scholarly journals Investigating Bacterial Volatilome for the Classification and Identification of Mycobacterial Species by HS-SPME-GC-MS and Machine Learning

Molecules ◽  
2021 ◽  
Vol 26 (15) ◽  
pp. 4600
Author(s):  
Marco Beccaria ◽  
Flavio A. Franchina ◽  
Mavra Nasir ◽  
Theodore Mellors ◽  
Jane E. Hill ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mycobacterium Tuberculosis Complex ◽  
Solid Phase ◽  
Ex Vivo ◽  
Quadrupole Mass ◽  
Tuberculosis Complex ◽  
Machine Learning Approach ◽  
Volatile Organic ◽  
Selection Of

Species of Mycobacteriaceae cause disease in animals and humans, including tuberculosis and leprosy. Individuals infected with organisms in the Mycobacterium tuberculosis complex (MTBC) or non-tuberculous mycobacteria (NTM) may present identical symptoms, however the treatment for each can be different. Although the NTM infection is considered less vital due to the chronicity of the disease and the infrequency of occurrence in healthy populations, diagnosis and differentiation among Mycobacterium species currently require culture isolation, which can take several weeks. The use of volatile organic compounds (VOCs) is a promising approach for species identification and in recent years has shown promise for use in the rapid analysis of both in vitro cultures as well as ex vivo diagnosis using breath or sputum. The aim of this contribution is to analyze VOCs in the culture headspace of seven different species of mycobacteria and to define the volatilome profiles that are discriminant for each species. For the pre-concentration of VOCs, solid-phase micro-extraction (SPME) was employed and samples were subsequently analyzed using gas chromatography–quadrupole mass spectrometry (GC-qMS). A machine learning approach was applied for the selection of the 13 discriminatory features, which might represent clinically translatable bacterial biomarkers.

scholarly journals A Machine Learning Approach for Efficient Selection of Enzyme Concentrations and Its Application for Flux Optimization

Catalysts ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 291 ◽  
Author(s):  
Anamya Ajjolli Nagaraja ◽  
Philippe Charton ◽  
Xavier F. Cadet ◽  
Nicolas Fontaine ◽  
Mathieu Delsaut ◽  
...  
Keyword(s):  
Machine Learning ◽  
Glass Ceiling ◽  
Principal Component ◽  
Enzyme Concentration ◽  
Learning Approach ◽  
Neural Network Approach ◽  
Free System ◽  
Machine Learning Approach ◽  
Selection Of

The metabolic engineering of pathways has been used extensively to produce molecules of interest on an industrial scale. Methods like gene regulation or substrate channeling helped to improve the desired product yield. Cell-free systems are used to overcome the weaknesses of engineered strains. One of the challenges in a cell-free system is selecting the optimized enzyme concentration for optimal yield. Here, a machine learning approach is used to select the enzyme concentration for the upper part of glycolysis. The artificial neural network approach (ANN) is known to be inefficient in extrapolating predictions outside the box: high predicted values will bump into a sort of “glass ceiling”. In order to explore this “glass ceiling” space, we developed a new methodology named glass ceiling ANN (GC-ANN). Principal component analysis (PCA) and data classification methods are used to derive a rule for a high flux, and ANN to predict the flux through the pathway using the input data of 121 balances of four enzymes in the upper part of glycolysis. The outcomes of this study are i. in silico selection of optimum enzyme concentrations for a maximum flux through the pathway and ii. experimental in vitro validation of the “out-of-the-box” fluxes predicted using this new approach. Surprisingly, flux improvements of up to 63% were obtained. Gratifyingly, these improvements are coupled with a cost decrease of up to 25% for the assay.

scholarly journals Solving the "right" problems for effective machine learning driven in vitro fertilization

2021 ◽  
Author(s):  
Itay Erlich ◽  
Assaf Ben-Meir ◽  
Iris Har-Vardi ◽  
James A Grifo ◽  
Assaf Zaritsky
Keyword(s):  
Machine Learning ◽  
In Vitro Fertilization ◽  
Clinical Factors ◽  
Vitro Fertilization ◽  
Data Intensive ◽  
Live Embryo ◽  
The Right ◽  
Visual Properties ◽  
Selection Of

