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Article ; Online: Printing of 3D biomimetic structures for the study of bone metastasis: A review.

Khanmohammadi, Mehdi / Volpi, Marina / Walejewska, Ewa / Olszewska, Alicja / Swieszkowski, Wojciech

2024 Volume 178, Page(s) 24–40

Abstract: Bone metastasis primarily occurs when breast, prostate, or lung cancers disseminate tumoral cells into bone tissue, leading to a range of complications in skeletal tissues and, in severe cases, paralysis resulting from spinal cord compression. ... ...

Abstract	Bone metastasis primarily occurs when breast, prostate, or lung cancers disseminate tumoral cells into bone tissue, leading to a range of complications in skeletal tissues and, in severe cases, paralysis resulting from spinal cord compression. Unfortunately, our understanding of pathophysiological mechanisms is incomplete and the translation of bone metastasis research into the clinic has been slow, mainly due to the lack of credible ex vivo and in vivo models to study the disease progression. Development of reliable and rational models to study how tumor cells become circulating cells and then invade and sequentially colonize the bone are in great need. Advances in tissue engineering technologies offers reliable 3D tissue alternatives which answer relevant research questions towards the understanding of cancer evolution and key functional properties of metastasis progression as well as prognosis of therapeutic approach. Here we performed an overview of cellular mechanisms involved in bone metastasis including a short summary of normal bone physiology and metastasis initiation and progression. Also, we comprehensively summarized current advances and methodologies in fabrication of reliable bone tumor models based on state-of-the-art printing technologies which recapitulate structural and biological features of native tissue. STATEMENT OF SIGNIFICANCE: This review provides a comprehensive summary of the collective findings in relation to various printed bone metastasis models utilized for investigating specific bone metastasis diseases, related characteristic functions and chemotherapeutic drug screening. These tumoral models are comprehensively evaluated and compared, in terms of their ability to recapitulate physiological metastasis microenvironment. Various biomaterials (natural and synthetic polymers and ceramic based substrates) and printing strategies and design architecture of models used for printing of 3D bone metastasis models are discussed here. This review clearly out-lines current challenges and prospects for 3D printing technologies in bone metastasis research by focusing on the required perspective models for clinical application of these technologies in chemotherapeutic drug screening.
MeSH term(s)	Humans ; Biomimetics ; Tissue Engineering ; Biocompatible Materials ; Bone Neoplasms ; Printing, Three-Dimensional ; Bioprinting/methods ; Tissue Scaffolds/chemistry ; Tumor Microenvironment
Chemical Substances	Biocompatible Materials
Language	English
Publishing date	2024-03-07
Publishing country	England
Document type	Journal Article ; Review
ZDB-ID	2173841-5
ISSN	1878-7568 ; 1742-7061
ISSN (online)	1878-7568
ISSN	1742-7061
DOI	10.1016/j.actbio.2024.02.046
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Hydrogel-Based Fiber Biofabrication Techniques for Skeletal Muscle Tissue Engineering.

Volpi, Marina / Paradiso, Alessia / Costantini, Marco / Świȩszkowski, Wojciech

ACS biomaterials science & engineering

2022 Volume 8, Issue 2, Page(s) 379–405

Abstract: The functional capabilities of skeletal muscle are strongly correlated with its well-arranged microstructure, consisting of parallelly aligned myotubes. In case of extensive muscle loss, the endogenous regenerative capacity is hindered by scar tissue ... ...

Abstract	The functional capabilities of skeletal muscle are strongly correlated with its well-arranged microstructure, consisting of parallelly aligned myotubes. In case of extensive muscle loss, the endogenous regenerative capacity is hindered by scar tissue formation, which compromises the native muscle structure, ultimately leading to severe functional impairment. To address such an issue, skeletal muscle tissue engineering (SMTE) attempts to fabricate
MeSH term(s)	Bioprinting/methods ; Hydrogels/chemistry ; Hydrogels/pharmacology ; Muscle, Skeletal ; Myoblasts ; Tissue Engineering/methods
Chemical Substances	Hydrogels
Language	English
Publishing date	2022-01-27
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Review
ISSN	2373-9878
ISSN (online)	2373-9878
DOI	10.1021/acsbiomaterials.1c01145
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Conference proceedings ; Online: Generic seismic mass-movement detection leveraging unsupervised statistical learning methods

Paitz, P. / Chmiel, M. / Husmann, L. / Volpi, M. / Kamper, F. / Walter, F.

XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG)

2023

Abstract: With global warming potentially increasing the severity and frequency of hazardous mass-movements, monitoring such hazards is crucial to the population and critical infrastructure – especially in alpine areas. Monitoring and early-warning systems have ... ...

Abstract	With global warming potentially increasing the severity and frequency of hazardous mass-movements, monitoring such hazards is crucial to the population and critical infrastructure – especially in alpine areas. Monitoring and early-warning systems have the potential to improve the resilience of mountain communities to catastrophic events. Increasing the spatial coverage of seismic monitoring networks enables new warning perspectives if efficient algorithms screening the seismic data streams for hazardous mass-movements in real-time are available. We propose to combine physical and statistical features of seismic ground velocity recordings from ground motion sensors such as seismometers. These features are then fed to an unsupervised workflow for mass movement detection. We evaluate the performance, consistency, and generalizability of unsupervised learning approaches by comparing a large number of fitted models obtained from various unsupervised methodologies. Focusing on debris-flow records at the Illgraben torrent in Switzerland, we present a mass-movement detector with high accuracy and early-warning capability that combines multiple statistical learning models into an ensemble classifier. Furthermore, our goal is to generalize this detector to measurements from other sites and thus to maximize its transferability. Since our results aim to enable mass-movement monitoring and early-warning worldwide, Open Research Data principles like Findability, Accessibility, Interoperability and Reusability (FAIR) are of high importance for this project. We discuss how using the Renku platform (renkulab.io) of the Swiss Data Science Center ensures FAIR data science principles in our investigation. This is a key step towards our goal to enable seismology-based early warning of mass-movements wherever it may be required.
Subject code	006
Language	English
Publishing country	de
Document type	Conference proceedings ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Full-scale industrial phage trial targeting Salmonella on pork carcasses

Volpi, Marta / Gambino, Michela / Kirkeby, Kirsten / Elsser-Gravesen, Anne / Brøndsted, Lone

Food Microbiology. 2023 June, v. 112 p.104240-

2023

Abstract: Phages have been suggested as promising biocontrol agents in food, but trials demonstrating the efficiency of phage treatment under industrial settings are missing. Here we performed a full-scale industrial trial to evaluate the efficacy of a commercial ... ...

Abstract	Phages have been suggested as promising biocontrol agents in food, but trials demonstrating the efficiency of phage treatment under industrial settings are missing. Here we performed a full-scale industrial trial to evaluate the efficacy of a commercial phage product to reduce the prevalence of naturally occurring Salmonella on pork carcasses. A total of 134 carcasses from potentially Salmonella positive finisher herds were chosen to be tested at the slaughterhouse based on the level of antibodies in the blood. During five consecutive runs, carcasses were directed into a cabin spraying phages, resulting in a dosage of approximately 2 × 10⁷ phages per cm² carcass surface. To evaluate the presence of Salmonella, a predefined area of one half of the carcass was swabbed before phage application and the other half 15 min after. A total of 268 samples were analysed by Real-Time PCR. Under these optimized test conditions, 14 carcasses were found positive before phage application, while only 3 carcasses were positive after. This work shows that phage application allows to achieve approximatively 79% reduction of Salmonella-positive carcasses and demonstrates that implementation of phage application in industrial settings can be used as an additional strategy to control foodborne pathogens.
Keywords	Salmonella ; bacteriophages ; biological control ; blood ; food microbiology ; pork ; quantitative polymerase chain reaction ; slaughterhouses ; Pork carcasses ; Slaughterhouse ; Industrial trial
Language	English
Dates of publication	2023-06
Publishing place	Elsevier Ltd
Document type	Article ; Online
ZDB-ID	50892-5
ISSN	1095-9998 ; 0740-0020
ISSN (online)	1095-9998
ISSN	0740-0020
DOI	10.1016/j.fm.2023.104240
Database	NAL-Catalogue (AGRICOLA)

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Book ; Online: Country-Scale Cropland Mapping in Data-Scarce Settings Using Deep Learning

Gajardo, Joaquin / Volpi, Michele / Onwude, Daniel / Defraeye, Thijs

A Case Study of Nigeria

2023

Abstract: Cropland maps are a core and critical component of remote-sensing-based agricultural monitoring, providing dense and up-to-date information about agricultural development. Machine learning is an effective tool for large-scale agricultural mapping, but ... ...

