Model with the GBDT for colorectal adenoma risk diagnosis

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Author(s): Junbo Gao*, Lifeng Zhang, Gaiqing Yu, Guoqiang Qu, Yanfeng Li, Xuebing Yang.

Journal Name: Current Bioinformatics

Abstract:

Background and objective: Colorectal cancer (CRC) is a common malignant tumor of the digestive system; it is associated with high morbidity and mortality. However, an early prediction of colorectal adenoma (CRA) that is a precancerous disease of most CRC patients provides an opportunity to make an appropriate strategy for prevention, early diagnosis and treatment. We aimed to build a machine learning model to predict CRA that could assist physicians in classifying high-risk patients and make informed choices, prevent CRC.

Methods: We instructed patients who had undergone a colonoscopy to fill out a questionnaire at the Sixth People Hospital of Shanghai in China from July 2018 to November 2018. A classification model with the gradient boosting decision tree (GBDT) was developed to predict CRA. This model was compared with three other models, namely, random forest (RF), support vector machine (SVM), and logistic regression (LR). The area under the receiver operating characteristic curve (AUC) was used to evaluate performance of the models.

Results: Among the 245 included patients, 65 patients had CRA. The area under the receiver operating characteristic (AUCs) of GBDT, RF, SVM ,and LR with 10 fold-cross validation were 0.8131, 0.74, 0.769 and 0.763. We also built an online prediction service, CRA Inference System, to substantialize the proposed solution for patients with CRA.

Conclusion: We developed and compared four classification models for CRA prediction, and the GBDT model showed the highest performance. Implementing a GBDT model for screening can reduce the cost of time and money and help physicians identify high-risk groups for primary prevention.

Keywords: Colorectal adenoma, Colorectal cancer, Gradient boosting decision tree, Prediction, Clinical data, Early prevention

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(E-pub Abstract Ahead of Print)
DOI: 10.2174/1574893614666191120142005
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An Information Gain-based Method for Evaluating the Classification Power of Features Towards Identifying Enhancers

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Author(s): Tianjiao Zhang, Rongjie Wang, Qinghua Jiang*, Yadong Wang.

Journal Name: Current Bioinformatics

Abstract:

Background: Enhancers are cis-regulatory elements that enhance gene expression on DNA sequences and are usually located far from transcription start sites. Like other regulatory elements, the regions around enhancers contain a variety of features.

Objective: The above features are widely used to predict the position of enhancers in existing algorithms. And the accuracies of these methods are significant affected by the selected features. Thus, it is urgent to filter the important features out, which can greatly help for enhancer recognition.

Method: To evaluate the classification power of these features for enhancer recognition, all of the features were divided into three categories: sequence features, transcriptional features, and epigenetic features. Here, we presented two evaluation methods involving information gain and single feature prediction accuracy. The information gain can effectively reflect the entropy change of enhancer recognition using different features. Single feature prediction accuracy can directly reflect the contribution of features for enhancers recognition.

Results: The average information gain of the sequence feature, transcriptional feature and epigenetic feature is 0.068, 0.213, and 0.299, respectively. The average AUC value corresponding to the sequence feature, transcriptional feature, and epigenetic feature is 0.534, 0.605, and 0.647, respectively.

Conclusion: In comparison with sequence features, epigenetic features are more effective for recognizing enhancers.

Keywords: Enhancer, Gene Expression Regulation, Sequence Features, Transcriptional Features, Epigenetic Features, Information Gain.

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DOI: 10.2174/1574893614666191120141032
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Stinging Insect Allergens

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Author(s): Cui Le, Xu Ying-Yang, Wang Xiu-Jie, Guan Kai*.