Automated live embryo imaging has transformed in-vitro fertilization (IVF) into a data-intensive field. Unlike clinicians who rank embryos from the same IVF cycle cohort based on the embryos visual quality and determine how many embryos to transfer based on clinical factors, machine learning solutions usually combine these steps by optimizing for implantation prediction and using the same model for ranking the embryos within a cohort. Here we establish that this strategy can lead to sub-optimal selection of embryos. We reveal that despite enhancing implantation prediction, inclusion of clinical properties hampers ranking. Moreover, we find that ambiguous labels of failed implantations, due to either low quality embryos or poor clinical factors, confound both the optimal ranking and even implantation prediction. To overcome these limitations, we propose conceptual and practical steps to enhance machine-learning driven IVF solutions. These consist of separating the optimizing of implantation from ranking by focusing on visual properties for ranking, and reducing label ambiguity.

scholarly journals Application of multi-omics data integration and machine learning approaches to identify epigenetic and transcriptomic differences between in vitro and in vivo produced bovine embryos

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0252096
Author(s):  
Maria B. Rabaglino ◽  
Alan O’Doherty ◽  
Jan Bojsen-Møller Secher ◽  
Patrick Lonergan ◽  
Poul Hyttel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Integration ◽  
Focal Adhesion ◽  
Learning Approach ◽  
Omics Data ◽  
Machine Learning Approach ◽  
Cluster 2 ◽  
Omics Data Integration

Pregnancy rates for in vitro produced (IVP) embryos are usually lower than for embryos produced in vivo after ovarian superovulation (MOET). This is potentially due to alterations in their trophectoderm (TE), the outermost layer in physical contact with the maternal endometrium. The main objective was to apply a multi-omics data integration approach to identify both temporally differentially expressed and differentially methylated genes (DEG and DMG), between IVP and MOET embryos, that could impact TE function. To start, four and five published transcriptomic and epigenomic datasets, respectively, were processed for data integration. Second, DEG from day 7 to days 13 and 16 and DMG from day 7 to day 17 were determined in the TE from IVP vs. MOET embryos. Third, genes that were both DE and DM were subjected to hierarchical clustering and functional enrichment analysis. Finally, findings were validated through a machine learning approach with two additional datasets from day 15 embryos. There were 1535 DEG and 6360 DMG, with 490 overlapped genes, whose expression profiles at days 13 and 16 resulted in three main clusters. Cluster 1 (188) and Cluster 2 (191) genes were down-regulated at day 13 or day 16, respectively, while Cluster 3 genes (111) were up-regulated at both days, in IVP embryos compared to MOET embryos. The top enriched terms were the KEGG pathway "focal adhesion" in Cluster 1 (FDR = 0.003), and the cellular component: "extracellular exosome" in Cluster 2 (FDR<0.0001), also enriched in Cluster 1 (FDR = 0.04). According to the machine learning approach, genes in Cluster 1 showed a similar expression pattern between IVP and less developed (short) MOET conceptuses; and between MOET and DKK1-treated (advanced) IVP conceptuses. In conclusion, these results suggest that early conceptuses derived from IVP embryos exhibit epigenomic and transcriptomic changes that later affect its elongation and focal adhesion, impairing post-transfer survival.

scholarly journals Bioactivity of volatile organic compounds by Aureobasidium species against gray mold of tomato and table grape

2020 ◽  
Vol 36 (11) ◽  
Author(s):  
A. Di Francesco ◽  
J. Zajc ◽  
N. Gunde-Cimerman ◽  
E. Aprea ◽  
F. Gasperi ◽  
...  
Keyword(s):  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Mycelial Growth ◽  
Solid Phase ◽  
Table Grape ◽  
Conidial Suspension ◽  
Vitro Assay ◽  
Volatile Organic

Abstract Aureobasidium strains isolated from diverse unconventional environments belonging to the species A. pullulans, A. melanogenum, and A. subglaciale were evaluated for Volatile Organic Compounds (VOCs) production as a part of their modes of action against Botrytis cinerea of tomato and table grape. By in vitro assay, VOCs generated by the antagonists belonging to the species A. subglaciale showed the highest inhibition percentage of the pathogen mycelial growth (65.4%). In vivo tests were conducted with tomatoes and grapes artificially inoculated with B. cinerea conidial suspension, and exposed to VOCs emitted by the most efficient antagonists of each species (AP1, AM10, AS14) showing that VOCs of AP1 (A. pullulans) reduced the incidence by 67%, partially confirmed by the in vitro results. Conversely, on table grape, VOCs produced by all the strains did not control the fungal incidence but were only reducing the infection severity (< 44.4% by A. pullulans; < 30.5% by A. melanogenum, and A. subglaciale). Solid-phase microextraction (SPME) and subsequent gas chromatography coupled to mass spectrometry identified ethanol, 3-methyl-1-butanol, 2-methyl-1-propanol as the most produced VOCs. However, there were differences in the amounts of produced VOCs as well as in their repertoire. The EC50 values of VOCs for reduction of mycelial growth of B. cinerea uncovered 3-methyl-1-butanol as the most effective compound. The study demonstrated that the production and the efficacy of VOCs by Aureobasidium could be directly related to the specific species and pathosystem and uncovers new possibilities for searching more efficient VOCs producing strains in unconventional habitats other than plants.