Abstract	Cropland maps are a core and critical component of remote-sensing-based agricultural monitoring, providing dense and up-to-date information about agricultural development. Machine learning is an effective tool for large-scale agricultural mapping, but relies on geo-referenced ground-truth data for model training and testing, which can be scarce or time-consuming to obtain. In this study, we explore the usefulness of combining a global cropland dataset and a hand-labeled dataset to train machine learning models for generating a new cropland map for Nigeria in 2020 at 10 m resolution. We provide the models with pixel-wise time series input data from remote sensing sources such as Sentinel-1 and 2, ERA5 climate data, and DEM data, in addition to binary labels indicating cropland presence. We manually labeled 1827 evenly distributed pixels across Nigeria, splitting them into 50\% training, 25\% validation, and 25\% test sets used to fit the models and test our output map. We evaluate and compare the performance of single- and multi-headed Long Short-Term Memory (LSTM) neural network classifiers, a Random Forest classifier, and three existing 10 m resolution global land cover maps (Google's Dynamic World, ESRI's Land Cover, and ESA's WorldCover) on our proposed test set. Given the regional variations in cropland appearance, we additionally experimented with excluding or sub-setting the global crowd-sourced Geowiki cropland dataset, to empirically assess the trade-off between data quantity and data quality in terms of the similarity to the target data distribution of Nigeria. We find that the existing WorldCover map performs the best with an F1-score of 0.825 and accuracy of 0.870 on the test set, followed by a single-headed LSTM model trained with our hand-labeled training samples and the Geowiki data points in Nigeria, with a F1-score of 0.814 and accuracy of 0.842.
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	006
Publishing date	2023-12-17
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Sources of information for innovation

Volpi, Massimiliano

Industry and innovation Vol. 24, No. 8 , p. 817-836

the role of companies’ motivations

2017 Volume 24, Issue 8, Page(s) 817–836

Author's details	Massimiliano Volpi
Keywords	Open innovation ; sources of information ; universities ; public research ; clients
Language	English
Publisher	Routledge
Publishing place	Abingdon
Document type	Article
ZDB-ID	1414341-0 ; 2020057-2
ISSN	1469-8390 ; 1366-2716
ISSN (online)	1469-8390
ISSN	1366-2716
Database	ECONomics Information System

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Article ; Online: Full-scale industrial phage trial targeting Salmonella on pork carcasses.

Volpi, Marta / Gambino, Michela / Kirkeby, Kirsten / Elsser-Gravesen, Anne / Brøndsted, Lone

Food microbiology

2023 Volume 112, Page(s) 104240

Abstract	Phages have been suggested as promising biocontrol agents in food, but trials demonstrating the efficiency of phage treatment under industrial settings are missing. Here we performed a full-scale industrial trial to evaluate the efficacy of a commercial phage product to reduce the prevalence of naturally occurring Salmonella on pork carcasses. A total of 134 carcasses from potentially Salmonella positive finisher herds were chosen to be tested at the slaughterhouse based on the level of antibodies in the blood. During five consecutive runs, carcasses were directed into a cabin spraying phages, resulting in a dosage of approximately 2 × 10
MeSH term(s)	Animals ; Abattoirs ; Bacteriophages ; Food Microbiology ; Meat ; Pork Meat ; Red Meat ; Salmonella ; Swine
Language	English
Publishing date	2023-02-17
Publishing country	England
Document type	Journal Article
ZDB-ID	50892-5
ISSN	1095-9998 ; 0740-0020
ISSN (online)	1095-9998
ISSN	0740-0020
DOI	10.1016/j.fm.2023.104240
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Article ; Online: Safety and Drugs: How Do We Record Medication Consumption and Prescription in Electronic Medical Records? A Look on Aspirin.

Volpi, Mercedes / Esteban, Santiago / Terrasa, Sergio

Studies in health technology and informatics

2020 Volume 270, Page(s) 1383–1384

Abstract: Background: documentation of aspirin consumption is usually not complete and clear, probably because it's a cheap drug that can be easily acquired.: Objective: To evaluate the quality of the record of aspirin consumption documented in the electronic ... ...

