LIVIVO - Search results -

Search results

Result 1 - 10 of total 126

Search options

Article ; Online: Environmental insults and compensative responses: when microbiome meets cancer.

Nagpal, Sunil / Mande, Sharmila S

Discover. Oncology

2023 Volume 14, Issue 1, Page(s) 130

Abstract: Tumor microenvironment has recently been ascribed a new hallmark-the polymorphic microbiome. Accumulating evidence regarding the tissue specific territories of tumor-microbiome have opened new and interesting avenues. A pertinent question is regarding ... ...

Abstract	Tumor microenvironment has recently been ascribed a new hallmark-the polymorphic microbiome. Accumulating evidence regarding the tissue specific territories of tumor-microbiome have opened new and interesting avenues. A pertinent question is regarding the functional consequence of the interface between host-microbiome and cancer. Given microbial communities have predominantly been explored through an ecological perspective, it is important that the foundational aspects of ecological stress and the fight to 'survive and thrive' are accounted for tumor-micro(b)environment as well. Building on existing evidence and classical microbial ecology, here we attempt to characterize the ecological stresses and the compensative responses of the microorganisms inside the tumor microenvironment. What insults would microbes experience inside the cancer jungle? How would they respond to these insults? How the interplay of stress and microbial quest for survival would influence the fate of tumor? This work asks these questions and tries to describe this underdiscussed ecological interface of the tumor and its microbiota. It is hoped that a larger scientific thought on the importance of microbial competition sensing vis-à-vis tumor-microenvironment would be stimulated.
Language	English
Publishing date	2023-07-15
Publishing country	United States
Document type	Journal Article ; Review
ISSN	2730-6011
ISSN (online)	2730-6011
DOI	10.1007/s12672-023-00745-9
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: Host-microbiome interactions: Gut-Liver axis and its connection with other organs.

Anand, Swadha / Mande, Sharmila S

NPJ biofilms and microbiomes

2022 Volume 8, Issue 1, Page(s) 89

Abstract: An understanding of connections between gut microbiome and liver has provided important insights into the pathophysiology of liver diseases. Since gut microbial dysbiosis increases gut permeability, the metabolites biosynthesized by them can reach the ... ...

Abstract	An understanding of connections between gut microbiome and liver has provided important insights into the pathophysiology of liver diseases. Since gut microbial dysbiosis increases gut permeability, the metabolites biosynthesized by them can reach the liver through portal circulation and affect hepatic immunity and inflammation. The immune cells activated by these metabolites can also reach liver through lymphatic circulation. Liver influences immunity and metabolism in multiple organs in the body, including gut. It releases bile acids and other metabolites into biliary tract from where they enter the systemic circulation. In this review, the bidirectional communication between the gut and the liver and the molecular cross talk between the host and the microbiome has been discussed. This review also provides details into the intricate level of communication and the role of microbiome in Gut-Liver-Brain, Gut-Liver-Kidney, Gut-Liver-Lung, and Gut-Liver-Heart axes. These observations indicate a complex network of interactions between host organs influenced by gut microbiome.
MeSH term(s)	Humans ; Dysbiosis ; Gastrointestinal Microbiome/physiology ; Liver ; Bile Acids and Salts ; Inflammation
Chemical Substances	Bile Acids and Salts
Language	English
Publishing date	2022-11-01
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	2817021-0
ISSN	2055-5008 ; 2055-5008
ISSN (online)	2055-5008
ISSN	2055-5008
DOI	10.1038/s41522-022-00352-6
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Article ; Online: BactInt: A domain driven transfer learning approach for extracting inter-bacterial associations from biomedical text.

Das Baksi, Krishanu / Pokhrel, Vatsala / Pudavar, Anand Eruvessi / Mande, Sharmila S / Kuntal, Bhusan K

Computational biology and chemistry

2024 Volume 109, Page(s) 108012

Abstract: Background: The healthy as well as dysbiotic state of an ecosystem like human body is known to be influenced not only by the presence of the bacterial groups in it, but also with respect to the associations within themselves. Evidence reported in ... ...