scholarly journals Current Challenges in Volatile Organic Compounds Analysis as Potential Biomarkers of Cancer

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Kamila Schmidt ◽  
Ian Podmore
Keyword(s):  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Clinical Symptoms ◽  
Ex Vivo ◽  
Critical Evaluation ◽  
Detection Techniques ◽  
Volatile Organic ◽  
Potential Biomarkers

An early diagnosis and appropriate treatment are crucial in reducing mortality among people suffering from cancer. There is a lack of characteristic early clinical symptoms in most forms of cancer, which highlights the importance of investigating new methods for its early detection. One of the most promising methods is the analysis of volatile organic compounds (VOCs). VOCs are a diverse group of carbon-based chemicals that are present in exhaled breath and biofluids and may be collected from the headspace of these matrices. Different patterns of VOCs have been correlated with various diseases, cancer among them. Studies have also shown that cancer cells in vitro produce or consume specific VOCs that can serve as potential biomarkers that differentiate them from noncancerous cells. This review identifies the current challenges in the investigation of VOCs as potential cancer biomarkers, by the critical evaluation of available matrices for the in vivo and in vitro approaches in this field and by comparison of the main extraction and detection techniques that have been applied to date in this area of study. It also summarises complementary in vivo, ex vivo, and in vitro studies conducted to date in order to try to identify volatile biomarkers of cancer.

Control of Postharvest Fungal Pathogens by Antifungal Compounds from Penicillium expansum

2013 ◽  
Vol 76 (11) ◽  
pp. 1879-1886 ◽  
Author(s):  
WAFA ROUISSI ◽  
LUISA UGOLINI ◽  
CAMILLA MARTINI ◽  
LUCA LAZZERI ◽  
MARTA MARI
Keyword(s):  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Fungal Pathogens ◽  
Solid Phase ◽  
Petri Dish ◽  
Pathogenic Fungi ◽  
Penicillium Expansum ◽  
Colletotrichum Acutatum ◽  
Volatile Organic

The fungicidal effects of secondary metabolites produced by a strain of Penicillium expansum (R82) in culture filtrate and in a double petri dish assay were tested against one isolate each of Botrytis cinerea, Colletotrichum acutatum, and Monilinia laxa and six isolates of P. expansum, revealing inhibitory activity against every pathogen tested. The characterization of volatile organic compounds released by the R82 strain was performed by solid-phase microextraction–gas chromatographic techniques, and several compounds were detected, one of them identified as phenethyl alcohol (PEA). Synthetic PEA, tested in vitro on fungal pathogens, showed strong inhibition at a concentration of 1,230 μg/ml of airspace, and mycelium appeared more sensitive than conidia; nevertheless, at the concentration naturally emitted by the fungus (0.726 ± 0.16 μg/ml), commercial PEA did not show any antifungal activity. Therefore, a combined effect between different volatile organic compounds produced collectively by R82 can be hypothesized. This aspect suggests further investigation into the possibility of exploiting R82 as a nonchemical alternative in the control of some plant pathogenic fungi.

scholarly journals In Vitro Activities of Linezolid against Clinical Isolates of Mycobacterium tuberculosis Complex Isolated in Taiwan over 10 Years

2008 ◽  
Vol 52 (6) ◽  
pp. 2226-2227 ◽  
Author(s):  
Tsi-Shu Huang ◽  
Yung-Ching Liu ◽  
Cheng-Len Sy ◽  
Yao-Shen Chen ◽  
Hui-Zin Tu ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Mycobacterium Tuberculosis Complex ◽  
Baseline Period ◽  
Multidrug Resistant ◽  
Clinical Isolates ◽  
Tuberculosis Complex

ABSTRACT Significant increases in the MIC90s of linezolid in multidrug-resistant Mycobacterium tuberculosis isolates were seen between the baseline period of 2001 to 2003 (0.5 μg/ml) and 2004 (2 μg/ml). The MICs were 4 μg/ml in three strains. Both fluoroquinolones (except levofloxacin) and kanamycin were found to have statistically significant degrees of concordance with linezolid.

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