Identification and Discrimination of Methicillin Resistant Staphylococcus aureus Strains Isolated from Burn Wound Sites Using PCR and Authentication with MALDI-TOF–MS

Charyulu, E. Madhava; Gnanamani, A.; Mandal, Asit Baran

doi:10.1007/s12088-011-0245-8

Cited by 25 publications

(6 citation statements)

References 31 publications

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“…6 (2867.9), 6574.5 (6576) (m/z features that appeared in previous works are listed in parentheses, tolerance = 0.002 Da). 7,11,16,24 Although many m/z peaks have been found to be significant in the determination of drug resistance, the contribution of these peaks involved in this study may not be completely consistent with previous studies (Tables 1 and 2 (PBP2A) is about 76 kDa. 6,42 Therefore, this is currently an inevitable defect in the detection of bacterial resistance by MALDI-TOF MS.…”

Section: The Rbf-svm Model Outperformed the Rf Model At The Low Falcontrasting

confidence: 60%

“…We noticed that a considerable percentage of the m/z features screened out in this work were also mentioned in previous works, such as the peaks at m/z 2288.0 (2288), 2327.4 (2330), 2649.9 (2647), 2865. 6 (2867.9), 6574.5 (6576) ( m/z features that appeared in previous works are listed in parentheses, tolerance = 0.002 Da) 7,11,16,24 . Although many m/z peaks have been found to be significant in the determination of drug resistance, the contribution of these peaks involved in this study may not be completely consistent with previous studies (Tables 1 and 2).…”

Section: Discussionmentioning

confidence: 42%

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Rapid identification and discrimination of methicillin‐resistant Staphylococcus aureus strains via matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry

Liu

Hsu

et al. 2020

Rapid Comm Mass Spectrometry

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Rationale Methicillin‐resistant Staphylococcus aureus (MRSA) is one of major clinical pathogens responsible for both hospital‐ and community‐acquired infections worldwide. A delay in targeted antibiotic treatment contributes to longer hospitalization stay, higher costs, and increasing in‐hospital mortality. Matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry (MALDI‐TOF MS) has been integrated into the routine workflow for microbial identification over the past decade, and it has also shown promising functions in the detection of bacterial resistance. Therefore, we describe a rapid MALDI‐TOF MS‐based methodology for MRSA screening with machine‐learning algorithms. Methods A total of 452 clinical S. aureus isolates were included in this study, of which 194 were MRSA and 258 were methicillin‐sensitive S. aureus (MSSA). The mass‐to‐charge ratio (m/z) features from MRSA and MSSA strains were binned and selected through Lasso regression. These features were then used to train a non‐linear support vector machine (SVM) with radial basis function (RBF) kernels to evaluate the discrimination performance. The classifiers' accuracy, sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve (AUC) were evaluated and compared with those from the random forest (RF) model. Results A total of 2601 unique spectral peaks of all isolates were identified and 38 m/z features were selected for the classifying model. The AUCs of the non‐linear RBF‐SVM model and the RF model were 0.89 and 0.87, respectively, and the accuracy ranged between 0.86 (RBF‐SVM) and 0.82 (RF). Conclusions Our study demonstrates that MALDI‐TOF MS coupled with machine‐learning algorithms could be used to develop a rapid and easy‐to‐use method to discriminate MRSA from MSSA. Considering that this method is easy to implement in routine microbiology laboratories, it suggests a cost‐effective and time‐efficient alternative to conventional resistance detection in the future to improve clinical treatment.

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Section: The Rbf-svm Model Outperformed the Rf Model At The Low Falcontrasting

confidence: 60%

Section: Discussionmentioning

confidence: 42%

Rapid identification and discrimination of methicillin‐resistant Staphylococcus aureus strains via matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry

Liu

Hsu

et al. 2020

Rapid Comm Mass Spectrometry

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“…They are networks with multiple loops in them, allowing information to continue. Though RNNs are capable of modeling long sequential data theoretically they fail to represent long sequences in real time applications [3].…”

Section: Introductionmentioning

confidence: 99%

Text based Sentiment Analysis using LSTM

Murthy¹,

Allu²,

Andhavarapu³

et al. 2020

IJERT

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Analyzing the big textual information manually is tougher and time-consuming. Sentiment analysis is a automated process that uses computing (AI) to spot positive and negative opinions from the text. Sentiment analysis is widely used for getting insights from social media comments, survey responses, and merchandise reviews to create data-driven decisions. Sentiment analysis systems are accustomed to add up to the unstructured text by automating business processes and saving hours of manual processing. In recent years, Deep Learning (DL) has garnered increasing attention within the industry and academic world for its high performance in various domains. Today, Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) are the foremost popular types of DL architectures used. We do sentiment analysis on text reviews by using Long Short-Term Memory (LSTM). Recently, thanks to their ability to handle large amounts of knowledge, neural networks have achieved a good success on sentiment classification. Especially long STM networks.

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“…RNNs are perfect to model sequential data as they are capable of remembering the input with its internal memory state and recurrent connections to learn and model sequential data as shown in Though RNNs are capable of modeling long sequential data theoretically they fail to represent long sequences in real time applications [3]. This is mainly due to the vanishing or exploding gradients problem.…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis from Movie Reviews Using LSTMs

Bodapati¹,

Naralasetti²,

Shaik³

2019

ISI

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With the advent of social networking and internet, it is very common for the people to share their reviews or feedback on the products they purchase or on the services they make use of or sharing their opinions on an event. These reviews could be useful for the others if analyzed properly. But analyzing the enormous textual information manually is impossible and automation is required. The objective of sentiment analysis is to determine whether the reviews or opinions given by the people give a positive sentiment or a negative sentiment. This has to be predicted based on the given textual information in the form of reviews or ratings. Earlier linear regression and SVM based models are used for this task but the introduction of deep neural networks has displaced all the classical methods and achieved greater success for the problem of automatically generating sentiment analysis information from textual descriptions. Most recent progress in this problem has been achieved through employing recurrent neural networks (RNNs) for this task. Though RNNs are able to give state of the art performance for the tasks like machine translation, caption generation and language modeling, they suffer from the vanishing or exploding gradients problems when used with long sentences. In this paper we use LSTMs, a variant of RNNs to predict the sentiment analysis for the task of movie review analysis. LSTMs are good in modeling very long sequence data. The problem is posed as a binary classification task where the review can be either positive or negative. Sentence vectorization methods are used to deal with the variability of the sentence length. In this paper we try to investigate the impact of hyper parameters like dropout, number of layers, activation functions. We have analyzed the performance of the model with different neural network configurations and reported their performance with respect to each configuration. IMDB bench mark dataset is used for the experimental studies.

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Identification and Discrimination of Methicillin Resistant Staphylococcus aureus Strains Isolated from Burn Wound Sites Using PCR and Authentication with MALDI-TOF–MS

Cited by 25 publications

References 31 publications

Rapid identification and discrimination of methicillin‐resistant Staphylococcus aureus strains via matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry

Rapid identification and discrimination of methicillin‐resistant Staphylococcus aureus strains via matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry

Text based Sentiment Analysis using LSTM

Sentiment Analysis from Movie Reviews Using LSTMs

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