Abstract	Background: The healthy as well as dysbiotic state of an ecosystem like human body is known to be influenced not only by the presence of the bacterial groups in it, but also with respect to the associations within themselves. Evidence reported in biomedical text serves as a reliable source for identifying and ascertaining such inter bacterial associations. However, the complexity of the reported text as well as the ever-increasing volume of information necessitates development of methods for automated and accurate extraction of such knowledge. Methods: A BioBERT (biomedical domain specific language model) based information extraction model for bacterial associations is presented that utilizes learning patterns from other publicly available datasets. Additionally, a specialized sentence corpus has been developed to significantly improve the prediction accuracy of the 'transfer learned' model using a fine-tuning approach. Results: The final model was seen to outperform all other variations (non-transfer learned and non-fine-tuned models) as well as models trained on BioGPT (a domain trained Generative Pre-trained Transformer). To further demonstrate the utility, a case study was performed using bacterial association network data obtained from experimental studies. Conclusion: This study attempts to demonstrate the applicability of transfer learning in a niche field of life sciences where understanding of inter bacterial relationships is crucial to obtain meaningful insights in comprehending microbial community structures across different ecosystems. The study further discusses how such a model can be further improved by fine tuning using limited training data. The results presented and the datasets made available are expected to be a valuable addition in the field of medical informatics and bioinformatics.
MeSH term(s)	Humans ; Ecosystem ; Deep Learning ; Computational Biology ; Medical Informatics ; Natural Language Processing
Language	English
Publishing date	2024-01-04
Publishing country	England
Document type	Journal Article
ISSN	1476-928X
ISSN (online)	1476-928X
DOI	10.1016/j.compbiolchem.2023.108012
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: Correction to: Visual exploration of microbiome data.

Kuntal, Bhusan K / Mande, Sharmila S

Journal of biosciences

2020 Volume 45

Abstract: Correction to: J Biosci (2019) 44:119 https://doi.org/10.1007/s12038-019-9933-z In the October 2019 Special Issue of the Journal of Biosciences on Current Trends in Microbiome Research, in the Review article titled "Visual exploration of microbiome data" ...

Abstract	Correction to: J Biosci (2019) 44:119 https://doi.org/10.1007/s12038-019-9933-z In the October 2019 Special Issue of the Journal of Biosciences on Current Trends in Microbiome Research, in the Review article titled "Visual exploration of microbiome data" by Bhusan K. Kuntal and Sharmila S. Mande (DOI: 10.1007/s12038-019-9933-z; Vol. 44, Article No. 119), affiliation 3 for Bhusan K. Kuntal was incorrectly mentioned as "Academy of Scientific and Innovative Research, CSIR-National Chemical Laboratory Campus, Pune 411008, India''. The correct affiliation should read as ''Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201 002, India".
Language	English
Publishing date	2020-11-13
Publishing country	India
Document type	Journal Article
ZDB-ID	756157-x
ISSN	0973-7138 ; 0250-5991
ISSN (online)	0973-7138
ISSN	0250-5991
Database	MEDical Literature Analysis and Retrieval System OnLINE

In stock of ZB MED Cologne/Königswinter

Zs.A 1907: Show issues

Location:
Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
Jg. 1995 - 2021: Lesesall (1.OG)
ab Jg. 2022: Lesesaal (EG)

Order via subito

Details ▾
- See ZB MED holdings
- Order with fees

Article ; Online: A compendium of predicted growths and derived symbiotic relationships between 803 gut microbes in 13 different diets.

Singh, Rohan / Dutta, Anirban / Bose, Tungadri / Mande, Sharmila S

Current research in microbial sciences

2022 Volume 3, Page(s) 100127

Abstract: Gut health is intimately linked to dietary habits and the microbial community (microbiota) that flourishes within. The delicate dependency of the latter on nutritional availability is also strongly influenced by interactions (such as, parasitic or ... ...

Abstract	Gut health is intimately linked to dietary habits and the microbial community (microbiota) that flourishes within. The delicate dependency of the latter on nutritional availability is also strongly influenced by interactions (such as, parasitic or mutualistic) between the resident microbes, often affecting their growth rate and ability to produce key metabolites. Since, cultivating the entire repertoire of gut microbes is a challenging task, metabolic models (genome-based metabolic reconstructions) could be employed to predict their growth patterns and interactions. Here, we have used 803 gut microbial metabolic models from the Virtual Metabolic Human repository, and subsequently optimized and simulated them to grow on 13 dietary compositions. The presented pairwise interaction data (https://osf.io/ay8bq/) and the associated bacterial growth rates are expected to be useful for (a) deducing microbial association patterns, (b) diet-based inference of personalised gut profiles, and (c) as a steppingstone for studying multi-species metabolic interactions.
Language	English
Publishing date	2022-03-23
Publishing country	Netherlands
Document type	Journal Article
ISSN	2666-5174
ISSN (online)	2666-5174
DOI	10.1016/j.crmicr.2022.100127
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: Visual exploration of microbiome data.