Abstract	Background: documentation of aspirin consumption is usually not complete and clear, probably because it's a cheap drug that can be easily acquired. Objective: To evaluate the quality of the record of aspirin consumption documented in the electronic medical records (EMRs) of a Private University Hospital of Argentina. Design: Qualitative and quantitative descriptive exploratory study. Results: principal findings were that 86 % of the notes mentioned aspirin as a chronic prescription. 12% mentioned its temporary suspension. Numerous EMRs mentioned the use of an "antiplatelet" drug without specifying which, and didn't specify abbreviations, consumed dose or when to start or stop aspirin. Conclusions: overall quality of aspirin related information registration in EMRs was poor. This is concerning since it's a frequently prescribed drug, not exempt from adverse events.
MeSH term(s)	Argentina ; Aspirin ; Documentation ; Electronic Health Records ; Prescriptions
Chemical Substances	Aspirin (R16CO5Y76E)
Language	English
Publishing date	2020-06-20
Publishing country	Netherlands
Document type	Journal Article
ISSN	1879-8365
ISSN (online)	1879-8365
DOI	10.3233/SHTI200453
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Using Machine Learning to generate an open-access cropland map from satellite images time series in the Indian Himalayan Region

Li, Danya / Gajardo, Joaquin / Volpi, Michele / Defraeye, Thijs

2022

Abstract: Crop maps are crucial for agricultural monitoring and food management and can additionally support domain-specific applications, such as setting cold supply chain infrastructure in developing countries. Machine learning (ML) models, combined with freely- ... ...

Abstract	Crop maps are crucial for agricultural monitoring and food management and can additionally support domain-specific applications, such as setting cold supply chain infrastructure in developing countries. Machine learning (ML) models, combined with freely-available satellite imagery, can be used to produce cost-effective and high spatial-resolution crop maps. However, accessing ground truth data for supervised learning is especially challenging in developing countries due to factors such as smallholding and fragmented geography, which often results in a lack of crop type maps or even reliable cropland maps. Our area of interest for this study lies in Himachal Pradesh, India, where we aim at producing an open-access binary cropland map at 10-meter resolution for the Kullu, Shimla, and Mandi districts. To this end, we developed an ML pipeline that relies on Sentinel-2 satellite images time series. We investigated two pixel-based supervised classifiers, support vector machines (SVM) and random forest (RF), which are used to classify per-pixel time series for binary cropland mapping. The ground truth data used for training, validation and testing was manually annotated from a combination of field survey reference points and visual interpretation of very high resolution (VHR) imagery. We trained and validated the models via spatial cross-validation to account for local spatial autocorrelation and selected the RF model due to overall robustness and lower computational cost. We tested the generalization capability of the chosen model at the pixel level by computing the accuracy, recall, precision, and F1-score on hold-out test sets of each district, achieving an average accuracy for the RF (our best model) of 87%. We used this model to generate a cropland map for three districts of Himachal Pradesh, spanning 14,600 km2, which improves the resolution and quality of existing public maps.
Keywords	Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning ; I.4.6 ; I.5.2
Subject code	006
Publishing date	2022-03-28
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Deep multi-task learning for a geographically-regularized semantic segmentation of aerial images

Volpi, Michele / Devis Tuia

International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) ISPRS journal of photogrammetry and remote sensing. 2018 Oct., v. 144

2018

Abstract: When approaching the semantic segmentation of overhead imagery in the decimeter spatial resolution range, successful strategies usually combine powerful methods to learn the visual appearance of the semantic classes (e.g. convolutional neural networks) ... ...

Abstract	When approaching the semantic segmentation of overhead imagery in the decimeter spatial resolution range, successful strategies usually combine powerful methods to learn the visual appearance of the semantic classes (e.g. convolutional neural networks) with strategies for spatial regularization (e.g. graphical models such as conditional random fields).In this paper, we propose a method to learn evidence in the form of semantic class likelihoods, semantic boundaries across classes and shallow-to-deep visual features, each one modeled by a multi-task convolutional neural network architecture. We combine this bottom-up information with top-down spatial regularization encoded by a conditional random field model optimizing the label space across a hierarchy of segments with constraints related to structural, spatial and data-dependent pairwise relationships between regions.Our results show that such strategy provide better regularization than a series of strong baselines reflecting state-of-the-art technologies. The proposed strategy offers a flexible and principled framework to include several sources of visual and structural information, while allowing for different degrees of spatial regularization accounting for priors about the expected output structures.
Keywords	learning ; neural networks ; remote sensing
Language	English
Dates of publication	2018-10
Size	p. 48-60.
Publishing place	Elsevier B.V.
Document type	Article
ZDB-ID	1007774-1
ISSN	0924-2716
ISSN	0924-2716
DOI	10.1016/j.isprsjprs.2018.06.007
Database	NAL-Catalogue (AGRICOLA)

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