11
Social media is a form of communication based on the internet to share information through content and images. Their choice of profile images and type of image they post can be closely connected to their personality. The user posted images are designated as personality traits. The objective of this study is to predict five factor model personality dimensions from profile images by using deep learning and neural networks. Developed a deep learning framework-based neural network for personality prediction. The personality types of the Big Five Factor model can be quantified from user profile images. To measure the effectiveness, proposed two models using convolution Neural Networks to classify each personality of the user. Done performance analysis among two different models for efficiently predict personality traits from profile image. It was found that VGG-69 CNN models are best performing models for producing the classification accuracy of 91% to predict user personality traits.
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Xie Guoyang,Yu Shuang,Li Wen,Mu Dan,Aguilar Zoraida P.,Xu Hengyi 한국미생물학회 2020 The journal of microbiology Vol.58 No.8
A multiplex polymerase chain reaction (mPCR) with propidium monoazide (PMA) and internal amplification control (IAC) for the simultaneous detection of waterborne pathogens Salmonella spp., Pseudomonas aeruginosa, Bacillus cereus, and Escherichia coli O157:H7, was developed. This PMA-IAC-mPCR assay used four new specific primers based on the genes for invA, ecfX, cesB, and fliC, respectively. A 16S rRNA primer was chosen for IAC to eliminate false negative results. The photosensitive dye, propidium monoazide (PMA) was used to exclude signals from dead bacteria that could lead to false positive results. In pure culture, the limits of detection (LOD) were 101 CFU/ml for P. aeruginosa, 102 CFU/ml for both Salmonella spp. and E. coli O157:H7, and 103 CFU/ml for B. cereus, respectively. In addition, with a 6–8 h enrichment of all four bacteria that were combined in a mixture that was spiked in water sample matrix, the LOD was 3 CFU/ml for Salmonella spp., 7 CFU/ml for E. coli O157:H7, 10 CFU/ml for B. cereus and 2 CFU/ml for P.aeruginosa. This PMA-IAC-mPCR assay holds potential for application in the multiplex assay of waterborne pathogens.
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