Kuntal, Bhusan K / Mande, Sharmila S

Journal of biosciences

2019 Volume 44, Issue 5

Abstract: A dramatic increase in large-scale cross-sectional and temporal-level metagenomic experiments has led to an improved understanding of the microbiome and its role in human well-being. Consequently, a plethora of analytical methods has been developed to ... ...

Abstract	A dramatic increase in large-scale cross-sectional and temporal-level metagenomic experiments has led to an improved understanding of the microbiome and its role in human well-being. Consequently, a plethora of analytical methods has been developed to decipher microbial biomarkers for various diseases, cluster different ecosystems based on microbial content, and infer functional potential of the microbiome as well as analyze its temporal behavior. Development of user-friendly visualization methods and frameworks is necessary to analyze this data and infer taxonomic and functional patterns corresponding to a phenotype. Thus, new methods as well as application of pre-existing ones has gained importance in recent times pertaining to the huge volume of the generated microbiome data. In this review, we present a brief overview of some useful visualization techniques that have significantly enriched microbiome data analytics.
MeSH term(s)	Metagenomics ; Microbiota
Language	English
Publishing date	2019-11-12
Publishing country	India
Document type	Journal Article ; Review
ZDB-ID	756157-x
ISSN	0973-7138 ; 0250-5991
ISSN (online)	0973-7138
ISSN	0250-5991
Database	MEDical Literature Analysis and Retrieval System OnLINE

In stock of ZB MED Cologne/Königswinter

Zs.A 1907: Show issues

Location:
Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
Jg. 1995 - 2021: Lesesall (1.OG)
ab Jg. 2022: Lesesaal (EG)

Order via subito

Details ▾
- See ZB MED holdings
- Order with fees

Article ; Online: Decoding the microbiome for the development of translational applications: Overview, challenges and pitfalls.

Haque, Mohammed Monzoorul / Mande, Sharmila S

Journal of biosciences

2019 Volume 44, Issue 5

Abstract: Recent studies have highlighted the potential of 'translational' microbiome research in addressing real-world challenges pertaining to human health, nutrition and disease. Additionally, outcomes of microbiome research have also positively impacted ... ...

Abstract	Recent studies have highlighted the potential of 'translational' microbiome research in addressing real-world challenges pertaining to human health, nutrition and disease. Additionally, outcomes of microbiome research have also positively impacted various aspects pertaining to agricultural productivity, fuel or energy requirements, and stability/preservation of various ecological habitats. Microbiome data is multi-dimensional with various types of data comprising nucleic and protein sequences, metabolites as well as various metadata related to host and or environment. This poses a major challenge for computational analysis and interpretation of data to reach meaningful, reproducible (and replicable) biological conclusions. In this review, we first describe various aspects of microbiomes that make them an attractive tool/target for developing various translational applications. The challenge of deciphering signatures from an information-rich resource like the microbiome is also discussed. Subsequently, we present three case-studies that exemplify the potential of microbiome- based solutions in solving real-world problems. The final part of the review attempts to familiarize readers with the importance of a robust study design and the diligence required during every stage of analysis for achieving solutions with potential translational value.
MeSH term(s)	Humans ; Microbiota ; Translational Medical Research
Language	English
Publishing date	2019-11-12
Publishing country	India
Document type	Journal Article ; Review
ZDB-ID	756157-x
ISSN	0973-7138 ; 0250-5991
ISSN (online)	0973-7138
ISSN	0250-5991
Database	MEDical Literature Analysis and Retrieval System OnLINE

In stock of ZB MED Cologne/Königswinter

Zs.A 1907: Show issues

Location:
Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
Jg. 1995 - 2021: Lesesall (1.OG)
ab Jg. 2022: Lesesaal (EG)

Order via subito

Details ▾
- See ZB MED holdings
- Order with fees

Article ; Online: Tracking mutational semantics of SARS-CoV-2 genomes.

Singh, Rohan / Nagpal, Sunil / Pinna, Nishal K / Mande, Sharmila S

Scientific reports

2022 Volume 12, Issue 1, Page(s) 15704

Abstract: Natural language processing (NLP) algorithms process linguistic data in order to discover the associated word semantics and develop models that can describe or even predict the latent meanings of the data. The applications of NLP become multi-fold while ... ...