Journal Name: Current Protein & Peptide Science

Abstract:

Hymenoptera venom allergy is one of the common causes of anaphylaxis. However, when physicians make the diagnosis of Hymenoptera venom allergy, the histories of being stung are not always consistent with the results of venom specific IgE. With the development of component-resolved diagnosis, it is possible to accurately localize an allergic reaction to certain sensitized proteins. This paper reviewed the studies that have addressed the identified allergenicity and cross-reactivity of Hymenoptera venom allergens accepted by the WHO/IUIS Nomenclature Sub-committee, the component-resolved diagnosis of Hymenoptera venom allergy and its predictive values for the efficacy and safety of venom immunotherapy. Also, we paid special attention to the progress of Hymenoptera venom allergy in Asian countries.

Keywords: Allergens, Cross Reactions, Hymenoptera, Hypersensitivity, Immunoglobulin E, Immunotherapy, Venoms

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DOI: 10.2174/1389203720666191120130209
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Natural Fused Heterocyclic Flavonoids: Potent Candidates As Anti-Inflammatory And Anti-Allergic Agents In The Treatment Of Asthma

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Author(s): Rajwinder Kaur*, Kirandeep Kaur, Rashmi Arora, Balraj Saini, Sandeep Arora.

Journal Name: Current Bioactive Compounds

Abstract:

In the last two decades, the flavanoids containing fused heterocyclic nucleus in their chemical structure have emerged to display a variety of pharmacological effects including anti-allergic and anti-inflammatory the most recent to the list. These polyphenolic compounds exert their pharmacological effect by various mechanisms including inhibition of human neutrophil elastase, cytokines (Interleukins- IL-3 and IL-4) and mast cells. Quercetin, Pycnogenol, Rutin and Kampferol are the few bio-flavonols out of hundreds of other compounds still under clinical trials that have been studied most. These flavonoids have been also reported to the other pharmacological effects like anti-cancer, anti-oxidants, anti-hypertensive, anti-viral, anti-ulcerogenic, anti-platelet, anti-hypotensive and anti-hepatotoxic. With all these versatile properties heterocyclic containing flavonoids may be a powerful candidate for the discovery of their utilization in other ailments like asthma.

Keywords: Flavonoids, Asthma, Lawsonia, Thuja orientaliFlavonoids, Thuja orientalis, candidate

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DOI: 10.2174/1573407215666191120125608
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Acknowledgements to Reviewers

Author(s): .

Journal Name: Current Green Chemistry

Volume 6 , Issue 3 , 2019

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VOLUME: 6
ISSUE: 3
Year: 2019
Page: [255 - 255]
Pages: 1
DOI: 10.2174/221334610603191120125514


Estimation of calorific values of some of Turkish Lignites by artificial neural network and multiple regression

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Author(s): Engin Özdemir*, Didem Eren Sarici.

Journal Name: Current Physical Chemistry

Abstract:

Background: The calorific value is the most important and effective factors of lignites in terms of energy resources. Humidity, ash content, volatile matter and sulfur content are the main factors affecting lignite's calorific values.

Objective: Determination of calorific value is a process that takes time and cost for businesses. Therefore, estimating the calorific value from the developed models by using other parameters will benefit enterprises in term of time, cost and labor

Method: In this study calorific values were estimated by using artificial neural network and multiple regression models by using lignite data of 30 different regions. As input parameters, humidity, ash content and volatile matter values are used. In addition, the mean absolute percentage error and the significance coefficient values were determined.

Results: Mean absolute percentage error values were found to be below 10%. There is a strong relationship between calorific values and other properties (R2> 90).

Conclusion: As a result, artificial neural network and multiple regression models proposed in this study was shown to successfully estimate the calorific value of lignites without performing laboratory analyses.

Keywords: Energy, multiple artificial neural network, calorific value regression, Turkish lignite

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DOI: 10.2174/1877946809666191120125450
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EDITORIAL: Organic Transformations by Following Green Credentials- Part 1 (A)

Author(s): Bubun Banerjee.

Journal Name: Current Green Chemistry

Volume 6 , Issue 3 , 2019

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VOLUME: 6
ISSUE: 3
Year: 2019
Page: [154 - 154]
Pages: 1
DOI: 10.2174/221334610603191120125019


Effects of Probiotics and Prebiotics on Frailty and Ageing: A Narrative Review

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Author(s): Kulapong Jayanama*, Olga Theou.

Journal Name: Current Clinical Pharmacology

Abstract:

Globally, the population over the age of 60 is growing fast, but people age in different ways. Frailty, shown by the accumulation of age-related deficits, is a state of increased vulnerability to adverse outcomes among people of the same chronological age. Ageing results in a decline in diversity and homeostasis of microbiomes, and gut flora changes are related to health deficit accumulation and adverse health outcomes. In older people, health deficits including inappropriate intake, sarcopenia, physical inactivity, polypharmacy, and social vulnerability are factors associated with gut dysbiosis. The use of probiotics and prebiotics is a cost-effective and widely available intervention. Intake of probiotics and prebiotics may improve the homeostasis of gut microflora and prevent frailty and unhealthy aging. However, health effects vary among probiotics and prebiotics and among individual populations. This narrative review summarizes recent evidence about the relationship of prebiotic and probiotic consumption with health outcomes in older people.

Keywords: Ageing, Frailty, Elderly, Gut microbiota, Probiotic, Prebiotic, Review

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DOI: 10.2174/1574884714666191120124548
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Ecofriendly Ethyl Cellulose Microsponges of Citronella oil: Preparation, Characterization and Evaluation of Cytotoxicity and Larvicidal assay

(E-pub Abstract Ahead of Print)

Author(s): Ruchi Sharma, Nitish Kumar, S.P Singh, Sunil Kumar, Rekha Rao*.

Journal Name: Current Pharmaceutical Biotechnology

Abstract:

Citronella oil (CO) was used by the Indian army as mosquito repellant to repel mosquitoes in the beginning of 20th century and later on, in 1948 registered in the USA for commercial purpose. Due to its safe nature, CO possesses an immense potential as mosquito repellent. It is a valuable alternative to synthetic mosquito repellents commonly used nowadays. However, its volatile nature, poor stability in air and at high temperature restricts its application. Its direct application on skin may lead to skin irritation. To surmount the above-mentioned issues, the present research aims to develop microsponge (MS), a novel dosage form for enhancing the utility and safety of CO. Quasi emulsion solvent diffusion method was chosen for crafting of MS using ethyl cellulose with various drug polymer ratios and characterized. In vitro cytotoxicity evaluation was also carried out to check the dermal safety of COMS. Present results revealed that size of all prepared formulation lie in the micro range (20 ± 3 to 41 ± 4 µm), with good pay load (42.09± 3.24 to 67.08± 6.43 %). Results of FE-SEM depicted that MS were spherical in shape with porous nature. Cytotoxicity results indicated that COMS were safe on skin cells when compared to pure CO. The optimized MS was also assessed for larvicidal assay against larvae of Anopheles culicifacies. Furthermore, the CO microformulations were found to possess enhanced stability of this oil. Entrapment of CO in MS resulted in better vehicle system in terms of safety, stability and handling benefits of this oil.

Keywords: Larvicidal Activity, Anopheles Culicifacies, Quasi Emulsion Solvent Diffusion Technique, Dermal Cytotoxicity

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DOI: 10.2174/1389201020666191120124314
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Realization methods of computer-aided diagnosis system of medical images

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Author(s): Muhammad Aqeel Ashraf*, Shahreen Kasim.

Journal Name: Recent Patents on Engineering

Abstract:

In this paper, medical images are used to realize the computer-aided diagnosis (CAD) system which develops targeted solutions to existing problems. Relying on the Mi COM platform, this system has collected and collated cases of all kinds, based on which a unified data model is constructed according to the gold standard derived by deducting each instance. Afterwards, the object segmentation algorithm is employed to segment the diseased tissues. Edge modification and feature extraction are performed for the tissue block segmented. The features extracted are classified by applying support vector machines or the Naive Bayesian classification algorithm. From the simulation results, the CAD system developed in this paper allows realization of diagnosis and treatment and sharing of data resources.

Keywords: CAD, Aided diagnosis system, DICOM, Medical imaging

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DOI: 10.2174/1872212113666191120124031
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