Abstract	Natural language processing (NLP) algorithms process linguistic data in order to discover the associated word semantics and develop models that can describe or even predict the latent meanings of the data. The applications of NLP become multi-fold while dealing with dynamic or temporally evolving datasets (e.g., historical literature). Biological datasets of genome-sequences are interesting since they are sequential as well as dynamic. Here we describe how SARS-CoV-2 genomes and mutations thereof can be processed using fundamental algorithms in NLP to reveal the characteristics and evolution of the virus. We demonstrate applicability of NLP in not only probing the temporal mutational signatures through dynamic topic modelling, but also in tracing the mutation-associations through tracing of semantic drift in genomic mutation records. Our approach also yields promising results in unfolding the mutational relevance to patient health status, thereby identifying putative signatures linked to known/highly speculated mutations of concern.
MeSH term(s)	COVID-19/virology ; Genome, Viral ; Humans ; Mutation ; SARS-CoV-2/genetics ; Semantics
Language	English
Publishing date	2022-09-20
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-022-20000-5
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Article: Corrigendum: Web-gLV: A Web Based Platform for Lotka-Volterra Based Modeling and Simulation of Microbial Populations.

Kuntal, Bhusan K / Gadgil, Chetan / Mande, Sharmila S

Frontiers in microbiology

2021 Volume 11, Page(s) 605308

Abstract: This corrects the article DOI: 10.3389/fmicb.2019.00288.]. ...

Abstract	[This corrects the article DOI: 10.3389/fmicb.2019.00288.].
Language	English
Publishing date	2021-01-08
Publishing country	Switzerland
Document type	Published Erratum
ZDB-ID	2587354-4
ISSN	1664-302X
ISSN	1664-302X
DOI	10.3389/fmicb.2020.605308
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Article ; Online: MarkerML - Marker Feature Identification in Metagenomic Datasets Using Interpretable Machine Learning.

Nagpal, Sunil / Singh, Rohan / Taneja, Bhupesh / Mande, Sharmila S

Journal of molecular biology

2022 Volume 434, Issue 11, Page(s) 167589

Abstract: Identification of environment specific marker-features is one of the key objectives of many metagenomic studies. It aims to identify such features in microbiome datasets that may serve as markers of the contrasting or comparable states. Hypothesis ... ...

Abstract	Identification of environment specific marker-features is one of the key objectives of many metagenomic studies. It aims to identify such features in microbiome datasets that may serve as markers of the contrasting or comparable states. Hypothesis testing and black-box machine learnt models which are conventionally used for identification of these features are generally not exhaustive, especially because they generally do-not provide any quantifiable relevance (context) of/between the identified features. We present MarkerML web-server, that seeks to leverage the emergence of interpretable machine learning for facilitating the contextual discovery of metagenomic features of interest. It does so through a comprehensive and automated application of the concept of Shapley Additive Explanations in companionship to the compositionality accounted hypothesis testing for the multi-variate microbiome datasets. MarkerML not only helps in identification of marker-features, but also enables insights into the role and inter-dependence of the identified features in driving the decision making of the supervised machine learnt model. Generation of high quality and intuitive visualizations spanning prediction effect plots, model performance reports, feature dependency plots, Shapley and abundance informed cladograms (Sungrams), hypothesis tested violin plots along-with necessary provisions for excluding the participant bias and ensuring reproducibility of results, further seek to make the platform a useful asset for the scientists in the field of microbiome (and even beyond). The MarkerML web-server is freely available for the academic community at https://microbiome.igib.res.in/markerml/.
MeSH term(s)	Datasets as Topic ; Humans ; Internet Use ; Machine Learning ; Metagenome ; Metagenomics ; Reproducibility of Results
Language	English
Publishing date	2022-04-18
Publishing country	Netherlands
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	80229-3
ISSN	1089-8638 ; 0022-2836
ISSN (online)	1089-8638
ISSN	0022-2836
DOI	10.1016/j.jmb.2022.167589
Database	MEDical Literature Analysis and Retrieval System OnLINE

Full text online

Accessible to users with ZB MED library card

In stock of ZB MED Cologne/Königswinter

Zs.A 867: Show issues

Location:
Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
Jg. 1995 - 2021: Lesesall (1.OG)
ab Jg. 2022: Lesesaal (EG)

Order via subito

Details ▾

To top

More links

Kategorien

Order via subito

Inter-library loan at ZB MED

More links

Kategorien

Order via subito

More links

Kategorien

Order via subito

Inter-library loan at ZB MED

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito

More links

Kategorien

Order via subito

Inter-library loan at ZB MED

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito

More links

Kategorien

Order via subito

More links

Kategorien

Order via subito

Full text online

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito